Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics for the North Shore coastal region from North Salisbury at the New Hampshire border to the west side of Deer Island in Boston Harbor (NorthShore_intersects_LTw.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics excluding the 1970-1979 and 1994 shorelines for the North Shore coastal region from North Salisbury at the New Hampshire border to the west side of Deer Island in Boston harbor (NorthShore_intersects_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Shorelines used to calculate shoreline change statistics from the North Shore B coastal region from the Annisquam River in Gloucester to the west side of Deer Island in Boston Harbor (NorthShoreB_shorelines.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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KauaiW_shorelines - Shorelines of the western coastal region of Kauai, Hawaii, from Oomano to Polihale, used in shoreline change analysis.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Onshore/offshore baseline for the North Shore coastal region generated to calculate shoreline change rates for the North Shore coastal region from North Salisbury at the New Hampshire border to the west side of Deer Island in Boston Harbor (NorthShore_baseline.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Shorelines used to calculate shoreline change statistics from the North Shore A coastal region from North Salisbury at the New Hampshire border to the Annisquam River in Gloucester (NorthShoreA_shorelines.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Digital Shoreline Analysis System version 4.3 transects and short-term (1970-2009) linear regression shoreline change statistics within the Nantucket coastal region including the Nantucket Sound- and Atlantic Ocean- facing coasts of Nantucket, Muskeget and Tuckernuck Islands (Nantucket_transects_rates_STlr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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MauiK_shorelines - Shorelines of the Kihei coastal region of Maui, Hawaii, from Maalaea to Makena, used in shoreline change analysis.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and short-term (1970-2009) end point shoreline change statistics within the Nantucket coastal region including the Nantucket Sound- and Atlantic Ocean- facing coasts of Nantucket, Muskeget and Tuckernuck Islands (Nantucket_transects_rates_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics for all data available within the Nantucket coastal region including the Nantucket Sound- and Atlantic Ocean- facing coasts of Nantucket, Muskeget and Tuckernuck Islands (Nantucket_transects_rates_LTw.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics without shorelines from 1970-1979 and 1994 within the Nantucket coastal region including the Nantucket Sound- and Atlantic Ocean- facing coasts of Nantucket, Muskeget and Tuckernuck Islands (Nantucket_transects_rates_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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MauiN_shorelines - Shorelines of the northern coastal region of Maui, Hawaii, from Waihee to Kuau, used in shoreline change analysis.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Shorelines used to calculate shoreline change statistics from within the Nantucket coastal region including the Nantucket Sound and Atlantic Ocean-facing coasts of Nantucket, Muskeget and Tuckernuck Islands (Nantucket_shorelines.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term (Linear Regression Rate) shoreline change statistics within the Nantucket coastal region including the Nantucket Sound- and Atlantic Ocean- facing coasts of Nantucket, Muskeget and Tuckernuck Islands (Nantucket_intersects_STlr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term (End Point Rate) shoreline change statistics within the Nantucket coastal region including the Nantucket Sound- and Atlantic Ocean- facing coasts of Nantucket, Muskeget and Tuckernuck Islands (Nantucket_intersects_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
MauiW_shorelines - Shorelines of the western coastal region of Maui, Hawaii, from Ukumehame to Honolua, used in shoreline change analysis.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics within the Nantucket coastal region including the Nantucket Sound- and Atlantic Ocean- facing coasts of Nantucket, Muskeget and Tuckernuck Islands (Nantucket_intersects_LTw.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics excluding the 1970-1979 and 1994 shorelines within the Nantucket coastal region including the Nantucket Sound- and Atlantic Ocean- facing coasts of Nantucket, Muskeget and Tuckernuck Islands Massachusetts-Rhode Island border (Nantucket_intersects_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Onshore/offshore baseline for Nantucket coastal region generated to calculate shoreline change rates for the Nantucket coastal region including the Nantucket Sound- and Atlantic Ocean- facing coasts of Nantucket, Muskeget and Tuckernuck Islands (Nantucket_baseline.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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OahuE_shorelines - Shorelines of the eastern coastal region of Oahu, Hawaii, from Kahuku to Makapuu, used in shoreline change analysis.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and short-term (1970-2009) linear regression shoreline change statistics within the Martha's Vineyard coastal region including the Vineyard Sound-, Nantucket Sound- and Atlantic Ocean- facing coasts of Martha's Vineyard and Nomans Land (MarthasVineyard_transects_rates_STlr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Intersects for the Buzzards Bay coastal region in Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Long-term and short-term shoreline change rates for the Buzzards Bay coastal region in Massachusetts calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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OahuN_shorelines - Shorelines of the northern coastal region of Oahu, Hawaii, from Camp Erdman to Kahuku, used in shoreline change analysis.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Intersects for the Cape Cod Bay coastal region in Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Shoreline Change Rates for Barnegat and Great Bay, NJ: 1839 to 2012 (ver 1.1, December 2017)
This dataset represents shoreline change rates for the New Jersey coastline (Point Pleasant, NJ to Longport, NJ) from 1839 to 2012. Shoreline data were obtained from multiple data sources, including the U.S. Geological Survey (USGS), National Oceanic and Atmospheric Administration (NOAA), and New Jersey Department of Environmental Protection (NJDEP). Datasets were compiled and analyzed using the R package Analyzing Moving Boundaries Using R (AMBUR) program. Rates of shoreline change can be used for ... |
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2011 profile-derived mean high water shorelines of the Outer Cape of MA used in shoreline change analysis
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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Midshore baseline for the exposed eastern Beaufort Sea coast of Alaska (U.S. Canadian Border to the Hulahula River) generated to calculate shoreline change rates
This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the exposed, open-ocean coast of Alaska between the U.S. Canadian Border to the Hulahula River for the time period 1947 to 2017. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates. |
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Shorelines from 1947 to 2017 for the central Beaufort Sea coast of Alaska (Hulahula River to the Colville River) used in shoreline change analysis
This dataset includes historical shoreline positions that span 70 years, from 1947 to 2017, for the north coast of Alaska between the Hulahula River and the Colville River. Shorelines were compiled from topographic survey sheets (T-sheets; National Oceanic and Atmospheric Administration (NOAA)), aerial orthophotographs (U.S. Geological Survey (USGS), National Aeronautics and Space Administration (NASA), Conoco-Philips (CP), British Petroleum Alaska (BPXA), and NOAA), satellite imagery (U.S. Fish and ... |
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Midshore baseline for the sheltered central Beaufort Sea coast of Alaska (Hulahula River to the Colville River) generated to calculate shoreline change rates
This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the mainland coast of Alaska sheltered by barrier islands from the Hulahula River and the Colville River for the time period 1947 to 2017. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates. |
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Midshore baseline for the exposed central Beaufort Sea coast of Alaska (Hulahula River to the Colville River) generated to calculate shoreline change rates
This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the exposed, open-ocean coast of Alaska between the Hulahula River and the Colville River for the time period 1947 to 2017. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates. |
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Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics for all data available within the South Cape Cod coastal region from Stage Harbor Light in Chatham to Nobska Point in Woods Hole (SouthCapeCod_transects_rates_LTw.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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2014 profile-derived mean high water shorelines of the Outer Cape of MA used in shoreline change analysis
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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2013 profile-derived mean high water shorelines of Martha's Vineyard, MA used in shoreline change analysis
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics without shorelines from 1970-1979 and 1994 within the South Cape Cod coastal region from Stage Harbor Light in Chatham to Nobska Point in Woods Hole (SouthCapeCod_transects_rates_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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2014 profile-derived mean high water shorelines of the south shore of Cape Cod, MA used in shoreline change analysis
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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2013 profile-derived mean high water shorelines of the north shore of Martha's Vineyard, MA used in shoreline change analysis
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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Shorelines used to calculate shoreline change statistics from the South Cape Cod coastal region of Massachusetts from Stage Harbor Light in Chatham to Nobska Point in Woods Hole (SouthCapeCod_shorelines.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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2010 profile-derived mean high water shorelines of the South Coast of MA used in shoreline change analysis
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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Shorelines used to calculate shoreline change statistics from the Outer Cape Cod coastal region from Long Point in Provincetown to Monomoy Island, Massachusetts (OuterCapeCod_shorelines.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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2013 profile-derived mean high water shorelines of the South Coast of MA used in shoreline change analysis.
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term (Linear Regression Rate) shoreline change statistics for the South Cape Cod coastal region from Stage Harbor Light in Chatham to Nobska Point in Woods Hole (SouthCapeCod_intersects_STlr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Digital Shoreline Analysis System version 4.3 transects and short-term (1970-2009) end point shoreline change statistics for the South Shore coastal region from Hewitts Cove in Hingham to the Cape Cod Canal in Sandwich (SouthShore_transects_rates_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term (Linear Regression Rate) shoreline change statistics for the Outer Cape Cod coastal region from Long Point in Provincetown to Monomoy Island (OuterCapeCod_intersects_STlr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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2011 profile-derived mean high water shorelines of the South Shore of MA used in shoreline change analysis
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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2013-2014 profile-derived mean high water shorelines of the South Shore of MA used in shoreline change analysis
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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2014 profile-derived mean high water shorelines of Cape Cod Bay, MA used in shoreline change analysis
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term (End Point Rate) shoreline change statistics for the South Cape Cod coastal region from Stage Harbor Light in Chatham to Nobska Point in Woods Hole (SouthCapeCod_intersects_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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2018 mean high water shoreline of the coast of MA used in shoreline change analysis
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast by compiling a database of historical (mid 1800's-1989) shoreline positions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline ... |
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2012 profile-derived mean high water shorelines of Nantucket, MA used in shoreline change analysis
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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Rate of shoreline change of marsh units in eastern Long Island salt marsh complex, New York (ver. 2.0, March 2024)
This data release contains coastal wetland synthesis products for the geographic region of eastern Long Island, New York, including the north and south forks, Gardiners Island, and Fishers Island. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, mean tidal range, and shoreline change rate are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific ... |
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Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics for the South Cape Cod coastal region from Stage Harbor Light in Chatham to Nobska Point in Woods Hole (SouthCapeCod_intersects_LTw.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics for all data available within the South Shore coastal region from Hewitts Cove in Hingham to the Cape Cod Canal in Sandwich (SouthShore_transects_rates_LTw.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Short-term shoreline change rates for the Georgia coastal region using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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2013 profile-derived mean high water shorelines of Nantucket, MA used in shoreline change analysis
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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Point shapefile of probability of shoreline change along the U.S. Atlantic Coast (ProbSLC_AtlanticData.shp)
During the 21st century, sea-level rise will have a wide range of effects on coastal environments, human development and infrastructure in coastal areas. Consequently there is a need to develop modeling or other analytical approaches that can be used to evaluate potential impacts to inform coastal management. This shapefile provides the data that were used to develop and evaluate the performance of a Bayesian network (BN) that was developed to predict long-term shoreline change associated with sea-level ... |
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Offshore baseline for Cape Cod coastal region generated to calculate shoreline change rates from Provincetown to the southern end of Monomoy Island, Massachusetts (CapeCod_baseline.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics excluding the 1970-1979 and 1994 shorelines within the South Cape Cod coastal region from Stage Harbor Light in Chatham to Nobska Point in Woods Hole (SouthCapeCod_intersects_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Shorelines of the Cape Cod coastal region from Provincetown to the southern end of Monomoy Island, Massachusetts, used in shoreline change analysis (CapeCod_shorelines.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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2013 profile-derived mean high water shorelines of the north shore of Nantucket, MA used in shoreline change analysis
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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Offshore baseline for Delmarva North coastal region generated to calculate shoreline change rates from Cape Henlopen, Delaware to the southern end of Assateague Island, Virginia (DelmarvaN_baseline.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics without shorelines from 1970-1979 and 1994 within the South Shore coastal region from Hewitts Cove in Hingham to the Cape Cod Canal in Sandwich (SouthShore_transects_rates_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Shorelines of the Delmarva North coastal region from Cape Henlopen, Delaware to the southern end of Assateague Island, Virginia, used in shoreline change analysis (DelmarvaN_shorelines.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Baseline for the South Cape Cod coastal region generated to calculate shoreline change rates from Stage Harbor Light in Chatham to Nobska Point in Woods Hole (SouthCapeCod_baseline.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Offshore baseline for the Delmarva South/Southern Virginia region generated to calculate shoreline change rates from Wallops Island, Virginia to the Virginia/North Carolina border (DelmarvaS_SVA_baseline.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Shorelines used to calculate shoreline change statistics from the South Shore coastal region from Hewitts Cove in Hingham to the Cape Cod Canal in Sandwich (SouthShore_shorelines.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Shorelines of the Delmarva South and Southern Virginia coastal region from Wallops Island, Virginia to the Virginia/North Carolina border, used in shoreline change analysis (DelmarvaS_SVA_shorelines.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term linear regression rate (LRR) shoreline change statistics for the South Shore coastal region from Hewitts Cove in Hingham to the Cape Cod Canal in Sandwich (SouthShore_intersects_STlr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Offshore baseline for Greater Boston coastal region from the southern side of Cape Ann, Massachusetts to Sandy Neck Beach in Sandwich, Massachusetts, generated to calculate shoreline change rates (GreaterBoston_baseline.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Shorelines of the Greater Boston coastal region from the southern side of Cape Ann, Massachusetts to Sandy Neck Beach in Sandwich, Massachusetts, used in shoreline change analysis (GreaterBoston_shorelines.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Offshore baseline for Long Island coastal region generated to calculate shoreline change rates for the Long Island region from Montauk Point to the entrance of Raritan Bay, New York (LongIsland_baseline.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Digital Shoreline Analysis System version 4.3 transects and short-term (1970-2009) linear regression shoreline change statistics for the Outer Cape Cod coastal region from Long Point in Provincetown to Monomoy Island (OuterCapeCod_transects_rates_STlr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Shorelines of the Long Island coastal region used in shoreline change analysis for the Long Island region from Montauk Point to the entrance of Raritan Bay, New York (LongIsland_shorelines.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term (End Point Rate)shoreline change statistics for the South Shore coastal region from Hewitts Cove in Hingham to the Cape Cod Canal in Sandwich (SouthShore_intersects_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Offshore baseline for Massachusetts Islands coastal region generated to calculate shoreline change rates for Martha's Vineyard and Nantucket (MA_Islands_baseline.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Baseline for the coastal region around Boston, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Shorelines of the Massachusetts Islands coastal region including Martha's Vineyard and Nantucket, used in shoreline change analysis (MA_Islands_shorelines.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Digital Shoreline Analysis System version 4.3 transects and short-term (1970-2009) end point rate shoreline change statistics for the Outer Cape Cod coastal region from Long Point in Provincetown to Monomoy Island (OuterCapeCod_transects_rates_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Offshore baseline for New England North coastal region from Popham Beach, Maine to the northern side of Cape Ann, Massachusetts, generated to calculate shoreline change rates (NE_North_baseline.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Shorelines of the New England North coastal region from Popham Beach, Maine to the northern side of Cape Ann, Massachusetts, used in shoreline change analysis (NewEnglandN_shorelines.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Offshore baseline for New England South coastal region from Dartmouth, Massachusetts to Napatree Point, Rhode Island, generated to calculate shoreline change rates (NE_South_baseline.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term (End Point Rate) shoreline change statistics for the Outer Cape Cod coastal region from Long Point in Provincetown to Monomoy Island (OuterCapeCod_intersects_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Shorelines of the New England South coastal region used in shoreline change analysis from Dartmouth, Massachusetts to Napatree Point, Rhode Island (NewEnglandS_shorelines.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics for the Outer Cape Cod coastal region from Long Point in Provincetown to Monomoy Island (OuterCapeCod_intersects_LTw.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Offshore baseline for New Jersey North coastal region generated to calculate shoreline change rates from Sandy Hook to Little Egg Inlet, New Jersey (NJN_baseline.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics excluding the 1970's and 1994 shorelines within the Outer Cape Cod coastal region from Long Point in Provincetown to Monomoy Island (OuterCapeCod_intersects_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Shorelines of the New Jersey North coastal region used in shoreline change analysis from Sandy Hook to Little Egg Inlet, New Jersey (NewJerseyN_shorelines.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Onshore/offshore baseline for the Outer Cape Cod coastal region generated to calculate shoreline change rates from Long Point in Provincetown to Monomoy Island (OuterCapeCod_baseline.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Offshore baseline for New Jersey South coastal region generated to calculate shoreline change rates from Little Egg Inlet to Cape May, New Jersey (NJS_baseline.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Intersects for coastal region around Boston, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Shorelines of the New Jersey South coastal region used in shoreline change analysis from Little Egg Inlet to Cape May, New Jersey (NewJerseyS_shorelines.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Long-term and short-term shoreline change rates for coastal region around Boston, Massachusetts calculated without the proxy-datum bias using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Digital Shoreline Analysis System version 4.3 transects and short-term (1970-2009) linear regression shoreline change statistics for all data available in the North Shore coastal region from North Salisbury at the New Hampshire border to the west side of Deer Island including the Boston Harbor Islands (NorthShore_transects_rates_STlr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Digital Shoreline Analysis System version 4.3 transects and short-term (1970-2009) end point shoreline change statistics for the North Shore coastal region from North Salisbury at the New Hampshire border to the west side of Deer Island in Boston Harbor (NorthShore_transects_rates_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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KauaiE_shorelines - Shorelines of the eastern coastal region of Kauai, Hawaii, from Papaa to Nawiliwili, used in shoreline change analysis.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics for all data available in the North Shore coastal region from North Salisbury at the New Hampshire border to the to the west side of Deer Island in Boston Harbor (NorthShore_transects_rates_LTw.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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2010-2022 New Jersey and New York Beach Shoreline Change
This dataset defines shoreline change rates for each 10-meter (m)-wide profile calculated via endpoint rate and linear regression from Himmelstoss and others (2018). Shoreline change rates were calculated for two time periods: pre-Sandy (2010-2012) and post-Sandy (2012-2022). The profiles were derived from light detection and ranging (lidar) digital elevation models (DEMs). Refer to Doran and others (2017) for more information about the source lidar data. These data support the National Fish and Wildlife ... |
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Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics without shorelines from 1970-1979 and 1994 within the North Shore coastal region from North Salisbury at the New Hampshire border to the west side of Deer Island in Boston Harbor (NorthShore_transects_rates_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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KauaiN_shorelines - Shorelines of the northern coastal region of Kauai, Hawaii, from Haena to Moloaa, used in shoreline change analysis
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term (Linear Regression Rate) shoreline change statistics for the North Shore coastal region from North Salisbury at the New Hampshire border to the west side of Deer Island in Boston Harbor (NorthShore_intersects_STlr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Baseline for the Buzzards Bay coastal region in Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term (End Point Rate) shoreline change statistics for the North Shore coastal region from North Salisbury at the New Hampshire border to the west side of Deer Island in Boston Harbor (NorthShore_intersects_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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KauaiS_shorelines - Shorelines of the southern coastal region of Kauai, Hawaii, from Waimea to Kipu Kai, used in shoreline change analysis.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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OahuS_shorelines - Shorelines of the southern coastal region of Oahu, Hawaii, from Barbers Point to Sandy Beach, used in shoreline change analysis.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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OahuW_shorelines - Shorelines of the western coastal region of Oahu, Hawaii, from Yokohama to Tracks Beach, used in shoreline change analysis.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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High-resolution shoreline change measurements (1997-2005) from Corolla to Cape Hatteras, NC (swash_shorelines.shp, geographic, WGS 84)
The northeastern North Carolina coastal system, from False Cape, Virginia, to Cape Lookout, North Carolina, has been studied by a cooperative research program that mapped the Quaternary geologic framework of the estuaries, barrier islands, and inner continental shelf. This information provides a basis to understand the linkage between geologic framework, physical processes, and coastal evolution at time scales from storm events to millennia. The study area attracts significant tourism to its parks and ... |
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Offshore baseline for the Oregon coastal region generated to calculate shoreline change rates (OR_baseline.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and short-term (1970-2009) end point shoreline change statistics within the Martha's Vineyard coastal region including the Vineyard Sound-, Nantucket Sound- and Atlantic Ocean- facing coasts of Martha's Vineyard and Nomans Land (MarthasVineyard_transects_rates_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Shorelines of the Oregon coastal region used in shoreline change analysis (OR_shorelines.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics for all data available within the Martha's Vineyard coastal region including the Vineyard Sound-, Nantucket Sound- and Atlantic Ocean- facing coasts of Martha's Vineyard and Nomans Land (MarthasVineyard_transects_rates_LTw.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics without shorelines from 1970-1979 and 1994 within the Martha's Vineyard coastal region including the Vineyard Sound-, Nantucket Sound- and Atlantic Ocean- facing coasts of Martha's Vineyard and Nomans Land (MarthasVineyard_transects_rates_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Offshore baseline for the Washington coastal region generated to calculate shoreline change rates (WA_baseline.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Baseline for the Cape Cod Bay coastal region in Massachusetts, generated to calculate shoreline change rates (without the proxy-datum bias) using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Shorelines of the Washington coastal region used in shoreline change analysis (WA_shorelines.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Baseline for the Cape Cod Bay coastal region in Massachusetts, generated to calculate shoreline change rates (with the proxy-datum bias) using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
Info |
Shorelines used to calculate shoreline change statistics from the Martha's Vineyard coastal region including Vineyard Sound, Nantucket Sound, and the Atlantic Ocean-facing coasts of Martha's Vineyard and Nomans Land (MarthasVineyard_shorelines.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Baseline_OpenOcean.shp - Baseline Along the Open-Ocean (South-Facing) Coast of Dauphin Island, Alabama, Generated to Calculate Shoreline Change Rates.
Analysis of shoreline change for Dauphin Island, Alabama was conducted using the U.S. Geological Survey (USGS) Digital Shoreline Analysis System (DSAS) v.4.3 for ArcMap (Thieler and others, 2009) and vector shorelines derived from air photos and lidar elevation surveys. DSAS-generated transects were cast at 100-meter intervals along a user defined shore-parallel baseline. The intersections of transects with the mean high water (MHW) shoreline positions are identified by intercept points. The rate of ... |
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Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term (Linear Regression Rate) shoreline change statistics within the Martha's Vineyard coastal region including the Vineyard Sound-, Nantucket Sound- and Atlantic Ocean- facing coasts of Martha's Vineyard and Nomans Land (MarthasVineyard_intersects_STlr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Baseline_BackBarrier.shp - Baseline Along the Back-Barrier (North-Facing) Coast of Dauphin Island, Alabama, Generated to Calculate Shoreline Change Rates.
Analysis of shoreline change for Dauphin Island, Alabama was conducted using the U.S. Geological Survey (USGS) Digital Shoreline Analysis System (DSAS) v.4.3 for ArcMap (Thieler and others, 2009) and vector shorelines derived from air photos and lidar elevation surveys. DSAS-generated transects were cast at 100-meter intervals along a user defined shore-parallel baseline. The intersections of transects with the mean high water (MHW) shoreline positions are identified by intercept points. The rate of ... |
Info |
Baseline for the coast of Puerto Rico's main island generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1 (ver. 2.0, March 2023)
The U.S. Geological Survey (USGS) maintains shoreline positions for the United States' coasts from both older sources, such as aerial photographs or topographic surveys, and contemporary sources, such as lidar-point clouds and digital elevation models. These shorelines are compiled and analyzed in the USGS Digital Shoreline Analysis System (DSAS), version 5.1 software to calculate rates of change. Keeping a record of historical shoreline positions is an effective method to monitor change over time, enabling ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term (End Point Rate) shoreline change statistics within the Martha's Vineyard coastal region including the Vineyard Sound-, Nantucket Sound- and Atlantic Ocean- facing coasts of Martha's Vineyard and Nomans Land (MarthasVineyard_intersects_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics within the Martha's Vineyard coastal region including the Vineyard Sound-, Nantucket Sound- and Atlantic Ocean- facing coasts of Martha's Vineyard and Nomans Land (MarthasVineyard_intersects_LTw.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Shoreline change rates for the coast of Puerto Rico's main island calculated using the Digital Shoreline Analysis System version 5.1 (ver. 2.0, March 2023)
The U.S. Geological Survey (USGS) maintains shoreline positions for the United States coasts from both older sources, such as aerial photographs or topographic surveys, and contemporary sources, such as lidar-point clouds and digital elevation models. These shorelines are compiled and analyzed in the USGS Digital Shoreline Analysis System (DSAS), version 5.1 software to calculate rates of change. Keeping a record of historical shoreline positions is an effective method to monitor change over time, enabling ... |
Info |
Baseline for the islands of Vieques and Culebra, Puerto Rico, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) maintains shoreline positions for the United States coasts from both older sources, such as aerial photographs or topographic surveys, and contemporary sources, such as lidar-point clouds and digital elevation models. These shorelines are compiled and analyzed in the USGS Digital Shoreline Analysis System (DSAS), version 5.1 software to calculate rates of change. Keeping a record of historical shoreline positions is an effective method to monitor change over time, enabling ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics excluding the 1970-1979 and 1994 shorelines within the Martha's Vineyard coastal region including the Vineyard Sound-, Nantucket Sound- and Atlantic Ocean- facing coasts of Martha's Vineyard and Nomans Land (MarthasVineyard_intersects_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Shoreline change rates for the islands of Vieques and Culebra, Puerto Rico, calculated using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) maintains shoreline positions for the United States coasts from both older sources, such as aerial photographs or topographic surveys, and contemporary sources, such as lidar-point clouds and digital elevation models. These shorelines are compiled and analyzed in the USGS Digital Shoreline Analysis System (DSAS), version 5.1 software to calculate rates of change. Keeping a record of historical shoreline positions is an effective method to monitor change over time, enabling ... |
Info |
Offshore baseline for Martha's Vineyard coastal region generated to calculate shoreline change rates within the Martha's Vineyard coastal region including the Vineyard Sound-, Nantucket Sound- and Atlantic Ocean- facing coasts of Martha's Vineyard and Nomans Land (MarthasVineyard_baseline.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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SOCAL_TRANSECTS_ST - Short-Term Shoreline Change Rates for Southern California Generated at a 50m Transect Spacing, 1971-1998
Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate ... |
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SOCAL_TRANSECTS_LT - Long-Term Shoreline Change Rates for Southern California Generated at a 50m Transect Spacing, 1852-1998
Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate ... |
Info |
SOCAL_INTERSECTS_ST - Short-Term Transect-Shoreline Intersection Points for Southern California Generated to Calculate Shoreline Change Rates
Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate ... |
Info |
SOCAL_INTERSECTS_LT - Long-Term Transect-Shoreline Intersection Points for Southern California Generated to Calculate Shoreline Change Rates
Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and short-term (1970-2009) end point shoreline change statistics for the Elizabeth Islands coastal region from Nonamesset Island southwest of Woods Hole to Cuttyhunk Island north of Martha's Vineyard (ElizabethIslands_transects_rates_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Offshore baseline for the northeastern Florida (FLne) coastal region generated to calculate shoreline change rates
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Shorelines of the northeastern Florida (FLne) coastal region used in shoreline change analysis
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics without shorelines from 1970-1979 and 1994 in the Elizabeth Islands coastal region from Nonamesset Island southwest of Woods Hole to Cuttyhunk Island north of Martha's Vineyard (ElizabethIslands_transects_rates_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Shorelines used to calculate shoreline change statistics from the Elizabeth Islands coastal region of Massachusetts from Nonamesset Island southwest of Woods Hole to Cuttyhunk Island north of Martha's Vineyard (ElizabethIslands_shorelines.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Baseline for the Virginia coastal region, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term (End Point Rate) shoreline change statistics for the Elizabeth Islands coastal region from Nonamesset Island southwest of Woods Hole to Cuttyhunk Island north of Martha's Vineyard (ElizabethIslands_intersects_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Offshore baseline for the southeastern Florida (FLse) coastal region generated to calculate shoreline change rates
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Shorelines of the southeastern Florida (FLse) coastal region used in shoreline change analysis
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics for the Elizabeth Islands coastal region from Nonamesset Island southwest of Woods Hole to Cuttyhunk Island north of Martha's Vineyard (ElizabethIslands_intersects_LTw.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics excluding the 1970-1979 and 1994 shorelines within the Elizabeth Islands coastal region from Nonamesset Island southwest of Woods Hole to Cuttyhunk Island north of Martha's Vineyard (ElizabethIslands_intersects_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Baseline for Elizabeth Islands coastal region generated to calculate shoreline change rates from Nonamesset Island southwest of Woods Hole to Cuttyhunk Island north of Martha's Vineyard (ElizabethIslands_baseline.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and short-term (1970-2009) linear regression rate shoreline change statistics within the Cape Cod Bay coastal region from the Cape Cod Canal in Sandwich to Long Point in Provincetown (CapeCodBay_transects_rates_STlr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Offshore baseline for the Georgia (GA) coastal region generated to calculate shoreline change rates
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Shorelines of the Georgia (GA) coastal region used in shoreline change analysis
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and short-term (1970-2009) end point rate shoreline change statistics within the Cape Cod Bay coastal region from the Cape Cod Canal in Sandwich to Long Point in Provincetown (CapeCodBay_transects_rates_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics for all data available within the Cape Cod Bay coastal region from the Cape Cod Canal in Sandwich to Long Point in Provincetown (CapeCodBay_transects_rates_LTw.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics without shorelines from 1970-1979 and 1994 within the Cape Cod Bay coastal region from the Cape Cod Canal in Sandwich to Long Point in Provincetown (CapeCodBay_transects_rates_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Shorelines used to calculate shoreline change statistics from Cape Cod Bay coastal region from the Cape Cod Canal in Sandwich to Long Point in Provincetown (CapeCodBay_shorelines.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Offshore baseline for the central North Carolina (NCcentral) coastal region generated to calculate shoreline change rates
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Shorelines of the central North Carolina (NCcentral) coastal region used in shoreline change analysis
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term (Linear Regression Rate) shoreline change statistics for the Cape Cod Bay coastal region from Cape Cod Canal in Sandwich to Long Point in Provincetown (CapeCodBay_intersects_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term (End Point Rate) shoreline change statistics for the Cape Cod Bay coastal region from the Cape Cod Canal in Sandwich to Long Point in Provincetown (CapeCodBay_intersects_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics for the Cape Cod Bay coastal region from Cape Cod Canal in Sandwich to Long Point in Provincetown (CapeCodBay_intersects_LTw.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics excluding the 1970-1979 and 1994 shorelines within the Cape Cod Bay coastal region from the Cape Cod Canal in Sandwich to Long Point in Provincetown (CapeCodBay_intersects_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Offshore baseline for the northern North Carolina (NCnorth) coastal region generated to calculate shoreline change rates
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Shorelines of the northern North Carolina (NCnorth) coastal region used in shoreline change analysis
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Onshore/offshore baseline for Cape Cod Bay coastal region generated to calculate shoreline change rates from Cape Cod Canal in Sandwich to Long Point in Provincetown (CapeCodBay_baseline.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Digital Shoreline Analysis System version 4.3 transects and short-term (1970-2009) linear regression shoreline change statistics for the Buzzards Bay coastal region from Nobska Point in Woods Hole, to Westport at the Rhode Island border (BuzzardsBay_transects_rates_STlr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Digital Shoreline Analysis System version 4.3 transects and short-term (1970-2009) end point shoreline change statistics for the Buzzards Bay coastal region from Nobska Point in Woods Hole, to Westport at the Rhode Island border (BuzzardsBay_transects_rates_STlr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics for all data available in the Buzzards Bay coastal region from Nobska Point in Woods Hole to Westport at the Rhode Island border (BuzzardsBay_transects_rates_LTw.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Offshore baseline for the southern North Carolina (NCsouth) coastal region generated to calculate shoreline change rates
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Shorelines of the southern North Carolina (NCsouth) coastal region used in shoreline change analysis
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics without shorelines from 1970-1979 and 1994 in the Buzzards Bay coastal region from Nobska Point in Woods Hole, to Westport at the Rhode Island border (BuzzardsBay_transects_rates_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Shorelines used to calculate shoreline change statistics from the Buzzards Bay coastal region of Massachusetts from Nobska Point in Woods Hole to Westport at the Rhode Island border (BuzzardsBay_shorelines.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term (Linear Regression Rate) shoreline change statistics for the Buzzards Bay coastal region from Nobska Point in Woods Hole, to Westport at the Rhode Island border (BuzzardsBay_intersects_STlr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term (End Point Rate) shoreline change statistics for the Buzzards Bay coastal region from Nobska Point in Woods Hole, to Westport at the Rhode Island border (BuzzardsBay_intersects_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Offshore baseline for the western North Carolina (NCwest) coastal region generated to calculate shoreline change rates
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Shorelines of the western North Carolina (NCwest) coastal region used in shoreline change analysis
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics for the Buzzards Bay coastal region from Nobska Point in Woods Hole, to Westport at the Rhode Island border (BuzzardsBay_intersects_LTw.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics excluding the 1970-1979 and 1994 shorelines within the Buzzards Bay coastal region from Nobska Point in Woods Hole, to Westport at the Rhode Island border (BuzzardsBay_intersects_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Baseline for Buzzards Bay coastal region generated to calculate shoreline change rates from Nobska Point in Woods Hole to Westport at the Massachusetts-Rhode Island border (BuzzardsBay_baseline.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Offshore baseline for the South Carolina (SC) coastal region generated to calculate shoreline change rates
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Shorelines of the South Carolina (SC) coastal region used in shoreline change analysis
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and short-term (1970-2009) linear regression shoreline change statistics for the Boston coastal region from Carson Beach in South Boston to Weymouth River, including the Boston Harbor Islands (Boston_transects_rates_STlr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Long-term and short-term shoreline change rates for the Cape Cod Bay coastal region in Massachusetts calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Baseline for the northern coast of Martha's Vineyard, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Baseline for the southern coast Martha's Vineyard, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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SOCAL_BASELINE - Offshore Baseline for Southern California Generated to Calculate Shoreline Change Rates
Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate ... |
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NORCAL_TRANSECTS_ST - Short-Term Shoreline Change Rates for Northern California Generated at a 50m Transect Spacing, 1952-2002
Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate ... |
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Offshore baseline for the Alabama coastal region generated to calculate shoreline change rates
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and short-term (1970-2009) end point rate shoreline change statistics for the Boston coastal region from Carson Beach in South Boston to Weymouth River, including the Boston Harbor Islands (Boston_transects_rates_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Shorelines of the Alabama coastal region used in shoreline change analysis
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Intersects for Martha's Vineyard, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Long-term and short-term shoreline change rates for Martha's Vineyard, Massachusetts calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Baseline for the northern coast of Nantucket, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Offshore baseline for the Florida north (FLnorth) coastal region generated to calculate shoreline change rates
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Baseline for the southern coast of Nantucket, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
Info |
Shorelines of the Florida north (FLnorth) coastal region used in shoreline change analysis
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Intersects for Nantucket, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
Info |
Long-term and short-term shoreline change rates for Nantucket, Massachusetts calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Baseline for the coastal region north of Boston, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Offshore baseline for the Florida west (FLwest) coastal region generated to calculate shoreline change rates
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Intersects for the coastal region north of Boston, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
Info |
Shorelines of the Florida west (FLwest) coastal region used in shoreline change analysis
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Long-term and short-term shoreline change rates for the coastal region north of Boston, Massachusetts calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Baseline for the South Carolina coastal region, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Baseline for the backshore of Outer Cape Cod, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Offshore baseline for the Louisiana coastal region generated to calculate shoreline change rates
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Intersects for the coastal region of South Carolina generated to calculate long-term shoreline change rates using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
Info |
Shorelines of the Louisiana coastal region used in shoreline change analysis
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Intersects for coastal region of South Carolina generated to calculate short-term shoreline change rates using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
Info |
Long-term shoreline change rate transects for the South Carolina coastal region, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
Info |
Short-term shoreline change rate transects for the South Carolina coastal region using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
Info |
Offshore baseline for the Mississippi coastal region generated to calculate shoreline change rates
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Baseline for the east facing coast of Cape Cod, Massachusetts, from Monomoy to Provincetown, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
Info |
Shorelines of the Mississippi coastal region used in shoreline change analysis
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
National Assessment of Hurricane-Induced Coastal Erosion Hazards: Puerto Rico
This dataset contains information on the probabilities of hurricane-induced erosion (collision, inundation and overwash) for each 100-meter (m) section of the Puerto Rico coast for category 1-5 hurricanes. The analysis is based on a storm-impact scaling model that uses observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast will respond to the direct landfall of category 1-5 hurricanes. Hurricane-induced water levels, due to both surge and waves, are ... |
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Intersects for Outer Cape Cod, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
Info |
Long-term and short-term shoreline change rates for Outer Cape Cod, Massachusetts calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
Info |
Offshore baseline for the Texas east (TXeast) coastal region generated to calculate shoreline change rates
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics for all data available in the Boston coastal region Carson Beach in South Boston to Weymouth River, including the Boston Harbor Islands (Boston_transects_rates_LTw.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
Info |
Shorelines of the Texas east (TXeast) coastal region used in shoreline change analysis
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics without shorelines from 1970-1979 and 1994 in the Boston coastal region from Carson Beach in South Boston to Weymouth River, including the Boston Harbor Islands (Boston_transects_rates_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Shorelines used to calculate shoreline change statistics Boston coastal region from Carson Beach in South Boston to Weymouth River, including the Boston Harbor Islands (Boston_shorelines.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Offshore baseline for the Texas west (TXwest) coastal region generated to calculate shoreline change rates
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term (Linear Regression Rate) shoreline change statistics for the Boston coastal region from Carson Beach in South Boston to Weymouth River, including the Boston Harbor Islands (Boston_intersects_STlr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Shorelines of the Texas west (TXwest) coastal region used in shoreline change analysis
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Baseline for the southern coast of Cape Cod, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate short-term (End Point Rate) shoreline change statistics for the Boston coastal region from Carson Beach in South Boston to Weymouth River, including the Boston Harbor Islands (Boston_intersects_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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NORCAL_TRANSECTS_LT - Long-Term Shoreline Change Rates for Northern California Generated at a 50 m Transect Spacing, 1854-2002
Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate ... |
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NORCAL_INTERSECTS_ST - Short-Term Transect-Shoreline Intersection Points for Northern California Generated to Calculate Shoreline Change Rates
Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate ... |
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NORCAL_INTERSECTS_LT - Long-Term Transect-Shoreline Intersection Points for Northern California Generated to Calculate Shoreline Change Rates
Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate ... |
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NORCAL_BASELINES - Offshore Baseline for Northern California Generated to Calculate Shoreline Change Rates
Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate ... |
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Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics for the Boston coastal region from Carson Beach in South Boston to Weymouth River, including the Boston Harbor Islands (Boston_intersects_LTw.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics excluding the 1970-1979 and 1994 shorelines within the Boston coastal region from Carson Beach in South Boston to Weymouth River, including the Boston Harbor Islands (Boston_intersects_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Offshore baseline for Boston coastal region generated to calculate shoreline change rates from Carson Beach in South Boston to Weymouth River on the Massachusetts mainland, and including the Boston Harbor Islands 9Boston_baseline.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Shoreline change rates in salt marsh units in Edwin B. Forsythe National Wildlife Refuge, New Jersey
Monitoring shoreline change is of interest in many coastal areas because it enables quantification of land loss over time. Evolution of shoreline position is determined by the balance between erosion and accretion along the coast. In the case of salt marshes, erosion along the water boundary causes a loss of ecosystem services, such as habitat provision, carbon storage, and wave attenuation. In terms of vulnerability, higher shoreline erosion rates indicate higher vulnerability. This dataset ... |
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CENCAL_TRANSECTS_ST - Short-Term Shoreline Change Rates for Central California Generated at a 50m Transect Spacing, 1971-1998
Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate ... |
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CENCAL_TRANSECTS_LT - Long-Term Shoreline Change Rates for Central California Generated at a 50 m Transect Spacing, 1853-2002
Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate ... |
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CENCAL_INTERSECTS_ST - Short-Term Transect-Shoreline Intersection Points for Central California Generated to Calculate Shoreline Change Rates
Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate ... |
Info |
CENCAL_INTERSECTS_LT - Long-Term Transect-Shoreline Intersection Points for Central California Generated to Calculate Shoreline Change Rates
Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate ... |
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CENCAL_BASELINE - Offshore Baseline for Central California Generated to Calculate Shoreline Change Rates
Rates of long-term and short-term shoreline change were generated in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2005-1304, Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Miller, T.M. The extension is designed to efficiently lead a user through the major steps of shoreline change analysis. This extension to ArcGIS contains three main components that define a baseline, generate ... |
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Shorelines for the northern Alaska coastal region used in shoreline change analysis, 1947 to 2005
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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WestBeaufort_sheltered_baselines.shp - Offshore baseline for the northern Alaska coastal region generated to calculate shoreline change rates along sheltered coastlines between the Colville River Delta and Point Barrow for the time period 1947 to 2005
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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Offshore baseline for the northern Alaska coastal region generated to calculate shoreline change rates along exposed coastlines between the Colville River Delta and Point Barrow for the time period 1947 to 2005
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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Shorelines for the northern Alaska coastal region used in shoreline change analysis, 1947 to 2012
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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Offshore baseline for the northern Alaska coastal region generated to calculate shoreline change rates along sheltered coastlines between Point Barrow and Icy Cape for the time period 1947 to 2012
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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Offshore baseline for the northern Alaska coastal region generated to calculate shoreline change rates along exposed coastlines between Point Barrow and Icy Cape for the time period 1947 to 2012
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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Shorelines for the northern Alaska coastal region used in shoreline change analysis, 1947 to 2003
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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Offshore baseline for the northern Alaska coastal region generated to calculate shoreline change rates along sheltered coastlines between the U.S.-Canadian border and the Okpilak-Hulahula River Delta for the time period 1947 to 2003
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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Offshore baseline for the northern Alaska coastal region generated to calculate shoreline change rates along exposed coastlines between the U.S.-Canadian border and the Okpilak-Hulahula River Delta for the time period 1947 to 2003
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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CentralBeaufort_shorelines.shp - Shorelines for the northern Alaska coastal region used in shoreline change analysis, 1947 to 2007
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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Offshore baseline for the northern Alaska coastal region generated to calculate shoreline change rates along sheltered coastlines between the Okpilak-Hulahula River Delta and the Colville River Delta for the time period 1947 to 2007
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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Offshore baseline for the northern Alaska coastal region generated to calculate shoreline change rates along exposed coastlines between the Okpilak-Hulahula River Delta and the Colville River Deltas for the time period 1947 to 2007
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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Shorelines from 1948 to 2016 for the north coast of Alaska, Icy Cape to Cape Prince Wales used in shoreline change analysis
This dataset includes shorelines that span 68 years, from 1948 to 2016, for the north coast of Alaska from Icy Cape to Cape Prince of Wales. Shorelines were compiled from topographic survey sheets (T-sheets; National Oceanic and Atmospheric Administration (NOAA)) and aerial orthophotographs (U.S. Geological Survey (USGS) and Alaska High Altitude Photography (AHAP)). Historical shoreline positions serve as easily understood features that can be used to describe the movement of beaches through time. These ... |
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Offshore baseline generated to calculate shoreline change rates for the north coast of Alaska, Icy Cape to Cape Prince of Wales
This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the exposed coast of Alaska from Icy Cape and Cape Prince Wales for the time period 1948 to 2016. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates. |
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Offshore baseline generated to calculate shoreline change rates near Barter Island, Alaska
This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the shorelines near Barter Island, Alaska for the time period 1947 to 2020. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates. |
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2015 Mean High Water Shorelines of the Puerto Rico coast used in Shoreline Change Analysis
The U.S. Geological Survey (USGS) maintains shoreline positions for the United States coasts from both older sources, such as aerial photos or topographic surveys, as well as contemporary sources like lidar point clouds and digital elevation models (DEMs). These shorelines are compiled and analyzed in the Digital Shoreline Analysis System (DSAS) software to compute rates of change. It is useful to keep a record of historical shoreline positions as a method of monitoring change over time to identify areas ... |
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2016 NOAA Mean High Water Shorelines of the Puerto Rico coast used in Shoreline Change Analysis
The U.S. Geological Survey (USGS) maintains shoreline positions for the United States coasts from both older sources, such as aerial photos or topographic surveys, as well as contemporary sources like lidar point clouds and digital elevation models (DEMs). These shorelines are compiled and analyzed in the Digital Shoreline Analysis System (DSAS) software to compute rates of change. It is useful to keep a record of historical shoreline positions as a method of monitoring change over time to identify areas ... |
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2016 USACE Mean High Water Shorelines of the Puerto Rico coast used in Shoreline Change Analysis
The U.S. Geological Survey (USGS) maintains shoreline positions for the United States coasts from both older sources, such as aerial photos or topographic surveys, as well as contemporary sources like lidar point clouds and digital elevation models (DEMs). These shorelines are compiled and analyzed in the Digital Shoreline Analysis System (DSAS) software to compute rates of change. It is useful to keep a record of historical shoreline positions as a method of monitoring change over time to identify areas ... |
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2018 Mean High Water Shorelines of the Puerto Rico coast used in Shoreline Change Analysis
The U.S. Geological Survey (USGS) maintains shoreline positions for the United States coasts from both older sources, such as aerial photos or topographic surveys, as well as contemporary sources like lidar point clouds and digital elevation models (DEMs). These shorelines are compiled and analyzed in the Digital Shoreline Analysis System (DSAS) software to compute rates of change. It is useful to keep a record of historical shoreline positions as a method of monitoring change over time to identify areas ... |
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Short-term shoreline change rates for Rincon, Puerto Rico 1994-2006 (st_transects.shp)
The 8 km of shoreline from Punta Higüero to Punta Cadena in Rincón, Puerto Rico is experiencing long-term coastal erosion. This study documents historical shoreline changes at Rincón for the period 1936-2006. Thirteen historical shoreline positions were compiled from existing data, new orthophotography, and GPS field surveys. Shoreline vectors represent the high water line at the time of the survey. |
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Intersects for the southern coastal region of Cape Cod Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Long-term and short-term shoreline change rates for the southern coastal region of Cape Cod, Massachusetts calculated without the proxy-datum bias using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Baseline for the coast south of Boston, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Intersects for coastal region of Virginia generated to calculate short-term shoreline change rates using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Long-term shoreline change rates for the Virginia coastal region, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Short-term shoreline change rates for the Virginia coastal region using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Shorelines of the Western Beaufort Sea, Alaska coastal region (Colville River to Point Barrow) used in shoreline change analysis
This dataset includes shorelines from 65 years ranging from 1947 to 2012 for the north coast of Alaska between the Colville River and Point Barrow. Shorelines were compiled from topographic survey sheets (T-sheets; National Oceanic and Atmospheric Administration (NOAA)), aerial orthophotographs (U.S. Geological Survey (USGS), National Aeronautics and Space Administration (NASA), and lidar elevation data(USGS). Historical shoreline positions serve as easily understood features that can be used to describe ... |
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Offshore baseline for the sheltered West Beaufort Sea, Alaska coastal region (Colville River to Point Barrow) generated to calculate shoreline change rates
This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the sheltered north coast of Alaska coastal region between the Colville River and Point Barrow for the time period 1947 to 2012. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates. |
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Offshore baseline for the exposed West Beaufort Sea, Alaska coastal region (Colville River to Point Barrow) generated to calculate shoreline change rates
This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the exposed north coast of Alaska coastal region between the Colville River and Point Barrow for the time period 1947 to 2012. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates. |
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Long and short-term shoreline change rate transects for the central North Carolina coastal region (NCcentral), calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Shorelines of the Eastern Chukchi Sea, Alaska coastal region (Point Barrow to Icy Cape) used in shoreline change analysis
This dataset includes shorelines from 65 years ranging from 1947 to 2012 for the north coast of Alaska between Point Barrow and Icy Cape. Shorelines were compiled from topographic survey sheets and Nautical Charts (T-sheet, Nautical Chart; National Oceanic and Atmospheric Administration (NOAA)), aerial orthophotographs (U.S. Geological Survey (USGS), National Aeronautics and Space Administration (NASA), satellite imagery (State of Alaska), and lidar elevation data (USGS). Historical shoreline positions ... |
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Baseline for the Florida east coast (FLec) coastal region generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Intersects for the Florida east coast (FLec) coastal region generated to calculate long-term shoreline change rates using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Intersects for the Florida east coast (FLec) coastal region generated to calculate short-term shoreline change rates using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Shorelines of the Florida east coast (FLec) coastal region used in shoreline change analysis
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The U.S. Geological Survey (USGS) Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane ... |
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Long-term shoreline change rates for the Florida east coast (FLec) coastal region using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Short-term shoreline change rates for the Florida east coast (FLec) coastal region using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Baseline for the Florida panhandle (FLph) coastal region generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Intersects for the Florida panhandle (FLph) coastal region generated to calculate long-term shoreline change rates using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Intersects for the Florida panhandle (FLph) coastal region generated to calculate short-term shoreline change rates using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Shorelines of the Florida panhandle (FLph) coastal region used in shoreline change analysis
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The U.S. Geological Survey (USGS) Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane ... |
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Long-term shoreline change rates for the Florida panhandle (FLph) coastal region using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Short-term shoreline change rates for the Florida panhandle (FLph) coastal region using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Baseline for the Florida west coast (FLwc) coastal region generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Intersects for the Florida west coast (FLwc) coastal region generated to calculate long-term shoreline change rates using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Intersects for the Florida west coast (FLwc) coastal region generated to calculate short-term shoreline change rates using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Shorelines of the Florida west coast (FLwc) coastal region used in shoreline change analysis
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The U.S. Geological Survey (USGS) Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane ... |
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Long-term shoreline change rates for the Florida west coast (FLwc) coastal region using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Short-term shoreline change rates for the Florida west coast (FLwc) coastal region using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Baseline for the Georgia coastal region generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Intersects for the Georgia coastal region generated to calculate long-term shoreline change rates using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Intersects for the Georgia coastal region generated to calculate short-term shoreline change rates using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Shorelines of the Georgia coastal region used in shoreline change analysis
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The U.S. Geological Survey (USGS) Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane ... |
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Long-term shoreline change rates for the Georgia coastal region using the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Intersects for the coast south of Boston, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Offshore baseline for the sheltered Eastern Chukchi Sea, Alaska coastal region (Point Barrow to Icy Cape) generated to calculate shoreline change rates
This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the sheltered north coast of Alaska coastal between Point Barrow and Icy Cape for the time period 1947 to 2012. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates. |
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Long-term and short-term shoreline change rates for the coast south of Boston, Massachusetts, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.0
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color ... |
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Digital Shoreline Analysis System version 4.3 transects and short-term (1970-2009) linear regression shoreline change statistics for the South Shore coastal region from Hewitts Cove in Hingham to the Cape Cod Canal in Sandwich (SouthShore_transects_rates_STlr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Offshore baseline for the exposed Eastern Chukchi Sea, Alaska coastal region (Point Barrow to Icy Cape) generated to calculate shoreline change rates
This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the exposed north coast of Alaska coastal region between Point Barrow and Icy Cape for the time period 1947 to 2012. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates. |
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2012-2014 contour-derived mean high water shorelines of the Massachusetts coast used in shoreline change analysis
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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Long and short-term shoreline change rate transects for the northern North Carolina coastal region (NCnorth), calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Shorelines of the Eastern Beaufort Sea, Alaska coastal region (U.S. Canadian Border to the Hulahula River) used in shoreline change analysis
This dataset includes shorelines from 63 years ranging from 1947 to 2010 for the north coast of Alaska between the U.S. Canadian Border and the Hulahula River. Shorelines were compiled from topographic survey sheets (T-sheets; National Oceanic and Atmospheric Administration (NOAA)), aerial orthophotographs (U.S. Geological Survey (USGS), National Aeronautics and Space Administration (NASA), satellite imagery (U.S. Fish and Wildlife Service (USFWS), State of Alaska), and lidar elevation data (USGS). ... |
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2010 profile-derived mean high water shorelines of the North Shore of MA used in shoreline change analysis
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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2014 profile-derived mean high water shorelines of the North Shore of MA used in shoreline change analysis
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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Offshore baseline for the sheltered East Beaufort Sea, Alaska coastal region (U.S. Canadian Border to the Hulahula River) generated to calculate shoreline change rates
This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the sheltered north coast of Alaska coastal region between the U.S. Canadian Border to the Hulahula River for the time period 1947 to 2010. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates. |
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Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics for the South Shore coastal region from Hewitts Cove in Hingham to the Cape Cod Canal in Sandwich
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Digital Shoreline Analysis System version 4.3 shoreline intersection points used to calculate long-term shoreline change statistics excluding the 1970-1979 and 1994 shorelines within the South Shore coastal region from Hewitts Cove in Hingham to the Cape Cod Canal in Sandwich (SouthShore_intersects_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Offshore baseline for the exposed East Beaufort Sea, Alaska coastal region (U.S. Canadian Border to the Hulahula River) generated to calculate shoreline change rates
This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the exposed north coast of Alaska coastal region between the U.S. Canadian Border to the Hulahula River for the time period 1947 to 2010. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates. |
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Shorelines of the Central Beaufort Sea, Alaska coastal region (Hulahula River to the Colville River) used in shoreline change analysis
This dataset includes shorelines from 63 years ranging from 1947 to 2010 for the north coast of Alaska between the Hulahula River and the Colville River. Shorelines were compiled from topographic survey sheets (T-sheets; National Oceanic and Atmospheric Administration (NOAA)), aerial orthophotographs (U.S. Geological Survey (USGS), National Aeronautics and Space Administration (NASA), Conoco-Philips (CP), British Petroleum Alaska (BPXA)), satellite imagery (State of Alaska), and lidar elevation data (USGS). ... |
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Offshore baseline for the sheltered Central Beaufort Sea, Alaska coastal region (Hulahula River to the Colville River) generated to calculate shoreline change rates
This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the sheltered north coast of Alaska coastal region between the Hulahula River and the Colville River for the time period 1947 to 2010. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates. |
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Offshore baseline for the exposed Central Beaufort Sea, Alaska coastal region (Hulahula River to the Colville River) generated to calculate shoreline change rates
This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the exposed north coast of Alaska coastal region between the Hulahula River and the Colville River for the time period 1947 to 2010. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates. |
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Waves, fetch, and associated shoreline change for the Point Aux Chenes and Grand Bay Estuaries in Mississippi and Alabama
This dataset represents a compilation of waves, fetch, and associated shoreline change rates from the Point Aux Chenes and Grand Bay estuaries (Mississippi and Alabama) for historical, modern, and long-term time periods. |
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Long-term shoreline change rates for the Southern California coastal region using the Digital Shoreline Analysis System version 5.0
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Rate of shoreline change of marsh units in Hudson Valley and New York City salt marsh complex, New York
This data release contains coastal wetland synthesis products for the geographic region of Hudson Valley and New York City, New York. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, mean tidal range, and shoreline change rate are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U ... |
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Shorelines of the Southern California coastal region (1852-2016) used in shoreline change analysis
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Baseline for the coastal region around Boston, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast by compiling a database of historical (mid 1800's-1989) shoreline positions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline ... |
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Intersects for the coastal region around Boston, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Long-term and short-term shoreline change rates for the coastal region around Boston, Massachusetts, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Baseline for the coastal region of Buzzards Bay, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast by compiling a database of historical (mid 1800's-1989) shoreline positions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline ... |
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Intersects for coastal region of Buzzards Bay, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Long-term and short-term shoreline change rates for the region of Buzzards Bay, Massachusetts, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Baseline for the region of Cape Cod Bay, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast by compiling a database of historical (mid 1800's-1989) shoreline positions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline ... |
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Intersects for coastal region of Cape Cod Bay, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Long-term and short-term shoreline change rates for the region of Cape Cod Bay, Massachusetts, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Baseline for the Elizabeth Islands, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast by compiling a database of historical (mid 1800's-1989) shoreline positions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline ... |
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Intersects for the region of the Elizabeth Islands, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Long-term and short-term shoreline change rates for the region of the Elizabeth Islands, Massachusetts, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Long and short-term shoreline change rate transects for the southern North Carolina coastal region (NCsouth), calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Baselines for the coast of Martha's Vineyard, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast by compiling a database of historical (mid 1800's-1989) shoreline positions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline ... |
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Intersects for coastal region of Martha's Vineyard, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Long-term and short-term shoreline change rates for the region of Martha's Vineyard, Massachusetts, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Baselines for the coast of Nantucket, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast by compiling a database of historical (mid 1800's-1989) shoreline positions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline ... |
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Intersects for coastal region of Nantucket, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Long-term and short-term shoreline change rates for the region of Nantucket, Massachusetts, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Baseline for the coastal region north of Boston, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast by compiling a database of historical (mid 1800's-1989) shoreline positions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline ... |
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Intersects for coastal region north of Boston, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Long-term and short-term shoreline change rates for the region north of Boston, Massachusetts, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Baselines for the Outer Cape Cod, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast by compiling a database of historical (mid 1800's-1989) shoreline positions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline ... |
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Intersects for the Outer Cape Cod, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Long-term and short-term shoreline change rates for the Outer Cape Cod, Massachusetts, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Baseline for the southern coast of Cape Cod, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast by compiling a database of historical (mid 1800's-1989) shoreline positions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline ... |
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Intersects for the southern coast of Cape Cod, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Long-term and short-term shoreline change rates for the southern coast of Cape Cod, Massachusetts, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Baseline for the coastal region south of Boston, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast by compiling a database of historical (mid 1800's-1989) shoreline positions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline ... |
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Intersects for coastal region south of Boston, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Long-term and short-term shoreline change rates for the coastal region south of Boston, Massachusetts, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of ... |
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Intersects for the Southern California coastal region generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Baseline for the Southern California coastal region generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Long-term shoreline change rates for the Northern California coastal region using the Digital Shoreline Analysis System version 5.0
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Shorelines of the Northern California coastal region (1854-2016) used in shoreline change analysis
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Intersects for the Northern California coastal region generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Baseline for the Northern California coastal region generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Long-term shoreline change rates for the Central California coastal region using the Digital Shoreline Analysis System version 5.0
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Intersects for the coastal region of Virginia generated to calculate long-term shoreline change rates using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics for all data available within the Outer Cape Cod coastal region from Long Point in Provincetown to Monomoy Island (OuterCapeCod_transects_rates_LTw.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Digital Shoreline Analysis System version 4.3 transects and long-term linear regression shoreline change statistics without shorelines from 1970s and 1994 within the Outer Cape Cod coastal region from Long Point in Provincetown to Monomoy Island (OuterCapeCod_transects_rates_LTwo.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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SouthShore_baseline.shp Offshore baseline for the South Shore coastal region generated to calculate shoreline change rates from Hewitts Cove in Hingham to the Cape Cod Canal in Sandwich (SouthShore_baseline.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Digital Shoreline Analysis System version 4.3 transects and short-term (1970-2009) linear regression shoreline change statistics for the South Cape Cod coastal region from Stage Harbor Light in Chatham to Nobska Point in Woods Hole (SouthCapeCod_transects_rates_STlr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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Long and short-term shoreline change rate transects for the western North Carolina coastal region (NCwest), calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Rate of shoreline change of marsh units in north shore Long Island salt marsh complex, New York
This data release contains coastal wetland synthesis products for the geographic region of north shore Long Island, New York. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, mean tidal range, and shoreline change rate are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. ... |
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Shorelines of the Central California coastal region (1852-2016) used in shoreline change analysis
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Long-term shoreline change rates for Rincon, Puerto Rico 1936-2006 (lt_transects.shp)
The 8 km of shoreline from Punta Higüero to Punta Cadena in Rincón, Puerto Rico is experiencing long-term coastal erosion. This study documents historical shoreline changes at Rincón for the period 1936-2006. Thirteen historical shoreline positions were compiled from existing data, new orthophotography, and GPS field surveys. Shoreline vectors represent the high water line at the time of the survey. |
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Intersects for the Central California coastal region generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Baseline for the Central California coastal region generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Shorelines from 1947 to 2017 for the eastern Beaufort Sea coast of Alaska (U.S. Canadian Border to the Hulahula River) used in shoreline change analysis
This dataset includes historical shoreline positions that span 70 years, from 1947 to 2017, for the north coast of Alaska between the U.S. Canadian Border to the Hulahula River. Shorelines were compiled from topographic survey sheets (T-sheets; National Oceanic and Atmospheric Administration (NOAA)), aerial orthophotographs (U.S. Geological Survey (USGS), National Aeronautics and Space Administration (NASA), Conoco-Philips (CP), British Petroleum Alaska (BPXA), and NOAA), satellite imagery (U.S. Fish and ... |
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Midshore baseline for the sheltered eastern Beaufort Sea coast of Alaska (U.S. Canadian Border to the Hulahula River) generated to calculate shoreline change rates
This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the mainland coast of Alaska sheltered by barrier islands from the U.S. Canadian Border to the Hulahula River for the time period 1947 to 2017. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate shoreline-change rates. |
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Digital Shoreline Analysis System version 4.3 transects and short-term end point shoreline change statistics for all data available within the South Cape Cod coastal region from Stage Harbor Light in Chatham to Nobska Point in Woods Hole (SouthCapeCod_transects_rates_STepr.shp)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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National Assessment of Hurricane-Induced Coastal Erosion Hazards: 2021 Update
This dataset contains information on the probabilities of hurricane-induced erosion (collision, inundation and overwash) for each 1-kilometer (km) section of the United States [Gulf of Mexico and Atlantic] coast for category 1-5 hurricanes. The analysis is based on a storm-impact scaling model that uses observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast will respond to the direct landfall of category 1-5 hurricanes. Hurricane-induced water levels, due ... |
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Transects with Shoreline Change Rates for the Grand Bay National Estuarine Research Reserve in Mississippi and Alabama from 1848 to 2017
This dataset contains shoreline change rates for the Grand Bay National Estuarine Research Reserve from 1848 to 2017. Shoreline data were obtained from multiple data sources, including the U.S. Geological Survey (USGS), National Oceanic and Atmospheric Administration (NOAA), the Grand Bay National Estuarine Research Reserve(GBNERR), and the Mississippi Office of Geology (MOG). Datasets were compiled and analyzed using the R package Analyzing Moving Boundaries Using R (AMBUR) program. Rates of shoreline ... |
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2012 profile-derived mean high water shorelines of Martha's Vineyard, MA used in shoreline change analysis
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Perpetual Hazards
Coastal resources are increasingly impacted by erosion, extreme weather events, sea-level rise, tidal flooding, and other potential hazards related to climate change. These hazards have varying impacts on coastal landscapes due to the numerous geologic, oceanographic, ecological, and socioeconomic factors that exist at a given location. Here, an assessment framework is introduced that synthesizes existing datasets describing the variability of the landscape and hazards that may act on it to evaluate the ... |
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Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Event Hazards
Coastal resources are increasingly impacted by erosion, extreme weather events, sea-level rise, tidal flooding, and other potential hazards related to climate change. These hazards have varying impacts on coastal landscapes due to the numerous geologic, oceanographic, ecological, and socioeconomic factors that exist at a given location. Here, an assessment framework is introduced that synthesizes existing datasets describing the variability of the landscape and hazards that may act on it to evaluate the ... |
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P23_Oct2012_Oct2014: Hurricane Sandy Beach Response and Recovery at Fire Island, New York: Shoreline and Beach Profile Data, October 2012 to October 2014.
This spreadsheet consists of Fire Island, NY pre- and post- storm cross-shore profiles collected from October 2012 to October 2014. This dataset contains a set of cross-shore profiles covering 16 dates from October 28 2012 to October 07 2014. As part of the assessment of beach and dune morphologic change associated with Hurricane Sandy and the series of winter storms that followed, DGPS elevation data were collected along ten shore-perpendicular profiles extending from just inland of the crest of the dune ... |
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OahuS_ST- Digital Shoreline Analysis System (DSAS) version 4.2 transects with short-term weighted linear regression rate calculations for the Oahu south region from Barbers Point to Sandy Beach, Hawaii.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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OahuW_baseline - Offshore baseline used to cast shore-perpendicular transects for measurement of historical shoreline positions along West Oahu, Hawaii (Yokohama to Tracks Beach)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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OahuW_LT- Digital Shoreline Analysis System (DSAS) version 4.2 transects with long-term weighted linear regression rate calculations for the Oahu west region from Yokohama to Tracks Beach, Hawaii
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Bias Feature for the Georgia coastal region containing proxy-datum bias information to be used in the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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OahuW_ST- Digital Shoreline Analysis System (DSAS) version 4.2 transects with short-term weighted linear regression rate calculations for the Oahu west region from Yokohama to Tracks Beach, Hawaii.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Tropical Storm Bill Assessment of Potential Coastal-Change Impacts: NHC Advisory 2, 0900 AM UTC MON JUN 16 2015
This dataset defines storm-induced coastal erosion hazards for the Texas and Louisiana coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Tropical Storm Bill in June 2015. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the three types of coastal change: ... |
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P22_Oct2012_Oct2014: Hurricane Sandy Beach Response and Recovery at Fire Island, New York: Shoreline and Beach Profile Data, October 2012 to October 2014.
This spreadsheet consists of Fire Island, NY pre- and post- storm cross-shore profiles collected from October 2012 to October 2014. This dataset contains a set of cross-shore profiles covering 16 dates from October 28 2012 to October 07 2014. As part of the assessment of beach and dune morphologic change associated with Hurricane Sandy and the series of winter storms that followed, DGPS elevation data were collected along ten shore-perpendicular profiles extending from just inland of the crest of the dune ... |
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Hurricane Sandy Assessment of Potential Coastal Change Impacts: NHC Advisory 29, 1100 AM EDT MON OCT 29 2012
This dataset defines hurricane-induced coastal erosion hazards for the Delaware, Maryland, New Jersey, New York, and Virginia coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Hurricane Sandy in October 2012. Hurricane-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the ... |
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Digital Shoreline Analysis System version 4.1 Transects with Long-Term Rate Calculations for the New England North region from Popham Beach, Maine to the northern side of Cape Ann, Massachusetts (NewEnglandN_LT.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Hurricane Nate Assessment of Potential Coastal Change Impacts: NHC Advisory 12, 0800 AM EDT SAT OCT 07 2017
This dataset defines storm-induced coastal erosion hazards for the Louisiana, Mississippi, Alabama and Florida coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Hurricane Nate in October 2017. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the three ... |
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Hurricane Michael Assessment of Potential Coastal Change Impacts: NHC Advisory 15, 0400 AM CDT WED OCT 10 2018
This dataset defines storm-induced coastal erosion hazards for the Alabama and Florida coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Hurricane Michael in October 2018. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the three types of coastal change: ... |
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P11_Oct2012_Oct2014: Hurricane Sandy Beach Response and Recovery at Fire Island, New York: Shoreline and Beach Profile Data, October 2012 to October 2014.
This spreadsheet consists of Fire Island, NY pre- and post- storm cross-shore profiles collected from October 2012 to October 2014. This dataset contains a set of cross-shore profiles covering 15 dates from October 28 2012 to October 07 2014. As part of the assessment of beach and dune morphologic change associated with Hurricane Sandy and the series of winter storms that followed, DGPS elevation data were collected along ten shore-perpendicular profiles extending from just inland of the crest of the dune ... |
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Hurricane Matthew Assessment of Potential Coastal Change Impacts: NHC Advisory 037, 800 AM EDT FRI OCT 07 2016
This dataset defines storm-induced coastal erosion hazards for the Florida, Georgia, South Carolina and North Carolina coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Hurricane Matthew in October 2016. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of ... |
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sand_ero - Erosion Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii
Erosion Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii |
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oahu_ero - Erosion Hazard Intensity Level in the coastal zone of Oahu, Hawaii
Erosion Hazard Intensity Level in the coastal zone of Oahu, Hawaii |
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Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Fabric Dataset
Coastal resources are increasingly impacted by erosion, extreme weather events, sea-level rise, tidal flooding, and other potential hazards related to climate change. These hazards have varying impacts on coastal landscapes due to the numerous geologic, oceanographic, ecological, and socioeconomic factors that exist at a given location. Here, an assessment framework is introduced that synthesizes existing datasets describing the variability of the landscape and hazards that may act on it to evaluate the ... |
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Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Hazard Impact Type
Coastal resources are increasingly impacted by erosion, extreme weather events, sea-level rise, tidal flooding, and other potential hazards related to climate change. These hazards have varying impacts on coastal landscapes due to the numerous geologic, oceanographic, ecological, and socioeconomic factors that exist at a given location. Here, an assessment framework is introduced that synthesizes existing datasets describing the variability of the landscape and hazards that may act on it to evaluate the ... |
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Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Maximum Change Likelihood
Coastal resources are increasingly impacted by erosion, extreme weather events, sea-level rise, tidal flooding, and other potential hazards related to climate change. These hazards have varying impacts on coastal landscapes due to the numerous geologic, oceanographic, ecological, and socioeconomic factors that exist at a given location. Here, an assessment framework is introduced that synthesizes existing datasets describing the variability of the landscape and hazards that may act on it to evaluate the ... |
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molo_ero - Erosion Hazard Intensity Level in the coastal zone of Molokai, Hawaii
Erosion Hazard Intensity Level in the coastal zone of Molokai, Hawaii |
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maui_ero - Erosion Hazard Intensity Level in the coastal zone of Maui, Hawaii
Erosion Hazard Intensity Level in the coastal zone of Maui, Hawaii |
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lanai_ero - Erosion Hazard Intensity Level in the coastal zone of Lanai, Hawaii
Erosion Hazard Intensity Level in the coastal zone of Lanai, Hawaii |
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kauai_ero - Erosion Hazard Intensity Level in the coastal zone of Kauai, Hawaii
Erosion Hazard Intensity Level in the coastal zone of Kauai, Hawaii |
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hawaii_ero - Erosion Hazard Intensity Level in the coastal zone of Hawaii, Hawaii
Erosion Hazard Intensity Level in the coastal zone of Hawaii, Hawaii |
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Model parameter input files to compare wave-averaged versus wave-resolving XBeach coastal flooding models for coral reef-lined coasts
This data release includes the XBeach input data files used to evaluate the importance of explicitly modeling sea-swell waves for runup. This was examined using a 2D XBeach short wave-averaged (surfbeat, XB-SB) and a wave-resolving (non-hydrostatic, XB-NH) model of Roi-Namur Island on Kwajalein Atoll in the Republic of Marshall Islands. Results show that explicitly modelling the sea-swell component (using XB-NH) provides a better approximation of the observed runup than XB-SB (which only models the time ... |
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Hurricane Maria Assessment of Potential Coastal Change Impacts: NHC Advisory 41, 0800 AM EDT TUE SEPT 26 2017
This dataset defines storm-induced coastal erosion hazards for the North Carolina, Virginia, Maryland and Delaware coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Hurricane Maria in September 2017. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the ... |
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Subtropical Storm Alberto Assessment of Potential Coastal Change Impacts: NHC Advisory 8, 0800 AM EDT SUN MAY 27 2018
This dataset defines storm-induced coastal erosion hazards for the Louisiana, Mississippi, Alabama and Florida coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Subtropical Storm Alberto in May 2018. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the ... |
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Baseline coastal oblique aerial photographs collected from Navarre Beach, Florida, to Breton Island, Louisiana, September 7, 2016
The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On September 7, 2016, the USGS conducted an oblique aerial photographic survey from Navarre Beach, Florida, to Breton Island, Louisiana, aboard a Maule MT57 aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect data for assessing incremental changes in the ... |
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Digital Shoreline Analysis System (DSAS) version 4.4 transects with short-term end-point rate-of-change calculations for the sheltered north coast of Alaska, from Icy Cape to Cape Prince of Wales
This dataset consists of short-term (less than 37 years) shoreline change rates for the sheltered north coast of Alaska from Icy Cape to Cape Prince of Wales. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.4, an ArcGIS extension developed by the U.S. Geological Survey. Rates of shoreline change were calculated using an end point rate-of-change (epr) method based on available shoreline data between 1980 and 2016. A reference baseline was used as the ... |
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Baseline Coastal oblique aerial photographs collected from Horseshoe Beach, Florida, to East Cape, Florida, May 19-20, 2010
The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On May 19-20, 2010, the USGS conducted an oblique aerial photographic survey from Horseshoe Beach, Florida, to East Cape, Florida, aboard a Piper Navajo Chieftain aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect data for assessing incremental changes ... |
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Baseline coastal oblique aerial photographs collected from False Cape State Park, Virginia, to Myrtle Beach, South Carolina, May 6, 2008
The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On May 6, 2008, the USGS conducted an oblique aerial photographic survey from False Cape State Park, Virginia, to Myrtle Beach, South Carolina, aboard a U.S. Coast Guard HH60 Helicopter aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission (08CH01) was conducted to collect data ... |
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Digital Shoreline Analysis System (DSAS) version 4.4 transects with short-term end-point rate-of-change calculations for the exposed north coast of Alaska, from Icy Cape to Cape Prince of Wales
This dataset consists of short-term (less than 37 years) shoreline change rates for the exposed coast of the north coast of Alaska from Icy Cape to Cape Prince of Wales. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.4, an ArcGIS extension developed by the U.S. Geological Survey. Rates of shoreline change were calculated using an end point rate-of-change (epr) method based on available shoreline data between 1980 and 2016. A reference baseline was ... |
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Extratropical Storm March 2018 Assessment of Potential Coastal Change Impacts: 0800 AM EST FRI MAR 02 2018
This dataset defines storm-induced coastal erosion hazards for the North Carolina, Virginia, Maryland, Delaware, New Jersey, New York, Rhode Island, Massachusetts, New Hampshire and Maine coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of an Extratropical Storm in March 2018. Storm-induced water levels, due to both surge and waves, were ... |
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Digital Shoreline Analysis System (DSAS) version 5.0 transects with shoreline rate change calculations at Barter Island Alaska, 1947 to 2020
This dataset consists of rate-of-change statistics for the shorelines at Barter Island, Alaska for the time period 1947 to 2020. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 5.0, an ArcGIS extension developed by the U.S. Geological Survey. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to ... |
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Hurricane Joaquin Assessment of Potential Coastal Change Impacts: NHC Advisory 27, 0800 AM EDT SUN OCT 04 2015
This dataset defines storm-induced coastal erosion hazards for the North Carolina, Virginia, Maryland, Delaware, New Jersey, New York, Rhode Island and Massachusetts coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Hurricane Joaquin in October 2015. Storm-induced water levels, due to both surge and waves, were compared to beach and dune ... |
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Extratropical Storm Jan2016 Assessment of Potential Coastal Change Impacts: 1200 PM EST FRI JAN 22 2016
This dataset defines storm-induced coastal erosion hazards for the Virginia, Maryland, Delaware, New Jersey and New York coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct impact of the Extratropical Storm in January 2016. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities ... |
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Hurricane Irma Assessment of Potential Coastal Change Impacts: NHC Advisory 41, 800 AM EDT SAT SEPT 9 2017
This dataset defines storm-induced coastal erosion hazards for the Florida, Georgia and South Carolina coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Hurricane Irma in September 2017. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the three types of ... |
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Effects of Late Holocene Climate and Coastal Change in Mobile Bay, Alabama: ADCIRC Model Input and Results (Initial_Elevations_RS_PH)
Using version 52.30 of the ADvanced CIRCulation (ADCIRC) numerical model (Luettich and others, 1992), astronomic tides were simulated at Mobile Bay, Alabama (AL), under scenarios of Holocene geomorphic configurations representing the period of 3500 to 2300 years before present including a breach in the Morgan Peninsula and a land bridge at Pass aux Herons, as described in Smith and others (2020). The two-dimensional ADCIRC model can be applied to coastal and estuarine systems to solve for time-dependent ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Coast Guard Beach, MA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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P09_Oct2012_Oct2014: Hurricane Sandy Beach Response and Recovery at Fire Island, New York: Shoreline and Beach Profile Data, October 2012 to October 2014.
This spreadsheet consists of Fire Island, NY pre- and post- storm cross-shore profiles collected from October 2012 to October 2014. This dataset contains a set of cross-shore profiles covering 16 dates from October 28 2012 to October 07 2014. As part of the assessment of beach and dune morphologic change associated with Hurricane Sandy and the series of winter storms that followed, DGPS elevation data were collected along ten shore-perpendicular profiles extending from just inland of the crest of the dune ... |
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Digital Shoreline Analysis System version 4.1 Transects with Short-Term Rate Calculations for the Greater Boston region from the southern side of Cape Ann, Massachusetts to Sandy Neck Beach in Sandwich, Massachusetts (GreaterBoston_ST.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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P08_Oct2012_Oct2014: Hurricane Sandy Beach Response and Recovery at Fire Island, New York: Shoreline and Beach Profile Data, October 2012 to October 2014.
This spreadsheet consists of Fire Island, NY pre- and post- storm cross-shore profiles collected from October 2012 to October 2014. This dataset contains a set if cross-shore profiles covering 16 dates from October 28 2012 to October 07 2014. As part of the assessment of beach and dune morphologic change associated with Hurricane Sandy and the series of winter storms that followed, DGPS elevation data were collected along ten shore-perpendicular profiles extending from just inland of the crest of the dune ... |
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Effects of Late Holocene Climate and Coastal Change in Mobile Bay, Alabama: ADCIRC Model Input and Results (Initial_Elevations_RS_MP_PH)
Using version 52.30 of the ADvanced CIRCulation (ADCIRC) numerical model (Luettich and others, 1992), astronomic tides were simulated at Mobile Bay, Alabama (AL), under scenarios of Holocene geomorphic configurations representing the period of 3500 to 2300 years before present including a breach in the Morgan Peninsula and a land bridge at Pass aux Herons, as described in Smith and others (2020). The two-dimensional ADCIRC model can be applied to coastal and estuarine systems to solve for time-dependent ... |
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Effects of Late Holocene Climate and Coastal Change in Mobile Bay, Alabama: ADCIRC Model Input and Results (Initial_Elevations_RS_MP)
Using version 52.30 of the ADvanced CIRCulation (ADCIRC) numerical model (Luettich and others, 1992), astronomic tides were simulated at Mobile Bay, Alabama (AL), under scenarios of Holocene geomorphic configurations representing the period of 3500 to 2300 years before present including a breach in the Morgan Peninsula and a land bridge at Pass aux Herons, as described in Smith and others (2020). The two-dimensional ADCIRC model can be applied to coastal and estuarine systems to solve for time-dependent ... |
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Effects of Late Holocene Climate and Coastal Change in Mobile Bay, Alabama: ADCIRC Model Input and Results (Initial_Elevations_RS)
Using version 52.30 of the ADvanced CIRCulation (ADCIRC) numerical model (Luettich and others, 1992), astronomic tides were simulated at Mobile Bay, Alabama (AL), under scenarios of Holocene geomorphic configurations representing the period of 3500 to 2300 years before present including a breach in the Morgan Peninsula and a land bridge at Pass aux Herons, as described in Smith and others (2020). The two-dimensional ADCIRC model can be applied to coastal and estuarine systems to solve for time-dependent ... |
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iCoast - Did the Coast Change? Crowd-sourced Coastal Classifications
On October 29, 2012, Hurricane Sandy made landfall as a post-tropical storm near Brigantine, New Jersey, with sustained winds of 70 knots (80 miles per hour) and tropical-storm-force winds extending 870 nautical miles in diameter (Blake and others, 2013). The effects of Hurricane Sandy’s winds and storm surge included erosion of the beaches and dunes as well as breaching of barrier islands in both natural and heavily developed areas of the coast (Spokin et. al., 2014). On November 4-6, 2012, the U.S. ... |
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A GIS compilation of vector shorelines for the Virginia coastal region from the 1840s to 2010s
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Digital Shoreline Analysis System (DSAS) version 5.0 transects with bluff rate change calculations for the north coast of Barter Island Alaska, 1950 to 2020
This dataset consists of rate-of-change statistics for the coastal bluffs at Barter Island, Alaska for the time period 1950 to 2020. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 5.0, an ArcGIS extension developed by the U.S. Geological Survey. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each bluff line establishing measurement points, which are then used to ... |
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Shoreline data for Ocean Beach, San Francisco, California, 2004 to 2021
This dataset contains historical shoreline positions (MHW - local Mean High Water, and MSL - local Mean Sea Level) that span 17 years, from 2004 to 2021, for Ocean Beach, San Francisco, California, USA. Shorelines were extracted from topographic elevation data collected by the USGS. Shoreline position data can be used to calculate rates of shoreline change (accretion or erosion) and to evaluate the performance of shoreline change models. |
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Tropical Storm Hermine Assessment of Potential Coastal Change Impacts: NHC Advisory 20, 0500 AM EDT FRI SEP 02 2016
This dataset defines storm-induced coastal erosion hazards for the Florida coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Tropical Storm Hermine in September 2016. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the three types of coastal change: ... |
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P07_Oct2012_Oct2014: Hurricane Sandy Beach Response and Recovery at Fire Island, New York: Shoreline and Beach Profile Data, October 2012 to October 2014.
This spreadsheet consists of Fire Island, NY pre- and post- storm cross-shore profiles collected from October 2012 to October 2014. This dataset contains a set of cross-shore profiles covering 16 dates from October 28 2012 to October 07 2014. As part of the assessment of beach and dune morphologic change associated with Hurricane Sandy and the series of winter storms that followed, DGPS elevation data were collected along ten shore-perpendicular profiles extending from just inland of the crest of the dune ... |
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Hurricane Harvey Assessment of Potential Coastal Change Impacts: NHC Advisory 020, 700 AM CDT FRI AUG 25 2017
This dataset defines storm-induced coastal erosion hazards for the Texas and Louisiana coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Hurricane Harvey in August 2017. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the three types of coastal change: ... |
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Transects with linear regression rates of change for GPS, Worldview, and aerial image shorelines for the Grand Bay National Estuarine Research Reserve in Mississippi from 2013-2020
Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into coastal management, including shoreline monitoring and change analysis. Geospatial shoreline data were created from a ... |
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Tropical Storm Gordon Assessment of Potential Coastal Change Impacts: NHC Advisory 8, 0700 AM CDT TUE SEP 04 2018
This dataset defines storm-induced coastal erosion hazards for the Louisiana, Mississippi, Alabama and Florida coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Tropical Storm Gordon in September 2018. Storm-induced water levels, due to both surge and waves, were compared to beach and dune elevations to determine the probabilities of the ... |
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Bias Feature for the Florida east coast (FLec) coastal region containing proxy-datum bias information to be used in the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Hurricane Florence Assessment of Potential Coastal Change Impacts: NHC Advisory 57, 1100 AM EDT THU SEP 13 2018
This dataset defines storm-induced coastal erosion hazards for the Georgia, South Carolina, North Carolina, Virginia, Maryland, Delaware, New Jersey and New York coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Hurricane Florence in September 2018. Storm-induced water levels, due to both surge and waves, were compared to beach and dune ... |
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Tropical Storm Colin Assessment of Potential Coastal Change Impacts: NHC Advisory 4, 0500 AM EDT MON JUN 06 2016
This dataset defines storm-induced coastal erosion hazards for the Florida coastline. The analysis was based on a storm-impact scaling model that used observations of beach morphology combined with sophisticated hydrodynamic models to predict how the coast would respond to the direct landfall of Tropical Storm Colin in June 2016. Storm-induced water levels, due to both surge and waves, are compared to beach and dune elevations to determine the probabilities of the three types of coastal change: collision ... |
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Digital Shoreline Analysis System version 4.1 Transects with Short-Term Rate Calculations for the New England North region from Popham Beach, Maine to the northern side of Cape Ann, Massachusetts (NewEnglandN_ST.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Digital Shoreline Analysis System version 4.1 Transects with Long-Term Rate Calculations for the Greater Boston region from the southern side of Cape Ann, Massachusetts to Sandy Neck Beach in Sandwich, Massachusetts (GreaterBoston_LT.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Shorelines_Oct2012_Sept2014: Hurricane Sandy Beach Response and Recovery at Fire Island, New York: Shoreline and Beach Profile Data, October 2012 to October 2014.
This shapefile consists of Fire Island, NY pre- and post-storm shoreline data collected from October 2012 to September 2014. This dataset contains 13 Mean High Water (MHW) shorelines for Fire Island, NY (A total of 15 dates, where two shorelines were collected over multiple days). Prior to and following Hurricane Sandy in October, 2012, continuous alongshore DGPS data were collected to assess the positional changes of the shoreline (MHW - 0.46 m NAVD88) and the upper portion of the beach. Over the course of ... |
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P26_Oct2012_Oct2014: Hurricane Sandy Beach Response and Recovery at Fire Island, New York: Shoreline and Beach Profile Data, October 2012 to October 2014.
This spreadsheet consists of Fire Island, NY pre- and post- storm cross-shore profiles collected from October 2012 to October 2014. This dataset contains a set of cross-shore profiles covering 16 dates from October 28 2012 to October 07 2014. As part of the assessment of beach and dune morphologic change associated with Hurricane Sandy and the series of winter storms that followed, DGPS elevation data were collected along ten shore-perpendicular profiles extending from just inland of the crest of the dune ... |
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Bias Feature for the Florida panhandle (FLph) coastal region containing proxy-datum bias information to be used in the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Transects with net change results for GPS and Worldview shorelines for the Grand Bay National Estuarine Research Reserve in Mississippi from 2013-2020
Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into coastal management, including shoreline monitoring and change analysis. Geospatial shoreline data were created from a ... |
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Effects of Late Holocene Climate and Coastal Change in Mobile Bay, Alabama: ADCIRC Model Input and Results (Water_Level_RS_PH)
Using version 52.30 of the ADvanced CIRCulation (ADCIRC) numerical model (Luettich and others, 1992), astronomic tides were simulated at Mobile Bay, Alabama (AL), under scenarios of Holocene geomorphic configurations representing the period of 3500 to 2300 years before present including a breach in the Morgan Peninsula and a land bridge at Pass aux Herons, as described in Smith and others (2020). The two-dimensional ADCIRC model can be applied to coastal and estuarine systems to solve for time-dependent ... |
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Effects of Late Holocene Climate and Coastal Change in Mobile Bay, Alabama: ADCIRC Model Input and Results (Water_Level_RS_MP_PH)
Using version 52.30 of the ADvanced CIRCulation (ADCIRC) numerical model (Luettich and others, 1992), astronomic tides were simulated at Mobile Bay, Alabama (AL), under scenarios of Holocene geomorphic configurations representing the period of 3500 to 2300 years before present including a breach in the Morgan Peninsula and a land bridge at Pass aux Herons, as described in Smith and others (2020). The two-dimensional ADCIRC model can be applied to coastal and estuarine systems to solve for time-dependent ... |
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P25_Oct2012_Oct2014: Hurricane Sandy Beach Response and Recovery at Fire Island, New York: Shoreline and Beach Profile Data, October 2012 to October 2014.
This spreadsheet consists of Fire Island, NY pre- and post- storm cross-shore profiles collected from October 2012 to October 2014. This dataset contains a set of cross-shore profiles covering 16 dates from October 28 2012 to October 07 2014. As part of the assessment of beach and dune morphologic change associated with Hurricane Sandy and the series of winter storms that followed, DGPS elevation data were collected along ten shore-perpendicular profiles extending from just inland of the crest of the dune ... |
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Effects of Late Holocene Climate and Coastal Change in Mobile Bay, Alabama: ADCIRC Model Input and Results (Water_Level_RS_MP)
Using version 52.30 of the ADvanced CIRCulation (ADCIRC) numerical model (Luettich and others, 1992), astronomic tides were simulated at Mobile Bay, Alabama (AL), under scenarios of Holocene geomorphic configurations representing the period of 3500 to 2300 years before present including a breach in the Morgan Peninsula and a land bridge at Pass aux Herons, as described in Smith and others (2020). The two-dimensional ADCIRC model can be applied to coastal and estuarine systems to solve for time-dependent ... |
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Effects of Late Holocene Climate and Coastal Change in Mobile Bay, Alabama: ADCIRC Model Input and Results (Water_Level_RS)
Using version 52.30 of the ADvanced CIRCulation (ADCIRC) numerical model (Luettich and others, 1992), astronomic tides were simulated at Mobile Bay, Alabama (AL), under scenarios of Holocene geomorphic configurations representing the period of 3500 to 2300 years before present including a breach in the Morgan Peninsula and a land bridge at Pass aux Herons, as described in Smith and others (2020). The two-dimensional ADCIRC model can be applied to coastal and estuarine systems to solve for time-dependent ... |
Info |
Bias Feature for the Florida west coast (FLwc) coastal region containing proxy-datum bias information to be used in the Digital Shoreline Analysis System version 5
During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The USGS Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion ... |
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Effects of Late Holocene Climate and Coastal Change in Mobile Bay, Alabama: ADCIRC Model Input and Results (Velocity_Residual_RS_PH)
Using version 52.30 of the ADvanced CIRCulation (ADCIRC) numerical model (Luettich and others, 1992), astronomic tides were simulated at Mobile Bay, Alabama (AL), under scenarios of Holocene geomorphic configurations representing the period of 3500 to 2300 years before present including a breach in the Morgan Peninsula and a land bridge at Pass aux Herons, as described in Smith and others (2020). The two-dimensional ADCIRC model can be applied to coastal and estuarine systems to solve for time-dependent ... |
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Effects of Late Holocene Climate and Coastal Change in Mobile Bay, Alabama: ADCIRC Model Input and Results (Velocity_Residual_RS_MP_PH)
Using version 52.30 of the ADvanced CIRCulation (ADCIRC) numerical model (Luettich and others, 1992), astronomic tides were simulated at Mobile Bay, Alabama (AL), under scenarios of Holocene geomorphic configurations representing the period of 3500 to 2300 years before present including a breach in the Morgan Peninsula and a land bridge at Pass aux Herons, as described in Smith and others (2020). The two-dimensional ADCIRC model can be applied to coastal and estuarine systems to solve for time-dependent ... |
Info |
Effects of Late Holocene Climate and Coastal Change in Mobile Bay, Alabama: ADCIRC Model Input and Results (Velocity_Residual_RS_MP)
Using version 52.30 of the ADvanced CIRCulation (ADCIRC) numerical model (Luettich and others, 1992), astronomic tides were simulated at Mobile Bay, Alabama (AL), under scenarios of Holocene geomorphic configurations representing the period of 3500 to 2300 years before present including a breach in the Morgan Peninsula and a land bridge at Pass aux Herons, as described in Smith and others (2020). The two-dimensional ADCIRC model can be applied to coastal and estuarine systems to solve for time-dependent ... |
Info |
Effects of Late Holocene Climate and Coastal Change in Mobile Bay, Alabama: ADCIRC Model Input and Results (Velocity_Residual_RS)
Using version 52.30 of the ADvanced CIRCulation (ADCIRC) numerical model (Luettich and others, 1992), astronomic tides were simulated at Mobile Bay, Alabama (AL), under scenarios of Holocene geomorphic configurations representing the period of 3500 to 2300 years before present including a breach in the Morgan Peninsula and a land bridge at Pass aux Herons, as described in Smith and others (2020). The two-dimensional ADCIRC model can be applied to coastal and estuarine systems to solve for time-dependent ... |
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OahuS_baseline - Offshore baseline used to cast shore-perpendicular transects for measurement of historical shoreline positions along South Oahu, Hawaii (Barbers Point to Sandy Beach)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
P24_Oct2012_Oct2014: Hurricane Sandy Beach Response and Recovery at Fire Island, New York: Shoreline and Beach Profile Data, October 2012 to October 2014.
This spreadsheet consists of Fire Island, NY pre- and post- storm cross-shore profiles collected from October 2012 to October 2014. This dataset contains a set of cross-shore profiles covering 16 dates from October 28 2012 to October 07 2014. As part of the assessment of beach and dune morphologic change associated with Hurricane Sandy and the series of winter storms that followed, DGPS elevation data were collected along ten shore-perpendicular profiles extending from just inland of the crest of the dune ... |
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OahuS_LT - Digital Shoreline Analysis System (DSAS) version 4.2 transects with long-term weighted linear regression rate calculations for the Oahu south region from Barbers Point to Sandy Beach, Hawaii
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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GULF - Coastal Vulnerability to Sea-Level Rise: U.S. Gulf Coast
The goal of this project is to quantify, at the National scale, the relative susceptibility of the Nation's coast to sea-level rise through the use of a coastal vulnerability index (CVI). This initial classification is based upon the variables geomorphology, regional coastal slope, tide range, wave height, relative sea-level rise and shoreline erosion and accretion rates. The combination of these variables and the association of these variables to each other furnish a broad overview of regions where ... |
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Wave thrust values at point locations along the shorelines of Massachusetts and Rhode Island
This product provides spatial variations in wave thrust along shorelines in Massachusetts and Rhode Island. Natural features of relevance along the State coast are salt marshes. In recent times, marshes have been eroding primarily through lateral erosion. Wave thrust represents a metric of wave attack acting on marsh edges. The wave thrust is calculated as the vertical integral of the dynamic pressure of waves. This product uses a consistent methodology with sufficient spatial resolution to include the ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for Cape Cod Bay, MA
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for the north shore of Martha's Vineyard, MA
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for the south shore of Martha's Vineyard, MA
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for the north shore of Nantucket, MA
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for the south shore of Nantucket, MA
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for the North Shore of MA
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for the Outer Cape of MA
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for the southern shoreline of Cape Cod, MA
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for the South Coast of MA
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for the South Shore of MA
The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using ... |
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Historical shoreline positions for the coast of MA, from 1844 - 2014.
The Massachusetts Office of Coastal Zone Management (MA CZM) launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast by compiling a database of historical (1800's-1989) shoreline positions and shoreline change maps. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- ... |
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Preliminary estimates of forecasted shoreline positions for Florida and Georgia
During Hurricane Irma, Florida and Georgia experienced substantial impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses from hurricanes result in increased vulnerability of coastal regions, including densely populated areas. Erosion may put critical infrastructure at risk of future flooding and may cause economic loss. The U.S. Geological Survey (USGS) Coastal and Marine Hazards Resources Program is working to assess shoreline erosion along the southeast U.S. ... |
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Uncertainty of forecasted shoreline positions for Florida and Georgia
During Hurricane Irma, Florida and Georgia experienced substantial impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses from hurricanes result in increased vulnerability of coastal regions, including densely populated areas. Erosion may put critical infrastructure at risk of future flooding and may cause economic loss. The U.S. Geological Survey (USGS) Coastal and Marine Hazards Resources Program is working to assess shoreline erosion along the southeast U.S. ... |
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1970s Shorelines for the Main Island of Puerto Rico
The U.S. Geological Survey (USGS) maintains shoreline positions for the United States coasts from both older sources, such as aerial photos or topographic surveys, as well as contemporary sources like lidar point clouds and digital elevation models (DEMs). These shorelines are compiled and analyzed in the Digital Shoreline Analysis System (DSAS) software to compute rates of change. It is useful to keep a record of historical shoreline positions as a method of monitoring change over time to identify areas ... |
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2010 Shorelines for Vieques, Culebra, and the Main Island of Puerto Rico
The U.S. Geological Survey (USGS) maintains shoreline positions for the United States coasts from both older sources, such as aerial photos or topographic surveys, as well as contemporary sources like lidar point clouds and digital elevation models (DEMs). These shorelines are compiled and analyzed in the Digital Shoreline Analysis System (DSAS) software to compute rates of change. It is useful to keep a record of historical shoreline positions as a method of monitoring change over time to identify areas ... |
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1970s Shorelines for Vieques and Culebra, Puerto Rico
The U.S. Geological Survey (USGS) maintains shoreline positions for the United States coasts from both older sources, such as aerial photos or topographic surveys, as well as contemporary sources like lidar point clouds and digital elevation models (DEMs). These shorelines are compiled and analyzed in the Digital Shoreline Analysis System (DSAS) software to compute rates of change. It is useful to keep a record of historical shoreline positions as a method of monitoring change over time to identify areas ... |
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Wave thrust values at point locations along the shorelines of Chesapeake Bay, Maryland and Virginia
This product provides spatial variations in wave thrust along shorelines in the Chesapeake Bay. Natural features of relevance along the Bay coast are salt marshes. In recent times, marshes have been eroding primarily through lateral erosion. Wave thrust represents a metric of wave attack acting on marsh edges. The wave thrust is calculated as the vertical integral of the dynamic pressure of waves. This product uses a consistent methodology with sufficient spatial resolution to include the distinct features ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Assateague Island, MD & VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Assateague Island, MD & VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Assawoman Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Assawoman Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Cape Hatteras, NC, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Cape Hatteras, NC, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Cape Lookout, NC, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Cape Lookout, NC, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Coast Guard Beach, MA, 2013-2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Coast Guard Beach, MA, 2013-2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Cobb Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Cobb Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Fisherman Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Fisherman Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Metompkin Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Metompkin Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Monomoy Island, MA, 2013-2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Monomoy Island, MA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Myrtle Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Myrtle Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Parramore Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Parramore Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Parker River, MA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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2010-2022 New Jersey and New York Beach Volumes
This dataset defines the volume of sand along a 10-meter (m) wide profile between the seaward-most dune toe and the mean high water shoreline derived from light detection and ranging (lidar) digital elevation models (DEMs). Refer to Doran and others (2017) for more information about the source lidar data. These data support the National Fish and Wildlife Foundation (NFWF)-funded project entitled “Monitoring Hurricane Sandy Beach and Marsh Resilience in New York and New Jersey” (NFWF project ID 2300.16 ... |
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2000 USACE Oahu Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2000 U.S. Army Corps of ... |
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Fall 2000 USGS Mid-Atlantic Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2000 Atlantic Coast U.S. ... |
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2001 USACE Oahu Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2001 U.S. Army Corps of ... |
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2002 USGS Virgina and Maryland Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2001 Gulf Coast USGS ... |
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Post-Hurricane Florence Digital Elevation Models of coastal North Carolina
This data release presents structure-from-motion (SFM) products derived from aerial imagery surveys with precise Global Navigation Satellite System (GNSS) navigation data flown in a piloted fixed wing aircraft taken along the North Carolina coast in response to Hurricane Florence (available here https://coastal.er.usgs.gov/data-release/doi-P91KB9SF/). USGS researchers use the elevation models and orthorectified imagery to assess future coastal vulnerability, nesting habitats for wildlife, and provide data ... |
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Post-Hurricane Florence RGB averaged orthoimagery of coastal North Carolina
This data release presents structure-from-motion (SFM) products derived from aerial imagery surveys with precise Global Navigation Satellite System (GNSS) navigation data flown in a piloted fixed wing aircraft taken along the North Carolina coast in response to Hurricane Florence (available here https://coastal.er.usgs.gov/data-release/doi-P91KB9SF/). USGS researchers use the elevation models and orthorectified imagery to assess future coastal vulnerability, nesting habitats for wildlife, and provide data ... |
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2003 NOAA Oahu Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2003 NOAA Oahu lidar ... |
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2004 Pre-Hurricane Ivan Eastern Gulf Coast United States Army Corps of Engineers (USACE) Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2004 Pre-Ivan Eastern ... |
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2004 USACE Post-Ivan Florida Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2004 U.S. Army Corps of ... |
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2004 Maine NOAA Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2004 NOAA Maine lidar ... |
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2004 Post-Hurricane Frances USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2004 Post-Hurricane ... |
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2004 Post-Hurricane Jeanne USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2004 Post-Hurricane ... |
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2005-2006 Atlantic Coast USACE Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2005-2006 Atlantic Coast ... |
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2005 EAARL Fire Island Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2005 Fire Island USGS ... |
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2005 Padre Island USGS EAARL Lidar-derived dune crest, toe and shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2005 Experimental ... |
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2006 FEMA Oahu Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2006 Federal Emergency ... |
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2007 Northeast Barrier Islands USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2007 Northeast Barrier ... |
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2007 South Florida FDEM Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2007 Florida Division of ... |
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2007 USACE Oahu Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2007 U.S. Army Corps of ... |
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September 2007 Northern Gulf of Mexico USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2007 Northern Gulf of ... |
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2008 USGS Post-Hurricane Ike Texas Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2008 USGS Post-Hurricane ... |
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2008 North Carolina and Virginia NOAA/NGS Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2008 Atlantic Coast ... |
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2008 Assateague Island USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2008 Assateague Island ... |
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2009 Cape Canaveral USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2009 Cape Canaveral ... |
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2009 North Carolina USACE Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2009 U.S. Army Corps of ... |
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2009 Florida USACE Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2009 Florida U.S. Army ... |
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2009 Post-NorIda USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2009 Post-NorIda USGS ... |
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2010 Northeast Atlantic USACE Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 Northeast Atlantic ... |
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2010 Southeast Atlantic USACE Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 Southeast Atlantic ... |
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2010 Virginia USACE Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 Virginia U.S. Army ... |
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2010 New York USACE Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 New York U.S. Army ... |
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2010 New Jersey USACE Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 New Jersey U.S. ... |
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2010 Delaware USACE Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 Delaware U.S. Army ... |
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2010 Maryland USACE Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 Maryland U.S. Army ... |
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2010 Assateague Island National Seashore USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 Assateague Island ... |
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July 2010 Dauphin Island USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 Dauphin Island U.S. ... |
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2011 USGS New York Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2011 Atlantic Coast ... |
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2011 East Coast New York/New Jersey NOAA/NGS Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2011 East Coast New York ... |
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2012 Post-Hurricane Sandy Fire Island, New York Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2012 Post-Hurricane ... |
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2012 Post-Hurricane Sandy Northeast Atlantic Coast USGS Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2012 Post ... |
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2012 Post-Sandy New York and New Jersey USACE NCMP Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2012 Post-Sandy New York ... |
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Orthomosaic representing Head of the Meadow Beach, Truro on March 10, 2022
These data map the beach and nearshore environment at Head of the Meadow Beach in Truro, MA, providing updated regional context for the 2019 CoastCam installation. CoastCam CACO-01 are two video cameras aimed at the beach that view the coast shared by beachgoers, shorebirds, seals, and sharks. These data were collected as part of field activity 2022-015-FA and a collaboration with the National Park Service at Cape Cod National Seashore to monitor the region. In March 2022, U.S. Geological Survey and Woods ... |
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2012 Pre-Hurricane Sandy Fire Island National Seashore, USGS Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2012 Post-Hurricane ... |
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2012 Pre-Sandy New York and New Jersey USGS Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2012 Pre Hurricane Sandy ... |
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2012 Post-Hurricane Sandy New Jersey USGS EAARL-B Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2012 Post-Hurricane ... |
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2013-2014 Northeast USGS Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2013-2014 Post� ... |
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2013 NOAA Oahu Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2013 National Oceanic ... |
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2013 USACE Oahu Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2013 U.S. Army Corps of ... |
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2013 Maine USACE/NAE Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2013 Maine United States ... |
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2014 East Coast Rhode Island NOAA/NGS ATM Lidar-Derived Dune Crest, Toe and Shoreline, post-Hurricane Sandy
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2014 East Coast Rhode ... |
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2014 East Coast Maine USACE/NAE ATM Lidar-Derived Dune Crest, Toe and Shoreline, post-Hurricane Sandy
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2014 East Coast Maine ... |
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2015 USACE Florida Gulf Coast Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2015 U.S. Army Corps of ... |
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Orthomosaic representing Marconi Beach, Wellfleet, MA March 11, 2022
The data in this release map the beach and nearshore environment at Marconi Beach in Wellfleet, MA and provide updated environmental context for the 2021 CoastCam installation that looks out at the coast shared by beachgoers, shorebirds, seals, and sharks. This is related to the field activity 2022-014-FA and a collaboration with the National Park Service at Cape Cod National Seashore to monitor the region that falls within the field of view of CoastCam CACO-02, which are two video cameras aimed at the ... |
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2016 Florida East Coast USACE Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2016 U.S. Army Corps of ... |
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2016 USACE Gulf Coast Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2016 U.S. Army Corps of ... |
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2017 East Coast USACE/FEMA ATM Lidar-Derived Dune Crest, Toe and Shoreline, post-Hurricane Irma
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2017 Atlantic Coast ... |
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2018 USGS Florida Panhandle Post-Michael Lidar-derived Dune Crest, Toe, and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2018 United States Army ... |
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Digital elevation models (DEMs) of coastal North Carolina, from 2019-08-30 to 2019-09-02, Pre-Hurricane Dorian
Digital elevation models (DEMs) were created from aerial imagery collected between August 30 and September 2, 2019, along the North Carolina coast between the Virginia-North Carolina border vicinity and Cape Lookout, North Carolina. These DEMs were created to document ground conditions prior to Hurricane Dorian, which made landfall on the North Carolina coast on September 6, 2019. The DEMs help researchers estimate the land surface before Hurricane Dorian and were created to document inter-annual changes in ... |
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RGB-averaged orthoimagery of coastal North Carolina, from 2019-08-30 to 2019-09-02, Pre-Hurricane Dorian
Orthoimages were created from aerial imagery collected between August 30 and September 2, 2019, along the North Carolina coast between the Virginia-North Carolina border vicinity and Cape Lookout, North Carolina. These RBG-averaged orthoimages were created to document ground conditions prior to Hurricane Dorian, which made landfall on the North Carolina coast on September 6, 2019. The RBG-averaged orthoimages help researchers estimate the land surface before Hurricane Dorian and were created to document ... |
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Digital elevation models (DEMs) of coastal North Carolina, from 2019-09-08 to 2019-09-13, Post-Hurricane Dorian
Digital elevation models (DEMs) were created from aerial imagery collected between September 08 and September 13, 2019, along the North Carolina coast between the Virginia-North Carolina border vicinity and Cape Lookout, North Carolina. These DEMs were created to document ground conditions post-Hurricane Dorian, which made landfall on the North Carolina coast on September 6, 2019. The DEMs help researchers estimate the land surface after Hurricane Dorian and were created to document inter-annual changes in ... |
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RGB-averaged orthoimagery of coastal North Carolina, from 2019-09-08 to 2019-09-13, Post-Hurricane Dorian
RGB-averaged ortho products were created from aerial imagery collected between September 8 and 13, 2019, along the North Carolina coast between the Virginia-North Carolina border vicinity and Cape Lookout, North Carolina. These RBG-averaged orthoimages were created to document ground conditions after Hurricane Dorian, which made landfall on the North Carolina coast on September 6, 2019. The RBG-averaged orthoimages help researchers estimate the land surface after Hurricane Dorian and were created to ... |
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1998 East Coast NASA/NOAA/USGS Winter ATM Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 1998 Atlantic Coast ... |
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1998 Atlantic coast NASA/NOAA/USGS Spring ATM Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 1998 Atlantic Coast ... |
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RGB-averaged orthoimagery of coastal North Carolina, on 2019-10-11, one-month post-Hurricane Dorian
RGB-averaged orthoimages were created from aerial imagery collected on October 11, 2019, along the North Carolina coast between the Virginia-North Carolina border vicinity and Cape Lookout, North Carolina. These RBG-averaged orthoimages were created to document ground conditions one-month after Hurricane Dorian, which made landfall on the North Carolina coast on September 6, 2019. The RBG-averaged orthoimages help researchers estimate the land surface after Hurricane Dorian and were created to document ... |
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Digital elevation models (DEMs) of coastal North Carolina, on 2019-11-26, two-months Post-Hurricane Dorian
Digital elevation models (DEMs) were created from aerial imagery collected November 26, 2019, along the North Carolina coast between the Virginia-North Carolina border vicinity and Cape Lookout, North Carolina. These DEMs were created to document ground conditions two-months after Hurricane Dorian, which made landfall on the North Carolina coast on September 6, 2019. The DEMs help researchers estimate the land surface one-month post-Hurricane Dorian and were created to document inter-annual changes in ... |
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RGB-averaged orthoimagery of coastal North Carolina, on 2019-11-26, two-months Post-Hurricane Dorian
RGB-averaged orthoimages were created from aerial imagery collected on November 26, 2019, along the North Carolina coast between the Virginia-North Carolina border vicinity and Cape Lookout, North Carolina. These RBG-averaged orthoimages were created to document ground conditions two-months after Hurricane Dorian, which made landfall on the North Carolina coast on September 6, 2019. The RBG-averaged orthoimages help researchers estimate the land surface after Hurricane Dorian and were created to document ... |
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Digital elevation models (DEMs) of coastal North Carolina, from 2020-02-08 to 2020-02-09
Digital elevation models (DEMs) were created from aerial imagery collected February 08 and 09, 2020, along the North Carolina coast between the Virginia-North Carolina border vicinity and Cape Lookout, North Carolina. These DEMs were created to document recovery ground conditions after Hurricane Dorian, which made landfall on the North Carolina coast on September 6, 2019. The DEMs help researchers document inter-annual changes in shoreline position and coastal morphology in response to storm events using ... |
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RGB-averaged orthoimagery of coastal North Carolina, from 2020-02-08 to 2020-02-09
RGB-averaged orthoimages were created from aerial imagery collected February 08 and 09, 2020, along the North Carolina coast between the Virginia-North Carolina border vicinity and Cape Lookout, North Carolina. These RGB-averaged orthoimages were created to document recovery ground conditions after Hurricane Dorian, which made landfall on the North Carolina coast on September 6, 2019. The RGB-averaged orthoimages help researchers document inter-annual changes in shoreline position and coastal morphology in ... |
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Digital elevation models (DEMs) of coastal North Carolina, from 2020-05-08 to 2020-05-09
Digital elevation models (DEMs) were created from aerial imagery collected May 08 and 09, 2020, along the North Carolina coast between the Virginia-North Carolina border vicinity and Cape Lookout, North Carolina. These DEMs were created to document recovery ground conditions after Hurricane Dorian, which made landfall on the North Carolina coast on September 6, 2019. The DEMs help researchers document inter-annual changes in shoreline position and coastal morphology in response to storm events using aerial ... |
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1998 Southeast ATM Lidar-derived dune crest, toe and shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 1998 Southeast USGS/NASA ... |
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1999 Atlantic Coast NASA/NOAA/USGS ATM Lidar-Derived Dune Crest, Toe and Shoreline, post-Hurricane Floyd
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 1999 Atlantic Coast ... |
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2018 South Texas USGS Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe, and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline derived from the 2018 United States ... |
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2018 Puerto Rico USACE Lidar-Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe, and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline ... |
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Tile index for Alaska coastal orthoimagery and elevation data: Icy Cape to Cape Prince of Wales, 2016
This part of the data release presents a shapefile that includes a spatial index of orthoimagery and elevation data describing the Alaskan coastline from Icy Cape to Cape Prince of Wales. The data products referenced in this index include orthoimagery, digital surface models, and elevation point clouds which were generated from aerial imagery using structure-from-motion methods. Fairbanks Fodar, a contracted mapping service, collected the aerial imagery in 2016 and created all of the data products ... |
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Elevation data collected in 2009 on the beach and foreshore in the vicinity of Wainwright, Alaska
Beach and foreshore elevation data were collected in the vicinity of Wainwright, Alaska. The area from the mouth of the Kuk River to about 8 km to the northeast was measured in August 2009. The area from the mouth of the Kuk River to about 5 km to the northeast was measured in October 2009. The elevation data were collected with Real-Time Kinematic (RTK) Global Positioning System (GPS) systems mounted on all-terrain vehicles. The GPS sampling rate was 1 Hz with vehicle speeds maintained at less than 15 km ... |
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Seabeach Amaranth Presence-Absence Data, Assateague Island National Seashore, 2008
Seabeach amaranth (Amaranthus pumilus) is a federally threatened plant species that was once prevalent on beaches of the U.S. mid-Atlantic coast. To re-establish a population at Assateague Island National Seashore (ASIS), seabeach amaranth cultivars were planted by ASIS natural resources staff for three growing seasons from 1999 to 2001 and have been monitored since 2001. Characteristics of favorable seabeach amaranth locations were assessed by sampling barrier island and habitat characteristics at sites ... |
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Seabeach Amaranth Presence-Absence Data, Assateague Island National Seashore, 2010
Seabeach amaranth (Amaranthus pumilus) is a federally threatened plant species that was once prevalent on beaches of the U.S. mid-Atlantic coast. To re-establish a population at Assateague Island National Seashore (ASIS), seabeach amaranth cultivars were planted by ASIS natural resources staff for three growing seasons from 1999 to 2001 and have been monitored since 2001. Characteristics of favorable seabeach amaranth locations were assessed by sampling barrier island and habitat characteristics at sites ... |
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Seabeach Amaranth Presence-Absence Data, Assateague Island National Seashore, 2014
Seabeach amaranth (Amaranthus pumilus) is a federally threatened plant species that was once prevalent on beaches of the U.S. mid-Atlantic coast. To re-establish a population at Assateague Island National Seashore (ASIS), seabeach amaranth cultivars were planted by ASIS natural resources staff for three growing seasons from 1999 to 2001 and have been monitored since 2001. Characteristics of favorable seabeach amaranth locations were assessed by sampling barrier island and habitat characteristics at sites ... |
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Digital elevation models (DEMs) of coastal North Carolina, on 2019-10-11, one month Post-Hurricane Dorian
Digital elevation models (DEMs) were created from aerial imagery collected October 11, 2019, along the North Carolina coast between the Virginia-North Carolina border vicinity and Cape Lookout, North Carolina. These DEMs were created to document ground conditions one-month after Hurricane Dorian, which made landfall on the North Carolina coast on September 6, 2019. The DEMs help researchers estimate the land surface one-month post-Hurricane Dorian and were created to document inter-annual changes in ... |
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Barrier island geomorphology and seabeach amaranth metrics at 50-m alongshore transects, and 5-m cross-shore points for 2008 — Assateague Island, MD and VA.
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for developing approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the effects of sea-level rise on shoreline change, barrier island geomorphology, and habitat availability for species such as piping plover ... |
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RBR sensor pressure and tidal data for two sites in the coastal marsh at Grand Bay National Estuarine Research Reserve, Mississippi, from April 2019 through January 2020
To better understand sediment deposition in marsh environments, scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) selected four study sites (Sites 5, 6, 7, and 8) along the Point Aux Chenes Bay shoreline of the Grand Bay National Estuarine Research Reserve (GNDNERR), Mississippi. These datasets were collected to serve as baseline data prior to the installation of a living shoreline (a subtidal sill). Each site consisted of five plots located along a ... |
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RBR sensor wave data for two sites in the coastal marsh at Grand Bay National Estuarine Research Reserve, Mississippi, from April 2019 through January 2020
To better understand sediment deposition in marsh environments, scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) selected four study sites (Sites 5, 6, 7, and 8) along the Point Aux Chenes Bay shoreline of the Grand Bay National Estuarine Research Reserve (GNDNERR), Mississippi. These datasets were collected to serve as baseline data prior to the installation of a living shoreline (a subtidal sill). Each site consisted of five plots located along a ... |
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Vegetation survey in a coastal marsh at the Grand Bay National Estuarine Research Reserve, Mississippi
To better understand sediment deposition in marsh environments, scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) selected four study sites (Sites 5, 6, 7, and 8) along the Point Aux Chenes Bay shoreline of the Grand Bay National Estuarine Research Reserve, Mississippi (GNDNERR). These datasets were collected to serve as baseline data prior to the installation of a living shoreline (a subtidal sill). Each site consisted of five plots located along a ... |
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Orthomosaic representing Marconi Beach, Wellfleet, MA on March 22, 2023
The data in this release re-map the beach and nearshore environment at Marconi Beach in Wellfleet, MA and provide updated environmental context for the 2021 CoastCam installation that looks out at the coast shared by beachgoers, shorebirds, seals, and sharks. This is related to the field activity 2022-014-FA and a collaboration with the National Park Service at Cape Cod National Seashore to monitor the region that falls within the field of view of CoastCam CACO-02, which are two video cameras aimed at the ... |
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Orthomosaic representing Head of the Meadow Beach, Truro, MA on March 10, 2023
The data in this release re-map the beach and nearshore environment at Head of the Meadow Beach in Truro, MA and provide updated environmental context for the 2020 CoastCam installation that looks out at the coast shared by beachgoers, shorebirds, seals, and sharks. This is related to the field activity 2022-015-FA and a collaboration with the National Park Service at Cape Cod National Seashore to monitor the region that falls within the field of view of CoastCam CACO-01, which are two video cameras aimed ... |
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AllScenarios_Bin1thru18_SSC: Modeling the Effects of Interior Headland Restoration on Estuarine Sediment Transport Processes in a Marine-Dominant Estuary: Delft3D Model Output
The effects of interior headland restoration on estuarine sediment transport processes are assessed through process-based numerical modeling. Three proposed interior headland restoration scenarios in the Grand Bay estuary (Mississippi/Alabama) are modeled using Delft3D (developed by Deltares) to understand impacts on suspended sediment concentrations, bed level morphology and sediment fluxes under present-day conditions and a sea level rise of 0.5 meters (m). Delft3D model output of suspended sediment ... |
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AllScenarios_Initial_and_Final_Bed_Elevations: Modeling the Effects of Interior Headland Restoration on Estuarine Sediment Transport Processes in a Marine-Dominant Estuary: Delft3D Model Output
The effects of interior headland restoration on estuarine sediment transport processes are assessed through process-based numerical modeling. Three proposed interior headland restoration scenarios in the Grand Bay estuary (Mississippi/Alabama) are modeled using Delft3D (developed by Deltares) to understand impacts on suspended sediment concentrations, bed level morphology and sediment fluxes under present-day conditions and a sea level rise of 0.5 meters (m). Delft3D model output of suspended sediment ... |
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AllScenarios_Sediment_Fluxes: Modeling the Effects of Interior Headland Restoration on Estuarine Sediment Transport Processes in a Marine-Dominant Estuary: Delft3D Model Output
The effects of interior headland restoration on estuarine sediment transport processes are assessed through process-based numerical modeling. Three proposed interior headland restoration scenarios in the Grand Bay estuary (Mississippi/Alabama) are modeled using Delft3D (developed by Deltares) to understand impacts on suspended sediment concentrations, bed level morphology and sediment fluxes under present-day conditions and a sea level rise of 0.5 meters (m). Delft3D model output of suspended sediment ... |
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AllScenarios_Spatial_Flow: Modeling the Effects of Interior Headland Restoration on Estuarine Sediment Transport Processes in a Marine-Dominant Estuary: Delft3D Model Output
The effects of interior headland restoration on estuarine sediment transport processes are assessed through process-based numerical modeling. Three proposed interior headland restoration scenarios in the Grand Bay estuary (Mississippi/Alabama) are modeled using Delft3D (developed by Deltares) to understand impacts on suspended sediment concentrations, bed level morphology and sediment fluxes under present-day conditions and a sea level rise of 0.5 meters (m). Delft3D model output of suspended sediment ... |
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AllScenarios_Spatial_Waves: Modeling the Effects of Interior Headland Restoration on Estuarine Sediment Transport Processes in a Marine-Dominant Estuary: Delft3D Model Output
The effects of interior headland restoration on estuarine sediment transport processes are assessed through process-based numerical modeling. Three proposed interior headland restoration scenarios in the Grand Bay estuary (Mississippi/Alabama) are modeled using Delft3D (developed by Deltares to understand impacts on suspended sediment concentrations, bed level morphology and sediment fluxes under present-day conditions and a sea level rise of 0.5 meters (m). Delft3D model output of suspended sediment ... |
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GrandBayModel_InputBathymetry: Modeling the Effects of Interior Headland Restoration on Estuarine Sediment Transport Processes in a Marine-Dominant Estuary: Delft3D Model Output
The effects of interior headland restoration on estuarine sediment transport processes are assessed through process-based numerical modeling. Three proposed interior headland restoration scenarios in the Grand Bay estuary (Mississippi/Alabama) are modeled using Delft3D (developed by Deltares) to understand impacts on suspended sediment concentrations, bed level morphology and sediment fluxes under present-day conditions and a sea level rise of 0.5 meters (m). Delft3D model output of suspended sediment ... |
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GrandBay_ValidationPeriod_Wave_WaterLevel: Modeling the Effects of Interior Headland Restoration on Estuarine Sediment Transport Processes in a Marine-Dominant Estuary: Delft3D Model Output
The effects of interior headland restoration on estuarine sediment transport processes are assessed through process-based numerical modeling. Three proposed interior headland restoration scenarios in the Grand Bay estuary (Mississippi/Alabama) are modeled using Delft3D (developed by Deltares) to understand impacts on suspended sediment concentrations, bed level morphology and sediment fluxes under present-day conditions and a sea level rise of 0.5 meters (m). Delft3D model output of suspended sediment ... |
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SC Bias Feature – Feature class containing South Carolina proxy-datum bias information to be used in the Digital Shoreline Analysis System
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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2010 lidar-derived mean high water shoreline for the coast of South Carolina
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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2017-2018 lidar-derived mean high water shoreline for the coast of South Carolina
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Intrinsic and Extrinsic Calibration Data From USGS CoastCam deployed at Madeira Beach, Florida
A digital video camera was installed at Madeira Beach, Florida (FL) and faced west along the beach. Every hour during daylight hours, daily from 2017 to 2022, the camera collected raw video and produced snapshots and time-averaged image products. This data release includes the necessary intrinsic orientation (IO) and extrinsic orientation (EO) calibration data to utilize imagery to make quantitative measurements. The camera is part of a U.S. Geological Survey (USGS) research project to study the beach and ... |
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Imagery from USGS CoastCam deployed at Madeira Beach, Florida
A digital video camera was installed at Madeira Beach, Florida (FL) and faced west along the beach. Every hour during daylight hours, daily from 2017 to 2022, the camera collected raw video and produced snapshots and time-averaged image products. The images included in this data release were collected from January 21, 2017, to December 31, 2017. The camera is part of a U.S. Geological Survey (USGS) research project to study the beach and nearshore environment. USGS researchers analyzed the imagery collected ... |
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Orthomosaic representing Head of the Meadow Beach, Truro from images collected during field activity 2021-014-FA on February 4, 2021
These data map the beach and nearshore environment at Head of the Meadow Beach in Truro, MA, providing updated regional context for the 2019 CoastCam installation. CoastCam CACO-01 are two video cameras aimed at the beach that view the coast shared by beachgoers, shorebirds, seals, and sharks. These data were collected as part of field activity 2021-014-FA and a collaboration with the National Park Service at Cape Cod National Seashore to monitor the region. In February 2021, U.S. Geological Survey and ... |
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Baseline for the North Carolina coastal region from Cape Hatteras to Cape Lookout (NCcentral)
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Bias feature containing proxy-datum bias information to be used in the Digital Shoreline Analysis System for the central coast of North Carolina from Cape Hatteras to Cape Lookout (NCcentral)
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Long and short-term shoreline intersect points for the central coast of North Carolina (NCcentral), calculated using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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2017 lidar-derived mean high water shoreline for the coast of North Carolina from Cape Hatteras to Cape Lookout (NCcentral)
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Baseline for the North Carolina coastal region from the Virginia border to Cape Hatteras (NCnorth)
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Bias feature containing proxy-datum bias information to be used in the Digital Shoreline Analysis System for the northern coast of North Carolina from the Virginia border to Cape Hatteras (NCnorth)
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Long and short-term shoreline intersect points for the northern coast of North Carolina (NCnorth), calculated using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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2017 lidar-derived mean high water shoreline for the coast of North Carolina from the Virginia border to Cape Hatteras (NCnorth)
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Baseline for the North Carolina coastal region from Cape Lookout to Cape Fear (NCsouth)
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Bias feature containing proxy-datum bias information to be used in the Digital Shoreline Analysis System for the southern coast of North Carolina from Cape Lookout to Cape Fear (NCsouth)
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Long and short-term shoreline intersect points for the southern coast of North Carolina (NCsouth), calculated using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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2017 lidar-derived mean high water shoreline for the southern coast of North Carolina from Cape Lookout to Cape Fear (NCsouth)
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Baseline for the North Carolina coastal region from Cape Fear to the South Carolina border (NCwest)
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Bias feature containing proxy-datum bias information to be used in the Digital Shoreline Analysis System for the western coast of North Carolina from Cape Fear to the South Carolina border (NCwest)
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Long and short-term shoreline intersect points for the western coast of North Carolina (NCwest), calculated using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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2017 lidar-derived mean high water shoreline for the coast of North Carolina from Cape Fear to the South Carolina border (NCwest)
The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This ... |
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Orthomosaic representing Marconi Beach, Wellfleet from images acquired during field activity 2021-022-FA on March 17, 2021
The data in this publication map the beach and nearshore environment at Marconi Beach in Wellfleet, MA and provide regional context for the 2021 CoastCam installation that looks out at the coast shared by beachgoers, shorebirds, seals, and sharks. These data were collected as part of field activity 2021-022-FA and a collaboration with the National Park Service at Cape Cod National Seashore to monitor the region that falls within the field of view of CoastCam CACO-02, which are two video cameras aimed at the ... |
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Low-altitude aerial imagery collected from a Helikite at Marconi Beach, Wellfleet, MA on March 22, 2023
The data in this release re-map the beach and nearshore environment at Marconi Beach in Wellfleet, MA and provide updated environmental context for the 2021 CoastCam installation that looks out at the coast shared by beachgoers, shorebirds, seals, and sharks. This is related to the field activity 2022-014-FA and a collaboration with the National Park Service at Cape Cod National Seashore to monitor the region that falls within the field of view of CoastCam CACO-02, which are two video cameras aimed at the ... |
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RGB-averaged orthoimagery of coastal North Carolina, from 2020-05-08 to 2020-05-09
RGB-averaged orthoimages were created from aerial imagery collected May 08 and 09, 2020, along the North Carolina coast between the Virginia-North Carolina border vicinity and Cape Lookout, North Carolina. These RGB-averaged orthoimages were created to document recovery ground conditions after Hurricane Dorian, which made landfall on the North Carolina coast on September 6, 2019. The RGB-averaged orthoimages help researchers document inter-annual changes in shoreline position and coastal morphology in ... |
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Unvegetated to vegetated ratio at Thompsons Beach and Stone Harbor, New Jersey from 2014 to 2018
In 2012, Hurricane Sandy struck the Northeastern US causing devastation among coastal ecosystems. Post-hurricane marsh restoration efforts have included sediment deposition, planting of vegetation, and restoring tidal hydrology. The work presented here is part of a larger project funded by the National Fish and Wildlife Foundation (NFWF) to monitor the post-restoration ecological resilience of coastal ecosystems in the wake of Hurricane Sandy. The U.S. Geological Survey Woods Hole Coastal and Marine Science ... |
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2001 Gulf Coast USGS/NASA ATM Lidar-Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline ... |
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2005 East Coast (DE, MD, NJ, NY, NC, and VA) USACE NCMP Topobathy Lidar-Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe, and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline ... |
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2012 Post-Hurricane Sandy Long Island, New York USACE Lidar-Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline ... |
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2016 USACE Post-Hurricane Matthew Lidar-Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline ... |
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2016 Massachusetts NOAA Lidar-Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe, and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline ... |
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2017 Georgia through New York USACE NCMP Lidar-Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline ... |
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2017 Florida West Coast NOAA Lidar-Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches.Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe, and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline ... |
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2018 Alabama and Florida USGS Lidar-Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe, and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline ... |
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2018 Florida USGS Lidar-Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe, and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline ... |
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2018 East Coast (VA, NC, SC) USACE NCMP Post-Florence Topobathy Lidar-Derived Dune Crest, Toe, and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe, and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline ... |
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2018 East Coast (NC) USACE NCMP Topobathy Lidar Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe, and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline ... |
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2018 Mississippi and Alabama USACE Lidar-Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe, and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline ... |
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2019 North Carolina and Virginia USACE Lidar-Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (L=lidar)-derived beach morphologic features such as dune crest, toe, and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline ... |
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2019 North Carolina and Virginia Post-Dorian USACE Lidar-Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe, and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline ... |
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2020 New Jersey and New York USACE Lidar-Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe, and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline ... |
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2021 New York State Lidar-Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline ... |
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2022 New Jersey and New York USACE USGS Lidar-Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline ... |
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2020 New Jersey USACE USGS Lidar-Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe, and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline ... |
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1998 MA, NY, MD, and VA USGS/NASA ATM2 Lidar-derived dune crest, toe and shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline ... |
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2014 Post-Hurricane Sandy SC to NY NOAA NGS Lidar-Derived Dune Crest, Toe and Shoreline
The storm-induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards (NACCH) project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Light detection and ranging (lidar)-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high-water shoreline ... |
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ATLANTIC - Coastal Vulnerability to Sea-Level Rise: A Preliminary Database for the U.S. Atlantic Coast
The goal of this project is to provide a preliminary overview, at a National scale, the relative susceptibility of the Nation's coast to sea-level rise through the use of a coastal vulnerability index (CVI). This initial classification is based upon the variables geomorphology, regional coastal slope, tide range, wave height, relative sea-level rise and shoreline erosion and accretion rates. The combination of these variables and the association of these variables to each other furnish a broad overview of ... |
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PACIFIC - Coastal Vulnerability to Sea-Level Rise: U.S. Pacific Coast
The goal of this project is to quantify, at the National scale, the relative susceptibility of the Nation's coast to sea-level rise through the use of a coastal vulnerability index (CVI). This initial classification is based upon the variables geomorphology, regional coastal slope, tide range, wave height, relative sea-level rise and shoreline erosion and accretion rates. The combination of these variables and the association of these variables to each other furnish a broad overview of regions where ... |
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Digital Shoreline Analysis System version 4.1 Transects with Long-Term Rate Calculations for the Cape Cod region from Provincetown to the southern end of Monomoy Island, Massachusetts (CapeCod_LT.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Digital Shoreline Analysis System version 4.1 Transects with Short-Term Rate Calculations for the Cape Cod region from Provincetown to the southern end of Monomoy Island, Massachusetts (CapeCod_ST.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Digital Shoreline Analysis System version 4.1 Transects with Long-Term Rate Calculations for the Delmarva North region from Cape Henlopen, Delaware to the southern end of Assateague Island, Virginia (DelmarvaN_LT.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Digital Shoreline Analysis System version 4.1 Transects with Short-Term Rate Calculations for the Delmarva North region from Cape Henlopen, Delaware to the southern end of Assateague Island, Virginia (DelmarvaN_ST.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Digital Shoreline Analysis System version 4.1 Transects with Long-Term Rate Calculations for the Delmarva South/Southern Virginia region from Wallops Island, Virginia to the Virginia/North Carolina border (DelmarvaS_SVA_LT.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.1 Transects with Short-Term Rate Calculations for the Delmarva South/Southern Virginia region from Wallops Island, Virginia to the Virginia/North Carolina border (DelmarvaS_SVA_ST.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.1 Transects with Long-Term Rate Calculations for the Long Island region from Montauk Point to the entrance of Raritan Bay, New York (LongIsland_LT.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.1 Transects with Rate Calculations for the Long Island region from Montauk Point to the entrance of Raritan Bay, New York (LongIsland_ST.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.1 Transects with Long-Term Rate Calculations for the Massachusetts Islands Region including Martha's Vineyard and Nantucket (MA_Islands_LT.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.1 Transects with Short-Term Rate Calculations for the Massachusetts Islands Region including Martha's Vineyard and Nantucket (MA_Islands_ST.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.1 Transects with Long-Term Rate Calculations for the New England South region from Dartmouth, Massachusetts to Napatree Point, Rhode Island (NewEnglandS_LT.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.1 Transects with Short-Term Rate Calculations for the New England South region from Dartmouth, Massachusetts to Napatree Point, Rhode Island (NewEnglandS_ST.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.1 Transects with Long-Term Rate Calculations for the New Jersey North region from Sandy Hook to Little Egg Inlet, New Jersey (NewJerseyN_LT.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.1 Transects with Short-Term Rate Calculations for the New Jersey North region from Sandy Hook to Little Egg Inlet, New Jersey (NewJerseyN_ST.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.1 Transects with Long-Term Rate Calculations for the New Jersey South region from Little Egg Inlet to Cape, May, New Jersey (NewJerseyS_LT.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.1 Transects with Short-Term Rate Calculations for the New Jersey South region from Little Egg Inlet to Cape, May, New Jersey (NewJerseyS_ST.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
KauaiE_baseline - Offshore baseline used to cast shore-perpendicular transects for measurement of historical shoreline positions along East Kauai, Hawaii (Papaa to Nawiliwili)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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KauaiE_LT - Digital Shoreline Analysis System (DSAS) version 4.2 transects with long-term weighted linear regression rate calculations for the Kauai east region from Papaa to Nawiliwili, Hawaii
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
KauaiE_ST- Digital Shoreline Analysis System (DSAS) version 4.2 transects with short-term weighted linear regression rate calculations for the Kauai east region from Papaa to Nawiliwili, Hawaii.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
KauaiN_baseline - Offshore baseline used to cast shore-perpendicular transects for measurement of historical shoreline positions along North Kauai, Hawaii (Haena to Moloaa)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
KauaiN_LT - Digital Shoreline Analysis System (DSAS) version 4.2 transects with long-term weighted linear regression rate calculations for the Kauai north region from Haena to Moloaa, Hawaii
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
KauaiN_ST - Digital Shoreline Analysis System (DSAS) version 4.2 transects with weighted linear regression short-term rate calculations for the Kauai north region from Haena to Moloaa, Hawaii.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
KauaiS_baseline - Offshore baseline used to cast shore-perpendicular transects for measurement of historical shoreline positions along South Kauai, Hawaii (Waimea to Kipu Kai)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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KauaiS_LT - Digital Shoreline Analysis System (DSAS) version 4.2 transects with long-term weighted linear regression rate calculations for the Kauai south region from Waimea to Kipu Kai, Hawaii
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
KauaiS_ST- Digital Shoreline Analysis System (DSAS) version 4.2 transects with short-term weighted linear regression rate calculations for the Kauai south region from Waimea to Kipu Kai, Hawaii.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
KauaiW_baseline - Offshore baseline used to cast shore-perpendicular transects for measurement of historical shoreline positions along West Kauai, Hawaii (Oomano to Polihale)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
KauaiW_LT - Digital Shoreline Analysis System (DSAS) version 4.2 transects with long-term weighted linear regression rate calculations for the Kauai west region from Oomano to Polihale, Hawaii
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
KauaiW_ST - Digital Shoreline Analysis System (DSAS) version 4.2 transects with short-term weighted linear regression rate calculations for the Kauai west region from Oomano to Polihale, Hawaii.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
MauiK_baseline - Offshore baseline used to cast shore-perpendicular transects for measurement of historical shoreline positions along the Kihei Coast of Maui, Hawaii (Maalaea to Makena)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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MauiK_LT - Digital Shoreline Analysis System (DSAS) version 4.2 transects with long-term weighted linear regression rate calculations for the Maui Kihei region from Maalaea to Makena, Hawaii
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
MauiK_ST - Digital Shoreline Analysis System (DSAS) version 4.2 transects with short-term weighted linear regression rate calculations for the Maui Kihei region from Maalaea to Makena, Hawaii.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
MauiN_baseline - Offshore baseline used to cast shore-perpendicular transects for measurement of historical shoreline positions along the North Coast of Maui, Hawaii (Waihee to Kuau)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
MauiN_LT- Digital Shoreline Analysis System (DSAS) version 4.2 transects with long-term weighted linear regression rate calculations for the Maui North region from Waihee to Kuau, Hawaii.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
MauiN_ST - Digital Shoreline Analysis System (DSAS) version 4.2 transects with short-term weighted linear regression rate calculations for the Maui North region from Waihee to Kuau, Hawaii.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
MauiW_baseline - Offshore baseline used to cast shore-perpendicular transects for measurement of historical shoreline positions along the West Coast of Maui, Hawaii (Ukumehame to Honolua)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
MauiW_LT- Digital Shoreline Analysis System (DSAS) version 4.2 transects with long-term weighted linear regression rate calculations for the Maui West region from Ukumehame to Honolua, Hawaii.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
MauiW_ST - Digital Shoreline Analysis System (DSAS) version 4.2 transects with short-term weighted linear regression rate calculations for the Maui West region from Ukumehame to Honolua, Hawaii.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
OahuE_baseline - Offshore baseline used to cast shore-perpendicular transects for measurement of historical shoreline positions along East Oahu, Hawaii (Kahuku to Makapuu)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
OahuE_LT - Digital Shoreline Analysis System (DSAS) version 4.2 transects with long-term weighted linear regression rate calculations for the Oahu East region from Kahuku to Makapuu, Hawaii
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
OahuE_ST - Digital Shoreline Analysis System (DSAS) version 4.2 transects with short-term weighted linear regression rate calculations for the Oahu East region from Kahuku to Makapuu, Hawaii.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
OahuN_baseline - Offshore baseline used to cast shore-perpendicular transects for measurement of historical shoreline positions along North Oahu, Hawaii (Camp Erdman to Kahuku)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
OahuN_LT - Digital Shoreline Analysis System (DSAS) version 4.2 transects with long-term weighted linear regression rate calculations for the Oahu north region from Camp Erdman to Kahuku, Hawaii.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
OahuN_ST - Digital Shoreline Analysis System (DSAS) version 4.2 transects with short-term weighted linear regression rate calculations for the Oahu North region from Camp Erdman to Kahuku, Hawaii.
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for Oregon (OR_shorelines_uncertainty.dbf)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.2 Transects with Long-Term Linear Regression Rate Calculations for Oregon (OR_transects_LT.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.2 Transects with Short-Term End Point Rate Calculations for Oregon (OR_transects_ST.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for Washington (WA_shorelines_uncertainty.dbf)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.2 Transects with Long-Term Linear Regression Rate Calculations for Washington (WA_transects_LT.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.2 Transects with Short-Term End Point Rate Calculations for Washington (WA_transects_ST.shp)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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USGS CoastCam at Madeira Beach, Florida: Timestack Imagery and Coordinate Data
A digital video camera was installed at Madeira Beach, Florida (FL) and faced west along the beach. Every hour during daylight hours, daily from 2017 to 2022, the camera collected raw video and produced snapshots and time-averaged image products. One such product is a "runup timestack". Runup timestacks are images created by sampling a cross-shore array of pixels from an image through time as waves propagate towards and run up a beach. Runup timestacks store the red, green, and blue or monochrome pixel ... |
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Shoreline intersects for the coast of Puerto Rico's main island generated by the Digital Shoreline Analysis System version 5.1 (ver. 2.0, March 2023)
The U.S. Geological Survey (USGS) maintains shoreline positions for the United States coasts from both older sources, such as aerial photographs or topographic surveys, and contemporary sources, such as lidar-point clouds and digital elevation models. These shorelines are compiled and analyzed in the USGS Digital Shoreline Analysis System (DSAS), version 5.1 software to calculate rates of change. Keeping a record of historical shoreline positions is an effective method to monitor change over time, enabling ... |
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Shorelines for Vieques, Culebra, and the main island of Puerto Rico from the 1900s to 2018 (ver. 2.0, March 2023)
The U.S. Geological Survey (USGS) maintains shoreline positions for the United States coasts from both older sources, such as aerial photographs or topographic surveys, and contemporary sources, such as lidar-point clouds and digital elevation models. These shorelines are compiled and analyzed in the USGS Digital Shoreline Analysis System (DSAS), version 5.1 software to calculate rates of change. Keeping a record of historical shoreline positions is an effective method to monitor change over time, enabling ... |
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Shoreline intersects for the islands of Vieques and Culebra, Puerto Rico, calculated using the Digital Shoreline Analysis System version 5.1
The U.S. Geological Survey (USGS) maintains shoreline positions for the United States coasts from both older sources, such as aerial photographs or topographic surveys, and contemporary sources, such as lidar-point clouds and digital elevation models. These shorelines are compiled and analyzed in the USGS Digital Shoreline Analysis System (DSAS), version 5.1 software to calculate rates of change. Keeping a record of historical shoreline positions is an effective method to monitor change over time, enabling ... |
Info |
2005 USGS Post-Hurricane Rita Texas and Louisiana Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2005 USGS Post-Hurricane ... |
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Marsh Shorelines of the Massachusetts Coast from 2013-14 Topographic Lidar Data
The Massachusetts Office of Coastal Zone Management (CZM) launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the Massachusetts coast. Seventy-six maps were produced in 1997 depicting a statistical analysis of shoreline change on ocean-facing shorelines from the mid-1800s to 1978 using multiple data sources. In 2001, a 1994 shoreline was added. More recently, in cooperation with CZM, the U.S. Geological Survey (USGS) delineated a new shoreline for Massachusetts using color ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for northeastern Florida (FLne)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Long-Term Linear Regression Rate Calculations for northeastern Florida (FLne)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term End Point Rate Calculations for northeastern Florida (FLne)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for northeastern Florida (FLne)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for southeastern Florida (FLse)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Long-Term Linear Regression Rate Calculations for southeastern Florida (FLse)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term End Point Rate Calculations for southeastern Florida (FLse)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for southeastern Florida (FLse)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for Georgia (GA)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Long-Term Linear Regression Rate Calculations for Georgia (GA)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term End Point Rate Calculations for Georgia (GA)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for Georgia (GA)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for central North Carolina (NCcentral)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Long-Term Linear Regression Rate Calculations for central North Carolina (NCcentral)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term End Point Rate Calculations for central North Carolina (NCcentral)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for central North Carolina (NCcentral)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for northern North Carolina (NCnorth)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Long-Term Linear Regression Rate Calculations for northern North Carolina (NCnorth)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term End Point Rate Calculations for northern North Carolina (NCnorth)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for northern North Carolina (NCnorth)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for southern North Carolina (NCsouth)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Long-Term Linear Regression Rate Calculations for southern North Carolina (NCsouth)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term End Point Rate Calculations for southern North Carolina (NCsouth)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for southern North Carolina (NCsouth)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for western North Carolina (NCwest)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Long-Term Linear Regression Rate Calculations for western North Carolina (NCwest)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for western North Carolina (NCwest)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for South Carolina (SC)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Long-Term Linear Regression Rate Calculations for South Carolina (SC)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term End Point Rate Calculations for South Carolina (SC)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for South Carolina (SC)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Long-Term Linear Regression Rate Calculations for Alabama
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for Alabama
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term End Point Rate Calculations for Alabama
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for Alabama
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Long-Term Linear Regression Rate Calculations for Florida north (FLnorth)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for Florida north (FLnorth)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term End Point Rate Calculations for Florida north (FLnorth)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for Florida north (FLnorth)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Long-Term Linear Regression Rate Calculations for Florida west (FLwest)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for Florida west (FLwest)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term End Point Rate Calculations for Florida west (FLwest)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for Florida west (FLwest)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Long-Term Linear Regression Rate Calculations for Louisiana
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for Louisiana
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term End Point Rate Calculations for Louisiana
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for Louisiana
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Long-Term Linear Regression Rate Calculations for Mississippi
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for Mississippi
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term End Point Rate Calculations for Mississippi
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for Mississippi
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Long-Term Linear Regression Rate Calculations for Texas east (TXeast)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for Texas east (TXeast)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for Texas east (TXeast)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Long-Term Linear Regression Rate Calculations for Texas west (TXwest)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for Texas west (TXwest)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Digital Shoreline Analysis System version 4.3 Transects with Short-Term Linear Regression Rate Calculations for Texas west (TXwest)
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
Info |
Change in salinity in salt marsh units in Edwin B. Forsythe National Wildlife Refuge, New Jersey during Hurricane Sandy
As part of the Hurricane Sandy Science Plan, the U.S. Geological Survey is expanding National Assessment of Coastal Change Hazards and forecast products to coastal wetlands. The intent is to provide federal, state, and local managers with tools to estimate the vulnerability of coastal wetlands to various factors and to evaluate their ecosystem service potential. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their ... |
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Change in salinity exposure of salt marsh units in Edwin B. Forsythe National Wildlife Refuge, New Jersey during Hurricane Sandy
As part of the Hurricane Sandy Science Plan, the U.S. Geological Survey is expanding National Assessment of Coastal Change Hazards and forecast products to coastal wetlands. The intent is to provide federal, state, and local managers with tools to estimate the vulnerability of coastal wetlands to various factors and to evaluate their ecosystem service potential. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their ... |
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Change in suspended sediment concentration over the salt marsh units in Edwin B. Forsythe National Wildlife Refuge, New Jersey during Hurricane Sandy
As part of the Hurricane Sandy Science Plan, the U.S. Geological Survey is expanding National Assessment of Coastal Change Hazards and forecast products to coastal wetlands. The intent is to provide federal, state, and local managers with tools to estimate the vulnerability of coastal wetlands to various factors and to evaluate their ecosystem service potential. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their ... |
Info |
Town Neck Beach, Massachusetts, 10 cm 2016-2017 Digital Elevation Models
Low-altitude (80-100 meters above ground level) Unmanned Aircraft Systems (UAS) imagery of Town Neck Beach in Sandwich, Massachusetts, were used in a structure-from-motion (SfM) photogrammetry workflow to create high-resolution topographic datasets. Imagery was collected at close to low tide on twelve days to observe changes in beach and dune morphology. Ground control points (GCPs), which are temporary targets on the ground located by using a real-time kinematic global navigation satellite system (RTK-GNSS ... |
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Town Neck Beach, Massachusetts, 5 cm 2016-2017 Orthomosaics
Low-altitude (80-100 meters above ground level) Unmanned Aircraft Systems (UAS) imagery of Town Neck Beach in Sandwich, Massachusetts, were used in a structure-from-motion (SfM) photogrammetry workflow to create high-resolution topographic datasets. Imagery was collected at close to low tide on twelve days to observe changes in beach and dune morphology. Ground control points (GCPs), which are temporary targets on the ground located by using a real-time kinematic global navigation satellite system (RTK-GNSS ... |
Info |
Historical Shorelines for Puerto Rico from 1901 to 1987
The U.S. Geological Survey (USGS) maintains shoreline positions for the United States coasts from both older sources, such as aerial photos or topographic surveys, as well as contemporary sources like lidar point clouds and digital elevation models (DEMs). These shorelines are compiled and analyzed in the Digital Shoreline Analysis System (DSAS) software to compute rates of change. It is useful to keep a record of historical shoreline positions as a method of monitoring change over time to identify areas ... |
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VA Bias_Feature – Feature class containing Virginia proxy-datum bias information to be used in the Digital Shoreline Analysis System.
Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This data release ... |
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Mean High Water Shorelines for the Outer Cape of Massachusetts from Nauset Inlet to Race Point (1998-2005)
This data release contains mean high water (MHW) shorelines for the Outer Cape of Cape Cod, Massachusetts, from Nauset Inlet to Race Point. From 1998-2005, the U.S. Geological Survey surveyed 45 kilometers of coastline 111 times using a ground-based system called Surveying Wide-Area Shorelines (SWASH). The SWASH system used a six-wheeled amphibious all-terrain vehicle as a platform for an array of Global Positioning System sensors. High-accuracy measurements of horizontal position, vertical position, and ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Parker River, MA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Rhode Island National Wildlife Refuge, RI, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Rhode Island National Wildlife Refuge, RI, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Ship Shoal Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Ship Shoal Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
Info |
points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Smith Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Smith Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Wreck Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Wreck Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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MA Bias_Feature – Feature class containing Massachusetts proxy-datum bias information to be used in the Digital Shoreline Analysis System.
The Digital Shoreline Analysis System (DSAS) is a freely available software application that works within the Environmental Systems Research Institute (ESRI) Geographic Information System (ArcGIS) software. DSAS computes rate-of-change statistics for a time series of shoreline vector data. Additionally, the DSAS application is useful for computing rates of change for any boundary-change problem that incorporates a clearly-identified feature position at discrete times, such as glacier limits, river banks, or ... |
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Climatological wave height, wave period and wave power along coastal areas of the East Coast of the United States and Gulf of Mexico
This U.S. Geological Survey data release provides data on spatial variations in climatological wave parameters (significant wave height, peak wave period, and wave power) for coastal areas along the United States East Coast and Gulf of Mexico. Significant wave height is the average wave height, from crest to trough, of the highest one-third of the waves in a specific time period. Peak wave period is the wave period associated with the most energetic waves in the wave spectrum in a specific time period. Wave ... |
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Bathymetric change of Central San Francisco Bay, California: 1971 to 2020
This 25-m-resolution surface presents bathymetric change of Central San Francisco Bay, California (hereafter referred to as Central Bay). This surface compares a 1-m-resolution digital elevation model (DEM) of the central portion of San Francisco Bay (Fregoso and others, 2020), comprised of bathymetry data in the Central Bay region from the time period referred to as the 2010s because the majority of the surveys were in that decade, to a 25-m-resolution DEM of Central Bay comprised of historic surveys from ... |
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Bathymetric change of San Pablo Bay, California: 1983 to 2015
This 25-m-resolution surface presents bathymetric change of San Pablo Bay, California, from 1983 to 2015. This surface compares a 1-m-resolution digital elevation model (DEM) of the northern portion of San Francisco Bay (Fregoso and others, 2020), comprised of bathymetry data in the San Pablo Bay region from the time period referred to as the 2010s because the majority of the surveys were in that decade, to a 25-m-resolution bathymetric DEM of San Pablo Bay comprised of historic surveys from 1983 to 1986 ... |
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Bathymetric change of South San Francisco Bay, California: 1979 to 2020
This 50-m-resolution surface presents bathymetric change of South San Francisco Bay, California (hereafter referred to as South Bay). This surface compares a 1-m-resolution digital elevation model (DEM) of the southern portion of San Francisco Bay (Fregoso and others, 2020), comprised of bathymetry data in the South Bay region from the time period referred to as the 2010s because the majority of the surveys were in that decade, to a 50-m-resolution DEM of South Bay comprised of historic surveys from 1979 to ... |
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Bathymetric change of Suisun Bay, California: 1988 to 2016
This 25-m-resolution surface presents bathymetric change of Suisun Bay, California, from 1988 to 2016. This surface compares a 1-m-resolution digital elevation model (DEM) of the northern portion of San Francisco Bay (Fregoso and others, 2020), comprised of bathymetry data in the Suisun region from the time period referred to as the 2010s because the majority of the surveys were in that decade, to a 25-m-resolution bathymetric DEM of Suisun Bay comprised of historic surveys from 1988 to 1990 (referred to as ... |
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Beach foreshore slope for the East Coast of the United States
This data release contains foreshore slopes for primarily open-ocean sandy beaches along the East Coast of the United States (Maine through Florida). The slopes were calculated while extracting shoreline position from lidar point cloud data collected between 1997 and 2018. The shoreline positions have been previously published, but the slopes have not. An along-shore reference baseline was defined, and then 20-meter spaced cross-shore beach transects were created perpendicular to the baseline. All data ... |
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Low-altitude aerial imagery collected from a Helikite at Marconi Beach, Wellfleet, MA on March 22, 2024
The data in this release re-map the beach and nearshore environment at Marconi Beach in Wellfleet, MA and provide updated environmental context for the 2021 CoastCam installation that looks out at the coast shared by beachgoers, shorebirds, seals, and sharks. This is related to the field activity 2024-016-FA and a collaboration with the National Park Service at Cape Cod National Seashore to monitor the region that falls within the field of view of two video cameras aimed at the beach (CoastCam CACO-02). In ... |
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2002 Post-Tropical Storm Fay University of Texas Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2002 University of Texas ... |
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2002 NOAA/NASA/USGS Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2002 Post-Hurricane Lili ... |
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2003 Pre- and Post-Hurricane Isabel USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2003 Pre- and Post ... |
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2004 Post-Hurricane Charley West Florida EAARL Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2004 Post-Hurricane ... |
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2005 Post-Hurricane Dennis Florida U.S. Army Corps of Engineers Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2005 USACE Post-Dennis ... |
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2005 Post-Hurricane Katrina EAARL Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2005 Post-Hurricane ... |
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September 2006 Post-Hurricane Wilma Florida U.S. Army Corps of Engineers Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2006 Post-Hurricane ... |
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March 2006 Mississippi and Alabama USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2006 USGS Mississippi ... |
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September 2006 Mississippi and Alabama USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2006 USGS Mississippi ... |
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September 2007 Southwest Florida Division of Emergency Management Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2007 Southwest Florida ... |
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June 2008 Alabama and Florida USGS EAARL Lidar-derived dune crest, toe and shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the June 2008 Louisiana, ... |
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2008 Post-Hurricane Gustav Northern Gulf of Mexico USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2008 Post-Hurricane ... |
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2008 South Louisiana USGS EAARL Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2008 South Louisiana ... |
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2009 Western Gulf of Mexico USACE Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2009 Western Gulf of ... |
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2010 Alabama and Florida USACE Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 Alabama and Florida ... |
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2010 Florida West Coast USACE Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 Florida West Coast ... |
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2010 Louisiana and Mississippi USACE Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2010 Louisiana and ... |
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2011 Northern Gulf Coast USACE Lidar-derived dune crest, toe and shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2011 Northern Gulf Coast ... |
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2012 Post-Hurricane Isaac USGS Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2012 Post-Hurricane ... |
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2013 Dauphin Island USGS Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2013 Dauphin Island ... |
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2014 USGS CMGP Post-Sandy Long Island Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2014 USGS CMGP Post ... |
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2014 Mobile County, Alabama Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2014 Mobile County, ... |
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2015 Mississippi and Alabama USGS Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2015 Mississippi and ... |
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1998 Fall Gulf Coast Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 1998 Fall Gulf Coast ... |
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1999 Fall Texas USGS/NASA/NOAA ATM Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 1999 Fall Gulf Coast ... |
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2004 Post-Hurricane Ivan Northern Gulf of Mexico EAARL Lidar-Derived Dune Crest, Toe and Shoreline
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2004 USGS Post-Ivan ... |
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Orthomosaics representing Nauset Light Beach, Eastham, MA on September 14 and 20, 2023, pre and post Hurricane Lee
The data in this release map Marconi Beach, Head of the Meadow Beach, and Nauset Light Beach, in Cape Cod National Seashore (CACO), Massachusetts, before and after Hurricane Lee in September 2023. U.S Geological Survey personnel conducted field surveys to collect topographic data using global navigation satellite systems (GNSS) at all three beaches. In addition, at Nauset Light Beach, an uncrewed aerial system (UAS) was used to collect images with a Ricoh GRII camera for use in structure from motion ... |
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SWASH Model Water Level Time Series at Wrightsville Beach, NC, USA for ILM site
This data release contains model output of water level elevations resulting from deterministic simulations at Wrightsville Beach, North Carolina (NC), USA. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Birchler and others (2024). |
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SWASH Model Water Level Time Series at Wrightsville Beach, NC, USA for MHX site
This data release contains model output of water level elevations resulting from deterministic simulations at Wrightsville Beach, North Carolina (NC), USA. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Birchler and others (2024). |
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SWASH Model Water Level Time Series at Wrightsville Beach, NC, USA for PIER site
This data release contains model output of water level elevations resulting from deterministic simulations at Wrightsville Beach, North Carolina (NC), USA. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Birchler and others (2024). |
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Digital Shoreline Analysis System (DSAS) version 5.1 transects with long-term linear regression rate calculations for the exposed central Beaufort Sea coast of Alaska from the Hulahula River to the Colville River
This dataset consists of long-term (70 years) shoreline change rates for the exposed, open-ocean coast of Alaska from the Hulahula River to the Colville River. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 5.1, an ArcGIS extension developed by the U.S. Geological Survey. Long-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1947 and 2017. A reference baseline was ... |
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Digital Shoreline Analysis System (DSAS) version 5.1 transects with short-term linear regression rate calculations for the exposed central Beaufort Sea coast of Alaska from the Hulahula River to the Colville River
This dataset consists of short-term (less than 39 years) shoreline change rates for the exposed, open-ocean coast of Alaska from the Hulahula River to the Colville River. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 5.1, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1979 and 2017. A reference ... |
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Digital Shoreline Analysis System (DSAS) version 5.1 transects with long-term linear regression rate calculations for the sheltered central Beaufort Sea coast of Alaska from the Hulahula River to the Colville River
This dataset consists of long-term (70 years) shoreline change rates for the mainland coast of Alaska sheltered by barrier islands from the Hulahula River to the Colville River. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 5.1, an ArcGIS extension developed by the U.S. Geological Survey. Long-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1947 and 2017. A ... |
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Digital Shoreline Analysis System (DSAS) version 5.1 transects with short-term linear regression rate calculations for the sheltered central Beaufort Sea coast of Alaska from the Hulahula River to the Colville River
This dataset consists of short-term (less than 39 years) shoreline change rates for the mainland coast of Alaska sheltered by barrier islands from the Hulahula River to the Colville River. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 5.1, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1979 and ... |
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Digital Shoreline Analysis System (DSAS) version 5.1 transects with long-term linear regression rate calculations for the exposed eastern Beaufort Sea coast of Alaska from the U.S. Canadian Border to the Hulahula River
This dataset consists of long-term (70 years) shoreline change rates for the exposed, open-ocean coast of Alaska from the U.S. Canadian Border to the Hulahula River. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 5.1, an ArcGIS extension developed by the U.S. Geological Survey. Long-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1947 and 2017. A reference ... |
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Digital Shoreline Analysis System (DSAS) version 5.1 transects with short-term linear regression rate calculations for the exposed eastern Beaufort Sea coast of Alaska from the U.S. Canadian Border to the Hulahula River
This dataset consists of short-term (less than 39 years) shoreline change rates for the exposed, open-ocean coast of Alaska from the U.S. Canadian Border to the Hulahula River. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 5.1, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1978 and 2017. A ... |
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Digital Shoreline Analysis System (DSAS) version 5.1 transects with long-term linear regression rate calculations for the sheltered eastern Beaufort Sea coast of Alaska from the U.S. Canadian Border to the Hulahula River
This dataset consists of long-term (70 years) shoreline change rates for the mainland coast of Alaska sheltered by barrier islands from the U.S. Canadian Border to the Hulahula River. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 5.1, an ArcGIS extension developed by the U.S. Geological Survey. Long-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1947 and 2017. A ... |
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Digital Shoreline Analysis System (DSAS) version 5.1 transects with short-term linear regression rate calculations for the sheltered eastern Beaufort Sea coast of Alaska from the U.S. Canadian Border to the Hulahula River
This dataset consists of short-term (less than 39 years) shoreline change rates for the mainland coast of Alaska sheltered by barrier islands from the U.S. Canadian Border to the Hulahula River. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 5.1, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1978 ... |
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Projected coastal flooding depths for 1-, 20-, and 100-year return interval storms and 0.00, +0.25, +0.50, +1.00, +1.50, +2.00, and +3.00 meter sea-level rise scenarios in American Samoa
This data release provides flood depth GeoTIFFs based on sea-level rise and wave-driven total water levels for the coast of the American Samoa’s most populated islands of Tutuila, Ofu-Olosega, and Tau. Oceanographic, coastal engineering, ecologic, and geospatial data and tools were combined to evaluate the increased risks of storm-induced coastal flooding in the populated American Samoan Islands due to climate change and sea-level rise. We followed risk-based valuation approaches to map flooding due to ... |
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Projected coastal flooding depths for 1-, 20-, and 100-year return interval storms and 0.00, +0.25, +0.50, +1.00, +1.50, +2.00, and +3.00 meter sea-level rise scenarios in the Mariana Islands
This data release provides flood depth GeoTIFFs based on sea-level rise and wave-driven total water levels for the coast of the most populated Mariana Islands of Guam and Saipan. Oceanographic, coastal engineering, ecologic, and geospatial data and tools were combined to evaluate the increased risks of storm-induced coastal flooding in the populated Mariana Islands due to climate change and sea-level rise. We followed risk-based valuation approaches to map flooding due to waves and storm surge at 10-m2 ... |
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Projected coastal flooding depths for 1-, 20-, and 100-year return interval storms and 0.00, +0.25, +0.50, +1.00, +1.50, +2.00, and +3.00 meter sea-level rise scenarios in the Hawaiian Islands (ver. 1.1, September 2024)
This data release provides flood depth GeoTIFFs based on sea-level rise and wave-driven total water levels for the coast of the most populated Hawaiian Islands of Oahu, Molokai, Kauai, Maui, and Big Island. Oceanographic, coastal engineering, ecologic, and geospatial data and tools were combined to evaluate the increased risks of storm-induced coastal flooding in the populated Hawaiian Islands due to climate change and sea-level rise. We followed risk-based valuation approaches to map flooding due to waves ... |
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Projected coastal flooding extents for 1-, 20-, and 100-year return interval storms and 0.00, +0.25, +0.50, +1.00, +1.50, +2.00, and +3.00 meter sea-level rise scenarios in American Samoa
This data release provides flooding extent polygons based on sea-level rise and wave-driven total water levels for the coast of American Samoa's most populated islands of Tutuila, Ofu-Olosega, and Tau. Oceanographic, coastal engineering, ecologic, and geospatial data and tools were combined to evaluate the increased risks of storm-induced coastal flooding due to climate change and sea-level rise. We followed risk-based valuation approaches to map flooding due to waves and storm surge at 10-m2 resolution ... |
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Projected coastal flooding extents for 1-, 20-, and 100-year return interval storms and 0.00, +0.25, +0.50, +1.00, +1.50, +2.00, and +3.00 meter sea-level rise scenarios in the Mariana Islands
This data release provides flooding extent polygons based on sea-level rise and wave-driven total water levels for the coast of the most populated Mariana Islands of Guam and Saipan. Oceanographic, coastal engineering, ecologic, and geospatial data and tools were combined to evaluate the increased risks of storm-induced coastal flooding due to climate change and sea-level rise. We followed risk-based valuation approaches to map flooding due to waves and storm surge at 10-m2 resolution along these islands' ... |
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Projected coastal flooding extents for 1-, 20-, and 100-year return interval storms and 0.00, +0.25, +0.50, +1.00, +1.50, +2.00, and +3.00 meter sea-level rise scenarios in the Hawaiian Islands
This data release provides flooding extent polygons based on sea-level rise and wave-driven total water levels for the coast of the most populated Hawaiian Islands of Oahu, Molokai, Kauai, Maui, and Big Island. Oceanographic, coastal engineering, ecologic, and geospatial data and tools were combined to evaluate the increased risks of storm-induced coastal flooding due to climate change and sea-level rise. We followed risk-based valuation approaches to map flooding due to waves and storm surge at 10-m2 ... |
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Projected coastal flooding inundation depths for +0.25, +0.50, +1.00, +1.50, +2.00, and +3.00 meter sea-level rise scenarios in American Samoa
This data release provides flood depth GeoTIFFs based on sea-level rise (SLR) for the coast of the most populated American Samoa s most populated islands of Tutuila, Ofu-Olosega, and Ta'u. Digital elevation models were used to extract SLR flooded areas at 10-m2 resolution along the coastlines for +0.25 m, +0.50 m, +1.00 m, +1.50 m, +2.00 m, and +3.00 m SLR scenarios. |
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Projected coastal flooding inundation depths for +0.25, +0.50, +1.00, +1.50, +2.00, and +3.00 meter sea-level rise scenarios in the Mariana Islands
This data release provides flood depth GeoTIFFs based on sea-level rise for the coast of the most populated Mariana Islands of Guam and Saipan. Digital elevation models were used to extract sea-level rise flooded areas at 10-m2 resolution along the coastlines for +0.25 m, +0.50 m, +1.00 m, +1.50 m, +2.00 m, and +3.00 m sea-level rise scenarios. |
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Projected coastal flooding inundation depths for +0.25, +0.50, +1.00, +1.50, +2.00, and +3.00 meter sea-level rise scenarios in the Hawaiian Islands
This data release provides flood depth GeoTIFFs based on potential future sea-level rise (SLR)for the coast of the most populated Hawaiian Islands of O'ahu, Moloka'i, Kaua'i, Maui, and Big Island. Digital elevation models were used to extract SLR flooded areas at 10-m2 resolution along the coastlines for +0.25 m, +0.50 m, +1.00 m, +1.50 m, +2.00 m, and +3.00 m SLR scenarios. |
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Reference baselines used to extract shorelines for the West Coast of the United States (ver. 1.1, September 2024)
This data release contains reference baselines for primarily open-ocean sandy beaches along the west coast of the United States (California, Oregon and Washington). The slopes were calculated while extracting shoreline position from lidar point cloud data collected between 2002 and 2011. The shoreline positions have been previously published, but the slopes have not. A reference baseline was defined and then evenly-spaced cross-shore beach transects were created. Then all data points within 1 meter of each ... |
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Beach foreshore slope for the West Coast of the United States (ver. 1.1, September 2024)
This data release contains foreshore slopes for primarily open-ocean sandy beaches along the west coast of the United States (California, Oregon and Washington). The slopes were calculated while extracting shoreline position from lidar point cloud data collected between 2002 and 2011. The shoreline positions have been previously published, but the slopes have not. A reference baseline was defined and then evenly-spaced cross-shore beach transects were created. Then all data points within 1 meter of each ... |
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Unprocessed aerial imagery from 9 December 2015 coastal survey of Central California.
This is a set of 1132 oblique aerial photogrammetric images and their derivatives, collected from Capitola to Pajaro Dunes with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 26 January 2016 coastal survey of Central California.
This is a set of 1836 oblique aerial photogrammetric images and their derivatives, collected from Ano Nuevo to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 5 February 2016 coastal survey of Central California.
This is a set of 3494 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 2 March 2016 coastal survey of Central California.
This is a set of 1309 oblique aerial photogrammetric images and their derivatives, collected from Santa Cruz to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 8 March 2016 coastal survey of Central California.
This is a set of 2753 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 15 September 2016 coastal survey of Central California.
This is a set of 1600 oblique aerial photogrammetric images and their derivatives, collected from Ano Nuevo to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 26 September 2016 coastal survey of Central California.
This is a set of 1569 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Ano Nuevo with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 1 December 2016 coastal survey of Central California.
This is a set of 3234 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 20 December 2016 coastal survey of Central California.
This is a set of 3036 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 25 January 2017 coastal survey of Central California.
This is a set of 4521 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Cape San Martin with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial ... |
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Unprocessed aerial imagery from 22 February 2017 coastal survey of Central California.
This is a set of 4808 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Lucia with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 8 March 2017 coastal survey of Central California.
This is a set of 5642 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 5 April 2017 coastal survey of Central California.
This is a set of 5044 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Cape San Martin with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial ... |
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Unprocessed aerial imagery from 8 May 2017 coastal survey of Central California.
This is a set of 1975 oblique aerial photogrammetric images and their derivatives, collected from Pedro Point to Sunset Beach with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 12 May 2017 coastal survey of Central California.
This is a set of 628 oblique aerial photogrammetric images and their derivatives, collected from SeaCliff Beach to Fort Ord with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 17 May 2017 coastal survey of Central California.
This is a set of 3045 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 19 May 2017 coastal survey of Central California.
This is a set of 3164 oblique aerial photogrammetric images and their derivatives, collected from Monterey to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, preceded ... |
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Unprocessed aerial imagery from 27 May 2017 coastal survey of Central California.
This is a set of 642 oblique aerial photogrammetric images and their derivatives, collected from Monterey to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, preceded ... |
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Unprocessed aerial imagery from 31 May 2017 coastal survey of Central California.
This is a set of 410 oblique aerial photogrammetric images and their derivatives, collected from Monterey to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, preceded ... |
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Unprocessed aerial imagery from 13 June 2017 coastal survey of Central California.
This is a set of 757 oblique aerial photogrammetric images and their derivatives, collected from Monterey to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, preceded ... |
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Unprocessed aerial imagery from 26 June 2017 coastal survey of Central California.
This is a set of 5069 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 18 December 2017 coastal survey of Central California.
This is a set of 2948 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 21 December 2017 coastal survey of Central California.
This is a set of 2072 oblique aerial photogrammetric images and their derivatives, collected from Monterey to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, preceded ... |
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Unprocessed aerial imagery from 29 January 2018 coastal survey of Central California.
This is a set of 5365 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 7 March 2018 coastal survey of Central California.
This is a set of 5355 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 28 May 2018 coastal survey of Central California.
This is a set of 3550 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 5 June 2018 coastal survey of Central California.
This is a set of 1533 oblique aerial photogrammetric images and their derivatives, collected from Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, preceded by the ... |
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Unprocessed aerial imagery from 10 September 2018 coastal survey of Central California.
This is a set of 5846 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 23 February 2019 coastal survey of Central California.
This is a set of 4734 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 4 March 2019 coastal survey of Central California.
This is a set of 2541 oblique aerial photogrammetric images and their derivatives, collected from Ano Nuevo to Monterey (x2) with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 11 March 2019 coastal survey of Central California.
This is a set of 1967 oblique aerial photogrammetric images and their derivatives, collected from Monterey to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, preceded ... |
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Unprocessed aerial imagery from 10 June 2019 coastal survey of Central California.
This is a set of 5042 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 15 October 2019 coastal survey of Central California.
This is a set of 3777 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 31 October 2019 coastal survey of Central California.
This is a set of 1911 oblique aerial photogrammetric images and their derivatives, collected from Monterey to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, preceded ... |
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Unprocessed aerial imagery from 29 November 2019 coastal survey of Central California.
This is a set of 1782 oblique aerial photogrammetric images and their derivatives, collected from Ano Nuevo to Davenport with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 30 November 2019 coastal survey of Central California.
This is a set of 1444 oblique aerial photogrammetric images and their derivatives, collected from Davenport to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 20 January 2020 coastal survey of Central California.
This is a set of 3072 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 25 January 2020 coastal survey of Central California.
This is a set of 1880 oblique aerial photogrammetric images and their derivatives, collected from Monterey to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, preceded ... |
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Unprocessed aerial imagery from 9 March 2020 coastal survey of Central California.
This is a set of 1979 oblique aerial photogrammetric images and their derivatives, collected from Ano Nuevo to Monterey (x2) with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 19 March 2020 coastal survey of Central California.
This is a set of 4835 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 19 April 2020 coastal survey of Central California.
This is a set of 2889 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 5 July 2020 coastal survey of Central California.
This is a set of 1890 oblique aerial photogrammetric images and their derivatives, collected from Monterey to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, preceded ... |
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Unprocessed aerial imagery from 30 September 2020 coastal survey of Central California.
This is a set of 3862 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 15 October 2020 coastal survey of Central California.
This is a set of 1982 oblique aerial photogrammetric images and their derivatives, collected from Natural Bridges to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 10 January 2021 coastal survey of Central California.
This is a set of 1896 oblique aerial photogrammetric images and their derivatives, collected from Monterey to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, preceded ... |
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Unprocessed aerial imagery from 11 January 2021 coastal survey of Central California.
This is a set of 3796 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey (x2) with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number ... |
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Unprocessed aerial imagery from 29 January 2021 coastal survey of Central California.
This is a set of 4919 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 3 March 2021 coastal survey of Central California.
This is a set of 2049 oblique aerial photogrammetric images and their derivatives, collected from Natural Bridges to Monterey (x2) with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial ... |
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Unprocessed aerial imagery from 26 March 2021 coastal survey of Central California.
This is a set of 5626 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 8 September 2021 coastal survey of Central California.
This is a set of 2678 oblique aerial photogrammetric images and their derivatives, collected from PigeonPt to Monterey (x2) with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 18 December 2021 coastal survey of Central California.
This is a set of 4722 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 20 January 2022 coastal survey of Central California.
This is a set of 2066 oblique aerial photogrammetric images and their derivatives, collected from Natural Bridges to Monterey (x2) with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial ... |
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Unprocessed aerial imagery from 4 February 2022 coastal survey of Central California.
This is a set of 2269 oblique aerial photogrammetric images and their derivatives, collected from Monterey to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 12 March 2022 coastal survey of Central California.
This is a set of 2098 oblique aerial photogrammetric images and their derivatives, collected from Natural Bridges to Monterey (x2) with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial ... |
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Unprocessed aerial imagery from 9 June 2022 coastal survey of Central California.
This is a set of 4595 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Big Sur with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 12-13 September 2022 coastal survey of Central California.
This is a set of 3661 oblique aerial photogrammetric images and their derivatives, collected from Natural Bridges to Monterey (x2) with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial ... |
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Unprocessed aerial imagery from 1 January 2023 coastal survey of Central California.
This is a set of 2076 oblique aerial photogrammetric images and their derivatives, collected from Point Lobos to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 5 January 2023 coastal survey of Central California.
This is a set of 2105 oblique aerial photogrammetric images and their derivatives, collected from Natural Bridges to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 16 January 2023 coastal survey of Central California.
This is a set of 2763 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 23 January 2023 coastal survey of Central California.
This is a set of 5039 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 1 February 2023 coastal survey of Central California.
This is a set of 2943 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 8 February 2023 coastal survey of Central California.
This is a set of 1939 oblique aerial photogrammetric images and their derivatives, collected from Monterey to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 2 March 2023 coastal survey of Central California.
This is a set of 1839 oblique aerial photogrammetric images and their derivatives, collected from Monterey to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 3 March 2023 coastal survey of Central California.
This is a set of 2758 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 13 March 2023 coastal survey of Central California.
This is a set of 2195 oblique aerial photogrammetric images and their derivatives, collected from Monterey to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 16 March 2023 coastal survey of Central California.
This is a set of 2915 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 17 March 2023 coastal survey of Central California.
This is a set of 2077 oblique aerial photogrammetric images and their derivatives, collected from Monterey to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 6 April 2023 coastal survey of Central California.
This is a set of 2374 vertical aerial photogrammetric images and their derivatives, collected from Half Moon Bay to Santa Cruz with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 8 June 2023 coastal survey of Central California.
This is a set of 2123 oblique aerial photogrammetric images and their derivatives, collected from Monterey to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 10 October 2023 coastal survey of Central California.
This is a set of 3929 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 11 October 2023 coastal survey of Central California.
This is a set of 4930 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 26 October 2023 coastal survey of Central California.
This is a set of 2869 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 23 December 2023 coastal survey of Central California.
This is a set of 4772 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 29 December 2023 coastal survey of Central California.
This is a set of 1821 oblique aerial photogrammetric images and their derivatives, collected from Ano Nuevo to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 1 January 2024 coastal survey of Central California.
This is a set of 2876 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 12 January 2024 coastal survey of Central California.
This is a set of 1965 oblique aerial photogrammetric images and their derivatives, collected from Point Lobos to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 9 February 2024 coastal survey of Central California.
This is a set of 4787 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 23 February 2024 coastal survey of Central California.
This is a set of 2323 oblique aerial photogrammetric images and their derivatives, collected from Monterey to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 24 February 2024 coastal survey of Central California.
This is a set of 3059 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 7 March 2024 coastal survey of Central California.
This is a set of 2161 oblique aerial photogrammetric images and their derivatives, collected from Natural Bridges to Monterey with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 6 April 2024 coastal survey of Central California.
This is a set of 2286 oblique aerial photogrammetric images and their derivatives, collected from Point Lobos to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 17 June 2024 coastal survey of Central California.
This is a set of 5140 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 18 August 2024 coastal survey of Central California.
This is a set of 2003 oblique aerial photogrammetric images and their derivatives, collected from Point Lobos to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 6 January 2023 coastal-landslides survey of Central California.
This is a set of 8762 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 12 January 2023 coastal-landslides survey of Central California.
This is a set of 11207 oblique aerial photogrammetric images and their derivatives, collected from San Francisco to Ragged Point with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number ... |
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Unprocessed aerial imagery from 29 March 2018 coastal survey of Central and southern California.
This is a set of 1160 oblique aerial photogrammetric images and their derivatives, collected from Mud Creek Slide to Santa Barbara Channel with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera ... |
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Unprocessed aerial imagery from 13 October 2018 coastal survey of Northern California to Washington.
This is a set of 11805 oblique aerial photogrammetric images and their derivatives, collected from OR-WA border to Mussel Rock CA with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial ... |
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Unprocessed aerial imagery from 19 April 2024 coastal survey of Northern California to Washington.
This is a set of 14032 oblique aerial photogrammetric images and their derivatives, collected from Hoh Head to Cape Mendocino with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 25 September 2016 coastal survey of Oregon and Washington.
This is a set of 1712 oblique aerial photogrammetric images and their derivatives, collected from Cape Falcon to Cascade Head with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 28 September 2017 coastal survey of Oregon and Washington.
This is a set of 2060 oblique aerial photogrammetric images and their derivatives, collected from OR-WA border to Nestucca River OR with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial ... |
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Unprocessed aerial imagery from 3 August 2020 coastal survey of Oregon and Washington.
This is a set of 2324 oblique aerial photogrammetric images and their derivatives, collected from Taholah WA to Seaside OR with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 3 September 2020 coastal survey of Oregon and Washington.
This is a set of 2158 oblique aerial photogrammetric images and their derivatives, collected from NW WA to Seaside OR with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, preceded ... |
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Unprocessed aerial imagery from 29 August 2022 coastal survey of Oregon and Washington.
This is a set of 2413 oblique aerial photogrammetric images and their derivatives, collected from Taholah WA to Seaside OR with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 1 June 2023 coastal survey of Oregon and Washington.
This is a set of 10139 oblique aerial photogrammetric images and their derivatives, collected from Salish Sea WA to Seaside OR with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 28 September 2016 coastal survey of Southern California.
This is a set of 2671 oblique aerial photogrammetric images and their derivatives, collected from ptConception to Ventura with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 1 March 2017 coastal survey of Southern California.
This is a set of 2979 oblique aerial photogrammetric images and their derivatives, collected from Point Conception to Ventura with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 27 December 2017 coastal survey of Southern California.
This is a set of 2392 oblique aerial photogrammetric images and their derivatives, collected from Point Conception to Santa Barbara with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial ... |
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Unprocessed aerial imagery from 13 September 2018 coastal survey of Southern California.
This is a set of 2062 oblique aerial photogrammetric images and their derivatives, collected from Santa Barbara Channel with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 6 May 2020 coastal survey of Southern California.
This is a set of 2167 oblique aerial photogrammetric images and their derivatives, collected from Santa Barbara Channel with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 18 September 2020 coastal survey of Southern California.
This is a set of 1968 oblique aerial photogrammetric images and their derivatives, collected from Santa Barbara Channel with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 2 March 2022 coastal survey of Southern California.
This is a set of 2212 oblique aerial photogrammetric images and their derivatives, collected from Santa Barbara Channel with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 28 September 2022 coastal survey of Southern California.
This is a set of 2032 oblique aerial photogrammetric images and their derivatives, collected from Point Conception to Point Mugu with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number ... |
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Unprocessed aerial imagery from 2 October 2022 coastal survey of Southern California.
This is a set of 1108 oblique aerial photogrammetric images and their derivatives, collected from Santa Rosa Island with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, preceded by ... |
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Unprocessed aerial imagery from 8 March 2023 coastal survey of Southern California.
This is a set of 2006 oblique aerial photogrammetric images and their derivatives, collected from Point Conception to Point Mugu with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number ... |
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Unprocessed aerial imagery from 12 October 2023 coastal survey of Southern California.
This is a set of 2013 oblique aerial photogrammetric images and their derivatives, collected from Point Conception to Port Hueneme with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial ... |
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Unprocessed aerial imagery from 5 January 2024 coastal survey of Southern California.
This is a set of 2061 oblique aerial photogrammetric images and their derivatives, collected from Point Conception to Point Mugu with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number ... |
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Unprocessed aerial imagery from 12 February 2024 coastal survey of Southern California.
This is a set of 2032 oblique aerial photogrammetric images and their derivatives, collected from Point Conception to Point Mugu with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number ... |
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Unprocessed aerial imagery from 23 February 2024 coastal survey of Southern California.
This is a set of 2371 oblique aerial photogrammetric images and their derivatives, collected from Point Conception to Point Mugu with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number ... |
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Unprocessed aerial imagery from 18 March 2024 coastal survey of Southern California.
This is a set of 2076 oblique aerial photogrammetric images and their derivatives, collected from Point Conception to Point Mugu with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number ... |
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Unprocessed aerial imagery from 4 August 2020 coastal survey of Washington.
This is a set of 645 oblique aerial photogrammetric images and their derivatives, collected from Elwha river mouth to Ediz Hook CG with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial ... |
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Unprocessed aerial imagery from 28 August 2022 coastal survey of Washington.
This is a set of 4116 oblique aerial photogrammetric images and their derivatives, collected from Salish Sea with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, preceded by the ... |
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Unprocessed aerial imagery from 29 August 2022 coastal survey of Washington.
This is a set of 4281 oblique and near nadir aerial photogrammetric images and their derivatives, collected from Elwha river mouth to Ediz Hook CG with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the ... |
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Unprocessed aerial imagery from 6 July 2024 coastal survey of Washington.
This is a set of 7809 oblique aerial photogrammetric images and their derivatives, collected from Salish Sea with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, preceded by the ... |
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Unprocessed aerial imagery from 31 August 2024 coastal survey of Washington.
This is a set of 6976 oblique aerial photogrammetric images and their derivatives, collected from Juan de Fuca Strait to Grays Harbor with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial ... |
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2014 East Coast New Hampshire USACE/NAE ATM Lidar-Derived Dune Crest, Toe and Shoreline, post-Hurricane Sandy
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2014 East Coast New ... |
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CACO2002_EAARLA_BE_z19_n88g12B_metadata: EAARL Coastal Topography--Cape Cod National Seashore, Massachusetts, 2002: Bare Earth
ASCII XYZ point cloud data were produced from remotely sensed, geographically referenced elevation measurements acquired cooperatively by the U.S. Geological Survey (USGS) and the National Park Service (NPS). Elevation measurements were collected over Cape Cod National Seashore using the first-generation National Aeronautics and Space Administration (NASA) Experimental Advanced Airborne Research Lidar (EAARL), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation ... |
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CACO2002_EAARLA_BE_z19_n88g12B_mosaic_metadata: EAARL Coastal Topography--Cape Cod National Seashore, Massachusetts, 2002: Bare Earth
A bare-earth topography Digital Elevation Model (DEM) mosaic for the Cape Cod National Seashore was produced from remotely sensed, geographically referenced elevation measurements acquired cooperatively by the U.S. Geological Survey (USGS) and the National Park Service (NPS). Elevation measurements were collected over Cape Cod National Seashore using the first-generation National Aeronautics and Space Administration (NASA) Experimental Advanced Airborne Research Lidar (EAARL), a pulsed laser ranging system ... |
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CACO2002_EAARLA_FS_z19_n88g12B_metadata: EAARL Coastal Topography--Cape Cod National Seashore, Massachusetts, 2002: First Surface
ASCII XYZ point cloud data were produced from remotely sensed, geographically referenced elevation measurements acquired cooperatively by the U.S. Geological Survey (USGS) and the National Park Service (NPS). Elevation measurements were collected over Cape Cod National Seashore using the first-generation National Aeronautics and Space Administration (NASA) Experimental Advanced Airborne Research Lidar (EAARL), a pulsed laser ranging system mounted onboard an aircraft to measure ground elevation, vegetation ... |
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CACO2002_EAARLA_FS_z19_n88g12B_mosaic_metadata: EAARL Coastal Topography--Cape Cod National Seashore, Massachusetts, 2002: First Surface
A first-surface topography Digital Elevation Model (DEM) mosaic for the Cape Cod National Seashore was produced from remotely sensed, geographically referenced elevation measurements acquired cooperatively by the U.S. Geological Survey (USGS) and the National Park Service (NPS). Elevation measurements were collected over Cape Cod National Seashore using the first-generation National Aeronautics and Space Administration (NASA) Experimental Advanced Airborne Research Lidar (EAARL), a pulsed laser ranging ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software version 5.0 for Central California
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software version 5.0 for Northern California
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software version 5.0 for Southern California
Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline ... |
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BEWARE2 database: A meta-process model to assess wave-driven flooding hazards on morphologically diverse, coral reef-lined coasts
This dataset contains the reef profiles and resulting hydrodynamic outputs of the "Broad-range Estimator of Wave Attack in Reef Environments" (BEWARE-2) meta-process modeling system. A process-based, wave-resolving hydrodynamic model (XBeach Non-Hydrostatic+, "XBNH+") was used to create a large synthetic database for use in BEWARE-2, relating incident hydrodynamics and coral reef geomorphology to coastal flooding hazards on reef-lined coasts. Building on previous work, BEWARE-2 improves system understanding ... |
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Marsh habitat change analysis for the Point Aux Chenes and Grand Bay Estuaries in Mississippi and Alabama from 1848 to 2022
Over time, as sea levels rise and land subsides, marsh transgression can occur. As shorelines erode and the marsh slowly transgresses landward into the upland, valuable coastal habitat simultaneously is lost and gained. If the shoreline erosion is faster than the rate of upland transgression, the result is a net loss in coastal wetlands. This dataset represents a marsh area change analysis for the Point Aux Chenes and Grand Bay estuaries in Mississippi and Alabama from 1848-1957/1958, 1848-2019/2022, and ... |
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Shorelines, shorepoints, and transects with rates for the Point Aux Chenes and Grand Bay Estuaries in Mississippi and Alabama from 1848 to 2023
This dataset represents a compilation of vector shorelines, shorepoints, and transects with rates for the Point Aux Chenes and Grand Bay estuaries in Mississippi and Alabama from 1848 to 2023. Shoreline data were obtained from multiple data sources, including the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the Grand Bay National Estuarine Research Reserve (GNDNERR), and the Mississippi Office of Geology (MOG). All shoreline data types have uncertainty ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Long-Term Linear Regression Rate Calculations for the Exposed Central Beaufort Sea coast of Alaska between the Hulahula River and the Colville River
This dataset consists of long-term (~63 years) shoreline change rates for the north coast of Alaska between the Hulahula River and the Colville River. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Long-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1947 and 2010. A reference baseline was used as ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Short-Term Linear Regression Rate Calculations for the Exposed Central Beaufort Sea coast of Alaska between the Hulahula River and the Colville River
This dataset consists of short-term (~31 years) shoreline change rates for the north coast of Alaska between the Hulahula River and the Colville River. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1979 and 2010. A reference baseline was used as ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Long-Term Linear Regression Rate Calculations for the Sheltered Central Beaufort Sea coast of Alaska between the Hulahula River and the Colville River
This dataset consists of long-term (~63 years) shoreline change rates for the north coast of Alaska between the Hulahula River and the Colville River. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Long-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1947 and 2010. A reference baseline was used as ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Short-Term Linear Regression Rate Calculations for the Sheltered Central Beaufort Sea coast of Alaska between the Hulahula River and the Colville River
This dataset consists of short-term (~31 years) shoreline change rates for the north coast of Alaska between the Hulahula River and the Colville River. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1979 and 2010. A reference baseline was used as ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Long-Term Linear Regression Rate Calculations for the Exposed East Beaufort Sea coast of Alaska between the U.S. Canadian Border and the Hulahula River
This dataset consists of long-term (~63 years) shoreline change rates for the north coast of Alaska between the U.S. Canadian Border and the Hulahula River. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Long-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1947 and 2010. A reference baseline was ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Short-Term Linear Regression Rate Calculations for the Exposed East Beaufort Sea coast of Alaska between the U.S. Canadian Border and the Hulahula River
This dataset consists of short-term (~32 years) shoreline change rates for the north coast of Alaska between the U.S. Canadian Border and the Hulahula River. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1978 and 2010. A reference baseline was ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Long-Term Linear Regression Rate Calculations for the Sheltered East Beaufort Sea coast of Alaska between the U.S. Canadian Border and the Hulahula River
This dataset consists of long-term (~63 years) shoreline change rates for the north coast of Alaska between the U.S. Canadian Border and the Hulahula River. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Long-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1947 and 2010. A reference baseline was ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Short-Term Linear Regression Rate Calculations for the Sheltered East Beaufort Sea coast of Alaska between the U.S. Canadian Border and the Hulahula River
This dataset consists of short-term (~32 years) shoreline change rates for the north coast of Alaska between the U.S. Canadian Border and the Hulahula River. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1978 and 2010. A reference baseline was ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Long-Term Linear Regression Rate Calculations for the Exposed East Chukchi Sea coast of Alaska between Point Barrow and Icy Cape
This dataset consists of long-term (~65 years) shoreline change rates for the north coast of Alaska between Point Barrow and Icy Cape. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Long-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1947 and 2012. A reference baseline was used as the originating ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Short-Term End Point Rate Calculations for the Exposed East Chukchi Sea coast of Alaska between Point Barrow and Icy Cape
This dataset consists of short-term (~32 years) shoreline change rates for the north coast of Alaska between Point Barrow and Icy Cape. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using an end point rate-of-change method based on available shoreline data between 1979 and 2011. A reference baseline was used as the originating point ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Short-Term Linear Regression Rate Calculations for the Exposed East Chukchi Sea coast of Alaska between the Point Barrow and Icy Cape
This dataset consists of short-term (~31 years) shoreline change rates for the north coast of Alaska between the Point Barrow and Icy Cape. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1979 and 2010. A reference baseline was used as the ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Long-Term Linear Regression Rate Calculations for the Sheltered East Chukchi Sea coast of Alaska between Point Barrow and Icy Cape
This dataset consists of long-term (~65 years) shoreline change rates for the north coast of Alaska between Point Barrow and Icy Cape. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Long-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1947 and 2012. A reference baseline was used as the originating ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Short-Term End Point Rate Calculations for the Sheltered East Chukchi Sea coast of Alaska between Point Barrow and Icy Cape
This dataset consists of short-term (~33 years) shoreline change rates for the north coast of Alaska between Point Barrow and Icy Cape. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using an end point rate-of-change method based on available shoreline data between 1979 and 2012. A reference baseline was used as the originating point ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Long-Term Linear Regression Rate Calculations for the Exposed West Beaufort Sea coast of Alaska between the Colville River and Point Barrow
This dataset consists of long-term (~65 years) shoreline change rates for the north coast of Alaska between the Colville River and Point Barrow. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Long-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1947 and 2012. A reference baseline was used as the ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Short-Term Linear Regression Rate Calculations for the Exposed West Beaufort Sea coast of Alaska between the Colville River and Point Barrow
This dataset consists of short-term (~33 years) shoreline change rates for the north coast of Alaska between the Colville River and Point Barrow. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1979 and 2012. A reference baseline was used as the ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Long-Term Linear Regression Rate Calculations for the Sheltered West Beaufort Sea coast of Alaska between the Colville River and Point Barrow
This dataset consists of long-term (~65 years) shoreline change rates for the north coast of Alaska between the Colville River and Point Barrow. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Long-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1947 and 2012. A reference baseline was used as the ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 Transects with Short-Term Linear Regression Rate Calculations for the Sheltered West Beaufort Sea coast of Alaska between the Colville River and Point Barrow
This dataset consists of short-term (~33 years) shoreline change rates for the north coast of Alaska between the Colville River and Point Barrow. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.3, an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using a linear regression rate-of-change method based on available shoreline data between 1979 and 2012. A reference baseline was used as the ... |
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Model parameter input files to compare locations of coral reef restoration on different reef profiles to reduce coastal flooding
This dataset consists of physics-based XBeach Non-hydrostatic hydrodynamic models input files used to study how coral reef restoration affects waves and wave-driven water levels over coral reefs, and the resulting wave-driven runup on the adjacent shoreline. Coral reefs are effective natural coastal flood barriers that protect adjacent communities. Coral degradation compromises the coastal protection value of reefs while also reducing their other ecosystem services, making them a target for restoration. ... |
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BEWARE database: A Bayesian-based system to assess wave-driven flooding hazards on coral reef-lined coasts
A process-based wave-resolving hydrodynamic model (XBeach Non-Hydrostatic, ‘XBNH’) was used to create a large synthetic database for use in a “Bayesian Estimator for Wave Attack in Reef Environments” (BEWARE), relating incident hydrodynamics and coral reef geomorphology to coastal flooding hazards on reef-lined coasts. Building on previous work, BEWARE improves system understanding of reef hydrodynamics by examining the intrinsic reef and extrinsic forcing factors controlling runup and flooding on ... |
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Historical shoreline vectors for barrier islands and spits along the north coast of Alaska between Cape Beaufort and the U.S.-Canadian border, 1947 to 2019
A suite of morphological metrics were derived from existing shoreline and elevation datasets for barrier islands and spits located along the north-slope coast of Alaska between Cape Beaufort and the U.S.-Canadian border. This dataset includes shoreline vectors, including data source and acquisition date, from five time periods: 1950s, 1980s, 2000s, 2010s, and 2020s. The shoreline vectors were combined to produce polygons upon which the metrics were calculated. |
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Model parameter input files to compare the influence of channels in fringing coral reefs on alongshore variations in wave-driven runup along the shoreline
An extensive set of physics-based XBeach Non-hydrostatic hydrodynamic model simulations (with input files here included) were used to evaluate the influence of shore-normal reef channels on flooding along fringing reef-lined coasts, specifically during extreme wave conditions when the risk for coastal flooding and the resulting impact to coastal communities is greatest. These input files accompany the modeling conducted for the following publication: Storlazzi, C.D., Rey, A.E., and van Dongeren, A.R., 2022, ... |
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Historical coastal bluff edge positions at Barter Island, Alaska for the years spanning 1950 to 2020
This dataset includes one vector shapefile delineating the position of the top edge of the coastal permafrost bluffs at Barter Island, Alaska spanning seven decades, between the years of 1950 and 2020. Bluff-edge positions delineated from a combination of aerial photography, declassified satellite photography, and very-high resolution satellite imagery can be used to quantify the movement of the bluff edge through time. These data were used to calculate rates of change every 10 meters alongshore using the ... |
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Offshore baseline generated to calculate bluff change rates for the north coast of Barter Island, Alaska
This dataset includes a reference baseline used by the Digital Shoreline Analysis System (DSAS) to calculate rate-of-change statistics for the coastal bluffs at Barter Island, Alaska for the time period 1950 to 2020. This baseline layer serves as the starting point for all transects cast by the DSAS application and can be used to establish measurement points used to calculate bluff-change rates. |
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Historical shoreline positions at Barter Island, Alaska for the years spanning 1947 to 2020
This dataset includes one vector shapefile delineating the position of the shorelines at Barter Island, Alaska spanning seven decades, between the years 1947 and 2020. Shoreline positions delineated from a combination of aerial photography, declassified satellite photography, and very-high resolution satellite imagery can be used to quantify the movement of the shoreline through time. These data were used to calculate rates of change every 10 meters alongshore using the Digital Shoreline Analysis System ... |
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Digital Shoreline Analysis System (DSAS) version 4.4 transects with long-term linear regression rate calculations for the exposed north coast of Alaska, from Icy Cape to Cape Prince of Wales
This dataset consists of long-term (less than 68 years) shoreline change rates for the exposed coast of the north coast of Alaska from Icy Cape to Cape Prince of Wales. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.4, an ArcGIS extension developed by the U.S. Geological Survey. Rates of shoreline change were calculated using a linear regression rate-of-change (lrr) method based on available shoreline data between 1948 and 2016. A reference baseline ... |
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Digital Shoreline Analysis System (DSAS) version 4.4 transects with short-term linear regression rate calculations for the exposed north coast of Alaska, from Icy Cape to Cape Prince of Wales
This dataset consists of short-term (less than 37 years) shoreline change rates for the north coast of Alaska from Icy Cape to Cape Prince of Wales. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.4, an ArcGIS extension developed by the U.S. Geological Survey. Rates of shoreline change were calculated using a linear regression rate-of-change (lrr) method based on available shoreline data between 1980s and 2016. A reference baseline was used as the ... |
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Digital Shoreline Analysis System (DSAS) version 4.4 transects with long-term linear regression rate calculations for the sheltered north coast of Alaska, from Icy Cape to Cape Prince of Wales
This dataset consists of long-term (less than 68 years) shoreline change rates for the sheltered north coast of Alaska from Icy Cape to Cape Prince of Wales. Rate calculations were computed within a GIS using the Digital Shoreline Analysis System (DSAS) version 4.4, an ArcGIS extension developed by the U.S. Geological Survey. Rates of shoreline change were calculated using a linear regression rate-of-change (lrr) method based on available shoreline data between 1948 and 2016. A reference baseline was used ... |
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Beach profile data collected in 2010 and 2011 in the vicinity of Arey Lagoon and Barter Island, Alaska
Beach elevation profiles were measured along 29 shore-normal transects on and around Arey and Barter Islands, Alaska in August 2010 and July 2011. Profile data are available in a single comma-delimited file and a zip file including multiple .jpg images that show a visual representation of the individual profiles. |
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Paleoshorelines--Monterey Canyon and Vicinity Map Area, California
This part of DS 781 presents data for the paleoshorelines for the geologic and geomorphic map of Monterey Canyon and Vicinity, California. The vector data file is included in "Paleoshorelines_MontereyCanyon.zip," which is accessible from https://doi.org/10.3133/ofr20161072. These data accompany the pamphlet and map sheets of Dartnell, P., Maier, K.L., Erdey, M.D., Dieter, B.E., Golden, N.E., Johnson, S.Y., Hartwell, S.R., Cochrane, G.R., Ritchie, A.C., Finlayson, D.P., Kvitek, R.G., Sliter, R.W., Greene, H ... |
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hawaii_oha - Overall Hazard Assessment in the coastal zone of Hawaii, Hawaii
Overall Hazard Assessment in the coastal zone of Hawaii, Hawaii |
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hawaii_sea - Sea Level Hazard Intensity Level in the coastal zone of Hawaii, Hawaii
Sea Level Hazard Intensity Level in the coastal zone of Hawaii, Hawaii |
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hawaii_stm - Storm Hazard Intensity Level in the coastal zone of Hawaii, Hawaii
Storm Hazard Intensity Level in the coastal zone of Hawaii, Hawaii |
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hawaii_tsu - Tsunami Hazard Intensity Level in the coastal zone of Hawaii, Hawaii
Tsunami Hazard Intensity Level in the coastal zone of Hawaii, Hawaii |
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hawaii_wav - High Wave Hazard Intensity Level in the coastal zone of Hawaii, Hawaii
High Wave Hazard Intensity Level in the coastal zone of Hawaii, Hawaii |
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kauai_oha - Overall Hazard Assessment in the coastal zone of Kauai, Hawaii
Overall Hazard Assessment in the coastal zone of Kauai, Hawaii |
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kauai_sea - Sea Level Hazard Intensity Level in the coastal zone of Kauai, Hawaii
Sea Level Hazard Intensity Level in the coastal zone of Kauai, Hawaii |
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kauai_stm - Storm Hazard Intensity Level in the coastal zone of Kauai, Hawaii
Storm Hazard Intensity Level in the coastal zone of Kauai, Hawaii |
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kauai_tsu - Tsunami Hazard Intensity Level in the coastal zone of Kauai, Hawaii
Tsunami Hazard Intensity Level in the coastal zone of Kauai, Hawaii |
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kauai_wav - High Wave Hazard Intensity Level in the coastal zone of Kauai, Hawaii
High Wave Hazard Intensity Level in the coastal zone of Kauai, Hawaii |
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lanai_oha - Overall Hazard Assessment in the coastal zone of Lanai, Hawaii
Overall Hazard Assessment in the coastal zone of Lanai, Hawaii |
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lanai_sea - Sea Level Hazard Intensity Level in the coastal zone of Lanai, Hawaii
Sea Level Hazard Intensity Level in the coastal zone of Lanai, Hawaii |
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lanai_stm - Storm Hazard Intensity Level in the coastal zone of Lanai, Hawaii
Storm Hazard Intensity Level in the coastal zone of Lanai, Hawaii |
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lanai_tsu - Tsunami Hazard Intensity Level in the coastal zone of Lanai, Hawaii
Tsunami Hazard Intensity Level in the coastal zone of Lanai, Hawaii |
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lanai_wav - High Wave Hazard Intensity Level in the coastal zone of Lanai, Hawaii
High Wave Hazard Intensity Level in the coastal zone of Lanai, Hawaii |
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maui_oha - Overall Hazard Assessment in the coastal zone of Maui, Hawaii
Overall Hazard Assessment in the coastal zone of Maui, Hawaii |
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maui_sea - Sea Level Hazard Intensity Level in the coastal zone of Maui, Hawaii
Sea Level Hazard Intensity Level in the coastal zone of Maui, Hawaii |
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maui_stm - Storm Hazard Intensity Level in the coastal zone of Maui, Hawaii
Storm Hazard Intensity Level in the coastal zone of Maui, Hawaii |
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maui_tsu - Tsunami Hazard Intensity Level in the coastal zone of Maui, Hawaii
Tsunami Hazard Intensity Level in the coastal zone of Maui, Hawaii |
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maui_wav - High Wave Hazard Intensity Level in the coastal zone of Maui, Hawaii
High Wave Hazard Intensity Level in the coastal zone of Maui, Hawaii |
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molo_oha - Overall Hazard Assessment in the coastal zone of Molokai, Hawaii
Overall Hazard Assessment in the coastal zone of Molokai, Hawaii |
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molo_sea - Sea Level Hazard Intensity Level in the coastal zone of Molokai, Hawaii
Sea Level Hazard Intensity Level in the coastal zone of Molokai, Hawaii |
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molo_stm - Storm Hazard Intensity Level in the coastal zone of Molokai, Hawaii
Storm Hazard Intensity Level in the coastal zone of Molokai, Hawaii |
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molo_tsu - Tsunami Hazard Intensity Level in the coastal zone of Molokai, Hawaii
Tsunami Hazard Intensity Level in the coastal zone of Molokai, Hawaii |
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molo_wav - High Wave Hazard Intensity Level in the coastal zone of Molokai, Hawaii
High Wave Hazard Intensity Level in the coastal zone of Molokai, Hawaii |
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oahu_oha - Overall Hazard Assessment in the coastal zone of Oahu, Hawaii
Overall Hazard Assessment in the coastal zone of Oahu, Hawaii |
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oahu_sea - Sea Level Hazard Intensity Level in the coastal zone of Oahu, Hawaii
Sea Level Hazard Intensity Level in the coastal zone of Oahu, Hawaii |
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oahu_stm - Storm Hazard Intensity Level in the coastal zone of Oahu, Hawaii
Storm Hazard Intensity Level in the coastal zone of Oahu, Hawaii |
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oahu_tsu - Tsunami Hazard Intensity Level in the coastal zone of Oahu, Hawaii
Tsunami Hazard Intensity Level in the coastal zone of Oahu, Hawaii |
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oahu_wav - High Wave Hazard Intensity Level in the coastal zone of Oahu, Hawaii
High Wave Hazard Intensity Level in the coastal zone of Oahu, Hawaii |
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sand_oha - Overall Hazard Assessment in the coastal zone of Sand Island (Oahu), Hawaii
Overall Hazard Assessment in the coastal zone of Sand Island (Oahu), Hawaii |
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sand_sea - Sea Level Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii
Sea Level Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii |
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sand_stm - Storm Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii
Storm Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii |
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sand_tsu - Tsunami Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii
Tsunami Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii |
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sand_wav - High Wave Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii
High Wave Hazard Intensity Level in the coastal zone of Sand Island (Oahu), Hawaii |
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Digital Shoreline Analysis System (DSAS) version 4.3 transects with end-point rate calculations for exposed shorelines between the Okpilak-Hulahula River Delta and Colville River Delta for the time period 1947 to 2007
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 transects with end-point rate calculations for sheltered shorelines between the Okpilak-Hulahula River Delta and Colville River Delta for the time period 1947 to 2007
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 transects with end-point rate calculations for exposed shorelines between the U.S.-Canadian border and the Okpilak-Hulahula river delta for the time period 1947 to 2003
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 transects with end-point rate calculations for sheltered shorelines between the U.S.-Canadian border and the Okpilak-Hulahula River Delta for the time period 1947 to 2003
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 transects with end-point rate calculations for exposed shorelines between Point Barrow and Icy Cape for the time period 1947 to 2012
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 transects with end-point rate calculations for sheltered shorelines between Point Barrow and Icy Cape for the time period 1947 to 2012
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 transects with end-point rate calculations for exposed shorelines between the Colville River Delta and Point Barrow for the time period 1947 to 2005
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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Digital Shoreline Analysis System (DSAS) version 4.3 transects with end-point rate calculations for sheltered shorelines between the Colville River Delta and Point Barrow for the time period 1947 to 2005
The Arctic Coastal Plain of northern Alaska is an area of strategic economic importance to the United States, is home to remote Native American communities, and encompasses unique habitats of global significance. Coastal erosion along the north coast of Alaska is chronic, widespread, may be accelerating, and is threatening defense and energy-related infrastructure, natural shoreline habitats, and Native communities. There is an increased demand for accurate information regarding past and present shoreline ... |
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CENCAL1853_1910 - Vectorized Shoreline of Central California Derived from 1853-1910 Source Data
There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the U.S. Geological Survey (USGS) has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. Each shoreline may represent a ... |
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CENCAL1929_1942 - Vectorized Shoreline of Central Califonia Derived from 1929-1942 Source Data
There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the U.S. Geological Survey (USGS) has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. Each shoreline may represent a ... |
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CENCAL1945_1976 - Vectorized Shoreline of Central California Derived from 1945-1976 Source Data
There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the U.S. Geological Survey (USGS) has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. Each shoreline may represent a ... |
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CENCAL_1998_2002 - Vectorized Shoreline of Central California Derived from 1998-2002 Lidar Source Data
There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the U.S. Geological Survey (USGS) has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. Each shoreline may represent a ... |
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CENCAL_BIASVALUES - Central California Shoreline Bias Values
The USGS has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. These shorelines were used to calculate long-term and short-term change rates in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS ... |
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NORCAL1854_1880 - Vectorized Shoreline of Northern California from 1854-1880 Source Data
There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the U.S. Geological Survey (USGS) has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. Each shoreline may represent a ... |
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NORCAL1928_1936 - Vectorized Shoreline of Northern California Derived from 1928-1936 Source Data
There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the USGS has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. Each shoreline may represent a compilation of data from one or ... |
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NORCAL1952_1971 - Vectorized Shoreline of Northern California Derived from 1952-1971 Source Data
There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the U.S. Geological Survey (USGS) has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. Each shoreline may represent a ... |
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NORCAL2002 - Vectorized Shoreline of Northern California Derived from 2002 Lidar Source Data
There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the U.S. Geological Survey (USGS) has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. Each shoreline may represent a ... |
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NORCAL_BIASVALUES - Northern California Shoreline Bias Values
The USGS has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. These shorelines were used to calculate long-term and short-term change rates in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS ... |
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SOCAL1852_1889 - Vectorized Shoreline of Southern California Derived from 1852-1889 Source Data
There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the U.S. Geological Survey (USGS) has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. Each shoreline may represent a ... |
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SOCAL1920_1934 - Vectorized Shoreline of Southern California Derived from 1920-1934 Source Data
There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the U.S. Geological Survey (USGS) has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. Each shoreline may represent a ... |
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SOCAL_1971_1976 - Vectorized Shoreline of Southern California Derived from 1971-1976 Source Data
There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the U.S. Geological Survey (USGS) has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. Each shoreline may represent a ... |
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SOCAL_1998 - Vectorized Shoreline of Southern California Derived from 1998 Lidar Source Data
There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the U.S. Geological Survey (USGS) has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. Each shoreline may represent a ... |
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SOCAL_BIASVALUES - Southern California Shoreline Bias Values
The USGS has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Shorelines are compiled by state and generally correspond to one of four time periods: 1800s, 1920s-1930s, 1970s, and 1998-2002. These shorelines were used to calculate long-term and short-term change rates in a GIS using the Digital Shoreline Analysis System (DSAS) version 3.0; An ArcGIS ... |
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Baseline coastal oblique aerial photographs collected from Navarre, Florida, to the Chandeleur Islands, Louisiana, and from Grand Point, Alabama, to St. Joseph Point, Mississippi, June 6, 2006
The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On June 6, 2006, the USGS conducted an oblique aerial photographic survey from Navarre, Florida, to the Chandeleur Islands, Louisiana, and from Grand Point, Alabama, to St. Joseph Point, Mississippi, aboard a U.S. Coast Guard HH60 Helicopter aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore ... |
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Baseline coastal oblique aerial photographs collected from Dauphin Island, Alabama, to Breton Island, Louisiana, September 26–27, 2006
The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On September 26-27, 2006, the USGS conducted an oblique aerial photographic survey from Dauphin Island, Alabama, to Breton Island, Louisiana, aboard a U.S. Coast Guard HH60 Helicopter aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect data for assessing ... |
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Baseline coastal oblique aerial photographs collected from the Harney River, Everglades National Park, Florida to Anclote Key, Florida, November 14, 2006
The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On November 14, 2006, the USGS conducted an oblique aerial photographic survey from the Harney River, Everglades National Park, Florida to Anclote Key, Florida, aboard a U.S. Coast Guard HH60 Helicopter aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect ... |
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Baseline coastal oblique aerial photographs collected from Dauphin Island, Alabama, to Breton Island, Louisiana, July 26–27, 2007
The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On July 26-27, 2007, the USGS conducted an oblique aerial photographic survey from Dauphin Island, Alabama, to Breton Island, Louisiana, aboard a U.S. Coast Guard HH60 Helicopter aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect data for assessing ... |
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Baseline coastal oblique aerial photographs collected from Dog Island, Florida, to Breton Island, Louisiana, June 24–25, 2008
The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On June 24–25, 2008, the USGS conducted an oblique aerial photographic survey from Dog Island, Florida, to Breton Island, Louisiana, aboard a U.S. Coast Guard HH60 Helicopter at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect data for assessing incremental ... |
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Post-Hurricane Gustav coastal oblique aerial photographs collected from the Chandeleur Islands, Louisiana, to Isles Dernieres Barrier Islands Refuge, Louisiana, September 4, 2008
The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On September 4, 2008, the USGS conducted an oblique aerial photographic survey from the Chandeleur Islands, Louisiana, to Isles Dernieres Barrier Islands Refuge, Louisiana, aboard a Beechcraft Super King Air 200 aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted ... |
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Baseline coastal oblique aerial photographs collected at the Chandeleur Islands, Louisiana, and Dauphin Island, Alabama, July 24, 2010
The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On July 24, 2010, the USGS conducted an oblique aerial photographic survey at the Chandeleur Islands, Louisiana, and Dauphin Island, Alabama, aboard a Beechcraft BE90 King Air aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect data for assessing ... |
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Baseline coastal oblique aerial photographs collected from Breton Island to the Chandeleur Islands, Louisiana, September 3, 2010
The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On September 3, 2010, the USGS conducted an oblique aerial photographic survey from Breton Island to the Chandeleur Islands, Louisiana, aboard a Cessna 210 aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect data for assessing incremental changes in the ... |
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Baseline coastal oblique aerial photographs collected from Tampa Bay to the Marquesas Keys, Florida, June 22–23, 2010
The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On June 22–23, 2010, the USGS conducted an oblique aerial photographic survey from Tampa Bay to the Marquesas Keys, Florida, aboard a Piper Navajo Chieftain aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect data for assessing incremental changes in ... |
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Baseline coastal oblique aerial photographs collected at Breton Island and the Chandeleur Islands, Louisiana, January 22, 2011
The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On January 22, 2011, the USGS conducted an oblique aerial photographic survey at Breton Island and the Chandeleur Islands, LA, aboard a Cessna 210 aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect data for assessing incremental changes in the beach and ... |
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Baseline coastal oblique aerial photographs collected from Ponte Vedra, Florida, to the South Carolina/North Carolina border, August 24, 2011
The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On August 24, 2011, the USGS conducted an oblique aerial photographic survey from Ponte Vedra, Florida, to the South Carolina/North Carolina border, aboard a Piper Navajo Chieftain aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect data for assessing ... |
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Beach Topography—Fire Island, New York, Pre-Hurricane Sandy, January 2012: Ground Based Lidar (1-Meter Digital Elevation Model)
The U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) and the U.S. Army Corps of Engineers Field Research Facility (USACE-FRF) of Duck, North Carolina collaborated to gather alongshore ground-based lidar beach topography at Fire Island, New York. This high-resolution, elevation dataset was collected on January 30, 2012, and was funded by SPCMSC. The USGS data release containing the aforementioned dataset includes the resulting, processed elevation point data (XYZ) and ... |
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Beach Topography—Fire Island, New York, Pre-Hurricane Sandy, January 2012: Ground Based Lidar (ASCII XYZ Point Data)
The U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) and the U.S. Army Corps of Engineers Field Research Facility (USACE-FRF) of Duck, North Carolina collaborated to gather alongshore ground-based lidar beach topography at Fire Island, New York. This high-resolution, elevation dataset was collected on January 30, 2012, and was funded by SPCMSC. The USGS data release containing the aforementioned dataset includes the resulting, processed elevation point data (XYZ) and an ... |
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Ground-Penetrating Radar Data and Differential Global Positioning System Data Collected from Long Beach Island, New Jersey, April 2015
Scientists from the United States Geological Survey, St. Petersburg Coastal and Marine Science Center, U.S. Geological Survey Pacific Coastal and Marine Science Center, and students from the University of Hawaii at Manoa collected sediment cores, sediment surface grab samples, ground-penetrating radar (GPR) and Differential Global Positioning System (DGPS) data from within the Edwin B. Forsythe National Wildlife Refuge-Holgate Unit located on the southern end of Long Beach Island, New Jersey, in April 2015 ... |
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Post-Hurricane Matthew coastal oblique aerial photographs collected from Port St. Lucie, Florida, to Kitty Hawk, North Carolina, October 13–15, 2016
The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On October 13–15, 2016, the USGS conducted an oblique aerial photographic survey from Port St. Lucie, Florida, to Kitty Hawk, North Carolina, aboard a Cessna 182 aircraft at an altitude of 500 feet (ft) and approximately 1,200 ft offshore. This mission was conducted to collect data for assessing incremental changes ... |
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Baseline coastal oblique aerial photographs collected U.S. Army Corps of Engineers Field Research Facility, Duck, North Carolina, June 9, 2017
The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On June 09, 2017, the USGS conducted an oblique aerial photographic survey of the U.S. Army Corps of Engineers Field Research Facility (USACE FRF), located in Duck, North Carolina, aboard a Cessna 182 aircraft at an altitude of approximately 1000 feet (ft). This mission was conducted to collect data for USACE FRF ... |
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Time Series of Aerial Imagery from Small Unmanned Aircraft Systems and Associated Ground Control Points: Madeira Beach, Florida, July 2017 to June 2018 (Surveyed GCPs)
Aerial imagery acquired with a small unmanned aircraft system (sUAS), in conjunction with surveyed ground control points (GCPs) visible in the imagery, can be processed with structure-from-motion (SfM) photogrammetry techniques to produce high-resolution orthomosaics, three-dimensional (3D) point clouds and digital elevation models (DEMs). This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides UAS survey data consisting of aerial ... |
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Archive of Chirp Subbottom Profile, Imagery, and Geospatial Data Collected in 2021 Near Pensacola Beach, Florida
From June 2 through 9, 2021, researchers from the U.S. Geological Survey (USGS) conducted an inshore and offshore geophysical survey to map the shoreface and determine Holocene stratigraphy near Pensacola Beach, Florida (FL). The Coastal Resource Evaluation for Management Applications (CREMA) project objective includes the investigation of nearshore geologic controls on surface morphology. This publication serves as an archive of high-resolution chirp subbottom trace data, survey trackline map, navigation ... |
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2021-322-FA_shots: Digital Chirp Subbottom Profile Shotpoint Data Collected During USGS Field Activity Number 2021-322-FA Offshore of Pensacola Beach, Florida, June 2021
From June 2 through 9, 2021, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and determine Holocene stratigraphy near Santa Rosa Island, Florida (FL). This shapefile represents a point dataset of field activity number (FAN) 2021-322-FA chirp subbottom profile 1,000-shot-interval locations collected inshore and offshore of Pensacola Beach, FL. |
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2021-322-FA_sol: Digital Chirp Subbottom Profile Start of Line Data Collected During USGS Field Activity Number 2021-322-FA Offshore of Pensacola Beach, Florida, June 2021
From June 2 through 9, 2021, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and determine Holocene stratigraphy near Santa Rosa Island, Florida (FL). This shapefile represents a point dataset of field activity number (FAN) 2021-322-FA chirp subbottom profile start of trackline locations collected inshore and offshore of Pensacola Beach, FL. |
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2021-322-FA_trkln: Digital Chirp Subbottom Profile Trackline Data Collected During USGS Field Activity Number 2021-322-FA Offshore of Pensacola Beach, Florida, June 2021
From June 2 through 9, 2021, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and determine Holocene stratigraphy near Santa Rosa Island, Florida (FL). This shapefile represents a line dataset of field activity number (FAN) 2021-322-FA chirp tracklines collected inshore and offshore of Pensacola Beach, FL. |
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Baseline coastal oblique aerial photographs collected from Fenwick Island State Park, Delaware, to Corolla, North Carolina, March 27, 1998
The U.S. Geological Survey (USGS) conducts baseline and storm-response photography missions to document and understand the changes in the vulnerability of the Nation's coasts to extreme storms. On March 27, 1998, the USGS conducted an oblique aerial photographic survey from Fenwick Island State Park, Delaware, to Corolla, North Carolina, aboard a U.S. Coast Guard HH60 Helicopter at an altitude of 500 feet (ft) and approximately 1,000 ft offshore. This mission was conducted to collect data for assessing ... |
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Aerial_Shorelines_1940_2015.shp - Dauphin Island, Alabama Shoreline Data Derived from Aerial Imagery from 1940 to 2015
Aerial_WDL_Shorelines.zip features digitized historic shorelines for the Dauphin Island coastline from October 1940 to November 2015. This dataset contains 10 Wet Dry Line (WDL) shorelines separated into 58 shoreline segments alongshore Dauphin Island, AL. The individual sections are divided according to location along the island and shoreline type: open-ocean, back-barrier, marsh shoreline. Imagery of Dauphin Island, Alabama was acquired from several sources including the United States Geological Survey ... |
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1869 Digitized Shoreline for Breton Island, Louisiana (Geographic, NAD83)
1869 Digitized Shoreline for Breton Island, Louisiana (Geographic, NAD83) consists of vector shoreline data that were derived from a set of National Ocean Service (NOS) raster shoreline maps (often called T-sheet or TP-sheet maps) created for Breton Island in 1869. In 2002, NOAA published digitized shorelines for T-sheet (T-1097), which were subsequently edited by USGS staff for input into the Digital Shoreline Analysis System (DSAS) Version 4.0, where area and shoreline change analyses could be conducted. |
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1922 Digitized Shoreline for Breton Island, Louisiana (Geographic, NAD83)
1922 Digitized Shoreline for Breton Island, Louisiana (Geographic, NAD83) consists of vector shoreline data that were derived from a set of National Ocean Service (NOS) raster shoreline maps (often called T-sheet or TP-sheet maps) created for Breton Island in 1922. In 2002, NOAA published digitized shorelines for T-sheet (T-3920), which were subsequently edited by USGS staff for input into the Digital Shoreline Analysis System (DSAS) Version 4.0, where area and shoreline change analyses could be conducted. |
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1950 Digitized Shoreline for Breton Island, Louisiana (Geographic, NAD83)
1950 Digitized Shoreline for Breton Island, Louisiana (Geographic, NAD83) consists of vector shoreline data that were derived from a set of National Ocean Service (NOS) raster shoreline maps (often called T-sheet or TP-sheet maps) created for Breton Island in 1950. In 2002, NOAA published digitized shorelines for T-sheet (T-9393), which were subsequently edited by USGS staff for input into the Digital Shoreline Analysis System (DSAS) Version 4.0, where area and shoreline change analyses could be conducted. |
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1983 Digitized Shoreline for Breton Island, Louisiana (Geographic, NAD83)
Shorelines were derived from the National High Altitude Photography (NHAP) program. The NHAP was coordinated by the U.S. Geological Survey as an interagency project to acquire cloud-free aerial photographs at a specific altitude above mean terrain elevation. Two different camera systems were used to obtain simultaneous coverage of black-and-white (BW) and color infrared (CIR) aerial photographs over the conterminous United States. Black-and-white aerial photographs were obtained on 9-inch film from an ... |
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1998 Digitized Shoreline for Breton Island, Louisiana(Geographic, NAD83)
Shorelines were derived from the U.S. Geological Survey Earth Resources Observation and Science (EROS) Center's Digital Orthophoto Quarter Quads (DOQQ) images collected on January 24, 1998. This dataset contains digitized shorelines created from the USGS imagery for Breton Island, Louisiana. Shorelines were digitized in ArcMap 10.2.2 so they could be used for area and shoreline change analysis using the Digital Shoreline Analysis System (DSAS) Version 4.0. |
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2001 Vectorized Shoreline for Breton Island, Louisiana (Geographic, NAD83)
A first-surface elevation map was produced cooperatively from remotely sensed, geographically referenced elevation measurements collected by the U.S. Geological Survey (USGS) and National Aeronautics and Space Administration (NASA) on September 07-09, 2001. Elevation measurements were collected over the area using the NASA Airborne Topographic Mapper (ATM), a scanning lidar system that measures high-resolution topography of the land surface. The ATM system is deployed on a Twin Otter or P-3 Orion aircraft ... |
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2004 Digitized Shoreline for Breton Island, Louisiana(Geographic, NAD83)
Shorelines were derived from the U.S. Geological Survey Earth Resources Observation and Science (EROS) Center’s Digital Orthophoto Quarter Quads (DOQQ) images collected on January 20, 2004. This dataset contains digitized shorelines created from the USGS imagery for Breton Island, Louisiana. Shorelines were digitized in ArcMap 10.2.2 so they could be used for area and shoreline change analysis, using the Digital Shoreline Analysis System (DSAS) version 4.0. |
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2005 Digitized Shoreline for Breton Island, Louisiana (Geographic, NAD83)
Shorelines were derived from the U.S. Geological Survey Earth Resources Observation and Science (EROS) Center’s Digital Orthophoto Quadrangle (DOQ) images collected on November 17, 2005. This dataset contains digitized shorelines created from the USGS imagery for Breton Island, Louisiana. Shorelines were digitized in ArcMap 10.2.2 so they could be used for area and shoreline change analysis, using the Digital Shoreline Analysis System (DSAS) version 4.0. |
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2007 Digitized Shoreline for Breton Island, Louisiana (Geographic, NAD83)
Shorelines were derived from the National Agriculture Imagery Program (NAIP) digital ortho imagery collected on October 11, 2007. This dataset contains digitized shorelines created from the NAIP imagery for Breton Island, Louisiana. Shorelines were digitized in ArcMap 10.2.2 so they could be used for area and shoreline change analysis using the Digital Shoreline Analysis System (DSAS) Version 4.0. |
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2008 Digitized Shoreline for Breton Island, Louisiana (Geographic, NAD83)
Shorelines were derived from the U.S. Geological Survey Earth Resources Observation and Science (EROS) Center high-resolution orthorectified images collected on October 01, 2008. This dataset contains digitized shorelines created from the USGS imagery for Breton Island, Louisiana. Shorelines were digitized in ArcMap 10.2.2 so they could be used for area and shoreline change analysis, using the Digital Shoreline Analysis System (DSAS) Version 4.0. |
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2010 Digitized Shoreline for Breton Island, Louisiana (Geographic, NAD83)
Shorelines were derived from the National Agriculture Imagery Program (NAIP) digital ortho imagery collected on May 10, 2010. This dataset contains digitized shorelines created from the NAIP imagery for Breton Island, Louisiana. Shorelines were digitized in ArcMap 10.2.2 so they could be used for area and shoreline change analysis using the Digital Shoreline Analysis System (DSAS) version 4.0. |
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2012 Digitized Shoreline for Breton Island, Louisiana(Geographic, NAD83)
Shorelines were derived from a U.S. Geological Survey Earth Resources Observations and Science Center (EROS) high-resolution orthorectified image that was collected on October 20, 2012 over Breton Island, Louisiana. Shorelines were digitized in ArcMap 10.2.2 so they could be used for area and shoreline change analysis using the Digital Shoreline Analysis System (DSAS) version 4.0. |
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2013 Vectorized Shoreline for Breton Island, Louisiana (Geographic, NAD83)
Shorelines were derived from a U.S. Geological Survey topographic lidar survey that was conducted on July 12-14, 2013 over Dauphin Island, Alabama and Chandeleur, Stake, Grand Gosier and Breton Islands, Louisiana and published in USGS Data Series 838. Photo Science, Inc., was contracted by the USGS to collect and process these data. Lidar data were acquired around portions of both the Alabama and Louisiana barrier islands; however, this dataset only contains shorelines created from data acquired from ... |
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2014 Vectorized Shoreline for Breton Island, Louisiana (Geographic, NAD83)
Shorelines were derived from a U.S. Geological Survey topographic lidar survey that was conducted on January 16-18, 2014 over Breton Island, Louisiana and released under USGS field activity number 14LGC01. Quantum Spatial was contracted by the USGS to collect and process these data. This dataset contains vectorized shorelines created from data acquired from Breton Island, Louisiana. Shorelines were vectorized in ArcMap 10.2.2 so they could be used for area and shoreline change analysis, using the Digital ... |
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Delineated Coastal Cliff Toes Derived from Post-Hurricane Maria Lidar Elevation Data Collected from Puerto Rico: 2018
The National Assessment of Coastal Change Hazards project aims to understand and forecast coastal landscape change. This dataset consists of delineated coastal cliff toes that may be used to assess the hazard posed by eroding coastal cliffs on the islands of Puerto Rico, Culebra, and Vieques. The delineation of cliff tops and toes can be used as an input into cliff hazard metrics and to measure overall cliff changes over time. Cliff tops and cliff toes were identified along three-dimensional (3D) transects ... |
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Delineated Coastal Cliff Tops Derived from Post-Hurricane Maria Lidar Elevation Data Collected from Puerto Rico: 2018
The National Assessment of Coastal Change Hazards project aims to understand and forecast coastal landscape change. This dataset consists of delineated coastal cliff tops that may be used to assess the hazard posed by eroding coastal cliffs on the islands of Puerto Rico, Culebra, and Vieques. The delineation of cliff tops and toes can be used as an input into cliff hazard metrics and to measure overall cliff changes over time. Cliff tops and cliff toes were identified along three-dimensional (3D) transects ... |
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Delineated Coastal Cliff Transects Derived from Post-Hurricane Maria Lidar Elevation Data Collected from Puerto Rico: 2018
The National Assessment of Coastal Change Hazards project aims to understand and forecast coastal landscape change. This dataset consists of delineated coastal cliff transects that may be used to assess the hazard posed by eroding coastal cliffs on the islands of Puerto Rico, Culebra, and Vieques. The delineation of cliff tops and toes can be used as an input into cliff hazard metrics and to measure overall cliff changes over time. Cliff tops and cliff toes were identified along three-dimensional (3D) ... |
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FIIS_Breach_Shorelines.shp - Fire Island National Seashore Wilderness Breach Shoreline Data Collected from Fire Island, New York, October 2014 to October 2017
Hurricane Sandy made U.S. landfall, coincident with astronomically high tides, near Atlantic City, New Jersey, on October 29, 2012. The storm, the largest on historical record in the Atlantic basin, affected an extensive area of the east coast of the United States. The highest waves and storm surge were focused along the heavily populated New York and New Jersey coasts. At the height of the storm, a record significant wave height of 9.6 meters (m) was recorded at the wave buoy offshore of Fire Island, New ... |
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FIIS_Breach_Shorelines.shp - Fire Island National Seashore Wilderness Breach Shoreline Data Collected from Fire Island, New York, October 2014 to September 2016
Hurricane Sandy made U.S. landfall, coincident with astronomical high tides, near Atlantic City, New Jersey, on October 29, 2012. The storm, the largest on historical record in the Atlantic basin, affected an extensive area of the east coast of the United States. The highest waves and storm surge were focused along the heavily populated New York and New Jersey coasts. At the height of the storm, a record significant wave height of 9.6 meters (m) was recorded at the wave buoy offshore of Fire Island, New ... |
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FIIS_Shorelines_Oct2012_Oct2017.shp: Fire Island, NY pre- and post-storm shoreline data from October 2012 to October 2017
Hurricane Sandy made U.S. landfall, coincident with astronomically high tides, near Atlantic City, New Jersey, on October 29, 2012. The storm, the largest on historical record in the Atlantic basin, affected an extensive area of the east coast of the United States. The highest waves and storm surge were focused along the heavily populated New York and New Jersey coasts. At the height of the storm, a record significant wave height of 9.6 meters (m) was recorded at the wave buoy offshore of Fire Island, New ... |
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Shorelines_Oct2012_Sep2016.shp: Fire Island, NY pre and post storm shoreline data from October 2012 to September 2016
Hurricane Sandy made U.S. landfall, coincident with astronomical high tides, near Atlantic City, New Jersey, on October 29, 2012. The storm, the largest on historical record in the Atlantic basin, affected an extensive area of the east coast of the United States. The highest waves and storm surge were focused along the heavily populated New York and New Jersey coasts. At the height of the storm, a record significant wave height of 9.6 meters (m) was recorded at the wave buoy offshore of Fire Island, New ... |
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Shapefile of Historical shorelines for Fire Island and Great South Bay, New York, derived from previously unpublished National Oceanic and Atmospheric Administration (NOAA) 1834-1875 topographic sheets
Topographic sheets (t-sheets) produced by the National Ocean Service (NOS) during the 1800s provide the position of past shorelines. The shoreline data can be vectorized into a geographic information system (GIS) and compared to modern shoreline data to calculate estimates of long-term shoreline rates of change. Many t-sheets were scanned and digitized by the National Oceanic and Atmospheric Administration (NOAA) and are available on the NOAA Shoreline website (https://shoreline.noaa.gov/data/datasheets/t ... |
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GrandBay_2010_Shoreline.shp - Grand Bay, Mississippi/Alabama, Shoreline Data Derived from 2010 Aerial Imagery
GrandBay_2010_Shoreline.zip features a digitized historical shoreline for the Grand Bay, Mississippi (MS) coastline (Pascagoula, MS to Point aux Pins, Alabama [AL]) derived from 2010 aerial imagery. Imagery of the Mississippi and Alabama coastlines was acquired from the National Agriculture Imagery Program (NAIP) and the city of Mobile, AL. Using ArcMap 10.3.1, the imagery was used to delineate and digitize the historical shoreline as either the Wet Dry Line (WDL) along sandy beaches or the vegetation edge ... |
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GrandBay_2012_Shoreline.shp - Grand Bay, Mississippi/Alabama, Shoreline Data Derived from 2012 Aerial Imagery
GrandBay_2012_Shoreline.zip features a digitized historical shoreline for the Grand Bay, Mississippi (MS) coastline (Pascagoula, MS to Bayou La Fourche Bay, Alabama [AL]) derived from 2012 aerial imagery. Imagery of the Mississippi and Alabama coastlines was acquired from the National Agriculture Imagery Program (NAIP). Using ArcMap 10.3.1, the imagery was used to delineate and digitize a coarse historical shoreline as either proximal Wet Dry Line along sandy beaches or proximal vegetation edge along the ... |
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Elevation data for four sites in the coastal marsh at Grand Bay National Estuarine Research Reserve, Mississippi, from July 2018 through January 2020
To better understand sediment deposition in marsh environments, scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) selected four study sites (Sites 5, 6, 7, and 8) along the Point Aux Chenes Bay shoreline of the Grand Bay National Estuarine Research Reserve (GNDNERR), Mississippi. These datasets were collected to serve as baseline data prior to the installation of a living shoreline (a subtidal sill). Each site consisted of five plots located along a ... |
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Elevation data for four sites in the coastal marsh at Grand Bay National Estuarine Research Reserve, Mississippi, from October 2016 through October 2017
To understand sediment deposition in marsh environments, scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) selected four study sites in the Grand Bay National Estuarine Research Reserve, Mississippi (GNDNERR). Each site consisted of four plots located along a transect perpendicular to the marsh-estuary shoreline at 5-meter (m) increments (5, 10, 15, and 20 m from the shoreline). Each plot contained four net sedimentation tiles (NST) that were secured ... |
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Shore proximal sediment deposition in coastal marsh at the Grand Bay National Estuarine Research Reserve, Mississippi: net sedimentation tile datasets from July 2018 to January 2020
To better understand sediment deposition in marsh environments, scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) selected four study sites (Sites 5, 6, 7, and 8) along the Point Aux Chenes Bay shoreline of the Grand Bay National Estuarine Research Reserve (GNDNERR), Mississippi. These datasets were collected to serve as baseline data prior to the installation of a living shoreline (a subtidal sill). Each site consisted of five plots located along a ... |
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Shore proximal sediment deposition in coastal marsh at the Grand Bay National Estuarine Research Reserve, Mississippi: net sedimentation tile datasets from October 2016 to October 2017
To understand sediment deposition in marsh environments, scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) selected four study sites in the Grand Bay National Estuarine Research Reserve, Mississippi (GNDNERR). Each site consisted of four plots located along a transect perpendicular to the marsh-estuary shoreline at 5-meter (m) increments (5, 10, 15, and 20 m from the shoreline). Each plot contained four net sedimentation tiles (NST) that were secured ... |
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Turbidity data for two sites in the coastal marsh at Grand Bay National Estuarine Research Reserve, Mississippi, from October 2016 through October 2017
To understand sediment deposition in marsh environments, scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) selected four study sites in the Grand Bay National Estuarine Research Reserve, Mississippi (GNDNERR). Each site consisted of four plots located along a transect perpendicular to the marsh-estuary shoreline at 5-meter (m) increments (5, 10, 15, and 20 m from the shoreline). Each plot contained four net sedimentation tiles (NST) that were secured ... |
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Water level data for four sites in the coastal marsh at Grand Bay National Estuarine Research Reserve, Mississippi, from October 2018 through January 2020
To better understand sediment deposition in marsh environments, scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) selected four study sites (Sites 5, 6, 7, and 8) along the Point Aux Chenes Bay shoreline of the Grand Bay National Estuarine Research Reserve (GNDNERR), Mississippi. These datasets were collected to serve as baseline data prior to the installation of a living shoreline (a subtidal sill). Each site consisted of five plots located along a ... |
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Water level and salinity data for four sites in the coastal marsh at Grand Bay National Estuarine Research Reserve, Mississippi, from October 2016 through October 2017
To understand sediment deposition in marsh environments, scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) selected four study sites in the Grand Bay National Estuarine Research Reserve, Mississippi (GNDNERR). Each site consisted of four plots located along a transect perpendicular to the marsh-estuary shoreline at 5-meter (m) increments (5, 10, 15, and 20 m from the shoreline). Each plot contained four net sedimentation tiles (NST) that were secured ... |
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Idealized Antecedent Topography Sensitivity Study: Initial Baseline and Modified Profiles Modeled with XBeach
Antecedent topography is an important aspect of coastal morphology when studying and forecasting coastal change hazards. The uncertainty in morphologic response of storm-impact models and their use in short-term hazard forecasting and decadal forecasting is important to account for when considering a coupled model framework. Mickey and others (2020) provided a methodology to investigate uncertainty of profile response within the storm impact model, XBeach, related to varying antecedent topographies. A ... |
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Wetland-Change Data Derived from Landsat Imagery, Assateague Island to Metompkin Island, Maryland and Virginia, 1984 to 2015: Land-cover Change Analysis
This U.S. Geological Survey (USGS) data release includes geospatial datasets that were created for the analysis of Virginia and Maryland Atlantic coastal wetland changes over time. Wetland change was determined by assessing two metrics: wetland persistence and land-cover switching. Because seasonal water levels, beach width, and vegetation differences can affect change analyses, only images acquired during the spring (March, April, and May) were included in the wetland-change metrics (N=10). Land-cover ... |
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Lidar_MHW_Shorelines_1998_2014.shp - Mean High Water (MHW) Shorelines Extracted from Lidar Data for Dauphin Island, Alabama from 1998 to 2014.
This shapefile consists of Dauphin Island, AL shorelines extracted from lidar data collected from November 1998 to January 2014. This dataset contains 14 Mean High Water (MHW) shorelines separated into 37 shoreline segments alongshore Dauphin Island, AL. The individual sections are divided according to location along the island and shoreline type: open ocean, back-barrier, marsh shoreline. Raw lidar point data was converted to a gridded surface, from which a contour of the operational MHW shoreline (0.24 ... |
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Time Series of Structure-from-Motion Products - Digital Elevation Models: Madeira Beach, Florida, July 2017 to June 2018
Aerial imagery acquired with a small unmanned aircraft system (sUAS), in conjunction with surveyed ground control points (GCP) visible in the imagery, can be processed with structure-from-motion (SfM) photogrammetry techniques to produce high-resolution orthomosaics, three-dimensional (3D) point clouds and digital elevation models (DEMs). This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides UAS survey data products consisting of DEMs ... |
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Time Series of Structure-from-Motion Products - Orthomosaics: Madeira Beach, Florida, July 2017 to June 2018
Aerial imagery acquired with a small unmanned aircraft system (sUAS), in conjunction with surveyed ground control points (GCP) visible in the imagery, can be processed with structure-from-motion (SfM) photogrammetry techniques to produce high-resolution orthomosaics, three-dimensional (3D) point clouds and digital elevation models (DEMs). This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides UAS survey data products consisting of ... |
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Time Series of Structure-from-Motion Products - Point Clouds: Madeira Beach, Florida, July 2017 to June 2018
Aerial imagery acquired with a small unmanned aircraft system (sUAS), in conjunction with surveyed ground control points (GCP) visible in the imagery, can be processed with structure-from-motion (SfM) photogrammetry techniques to produce high-resolution orthomosaics, three-dimensional (3D) point clouds and digital elevation models (DEMs). This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides UAS survey data products consisting of ... |
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Time Series of Aerial Imagery from Small Unmanned Aircraft Systems and Associated Ground Control Points: Madeira Beach, Florida, July 2017 to June 2018 (Aerial Imagery)
Aerial imagery acquired with a small unmanned aircraft system (sUAS), in conjunction with surveyed ground control points (GCPs) visible in the imagery, can be processed with structure-from-motion (SfM) photogrammetry techniques to produce high-resolution orthomosaics, three-dimensional (3D) point clouds and digital elevation models (DEMs). This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides UAS survey data consisting of aerial ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Cat Island, Mississippi (Polyline: Combined Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Cat Island, Mississippi (Polyline: Combined Dates) is a line shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Cat Island, Mississippi (dates_meta.txt)
Shorelines Extracted from 1984-2015 Landsat Imagery: Cat Island, Mississippi (Polyline: Individual Dates) is a line shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Cat Island, Mississippi (Polygon: Combined Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Cat Island, Mississippi (Polygon: Combined Dates) is a polygon shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Cat Island, Mississippi (Polygon: Individual Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Cat Island, Mississippi (Polygon: Individual Dates) is a dataset consisting of 268 polygon shapefiles representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Dauphin Island, Alabama (Polyline: Combined Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Dauphin Island, Alabama (Polyline: Combined Dates) is a line shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Dauphin Island, Alabama (Polyline: Individual Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Dauphin Island, Alabama (Polyline: Individual Dates) is a line shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Dauphin Island, Alabama (Polygon: Combined Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Dauphin Island, Alabama (Polygon: Combined Dates) is a polygon shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Dauphin Island, Alabama (Polygon: Individual Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Dauphin Island, Alabama (Polygon: Individual Dates) is a dataset consisting of 223 polygon shapefiles representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Horn Island, Mississippi (Polyline: Combined Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Horn Island, Mississippi (Polyline: Combined Dates) is a line shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Horn Island, Mississippi (Polyline: Individual Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Horn Island, Mississippi (Polyline: Individual Dates) is a line shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Horn Island, Mississippi (Polygon: Combined Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Horn Island, Mississippi (Polygon: Combined Dates) is a polygon shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Horn Island, Mississippi (Polygon: Individual Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Horn Island, Mississippi (Polygon: Individual Dates) is a dataset consisting of 254 polygon shapefiles representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Petit Bois Island, Mississippi (Polyline: Combined Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Petit Bois Island, Mississippi (Polyline: Combined Dates) is a line shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Petit Bois Island, Mississippi (Polyline: Individual Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Petit Bois Island, Mississippi (Polyline: Individual Dates) is a line shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Petit Bois Island, Mississippi (Polygon: Combined Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Petit Bois Island, Mississippi (Polygon: Combined Dates) is a polygon shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Petit Bois Island, Mississippi (Polygon: Individual Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Petit Bois Island, Mississippi (Polygon: Individual Dates) is a dataset consisting of 271 polygon shapefiles representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Ship Island, Mississippi (Polyline: Combined Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Ship Island, Mississippi (Polyline: Combined Dates) is a line shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Ship Island, Mississippi (Polyline: Individual Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Ship Island, Mississippi (Polyline: Individual Dates) is a line shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Ship Island, Mississippi (Polygon: Combined Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Ship Island, Mississippi (Polygon: Combined Dates) is a polygon shapefile representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from these images and can indicate ... |
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Shorelines Extracted from 1984-2015 Landsat Imagery: Ship Island, Mississippi (Polygon: Individual Dates)
Shorelines Extracted from 1984-2015 Landsat Imagery: Ship Island, Mississippi (Polygon: Individual Dates) is a dataset consisting of 280 polygon shapefiles representing shorelines generated from satellite imagery that was collected from 1984 to 2015. The sample frequency of satellite imagery is much higher, and the coverage much greater, than most routine high-resolution topographic surveys. Certain aspects of barrier island morphology, such as island size, shape and position, can be determined from ... |
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Shorelines Derived from Continuous Video-Imagery at the NASA-Kennedy Space Center, Florida From August 2011 to July 2012
In 2010, a video camera was installed near the northern boundary of the National Aeronautics and Space Administration-Kennedy Space Center (NASA-KSC) property along the Atlantic coast of Florida. A region extending 1 kilometer (km) to the south of the camera was established as the region of interest for the video image observations. During every daylight hour of camera operation from August 8, 2011 to July 24, 2012, a time exposure (timex) image product was created by averaging pixel color intensity for all ... |
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Historical Shoreline for New Jersey (1971 to 1978): Vector Digital Data
New_Jersey_1971_78_Digitized_Shoreline.zip features a digitized historic shoreline for the New Jersey coastline (Point Pleasant, NJ to Longport, NJ) from 1971 to 1978. Imagery of the New Jersey coastline was acquired from the New Jersey Geographic Information Network (NJGIN) as two images: “1970 NJDEP Wetlands Basemap” (1971-78) and the “1977 Tidelands Basemaps” (1977-78). These images are available as a web mapping service (WMS) through the NJGIN website (https://njgin.state.nj.us/NJ_NJGINExplorer ... |
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Shorelines for Barnegat and Great Bay, NJ: 1839 to 2012 (ver 1.1, December 2017)
This data set represents vector shorelines for the New Jersey coastline (Point Pleasant, NJ to Longport, NJ) from 1839 to 2012. Data were obtained from multiple data sources, including the U.S. Geological Survey (USGS), National Oceanic and Atmospheric Administration (NOAA), and New Jersey Department of Environmental Protection (NJDEP). Shorelines were obtained from the original provider and merged into a single file in order to conduct shoreline change analysis for the open-ocean and estuarine shorelines ... |
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Point based shorelines derived from global positioning system data with nearest WorldView shoreline distance for the Grand Bay National Estuarine Research Reserve in Mississippi from 2013-2020
Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into coastal management, including shoreline monitoring and change analysis. Geospatial shoreline data were created from a ... |
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Vectorized Marsh Shorelines for the Grand Bay National Estuarine Research Reserve in Mississippi and Alabama from 1848 to 2017
This dataset represents a compilation of vector shorelines in the Grand Bay National Estuarine Research Reserve (Mississippi and Alabama) from 1848 to 2017. Shoreline data were obtained from multiple data sources, including the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Administration (NOAA), the Grand Bay National Estuarine Research Reserve (GBNERR), and the Mississippi Office of Geology (MOG). All shoreline data types have uncertainty associated with delineating the shoreline ... |
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Vectorized marsh shorelines derived from high resolution aerial imagery for the Grand Bay National Estuarine Research Reserve in Mississippi from 2014-2020
Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into coastal management, including shoreline monitoring and change analysis. Geospatial shoreline data were created from a ... |
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Vectorized marsh shorelines derived from global positioning system data for the Grand Bay National Estuarine Research Reserve in Mississippi from 2013-2020
Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into coastal management, including shoreline monitoring and change analysis. Geospatial shoreline data were created from a ... |
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Vectorized Marsh Shorelines derived from WorldView imagery for the Grand Bay National Estuarine Research Reserve in Mississippi from 2013-2020
Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into coastal management, including shoreline monitoring and change analysis. Geospatial shoreline data were created from a ... |
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Transects_BackBarrier.shp - Digital Shoreline Analysis System version 4.3 Transects with Linear Regression Rate Calculations for the Back-Barrier (North-Facing) coast of Dauphin Island, Alabama.
Rates of shoreline change for Dauphin Island, Alabama were generated for three analysis periods, using two different shoreline proxy datasets. Mean High Water line (MHW) shorelines were generated from 14 lidar datasets (1998-2014) and Wet Dry Line (WDL) shorelines were digitized from ten sets of georeferenced aerial images (1940-2015). Rates of change were generated for three groups of shorelines: MHW (lidar), WDL (aerial) and MHW and WDL shorelines combined. These data will aid in developing an ... |
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Transects_OpenOcean.shp - Digital Shoreline Analysis System version 4.3 Transects with Linear Regression Rate Calculations for the Open Ocean coast of Dauphin Island, Alabama.
Rates of shoreline change for Dauphin Island, Alabama were generated for three analysis periods, using two different shoreline proxy datasets. Mean High Water line (MHW) shorelines were generated from 14 lidar datasets (1998-2014) and Wet Dry Line (WDL) shorelines were digitized from ten sets of georeferenced aerial images (1940-2015). Rates of change were generated for three groups of shorelines: MHW (lidar), WDL (aerial) and MHW and WDL shorelines combined. These data will aid in developing an ... |
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Wetland-Change Data Derived from Landsat Imagery, Assateague Island to Metompkin Island, Maryland and Virginia, 1984 to 2015: Wetland Persistence Analysis
This U.S. Geological Survey (USGS) data release includes geospatial datasets that were created for the analysis of Virginia and Maryland Atlantic coastal wetland changes over time. Wetland change was determined by assessing two metrics: wetland persistence and land-cover switching. Because seasonal water levels, beach width, and vegetation differences can affect change analyses, only images acquired during the spring (March, April, and May) were included in the wetland-change metrics (N=10). To assess ... |
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XYZ point data - Post Hurricane Sandy Beach Profile Survey Fire Island Inlet to Moriches Inlet 2013
The U.S. Army Corps of Engineers(USACE) contracted a beach survey of Fire Island, New York from September 17–October 6, 2013, for the purpose of planning a beach reconstruction project following Hurricane Sandy. This dataset contains elevation data of subaerial morphology and nearshore bathymetry collected using real time kinematic global positioning system (RTK-GPS) and hydrography techniques. The data were provided to the U.S. Geological Survey(USGS) to contribute to an existing monitoring dataset of ... |
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Historic shoreline positions for Rincon, Puerto Rico 1936-2006 (shorelines.shp)
The 8 km of shoreline from Punta Higüero to Punta Cadena in Rincón, Puerto Rico is experiencing long-term coastal erosion. This study documents historical shoreline changes at Rincón for the period 1936-2005. Twelve historical shoreline positions were compiled from existing data, new orthophotography, and GPS field surveys. Shoreline vectors represent the high water line at the time of the survey. |
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Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for the Cape Cod region from Provincetown to the southern end of Monomoy Island, Massachusetts (OuterCapeCod_shorelines_uncertainty.dbf)
Due to continued coastal population growth and increased threats of erosion, current data on trends and rates of shoreline movement are required to inform shoreline and floodplain management. The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals along ocean-facing sections of the Massachusetts coast. ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Cedar Island, VA, 2010
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Cedar Island, VA, 2012–2013
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Cedar Island, VA, 2012
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Cedar Island, VA, 2013–2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Cedar Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Edwin B. Forsythe NWR, NJ, 2010
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Edwin B. Forsythe NWR, NJ, 2010
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Edwin B. Forsythe NWR, NJ, 2012
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Edwin B. Forsythe NWR, NJ, 2012
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Edwin B. Forsythe NWR, NJ, 2013–2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Edwin B. Forsythe NWR, NJ, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Fire Island, NY, 2010–2011
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Fire Island, NY, 2010
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Fire Island, NY, 2012
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Fire Island, NY, 2012
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Fire Island, NY, 2014–2015
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Fire Island, NY, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Rockaway Peninsula, NY, 2010–2011
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Rockaway Peninsula, NY, 2010
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Rockaway Peninsula, NY, 2012
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Rockaway Peninsula, NY, 2012
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Rockaway Peninsula, NY, 2013–2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Rockaway Peninsula, NY, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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Discharge measurements collected in the Stillaguamish River Delta, Port Susan, Washington, USA in March, April, and May 2014
Tidal water discharge within two breaches constructed in a former flood-control levee of a restored agricultural area in Port Susan, Washington, was measured repeatedly during several tidal cycles. Measurements were made on March 27, 2014, April 16, 2014, May 18, 2014, and May 29, 2014 at breach PSB1, and on May 29, 2014 at breach PSB2. These data were collected using a boat-mounted Teledyne RDI RiverRay 600 kHz acoustic Doppler current profiler (ADCP) or a Teledyne RDI StreamPro 2000 kHz ADCP, depending on ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Cedar Island, VA, 2013–2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Year_30_Elevations_NA)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Cedar Island, VA, 2012–2013
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Year_30_Elevations_N)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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Archive of Chirp Subbottom Profile, Imagery, and Geospatial Data Collected in 2022 Offshore of Breton Island, Louisiana
On August 5, 2022, researchers from the U.S. Geological Survey (USGS) conducted an offshore geophysical survey to map the shoreface and determine Holocene stratigraphy near Breton Island, Louisiana (LA). The Breton Island Post Construction Monitoring project objective includes the investigation of nearshore geologic controls on surface morphology in addition to mapping the seafloor to evaluate coastal change. This publication (Forde and others, 2023) serves as an archive of high-resolution chirp subbottom ... |
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Geometrically corrected image mosaic of 1936 aerial photographs of Rincon, Puerto Rico (mosaic_1936.tif)
The 8 km of shoreline from Punta Higuero to Punta Cadena in Rincón, Puerto Rico is experiencing long-term coastal erosion. This study documents historical shoreline changes at Rincón for the period 1936-2005. Twelve historical shoreline positions were compiled from existing data, new orthophotography, and GPS field surveys. |
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Geometrically corrected image mosaic of 1983 aerial photographs of Rincon, Puerto Rico (mosaic_1983.tif)
The 8 km of shoreline from Punta Higuero to Punta Cadena in Rincón, Puerto Rico is experiencing long-term coastal erosion. This study documents historical shoreline changes at Rincón for the period 1936-2005. Twelve historical shoreline positions were compiled from existing data, new orthophotography, and GPS field surveys. |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Cedar Island, VA, 2010–2011
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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Geometrically corrected image mosaic of 1987 aerial photograps of Rincon, Puerto Rico (mosiac_1987.tif)
The 8 km of shoreline from Punta Higuero to Punta Cadena in Rincón, Puerto Rico is experiencing long-term coastal erosion. This study documents historical shoreline changes at Rincón for the period 1936-2005. Twelve historical shoreline positions were compiled from existing data, new orthophotography, and GPS field surveys. |
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Conceptual marsh units for Assateague Island National Seashore and Chincoteague Bay, Maryland and Virginia
The salt marsh complex of Assateague Island National Seashore (ASIS) and Chincoteague Bay was delineated to smaller, conceptual marsh units by geoprocessing of surface elevation data. Flow accumulation based on the relative elevation of each location is used to determine the ridge lines that separate each marsh unit while the surface slope is used to automatically assign each unit a drainage point, where water is expected to drain through. Through scientific efforts initiated with the Hurricane Sandy ... |
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Elevation of marsh units in Fire Island National Seashore and central Great South Bay salt marsh complex, New York
Elevation distribution in the Fire Island National Seashore and central Great South Bay salt marsh complex is given in terms of mean elevation of conceptual marsh units defined by Defne and Ganju (2018). The elevation data is based on the 1-meter resolution Coastal National Elevation Database (CoNED). Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands ... |
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Elevation of marsh units in Cape Cod National Seashore salt marsh complex, Massachusetts
Elevation distribution in the Cape Cod National Seashore (CACO) salt marsh complex and approximal wetlands is given in terms of mean elevation of conceptual marsh units defined by Defne and Ganju (2019). The elevation data is based on the 1-meter resolution Coastal National Elevation Database (CoNED), where data gaps exist. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast ... |
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Conceptual salt marsh units for wetland synthesis: Edwin B. Forsythe National Wildlife Refuge, New Jersey
The salt marsh complex of the Edwin B. Forsythe National Wildlife Refuge (EBFNWR), which spans over Great Bay, Little Egg Harbor, and Barnegat Bay (New Jersey, USA), was delineated to smaller, conceptual marsh units by geoprocessing of surface elevation data. Flow accumulation based on the relative elevation of each location is used to determine the ridge lines that separate each marsh unit while the surface slope is used to automatically assign each unit a drainage point, where water is expected to drain ... |
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Oceanographic time-series measurements collected in the Stillaguamish River Delta, Port Susan, Washington, USA from March 2014 to July 2015
Water level, flow velocity, temperature, salinity, and turbidity were measured in a breach constructed in a flood-protection levee surrounding a restored former agricultural area in Port Susan, Washington, USA, near the mouth of the Stillaguamish River. Data were collected in a breach known as PSB1 at 15-minute intervals from March 21, 2014 to July 1, 2015 using a SonTek Argonaut-SW current meter, an In-Situ Aqua TROLL 200 pressure, conductivity, and temperature sensor, and an FTS DTS-12 turbidity sensor. |
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Wave power on marsh units in Connecticut salt marshes
This data release contains coastal wetland synthesis products for the state of Connecticut. Metrics for resiliency, including the unvegetated to vegetated ratio (UVVR), marsh elevation, tidal range, wave power, and exposure potential to environmental health stressors are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change ... |
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Unvegetated to vegetated ratio of marsh units in Connecticut salt marshes
This data release contains coastal wetland synthesis products for the state of Connecticut. Metrics for resiliency, including the unvegetated to vegetated ratio (UVVR), marsh elevation, tidal range, wave power, and exposure potential to environmental health stressors are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change ... |
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Mean tidal range of marsh units in Connecticut salt marshes
This data release contains coastal wetland synthesis products for the state of Connecticut. Metrics for resiliency, including the unvegetated to vegetated ratio (UVVR), marsh elevation, tidal range, wave power, and exposure potential to environmental health stressors are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change ... |
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Inferred hydrodynamic residence time in salt marsh units in Edwin B. Forsythe National Wildlife Refuge, New Jersey
As part of the Hurricane Sandy Science Plan, the U.S. Geological Survey is expanding National Assessment of Coastal Change Hazards and forecast products to coastal wetlands. The intent is to provide federal, state, and local managers with tools to estimate the vulnerability of coastal wetlands to various factors and to evaluate their ecosystem service potential. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their ... |
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Elevation of marsh units in Connecticut salt marshes
This data release contains coastal wetland synthesis products for the state of Connecticut. Metrics for resiliency, including the unvegetated to vegetated ratio (UVVR), marsh elevation, tidal range, wave power, and exposure potential to environmental health stressors are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change ... |
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Mean tidal range in salt marsh units of Edwin B. Forsythe National Wildlife Refuge, New Jersey (polygon shapefile)
Biomass production is positively correlated with mean tidal range in salt marshes along the Atlantic coast of the United States of America. Recent studies support the idea that enhanced stability of the marshes can be attributed to increased vegetative growth due to increased tidal range. This dataset displays the spatial variation mean tidal range (i.e. Mean Range of Tides, MN) in the Edwin B. Forsythe National Wildlife Refuge (EBFNWR), which spans over Great Bay, Little Egg Harbor, and Barnegat Bay in New ... |
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Raster image of mean tidal range in the Edwin B. Forsythe National Wildlife Refuge, New Jersey (32-bit GeoTIFF)
Biomass production is positively correlated with mean tidal range in salt marshes along the Atlantic coast of the United States of America. Recent studies support the idea that enhanced stability of the marshes can be attributed to increased vegetative growth due to increased tidal range. This dataset displays the spatial variation mean tidal range (i.e. Mean Range of Tides, MN) in the Edwin B. Forsythe National Wildlife Refuge (EBFNWR), which spans over Great Bay, Little Egg Harbor, and Barnegat Bay in New ... |
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Elevation of salt marsh units in Edwin B. Forsythe National Wildlife Refuge, New Jersey
Elevation distribution in the Edwin B. Forsythe National Wildlife Refuge (EBFNWR), which spans over Great Bay, Little Egg Harbor, and Barnegat Bay in New Jersey, USA is given in terms of mean elevation of conceptual marsh units defined by Defne and Ganju (2016). The elevation data is based on the 1-meter resampled 1/9 arc-second resolution USGS National Elevation Data. As part of the Hurricane Sandy Science Plan, the U.S. Geological Survey is expanding National Assessment of Coastal Change Hazards and ... |
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Raster image of exposure potential to environmental health stressors in Edwin B. Forsythe National Wildlife Refuge (32-bit GeoTIFF)
Natural and anthropogenic contaminants, pathogens, and viruses are found in soils and sediments throughout the United States. Enhanced dispersion and concentration of these environmental health stressors in coastal regions can result from sea level rise and storm-derived disturbances. The combination of existing environmental health stressors and those mobilized by natural or anthropogenic disasters could adversely impact the health and resilience of coastal communities and ecosystems. This dataset displays ... |
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Conceptual marsh units of Connecticut salt marshes
This data release contains coastal wetland synthesis products for the state of Connecticut. Metrics for resiliency, including the unvegetated to vegetated ratio (UVVR), marsh elevation, tidal range, wave power, and exposure potential to environmental health stressors are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change ... |
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Optically stimulated luminescence (OSL) age data from vibracores collected offshore central California, during field activity 2019-651-FA (ver 2.0, August 2023)
This dataset includes optically stimulated luminescence (OSL) age data from sediment cores collected offshore central California in the vicinity of Morro Bay. These data were collected aboard the M/V Bold Horizon in October 2019 for use in regional hazard assessments relating to the Hosgri Fault. |
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Coordinates of vibracores collected offshore central California, during field activity 2019-651-FA (ver 2.0, August 2023)
This dataset includes coordinate information for sediment cores collected offshore central California in the vicinity of Morro Bay. These data were collected aboard the M/V Bold Horizon in October 2019 for use in regional hazard assessments relating to the Hosgri Fault. |
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Radiocarbon age data from vibracores collected offshore central California, during field activity 2019-651-FA (ver 2.0, August 2023)
This dataset includes radiocarbon age data from sediment cores collected offshore central California in the vicinity of Morro Bay. These data were collected aboard the M/V Bold Horizon in October 2019 for use in regional hazard assessments relating to the Hosgri Fault. |
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Exposure potential of salt marsh units in Edwin B. Forsythe National Wildlife Refuge to environmental health stressors (polygon shapefile)
Natural and anthropogenic contaminants, pathogens, and viruses are found in soils and sediments throughout the United States. Enhanced dispersion and concentration of these environmental health stressors in coastal regions can result from sea level rise and storm-derived disturbances. The combination of existing environmental health stressors and those mobilized by natural or anthropogenic disasters could adversely impact the health and resilience of coastal communities and ecosystems. This dataset displays ... |
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Slope Values Across Marsh-Forest Boundary in Chesapeake Bay Region, USA
The marsh-forest boundary in the Chesapeake Bay was determined by geoprocessing high-resolution (1 square meter) land use and land cover data sets. Perpendicular transects were cast at standard intervals (30 meters) along the boundary within a GIS by repurposing the Digital Shoreline Analysis System (DSAS) Version 5.0, an ArcGIS extension developed by the U.S. Geological Survey. Average and maximum slope values were assigned to each transect from surface elevation data. The same values were also provided as ... |
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Multi-sensor core logger (MSCL) data of vibracores and bob-cores collected in Lake Ozette, from 2019 to 2021
This dataset includes multi-sensor core logger (MSCL) data from sediment cores collected in Lake Ozette, Washington. The sediment cores were collected during USGS field activities 2019-622-FA and 2021-641-FA for investigating submarine landslide deposits triggered by large Cascadia Subduction Zone earthquakes. |
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Grainsize data from vibracores collected in Ozette Lake, Washington, in 2019
Grainsize data were collected from select sediment cores from Ozette Lake, Washington, in 2019. These data were used to investigate submarine landslide deposits triggered by large Cascadia Subduction Zone earthquakes. |
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Hurricane Sally Overwash Extents
The National Assessment of Coastal Change Hazards project at the U.S. Geological Survey (USGS) exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the Florida and Alabama coast and attributed to coastal processes during [Atlantic Basin] Hurricane Sally, which made landfall in the U.S. on September 16, 2020. |
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Hurricane Matthew Overwash Extents (version 2.0, 20210916)
The National Assessment of Coastal Change Hazards project exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the Florida, Georgia, North Carolina,and South Carolina coasts and attributed to coastal processes during [Atlantic Basin] Hurricane Matthew, which made landfall in the U.S. on October 8, 2018. |
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Time series for the central Beaufort Sea coast, Alaska
Time series output from a spectral wave model (Simulating Waves WAves Nearshore [SWAN]; Booij and others 1999), implemented for the central Beaufort Sea coast of Alaska from 1979 to 2019, are provided. The variables include significant wave heights, mean wave periods, mean wave directions, wave steepness, and orbital velocities. Additionally, water depths, x (east-west) and y (north-south) components of the wind, and sea ice concentrations are provided. Further information can be found in Nederhoff and ... |
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Shore Proximal Marsh Sediment Deposition and Ancillary Data From Grand Bay National Estuarine Research Reserve, Mississippi: grain size analysis
To better understand sediment deposition in marsh environments, scientists from the U.S. Geological Survey, St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) selected four study sites (Sites 5, 6, 7, and 8) along the Point Aux Chenes Bay shoreline of the Grand Bay National Estuarine Research Reserve (GNDNERR), Mississippi. These datasets were collected to serve as baseline data prior to the installation of a living shoreline (a subtidal sill). Each site consisted of five plots located along a ... |
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Water_Level_na: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Water_Level_na_SLR: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Hurricane Isaias Overwash Extents
The National Assessment of Coastal Change Hazards project at the U.S. Geological Survey (USGS) exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the coast of the Carolinas and attributed to coastal processes during [Atlantic Basin] Hurricane Isaias, which made landfall in the U.S. on August 4, 2020. |
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Water_Level_all: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Water_Level_all_SLR: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Water_Level_GBI: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Water_Level_GBI_SLR: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Hurricane Florence Overwash Extents
The National Assessment of Coastal Change Hazards project at the U.S. Geological Survey (USGS) exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the southeast coast of the United States from North Carolina to Virginia and attributed to coastal processes during [Atlantic Basin] Hurricane Florence, which made landfall in the U.S. on September 14, 2018. |
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Velocity_na: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Velocity_na_SLR: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Velocity_all: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Ground Penetrating Radar (GPR) Profile Trace Data Collected from Dauphin Island, Alabama in April 2013
From April 13-20, 2013, scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS-SPCMSC) conducted geophysical and sediment sampling surveys on Dauphin Island, Alabama, as part of field activity number 13BIM01. This dataset, Ground Penetrating Radar (GPR) Profile Trace Data Collected from Dauphin Island, Alabama in April 2013, contains the unprocessed, raw profile trace data obtained during this survey. |
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Sediment and Radiochemical Characteristics from Shore-Perpendicular Estuarine and Marsh Transects in the Grand Bay National Estuarine Research Reserve, Mississippi
To examine sediment transport and provenance between a marsh and estuary, surface sediments were collected along two transects in the Grand Bay National Estuarine Research Reserve, Mississippi (GNDNERR). Each shore-perpendicular transect consisted of fifteen surface samples, collected every 2.5 meters (m) from 10-m out into the estuary to 25-m into the marsh from the shoreline. Sediment samples were analyzed for their physical and radiochemical properties or signatures. Sediment samples were collected ... |
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Velocity_all_SLR: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Velocity_GBI: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Archive of Chirp Subbottom Profile Data Collected in 2015 from the Northern Chandeleur Islands, Louisiana
From September 14 to 28, 2015, the U.S. Geological Survey (USGS) conducted a geophysical survey to investigate the geologic controls on barrier island evolution and medium-term and interannual sediment transport along the sand berm constructed in 2011 (offshore, at the northern end of the Chandeleur Islands, Louisiana) as mitigation of the Deepwater Horizon oil spill. This investigation is part of a broader USGS project, which seeks to better understand barrier island evolution over medium time scales ... |
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Velocity_GBI_SLR: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Archive of Ground Penetrating Radar and Differential Global Positioning System Data Collected in April 2016 from Fire Island, New York
Researchers from the U.S. Geological Survey (USGS) conducted a long-term, coastal morphologic-change study at Fire Island, New York, prior to and after Hurricane Sandy impacted the area in October 2012. The Fire Island Coastal Change project (https://coastal.er.usgs.gov/fire-island/) objectives include understanding the morphologic evolution of the barrier island system on a variety of time scales (months to centuries) and resolving storm-related impacts, post-storm beach response, and recovery. In April ... |
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Archive of Chirp Subbottom Profile Data Collected in 2016 from the Northern Chandeleur Islands, Louisiana
From June 10 to 19, 2016, the U.S. Geological Survey (USGS) conducted a geophysical survey to investigate the geologic controls on barrier island evolution and medium-term and interannual sediment transport along the sand berm constructed in 2011 (offshore, at the northern end of the Chandeleur Islands, Louisiana) as mitigation of the Deepwater Horizon oil spill. This investigation is part of a broader USGS project, which seeks to better understand barrier island evolution over medium time scales (months to ... |
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Archive of Chirp Sub-Bottom Profile, Imagery, and Navigational Data Collected in June and August 2023 from the Chandeleur Islands, Louisiana
As part of the 2022 Disaster Supplemental project, scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) conducted a nearshore geophysical survey to map the shoreface and inner shelf, as well as characterize stratigraphy near the Chandeleur Islands, Louisiana (LA) in June and August 2023. The purpose of this study was to conduct a morphologic and geologic assessment of the impacts of the 2020 and 2021 hurricane seasons within part of the Breton National ... |
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Salinity_na_tropical: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Salinity_na_tropical_SLR: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Salinity_na_frontal: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Hurricane Zeta Overwash Extents
The National Assessment of Coastal Change Hazards project exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the Louisiana coast and attributed to coastal processes during [Atlantic Basin] Hurricane Zeta, which made landfall in the U.S. on October 28, 2020. |
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Archive of Chirp Subbottom Profile Data Collected in 2017 From the Northern Chandeleur Islands, Louisiana
From August 7 to 16, 2017, the U.S. Geological Survey (USGS) conducted a geophysical survey to investigate the geologic controls on barrier island evolution and medium-term and interannual sediment transport along the sand berm constructed in 2011 (offshore, at the northern end of the Chandeleur Islands, Louisiana) as mitigation of the Deepwater Horizon oil spill. This investigation is part of a broader USGS project, which seeks to better understand barrier island evolution over medium time scales (months ... |
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Archive of Chirp Subbottom Profile Data Collected in 2017 from the Louisiana Chenier Plain
June 2–10 and July 2, 2017, the U.S. Geological Survey (USGS) conducted geophysical surveys offshore of the Louisiana Chenier Plain to document the changing morphology of the coastal environment. Data were collected under the Barrier Island Coastal Monitoring (BICM) program, an ongoing collaboration between the State of Louisiana Coastal Protection and Restoration Authority (CPRA), the University of New Orleans (UNO) Pontchartrain Institute for Environmental Sciences (PIES), and the USGS. Project ... |
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USGS CoastCam at DUNEX: Timestack Imagery and Coordinate Data (Camera 1)
Two digital video cameras were temporarily installed at the U.S. Fish and Wildlife Service (USFWS) Pea Island National Wildlife Refuge (PINWR) in North Carolina (NC), as part of the DUring Nearshore Event eXperiment (DUNEX). DUNEX was a collaborative community-led experiment that took place in the fall of 2021 along the Outer Banks of NC, with the goal of improving the understanding, observational techniques, and predictive capabilities for extreme storm processes and impacts within the coastal environment. ... |
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USGS CoastCam at DUNEX: Timestack Imagery and Coordinate Data (Camera 2)
Two digital video cameras were temporarily installed at the U.S. Fish and Wildlife Service (USFWS) Pea Island National Wildlife Refuge (PINWR) in North Carolina (NC), as part of the DUring Nearshore Event eXperiment (DUNEX). DUNEX was a collaborative community-led experiment that took place in the fall of 2021 along the Outer Banks of NC, with the goal of improving the understanding, observational techniques, and predictive capabilities for extreme storm processes and impacts within the coastal environment. ... |
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Post-Hurricane Florence Aerial Imagery: Cape Fear to Duck, North Carolina, October 6-8, 2018
The U.S. Geological Survey (USGS) Remote Sensing Coastal Change (RSCC) project collects aerial imagery along coastal swaths, in response to storm events, with optimized endlap/sidelap and precise position information to create high-resolution orthomosaics, three-dimensional (3D) point clouds, and digital elevation/surface models (DEMs/DSMs) using Structure-from-Motion (SfM) photogrammetry methods. These products are valuable for measuring topographic change, and for understanding coastal vulnerability and ... |
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Archive of Chirp Subbottom Profile Data Collected in June 2018 From Fire Island, New York
Researchers from the U.S. Geological Survey (USGS) conducted a long-term, coastal morphologic-change study at Fire Island, New York, prior to and after Hurricane Sandy impacted the area in October 2012. The Fire Island Coastal System Change project (https://coastal.er.usgs.gov/fire-island/) objectives include understanding the morphologic evolution of the barrier island system on a variety of time scales (months to centuries) and resolving storm-related impacts, post-storm beach response, and recovery. From ... |
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Archive of Chirp Subbottom Profile Data Collected in 2018 from the Northern Chandeleur Islands, Louisiana
From August 16 to 21, 2018, the U.S. Geological Survey (USGS) conducted a geophysical survey to investigate the geologic controls on barrier island evolution and medium-term and interannual sediment transport along the sand berm constructed in 2011 (offshore, at the northern end of the Chandeleur Islands, Louisiana) as mitigation of the Deepwater Horizon oil spill. This investigation is part of a broader USGS project, which seeks to better understand barrier island evolution over medium time scales (months ... |
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Hurricane Michael Overwash Extents
The National Assessment of Coastal Change Hazards project at the U.S. Geological Survey (USGS) exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the Florida coast and attributed to coastal processes during [Atlantic Basin] Hurricane Michael, which made landfall in the U.S. on October 10, 2018. |
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Aerial Imagery of the North Carolina Coast: 2019-08-30 and 2019-09-02, Pre-Hurricane Dorian
The U.S. Geological Survey (USGS) Remote Sensing Coastal Change (RSCC) project collects aerial imagery along coastal swaths, in response to storm events, with optimized endlap/sidelap and precise position information to create high-resolution orthomosaics, three-dimensional (3D) point clouds, and digital elevation/surface models (DEMs/DSMs) using Structure-from-Motion (SfM) photogrammetry methods. These products are valuable for measuring topographic and landscape change, and for understanding coastal ... |
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Aerial Imagery of the North Carolina Coast: 2019-09-08 to 2019-09-13, Post-Hurricane Dorian
The U.S. Geological Survey (USGS) Remote Sensing Coastal Change (RSCC) project collects aerial imagery along coastal swaths, in response to storm events, with optimized endlap/sidelap and precise position information to create high-resolution orthomosaics, three-dimensional (3D) point clouds, and digital elevation/surface models (DEMs/DSMs) using Structure-from-Motion (SfM) photogrammetry methods. These products are valuable for measuring topographic and landscape change, and for understanding coastal ... |
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Hurricane Laura Overwash Extents
The National Assessment of Coastal Change Hazards project project at the U.S. Geological Survey (USGS) exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the Louisiana coast and attributed to coastal processes during [Atlantic Basin] Hurricane Laura, which made landfall in the U.S. on August 27, 2020. |
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Aerial Imagery of the North Carolina Coast: 2019-10-11
The U.S. Geological Survey (USGS) Remote Sensing Coastal Change (RSCC) project collects aerial imagery along coastal swaths, in response to storm events, with optimized endlap/sidelap and precise position information to create high-resolution orthomosaics, three-dimensional (3D) point clouds, and digital elevation/surface models (DEMs/DSMs) using Structure-from-Motion (SfM) photogrammetry methods. These products are valuable for measuring topographic and landscape change, and for understanding coastal ... |
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Aerial Imagery of the North Carolina Coast: 2019-11-26
The U.S. Geological Survey (USGS) Remote Sensing Coastal Change (RSCC) project collects aerial imagery along coastal swaths, in response to storm events, with optimized endlap/sidelap and precise position information to create high-resolution orthomosaics, three-dimensional (3D) point clouds, and digital elevation/surface models (DEMs/DSMs) using Structure-from-Motion (SfM) photogrammetry methods. These products are valuable for measuring topographic and landscape change, and for understanding coastal ... |
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Hurricane Irma Overwash Extents
The National Assessment of Coastal Change Hazards project at the U.S. Geological Survey (USGS) exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the Florida coast and attributed to coastal processes during [Atlantic Basin] Hurricane Irma, which made landfall in the U.S. on September 9, 2017. |
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Hurricane Delta Overwash Extents
The National Assessment of Coastal Change Hazards project at the U.S. Geological Survey (USGS) exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the Louisiana coast and attributed to coastal processes during [Atlantic Basin] Hurricane Delta, which made landfall in the U.S. on October 9, 2020. |
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Archive of Chirp Subbottom Profile Data Collected in 2019 from Cedar Island, Virginia
From August 9 to 14, 2019, researchers from the U.S. Geological Survey (USGS) conducted a geophysical survey to investigate shoreface morphology and geology near Cedar Island, Virginia. The Coastal Sediment Availability and Flux project objectives include understanding the morphologic evolution of the barrier island system on a variety of time scales (months to centuries) and resolving storm-related impacts, post-storm beach response, and recovery. This publication serves as an archive of high-resolution ... |
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Archive of Chirp Subbottom Profile, Imagery, and Geospatial Data Collected in 2019 from Rockaway Peninsula, New York
From September 27 through October 5, 2019, researchers from the U.S. Geological Survey (USGS) conducted a geophysical survey to investigate shoreface morphology and geology near the Rockaway Peninsula, New York. The Coastal Sediment Availability and Flux project objectives include understanding the morphologic evolution of the barrier island system on a variety of time scales (months to centuries) and resolving storm-related impacts, post-storm beach response, and recovery. This publication serves as an ... |
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Core descriptions and sedimentologic data from vibracores and sand augers collected in 2021 and 2022 from Fire Island, New York
In 2021 and 2022, scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) and the USGS New York Water Science Center (NYWSC), on behalf of SPCMSC, conducted sediment sampling and ground penetrating radar (GPR) surveys at Point O' Woods and Ho-Hum Beach (NYWSC, 2021) and Watch Hill, Long Cove, and Smith Point (SPCMSC, 2022), Fire Island, New York. These data complement previous SPCMSC GPR and sediment sampling surveys conducted at Fire Island in 2016 ... |
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2019-333-FA_shots: Digital Chirp Subbottom Profile Shotpoint Data Collected During USGS Field Activity Number 2019-333-FA Offshore of the Rockaway Peninsula, New York, September–October 2019
From September 27 through October 5, 2019, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport offshore of the Rockaway Peninsula, New York. This shapefile represents a point dataset of field activity number (FAN) 2019-333-FA chirp subbottom profile 1,000-shot-interval locations. |
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2019-333-FA_sol: Digital Chirp Subbottom Profile Start of Line Data Collected During USGS Field Activity Number 2019-333-FA Offshore of the Rockaway Peninsula, New York, September–October 2019
From September 27 through October 5, 2019, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport offshore of the Rockaway Peninsula, New York. This shapefile represents a point dataset of field activity number (FAN) 2019-333-FA chirp subbottom profile start of trackline locations. |
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USGS CoastCam at DUNEX: Intrinsic and Extrinsic Calibration Data (Camera 2)
Two digital video cameras were temporarily installed at the U.S. Fish and Wildlife Service (USFWS) Pea Island National Wildlife Refuge (PINWR) in North Carolina (NC), as part of the DUring Nearshore Event eXperiment (DUNEX). DUNEX was a collaborative community-led experiment that took place in the fall of 2021 along the Outer Banks of NC, with the goal of improving the understanding, observational techniques, and predictive capabilities for extreme storm processes and impacts within the coastal environment. ... |
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2019-333-FA_trkln: Digital Chirp Subbottom Profile Trackline Data Collected During USGS Field Activity Number 2019-333-FA Offshore of the Rockaway Peninsula, New York, September–October 2019
From September 27 through October 5, 2019, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport offshore of the Rockaway Peninsula, New York. This shapefile represents a line dataset of field activity number (FAN) 2019-333-FA chirp tracklines. |
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Aerial Imagery of the North Carolina Coast: 2020-02-08 to 2020-02-09
The U.S. Geological Survey (USGS) Remote Sensing Coastal Change (RSCC) project collects aerial imagery along coastal swaths, in response to storm events, with optimized endlap/sidelap and precise position information to create high-resolution orthomosaics, three-dimensional (3D) point clouds, and digital elevation/surface models (DEMs/DSMs) using Structure-from-Motion (SfM) photogrammetry methods. These products are valuable for measuring topographic and landscape change, and for understanding coastal ... |
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Aerial Imagery of the North Carolina Coast: 2020-05-08 to 2020-05-09
The U.S. Geological Survey (USGS) Remote Sensing Coastal Change (RSCC) project collects aerial imagery along coastal swaths, in response to storm events, with optimized endlap/sidelap and precise position information to create high-resolution orthomosaics, three-dimensional (3D) point clouds, and digital elevation/surface models (DEMs/DSMs) using Structure-from-Motion (SfM) photogrammetry methods. These products are valuable for measuring topographic and landscape change, and for understanding coastal ... |
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Salinity_na_frontal_SLR: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Salinity_all_tropical: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Salinity_all_tropical_SLR: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Salinity_all_frontal: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Salinity_all_frontal_SLR: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Initial and Future Marsh Productivity Conditions Under Three Sea-Level Rise Scenarios (Intermediate-Low, Intermediate, and Intermediate-High) from 2020 to 2100 in the Apalachicola-Big-Bend Region
Using the Hydro-MEM (Hydrodynamic-Marsh Equilibrium Model) (Alizad and others, 2016a; 2016b), the wetlands system within the Apalachicola-Big-Bend (ABB) region of Florida (FL) was assessed using initial and three sea-level rise (SLR) scenarios from the National Oceanic and Atmospheric Administration (NOAA) (Sweet and others, 2017). The initial (init) scenario represents the present conditions in the year 2020. The intermediate-low (int-low) scenario projects 50 centimeters (cm) of SLR by 2100, the ... |
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Projected sea-level rise flooding inundation extents for +0.25, +0.50, +1.00, +1.50, +2.00, and +3.00 meter sea-level rise scenarios in American Samoa
This data release provides flooding extent polygons based on sea-level rise (SLR) water levels for the coast of American Samoa's most populated islands of Tutuila, Ofu-Olosega, and Ta'u. Digital elevation models were used to predict SLR flooding extents for +0.25 m, +0.50 m, +1.00 m, +1.50 m, +2.00 m, and +3.00 m SLR scenarios. |
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Projected sea-level rise flooding inundation extents for +0.25, +0.50, +1.00, +1.50, +2.00, and +3.00 meter in the Mariana Islands
This data release provides flooding extent polygons based on potential future sea-level rise (SLR) rise water levels for the coast of the most populated Mariana Islands of Guam and Saipan. Digital elevation models were used to predict SLR flooding extents for +0.25 m, +0.50 m, +1.00 m, +1.50 m, +2.00 m, and +3.00 m SLR rise scenarios. |
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Projected sea-level rise flooding inundation extents for +0.25, +0.50, +1.00, +1.50, +2.00, and +3.00 meter sea-level rise scenarios in the Hawaiian Islands
This data release provides flooding extent polygons based on potential future sea-level rise (SLR) water levels for the coast of the most populated Hawaiian Islands of O'ahu, Moloka'i, Kaua'i, Maui, and Big Island. Digital elevation models were used to extract SLR flooded areas along the coastlines at 10-m2 resolution and converted to polygon shapefiles of the extents for +0.25 m, +0.50 m, +1.00 m, +1.50 m, +2.00 m, and +3.00 m SLR scenarios. |
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Initial and Future Marsh Vegetation Conditions Under Three Sea-Level Rise Scenarios (Intermediate-Low, Intermediate, and Intermediate-High) from 2020 to 2100 in the Apalachicola-Big-Bend Region
Using the Hydro-MEM (Hydrodynamic-Marsh Equilibrium Model) (Alizad and others, 2016a; 2016b), the wetlands system within the Apalachicola-Big-Bend (ABB) region of Florida (FL) was assessed using initial and three sea-level rise (SLR) scenarios from the National Oceanic and Atmospheric Administration (NOAA) (Sweet and others, 2017). The initial (init) scenario represents the present conditions in the year 2020. The intermediate-low (int-low) scenario projects 50 centimeters (cm) of SLR by 2100, the ... |
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Salinity_GBI_tropical: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Geospatial Navigational Data Associated with Chirp Sub-Bottom Profiles Collected During USGS Field Activity Number 2023-325-FA in June and August 2023 from the Chandeleur Islands, Louisiana
As part of the 2022 Disaster Supplemental project, scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) conducted a nearshore geophysical survey to map the shoreface and inner shelf, as well as characterize stratigraphy near the Chandeleur Islands, Louisiana (LA) in June and August 2023. The purpose of this study was to conduct a morphologic and geologic assessment of the impacts of the 2020 and 2021 hurricane seasons within part of the Breton National ... |
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USGS CoastCam at Sand Key, Florida: Timestack Imagery and Coordinate Data (Camera 2)
Two digital video cameras were installed at Sand Key, Florida (FL), facing south (camera 1) and north (camera 2) along the beach. Every hour during daylight hours, daily from 2018 to 2022, the cameras collected raw video and produced snapshots and time-averaged image products. For camera 2, one such product that is created is a "runup timestack". Runup timestacks are images created by sampling a cross-shore array of pixels from an image through time as waves propagate towards and run up a beach. Runup ... |
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USGS CoastCam at Sand Key, Florida: Intrinsic and Extrinsic Calibration Data (Camera 2)
Two digital video cameras were installed at Sand Key, Florida (FL), facing south (camera 1) and north (camera 2) along the beach. Every hour during daylight hours, the cameras collected raw video and produced snapshots and time-averaged image products. This data release includes the necessary intrinsic orientation (IO) and extrinsic orientation (EO) calibration data to utilize imagery to make quantitative measurements. The images included in this data release were collected by camera 2 (c2) from May 29, ... |
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Bathymetric Grid for a Wave Exposure Model of Grand Bay, Mississippi
Coastal marshes are highly dynamic and ecologically important ecosystems that are subject to pervasive and often harmful disturbances, including shoreline erosion. Shoreline erosion can result in an overall loss of coastal marsh, particularly in estuaries with moderate- or high-wave energy. Not only can waves be important physical drivers of shoreline change they can also influence shore-proximal vertical accretion through sediment delivery. For these reasons, estimates of wave energy can provide a ... |
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Shapefile of Historical Bathymetric Soundings for Mississippi and Alabama Derived from National Ocean Service (NOS) Hydrographic Sheets
Hydrographic sheets (H-sheets) and nautical charts produced by the National Ocean Service (NOS) during the 1800s provide historic sounding (water depth) measurements of coastal areas. The data can be vectorized into a geographic information system (GIS), adjusted to a modern vertical datum, and converted into a digital elevation model to provide an interpretation of the historic seafloor elevation. These data were produced to provide an estimate of historical bathymetry for the Mississippi-Alabama coastal ... |
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Location and depth data for piston and gravity cores collected in September 2019 offshore of south-central California (USGS FAN 2019-642-FA)
This dataset includes the location and depth information for 39 piston and gravity cores that were collected as part of a groundtruthing survey in September 2019 aboard the R/V Bold Horizon. This dataset is one of several collected as part of the Bureau of Ocean Energy Management (BOEM)-funded California Deepwater Investigations and Groundtruthing (Cal DIG I) project. The purpose of the study is to assess shallow geohazards, benthic habitats, and thereby the potential for alternative energy infrastructure ... |
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Salinity_GBI_tropical_SLR: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Salinity_GBI_frontal: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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Salinity_GBI_frontal_SLR: Modeling the effects of large-scale interior headland restoration on tidal hydrodynamics and salinity transport in an open coast, marine-dominant estuary: model input and results
Using version 20.1_19 of the Discontinuous-Galerkin Shallow Water Equations Model (DG-SWEM) (Kubatko and others, 2006), astronomic tides and salinity transport were simulated at Grand Bay, Alabama (AL), under scenarios of interior headland restoration and sea level rise, as described in Passeri and others (2023). The two-dimensional DG-SWEM model can be applied to coastal and estuarine systems to solve for time-dependent hydrodynamic circulation and salinity transport. The DG-SWEM model uses the ADCIRC ... |
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OpenFOAM models of low- and high-relief sites from the coral reef flat off Waiakane, Molokai, Hawaii
OpenFOAM Computational Fluid Dynamics (CFD) models were developed to simulate wave energy dissipation across natural rough reef surfaces on the reef flat off Waiakane, Molokai, Hawaii, to understand this process in the context of reef restoration design. A total of 140 models were developed (70 per low- and 70 per high-bed-relief domains). Models were calibrated and validated with oceanographic datasets collected in 2018. This data release presents the 140 model scenarios that can be readily input into ... |
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Nearshore parametric wave setup hindcast data (1979-2019) for the North and South Carolina coasts
This dataset presents alongshore wave setup timeseries for the North and South Carolina coastlines. Wave setup was modelled using parameterization for open coast sandy beaches as presented in Stockdon and others (2006). The parameterization relates onshore wave setup to offshore wave conditions and beach characteristics. Wave conditions were extracted at approximately the 10 m depth contour and reverse shoaled to the deep-water condition. These data were then matched to cross-shore transects spaced at ... |
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Nearshore parametric wave setup future projections (2020-2050) for the North and South Carolina coasts
This dataset presents alongshore wave setup timeseries for the North and South Carolina coastlines. Wave setup was modelled using parameterization for open coast sandy beaches as presented in Stockdon and others (2006). The parameterization relates onshore wave setup to offshore wave conditions and beach characteristics. Wave conditions were extracted at approximately the 10 m depth contour and reverse shoaled to the deep-water condition. These data were then matched to cross-shore transects spaced at ... |
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Nearshore water level, tide, and non-tidal residual hindcasts (1979-2016) for the North and South Carolina coasts
A dataset of modeled nearshore water levels (WLs) was developed for the North and South Carolina coastlines. Water levels, defined for this dataset as the linear sum of tides and non-tidal residuals (NTR), were produced by Muis and others (2016) using a global tide and surge model (GTSM) forced by global atmospheric fields -. Water level outputs were extracted from the global grid at approximately 20 km resolution along the coastlines. These data were then statistically downscaled using a signal-specific ... |
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Nearshore water level, tide, and non-tidal residual future projections (2016-2050) for the North and South Carolina coasts
A dataset of modeled nearshore water levels (WLs) was developed for the North and South Carolina coastlines. Water levels, defined for this dataset as the linear sum of tides and non-tidal residuals (NTR), were produced by Muis and others (2016) using a global tide and surge model (GTSM) forced by global atmospheric fields. Water level outputs were extracted from the global grid at approximately 20 km resolution along the southeast Atlantic coastline. These data were then statistically downscaled using a ... |
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Nearshore parametric wave setup hindcast data (1979-2019) for the U.S. Atlantic coast
This dataset presents alongshore wave setup timeseries for three states (Virginia, Georgia, and Florida) along the U.S. Atlantic coast. Wave setup was modelled using parameterization for open coast sandy beaches as presented in Stockdon and others (2006). The parameterization relates onshore wave setup to offshore wave conditions and beach characteristics. Wave conditions were extracted at approximately the 10 m depth contour and reverse shoaled to the deep-water condition. These data were then matched to ... |
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Nearshore parametric wave setup future projections (2020-2050) for the U.S. Atlantic coast
This dataset presents alongshore wave setup timeseries for three states (Virginia, Georgia, and Florida) along the U.S. Atlantic coast. Wave setup was modelled using parameterization for open coast sandy beaches as presented in Stockdon and others (2006). The parameterization relates onshore wave setup to offshore wave conditions and beach characteristics. Wave conditions were extracted at approximately the 10 m depth contour and reverse shoaled to the deep-water condition. These data were then matched to ... |
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Nearshore water level, tide, and non-tidal residual hindcasts (1979-2016) for the U.S. Atlantic coast
A dataset of modeled nearshore water levels (WLs) was developed for three states (Virginia, Georgia, and Florida) along the U.S. Atlantic coast. Water levels, defined for this dataset as the linear sum of tides and non-tidal residuals (NTR), were produced by Muis and others (2016) using a global tide and surge model (GTSM) forced by global atmospheric fields -. Water level outputs were extracted from the global grid at approximately 20 km resolution along the coastlines. These data were then statistically ... |
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Nearshore water level, tide, and non-tidal residual future projections (2016-2050) for the U.S. Atlantic coast
A dataset of modeled nearshore water levels (WLs) was developed for three states (Virginia, Georgia, and Florida) along the U.S. Atlantic coast. Water levels, defined for this dataset as the linear sum of tides and non-tidal residuals (NTR), were produced by Muis and others (2016) using a global tide and surge model (GTSM) forced by global atmospheric fields. Water level outputs were extracted from the global grid at approximately 20 km resolution along the Atlantic coastline. These data were then ... |
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Ground Penetrating Radar and Global Positioning System Data Collected from Fire Island, New York, March-April 2021
Fire Island, New York (NY) is a 50-kilometer (km) long barrier island system fronting the southern coast of Long Island, NY with relatively complex geology. In 2016, the U.S. Geological Survey (USGS) conducted ground penetrating radar (GPR) surveys and sediment sampling at Fire Island to characterize and quantify spatial variability in the subaerial geology (Forde and others, 2018; Buster and others, 2018). These surveys, in combination with historical data, allowed for a preliminary reconstruction of the ... |
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Attendee Survey Results from the April and May 2020 Gulf Islands National Seashore Workshop
In early 2020, scientists gathered to advance sediment budget modeling efforts by conducting a “Needs Assessment Workshop” for the Gulf Island National Seashore (GINS) to understand the coastal processes affecting island resiliency. The “Gulf Islands Sediment Budget Needs Assessment Workshop” was held, virtually, April 23–24 and May 27–28, 2020. The workshop series was organized by researchers from North Carolina State University in collaboration with National Park Service (NPS) and U.S. ... |
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CatIsland 2010 single-beam bathymetry tracklines
In September and October of 2010, the U.S. Geological Survey (USGS), in cooperation with the Army Corps of Engineers (USACE), conducted geophysical surveys around Cat Island, Miss. to collect bathymetry, acoustical backscatter, and seismic reflection data (seismic-reflection data have been published separately, Forde and others, 2012). The geophysical data along with sediment vibracore data (yet to be published) will be integrated to analyze and produce a report describing the geomorphology and geologic ... |
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CatIsland_2010_Bathy_Swath_tracklines
In September and October of 2010, the U.S. Geological Survey (USGS), in cooperation with the Army Corps of Engineers (USACE), conducted geophysical surveys around Cat Island, Miss. to collect bathymetry, acoustical backscatter, and seismic reflection data (seismic-reflection data have been published separately, Forde and others, 2012). The geophysical data along with sediment vibracore data (yet to be published) will be integrated to analyze and produce a report describing the geomorphology and geologic ... |
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Model parameter input files to compare the influence of coral reef carbonate budgets on alongshore variations in wave-driven total water levels on Buck Island Reef National Monument
A set of physics-based XBeach Non-hydrostatic hydrodynamic model simulations (with input files here included) were used to evaluate how varying carbonate budgets, and thus coral reef accretion and degradation, affect alongshore variations in wave-driven water levels along the adjacent shoreline of Buck Island Reef National Monument (BUIS) for a number of sea-level rise scenarios, specifically during extreme wave conditions when the risk for coastal flooding and the resulting impact to coastal communities is ... |
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Lifespan of Massachusetts salt marsh units
Lifespan of salt marshes in Massachusetts (MA) are calculated using conceptual marsh units defined by Ackerman and others (2022). The lifespan calculation is based on estimated sediment supply and sea-level rise (SLR) predictions after Ganju and others (2020). Sea level predictions are local estimates which correspond to the 0.3, 0.5, and 1.0 meter increase in Global Mean Sea Level (GMSL) scenarios by 2100 from Sweet and others (2022). The U.S. Geological Survey has been expanding national assessment of ... |
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Elevation of marsh units in Eastern Shore of Virginia salt marshes
This data release contains coastal wetland synthesis products for the Atlantic-facing Eastern Shore of Virginia (the data release for the Chesapeake Bay-facing portion of the Eastern Shore of Virginia is found here: https://doi.org/10.5066/P997EJYB). Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and tidal range are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland ... |
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USGS CoastCam at Sand Key, Florida: Intrinsic and Extrinsic Calibration Data (Camera 1)
Two digital video cameras were installed at Sand Key, Florida (FL), facing south (camera 1) and north (camera 2) along the beach. Every hour during daylight hours, the cameras collected raw video and produced snapshots and time-averaged image products. This data release includes the necessary intrinsic orientation (IO) and extrinsic orientation (EO) calibration data to utilize imagery to make quantitative measurements.. The cameras are part of a U.S. Geological Survey (USGS) research project to study the ... |
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Time Series of Structure-from-Motion Products - Digital Elevation Models: Little Dauphin Island and Pelican Island, Alabama, September 2018 to April 2019
Aerial imagery acquired with a small unmanned aircraft system (sUAS), in conjunction with surveyed ground control points (GCP) visible in the imagery, can be processed with structure-from-motion (SfM) photogrammetry techniques to produce high-resolution orthomosaics, three-dimensional (3D) point clouds and digital elevation models (DEMs). This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides UAS survey data products consisting of DEMs ... |
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Time Series of Structure-from-Motion Products - Multispectral Orthomosaics: Little Dauphin Island and Pelican Island, Alabama, September 2018 to April 2019
Aerial imagery acquired with a small unmanned aircraft system (sUAS), in conjunction with surveyed ground control points (GCP) visible in the imagery, can be processed with structure-from-motion (SfM) photogrammetry techniques to produce high-resolution orthomosaics, three-dimensional (3D) point clouds and digital elevation models (DEMs). This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides UAS survey data products consisting of ... |
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Time Series of Structure-from-Motion Products - RGB Orthomosaics: Little Dauphin Island and Pelican Island, Alabama, September 2018 to April 2019
Aerial imagery acquired with a small unmanned aircraft system (sUAS), in conjunction with surveyed ground control points (GCP) visible in the imagery, can be processed with structure-from-motion (SfM) photogrammetry techniques to produce high-resolution orthomosaics, three-dimensional (3D) point clouds and digital elevation models (DEMs). This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides UAS survey data products consisting of red ... |
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10cct01_v2rbf_50m.tif: 50-Meter Resolution Grid of Swath Bathymetry Data Collected Offshore of Cat Island, Mississippi in March 2010
In March of 2010, the U.S. Geological Survey (USGS) conducted geophysical surveys east of Cat Island, Mississippi. The efforts were part of the USGS Gulf of Mexico Science Coordination partnership with the U. S. Army Corps of Engineers (USACE) to assist the Mississippi Coastal Improvements Program (MsCIP) and the Northern Gulf of Mexico (NGOM) Ecosystem Change and Hazards Susceptibility Project by mapping the shallow geological stratigraphic framework of the Mississippi Barrier Island Complex. The data ... |
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CatIsland_2010_Bathy_NAVD88_grid.tif
In September and October of 2010, the U.S. Geological Survey (USGS), in cooperation with the Army Corps of Engineers (USACE), conducted geophysical surveys around Cat Island, Miss. to collect bathymetry, acoustical backscatter, and seismic reflection data (seismic-reflection data have been published separately, Forde and others, 2012). The geophysical data along with sediment vibracore data (yet to be published) will be integrated to analyze and produce a report describing the geomorphology and geologic ... |
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Offshore baselines for Assateague Island, Maryland and Virginia (projected, UTM Zone 18 (NAD83))
Assessing the physical change to shorelines and wetlands is critical in determining the resiliency of wetland systems that protect adjacent habitat and communities. The wetland and back-barrier shorelines of Assateague Island, located in Maryland and Virginia, changed as a result of wave action and storm surge that occurred during Hurricane Sandy, which made landfall on October 29, 2012. The impact of Hurricane Sandy will be assessed and placed in its historical context to understand the future ... |
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Transect Lines for Assateague Island, Maryland and Virginia
Assessing the physical change to shorelines and wetlands is critical in determining the resiliency of wetland systems that protect adjacent habitat and communities. The wetland and back-barrier shorelines of Assateague Island, located in Maryland and Virginia, changed as a result of wave action and storm surge that occurred during Hurricane Sandy, which made landfall on October 29, 2012. The impact of Hurricane Sandy will be assessed and placed in its historical context to understand the future ... |
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Wave time-series: ERA5 hindcast period 1979-2019 - U.S. Canada border to Bering Strait
Modeled wave time series data are presented for the hindcast period of 1979 to 2019 from the U.S. Canada border to the Bering Strait close to the 5 and 10 m isobaths. Outputs include three-hourly nearshore significant wave heights (Hs), mean wave periods (Tm) and mean wave directions (Dm) for 6424 locations. Data are available as netCDF files and are packaged for the Beaufort Sea region from the U.S. Canada border to Nuvuk (Point Barrow), and for the Chukchi Sea region from Nuvuk to Kotzebue Sound and from ... |
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Wave model input files
Provided here are the required input files to run a standalone wave model (Simulating Waves WAves Nearshore [SWAN]; Booij and others, 1999) on eleven model domains from the Canada-U.S. border to Norton Sound, Alaska to create a downscaled wave database (DWDB). The DWDB, in turn, can be used to reconstruct hindcast (1979-2019) and projected (2020-2050) time series at each point in the model domains see Engelstad and others, 2023 for further information on reconstruction of time-series. The model forcing ... |
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USGS CoastCam at Waiakāne, Moloka'i, Hawai'i: 2018 Intrinsic and Extrinsic Calibration Data
A digital video camera was installed at Waiakāne, Moloka'i, Hawai'i (HI) and faced west along the beach. Every hour during daylight hours, daily from June 26, 2018, to September 20, 2018, the camera collected raw video and produced snapshots and time-averaged image products. This data release includes the necessary intrinsic orientation (IO) and extrinsic orientation (EO) calibration data to utilize imagery to make quantitative measurements. The camera is part of a U.S. Geological Survey (USGS) research ... |
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P10_Nov2012_Oct2014: Fire Island, NY pre- and post- storm cross-shore profiles from November 2012 to October 2014.
This spreadsheet consists of Fire Island, NY pre- and post- storm cross-shore profiles collected from November 2012 to October 2014. This dataset contains a set of cross-shore profiles covering 14 dates from November 04 2012 to October 07 2014. As part of the assessment of beach and dune morphologic change associated with Hurricane Sandy and the series of winter storms that followed, DGPS elevation data were collected along ten shore-perpendicular profiles extending from just inland of the crest of the dune ... |
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Unvegetated to vegetated ratio of marsh units in eastern Long Island salt marsh complex, New York (ver. 2.0, March 2024)
This data release contains coastal wetland synthesis products for the geographic region of eastern Long Island, New York, including the north and south forks, Gardiners Island, and Fishers Island. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with ... |
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Exposure potential of marsh units to environmental health stressors in eastern Long Island salt marsh complex, New York (ver. 2.0, March 2024)
This data release contains coastal wetland synthesis products for the geographic region of eastern Long Island, New York, including the north and south forks, Gardiners Island, and Fishers Island. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with ... |
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Mean tidal range of marsh units in eastern Long Island salt marsh complex, New York (ver. 2.0, March 2024)
This data release contains coastal wetland synthesis products for the geographic region of eastern Long Island, New York, including the north and south forks, Gardiners Island, and Fishers Island. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with ... |
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Elevation of marsh units in eastern Long Island salt marsh complex, New York (ver. 2.0, March 2024)
This data release contains coastal wetland synthesis products for the geographic region of eastern Long Island, New York, including the north and south forks, Gardiners Island, and Fishers Island. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with ... |
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Conceptual marsh units of eastern Long Island salt marsh complex, New York (ver. 2.0, March 2024)
This data release contains coastal wetland synthesis products for the geographic region of eastern Long Island, New York, including the north and south forks, Gardiners Island, and Fishers Island. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with ... |
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Ground Penetrating Radar and Global Positioning System Data Collected from Central Florida Gulf Coast Barrier Islands, Florida, February-March 2021
A morphologically diverse and dynamic group of barrier islands along the Central Florida (FL) Gulf Coast (CFGC) form a 75-kilometer-long chain stretching from Anclote Key in the north to Egmont Key in the south. In 2021, scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) conducted ground penetrating radar (GPR) surveys on barrier islands located along the CFGC, in Pinellas County, FL. This study investigated the past evolution of the CFGC from field ... |
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USGS CoastCam at Tres Palmas, Rincón, Puerto Rico: Intrinsic and Extrinsic Calibration Data
A digital video camera was installed at Tres Palmas, Rincón, Puerto Rico (PR) and faced west to view the beach and water offshore. Every hour during daylight hours, daily from August 27, 2019 to March 10, 2020, the camera collected raw video and produced snapshots and time-averaged image products. This data release includes the necessary intrinsic orientation (IO) and extrinsic orientation (EO) calibration data to utilize imagery to make quantitative measurements. The camera is part of a U.S. Geological ... |
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Offshore Baselines for the Undeveloped Areas of New Jersey's Barrier Islands (projected, UTM Zone 18N (NAD83))
Assessing the physical change to shorelines and wetlands is critical in determining the resiliency of wetland systems that protect adjacent habitat and communities. The wetland and back-barrier shorelines of New Jersey changed as a result of wave action and storm surge that occurred during Hurricane Sandy, which made landfall on October 29, 2012. The impact of Hurricane Sandy will be assessed and placed in its historical context to understand the future vulnerability of wetland systems. Making these ... |
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Transect Lines for the Undeveloped Areas of New Jersey's Barrier Islands (projected, UTM Zone 18N (NAD83))
Assessing the physical change to shorelines and wetlands is critical in determining the resiliency of wetland systems that protect adjacent habitat and communities. The wetland and back-barrier shorelines of New Jersey changed as a result of wave action and storm surge that occurred during Hurricane Sandy, which made landfall on October 29, 2012. The impact of Hurricane Sandy will be assessed and placed in its historical context to understand the future vulnerability of wetland systems. Making these ... |
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Vibracore locations collected in 2014 from Barnegat Bay, New Jersey
In response to the 2010 Governor’s Action Plan to clean up the Barnegat Bay–Little Egg Harbor (BBLEH) estuary in New Jersey, the U.S. Geological Survey (USGS) partnered with the New Jersey Department of Environmental Protection in 2011 to begin a multidisciplinary research project to understand the physical controls on water quality in the bay. Between 2011 and 2013, USGS scientists mapped the geological and morphological characteristics of the seafloor of the BBLEH estuary using a suite of geophysical ... |
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Grain-size data from vibracores collected in 2014 from Barnegat Bay, New Jersey
In response to the 2010 Governor’s Action Plan to clean up the Barnegat Bay–Little Egg Harbor (BBLEH) estuary in New Jersey, the U.S. Geological Survey (USGS) partnered with the New Jersey Department of Environmental Protection in 2011 to begin a multidisciplinary research project to understand the physical controls on water quality in the bay. Between 2011 and 2013, USGS scientists mapped the geological and morphological characteristics of the seafloor of the BBLEH estuary using a suite of geophysical ... |
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Sediment grain-size data from sand augers collected in March/April and October 2014 from Assateague Island, Maryland (U.S. Geological Survey Field Activity Numbers [FAN] 2014-301-FA and 2014-322-FA)
The U.S. Geological Survey has a long history of responding to and documenting the impacts of storms along the Nation’s coasts and incorporating these data into storm impact and coastal change vulnerability assessments. Although physical changes caused by tropical and extratropical storms to the sandy beaches and dunes fronting barrier islands are generally well documented, the interaction between sandy shoreline erosion and overwash with the back-barrier wetland and estuarine environments is poorly ... |
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Archive of Digitized Analog Boomer Seismic-Reflection Data Collected During U.S. Geological Survey Cruises Erda 92-2 and Erda 92-4 in Mississippi Sound, June and August 1992
The U.S. Geological Survey (USGS) Coastal and Marine Geology Program has actively collected geophysical and sedimentological data in the northern Gulf of Mexico for several decades, including shallow subsurface data in the form of high-resolution seismic reflection profiles (HRSP). Prior to the mid-1990s most HRSP data were collected in analog format as paper rolls of continuous profiles up to 25 meters long. As part of the National Geological and Geophysical Data Preservation Program (NGGDPP) (https:/ ... |
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Unvegetated to vegetated ratio of marsh units in Eastern Shore of Virginia salt marshes
This data release contains coastal wetland synthesis products for the Atlantic-facing Eastern Shore of Virginia (the data release for the Chesapeake Bay-facing portion of the Eastern Shore of Virginia is found here: https://doi.org/10.5066/P997EJYB). Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and tidal range are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland ... |
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Mean tidal range of marsh units in Eastern Shore of Virginia salt marshes
This data release contains coastal wetland synthesis products for the Atlantic-facing Eastern Shore of Virginia (the data release for the Chesapeake Bay-facing portion of the Eastern Shore of Virginia is found here: https://doi.org/10.5066/P997EJYB). Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and tidal range are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland ... |
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Conceptual marsh units of salt marshes on the Eastern Shore of Virginia
This data release contains coastal wetland synthesis products for the Atlantic-facing Eastern Shore of Virginia (the data release for the Chesapeake Bay-facing portion of the Eastern Shore of Virginia is found here: https://doi.org/10.5066/P997EJYB). Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and tidal range are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland ... |
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Static chamber gas fluxes and carbon and nitrogen isotope content of age-dated sediment cores from a Phragmites wetland in Sage Lot Pond, Massachusetts, 2013-2015
Coastal wetlands are major global carbon sinks; however, quantification of carbon flux can be difficult in these heterogeneous and dynamic ecosystems. To characterize spatial and temporal variability in a New England salt marsh, static chamber measurements of greenhouse gas (GHG) fluxes were compared among major plant-defined zones (high marsh dominated by Distichlis spicata and a zone of invasive Phragmites australis) during 2013 and 2014 growing seasons. Two sediment cores were collected in 2015 from the ... |
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Dauphin Island Decadal Hindcast Model Inputs and Results: Final DEM
The model output of bathymetry and topography values resulting from a deterministic simulation at Dauphin Island, Alabama, as described in USGS Open-File Report 2019–1139 (https://doi.org/10.3133/ofr20191139), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry refer to Mickey and others (2020). |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Initial_Elevations_NA)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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Georeferenced scans of National Oceanic and Atmospheric Administration (NOAA) topographic sheets (T-Sheets) Collected Along the Fire Island and Great South Bay, New York, Coastline from 1834-1875
Topographic sheets (t-sheets) produced by the National Ocean Service (NOS) during the 1800s provide the position of past shorelines. The shoreline data can be vectorized into a geographic information system (GIS) and compared to modern shoreline data to calculate estimates of long-term shoreline rates of change. Many t-sheets were scanned and digitized by the National Oceanic and Atmospheric Administration (NOAA) and are available on the NOAA Shoreline web site (https://shoreline.noaa.gov/data/datasheets/t ... |
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USGS CoastCam at Isla Verde, Puerto Rico: 2018-2019 Intrinsic and Extrinsic Calibration Data
A digital video camera was installed at Isla Verde, Puerto Rico (PR) and faced northeast along the beach. Every hour during daylight hours, daily from February 1, 2019, to July 15, 2019, the camera collected raw video and produced snapshots and time-averaged image products. This data release includes the necessary intrinsic orientation (IO) and extrinsic orientation (EO) calibration data to utilize imagery to make quantitative measurements. The camera is part of a U.S. Geological Survey (USGS) research ... |
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Archive of Digitized Analog Boomer and Minisparker Seismic Reflection Data Collected from the Northern Gulf of Mexico: 1981, 1990 and 1991
The U.S. Geological Survey (USGS) Coastal and Marine Geology Program has actively collected geophysical and sedimentological data in the northern Gulf of Mexico for several decades, including shallow subsurface data in the form of high-resolution seismic reflection profiles (HRSP). Prior to the mid-1990s most HRSP data were collected in analog format as paper rolls of continuous profiles up to 25 meters long. As part of the National Geological and Geophysical Data Preservation Program (https:/ ... |
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USGS CoastCam at Waiakāne, Moloka'i, Hawai'i: 2018 Timestack Imagery and Coordinate Data
A digital video camera was installed at Waiakāne, Moloka'i, Hawai'i (HI) and faced west along the beach. Every hour during daylight hours, daily from June 26, 2018, to September 20, 2018, the camera collected raw video and produced snapshots and time-averaged image products. One such product is a "runup timestack". Runup timestacks are images created by sampling a cross-shore array of pixels from an image through time as waves propagate towards and run up a beach. Runup timestacks store the red, green, and ... |
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USGS CoastCam at Isla Verde, Puerto Rico: 2018-2019 Timestack Imagery and Coordinate Data
A digital video camera was installed at Isla Verde Beach in San Juan, Puerto Rico (PR) and faced northeast along the beach. Every hour during daylight hours, daily from February 1, 2019, to July 15, 2019, the camera collected raw video and produced snapshots and time-averaged image products. One such product is a "runup timestack". Runup timestacks are images created by sampling a cross-shore array of pixels from an image through time as waves propagate towards and run up a beach. Runup timestacks store the ... |
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Model parameter input files to compare effects of stream discharge scenarios on sediment deposition and concentrations around coral reefs off west Maui, Hawaii
This dataset consists of physics-based Delft3D model and Delwaq model input files used in modeling sediment deposition and concentrations around the coral reefs of west Maui, Hawaii. The Delft3D models were used to simulate waves and currents under small (SC1) and large (‘SC2’) wave conditions for current stream discharge (‘Alt1’) and stream discharge with watershed restoration (‘Alt3’). Delft3D model results were subsequently used as forcing conditions for Delwaq models to simulate sediment ... |
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Lifespan of Chesapeake Bay salt marsh units
Lifespan distribution in the Chesapeake Bay (CB) salt marsh complex is presented in terms of lifespan of conceptual marsh units defined by Ackerman and others (2022). The lifespan calculation is based on estimated sediment supply and sea-level rise (SLR) predictions after Ganju and others (2020). Sea level predictions are present day estimates at the prescribed rate of SLR, which correspond to the 0.3, 0.5, and 1.0 meter increase in Global Mean Sea Level (GMSL) scenarios by 2100 from Sweet and others (2022) ... |
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Subbottom and Sidescan Sonar Data Acquired in 2015 From Grand Bay, Mississippi and Alabama
From May 28 to June 3, 2015, the U.S. Geological Survey (USGS) conducted a geophysical survey to investigate the geologic evolution and estuarine sediment thickness in Grand Bay, Alabama and Mississippi. Specific objectives were to document the age and accumulation patterns of estuarine sediment to advance our understanding of sediment exchange with the adjacent marsh and sources of sediment to the coastal ocean. This investigation is part of the USGS Sea-level and Storm Impacts on Estuarine Environments ... |
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Hydrodynamic and sediment transport model of the mouth of the Columbia River, Washington and Oregon, 2020-2021
A three-dimensional hydrodynamic and sediment transport model application of the mouth of the Columbia River (MCR) was constructed using the Delft3D4 (D3D) modeling suite (Deltares, 2021) to simulate water levels, flow, waves, and sediment transport for time period of September 22, 2020, to March 10, 2021. The model was used to predict the dispersal of sediment from a submerged, nearshore berm composed of sediment that was dredged from the entrance to the MCR navigation channel and placed on the northern ... |
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Mineralogical point-count data from vibracores collected offshore central California, during field activity 2019-651-FA
This dataset includes mineralogical point-count data from sediment cores collected offshore central California in the vicinity of Morro Bay. These data were collected aboard the M/V Bold Horizon in October 2019 for use in regional hazard assessments relating to the Hosgri Fault. |
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Photographs of vibracores collected offshore central California, during field activity 2019-651-FA
This dataset includes photographs of sediment cores collected offshore central California in the vicinity of Morro Bay. These data were collected aboard the M/V Bold Horizon in October 2019 for use in regional hazard assessments relating to the Hosgri Fault. |
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Grain-size data of vibracores collected offshore central California, during field activity 2019-651-FA
This dataset includes grain-size data of sediment cores collected offshore central California in the vicinity of Morro Bay. These data were collected aboard the M/V Bold Horizon in October 2019 for use in regional hazard assessments relating to the Hosgri Fault. |
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Site description and associated GPS data collected at eleven study sites within the Grand Bay National Estuarine Research Reserve in Mississippi
Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into coastal management, including shoreline monitoring and change analysis. Geospatial shoreline data were created from a ... |
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Multi-sensor core logger (MSCL) data of vibracores collected offshore central California, during field activity 2019-651-FA
This dataset includes multi-sensor core logger (MSCL) data from sediment cores collected offshore central California in the vicinity of Morro Bay. The sediment cores were collected aboard the M/V Bold Horizon in October 2019 for use in regional hazard assessments relating to the Hosgri Fault. |
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Computed tomography (CT) scans of vibracores collected offshore central California, during field activity 2019-651-FA
This dataset includes computed tomography (CT) scans of sediment cores collected offshore central California in the vicinity of Morro Bay. These data were collected aboard the M/V Bold Horizon in October 2019 for use in regional hazard assessments relating to the Hosgri Fault. |
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Vertical land motion rates for the years 2007 to 2020 for the North and South Carolina coasts
Rates of land subsidence and uplift for the North and South Carolina coasts are derived from Sentinel-1A/B (2015-2020) and ALOS (2007-2011) synthetic aperture radar (SAR) satellites, at approximately 50-75 m resolution and mm-level precision. The data consist of vertical land motion (VLM) rates and the 1-sigma error in land motion rates and are available as csv files. |
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Grid File of Historical Bathymetric Soundings for Mississippi and Alabama Derived from National Ocean Service (NOS) Hydrographic Sheets
Hydrographic sheets (H-sheets) and nautical charts produced by the National Ocean Service (NOS) during the 1800s provide historic sounding (water depth) measurements of coastal areas. The data can be vectorized into a geographic information system (GIS), adjusted to a modern vertical datum, and converted into a digital elevation model to provide an interpretation of the historic seafloor elevation. These data were produced to provide an estimate of historical bathymetry for the Mississippi-Alabama coastal ... |
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USGS CoastCam at Tres Palmas, Rincón, Puerto Rico: Timestack Imagery and Coordinate Data
A digital video camera was installed at Tres Palmas, Rincón, Puerto Rico (PR) and faced west along the beach. Every hour during daylight hours, daily from August 27, 2019, to March 10, 2020, the camera collected raw video and produced snapshots and time-averaged image products. One such product is a "runup timestack". Runup timestacks are images created by sampling a cross-shore array of pixels from an image through time as waves propagate towards and run up a beach. Runup timestacks store the red, green, ... |
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Unvegetated to vegetated ratio of marsh units in Maine salt marshes
This data release contains coastal wetland synthesis products for the state of Maine. Metrics for resiliency, including the unvegetated to vegetated ratio (UVVR), marsh elevation, tidal range, and lifespan, are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the ... |
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Hurricane Matthew Overwash Extents
The National Assessment of Coastal Change Hazards project exists to understand and predict storm impacts to our nation's coastlines. This geospatial dataset defines the alongshore extent of overwash sediments deposited along the southeast coast of the United States from Florida to North Carolina and attributed to coastal processes during [Atlantic Basin] Hurricane Matthew, which made landfall in the U.S. on October 8, 2016. |
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Georeferenced National Ocean Service (NOS) Hydrographic Sheets for Grand Bay, Mississippi, and Surrounding Areas
Hydrographic sheets (H-sheets) and nautical charts produced by the National Ocean Service (NOS) during the 1800s provide historic sounding (water depth) measurements of coastal areas. The data can be vectorized into a geographic information system (GIS), adjusted to a modern vertical datum, and converted into a digital elevation model to provide an interpretation of the historic seafloor elevation. These data were produced to provide an estimate of historical bathymetry for the Mississippi-Alabama coastal ... |
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Mean tidal range of marsh units in Maine salt marshes
This data release contains coastal wetland synthesis products for the state of Maine. Metrics for resiliency, including the unvegetated to vegetated ratio (UVVR), marsh elevation, tidal range, and lifespan, are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the ... |
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Vertical land motion rates for the years 2007 to 2020 for the U.S. Atlantic coast
This dataset contains rates of land subsidence and uplift derived from Sentinel-1A/B (2015-2020) and ALOS (2007-2011) synthetic aperture radar (SAR) satellites, at approximately 50-75 m resolution and mm-level precision for the U.S. Atlantic coast except for the states of North and South Carolina. The data consist of vertical land motion (VLM) rates and the 1-sigma error in land motion rates and are available as csv files. Similar vertical land motion rates for North Carolina and South Carolina are ... |
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Dauphin Island Decadal Hindcast Model Inputs and Results: Initial DEM
The model input for the bathymetry and topography values resulting from a deterministic simulation at Dauphin Island, Alabama, as described in U.S. Geological Survey (USGS) Open-File Report 2019-1139 (https://doi.org/10.3133/ofr20191139), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry refer to Mickey and others (2020). |
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Dauphin Island Decadal Forecast Evolution Model Inputs and Results: Initial DEMs with and without restoration alternatives R2-R7
The model input and output of topography and bathymetry values resulting from forecast simulations of coupled modeling scenarios occurring between 2015 and 2025 at Dauphin Island, Alabama, and described in U.S. Geological Survey (USGS) Open-File Report 2020–1001 (https://doi.org/10.3133/ofr20201001), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Mickey and others (2020). |
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Lifespan of marsh units in Maine salt marshes
This data release contains coastal wetland synthesis products for the state of Maine. Metrics for resiliency, including the unvegetated to vegetated ratio (UVVR), marsh elevation, tidal range, and lifespan, are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the ... |
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Elevation of marsh units in Maine salt marshes
This data release contains coastal wetland synthesis products for the state of Maine. Metrics for resiliency, including the unvegetated to vegetated ratio (UVVR), marsh elevation, tidal range, and lifespan, are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the ... |
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Conceptual marsh units of Maine salt marshes
This data release contains coastal wetland synthesis products for the state of Maine. Metrics for resiliency, including the unvegetated to vegetated ratio (UVVR), marsh elevation, tidal range, and lifespan, are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the ... |
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2023-310-FA_trkln: Digital Chirp Subbottom Profile Trackline Data Collected During USGS Field Activity Number 2023-310-FA Offshore of Kailua, Hawaii, May 2023
From May 7-13, 2022, the U.S. Geological Survey (USGS) conducted a geologic assessment, including bathymetric mapping, near Kailua, Hawaii in support of efforts to construct an artificial coral reef offshore of Marine Corps Base Hawaii (MCBH). Geophysical data were collected as part of the Coastal Sediment Availability and Flux and Defense Advanced Research Protection Agency (DARPA) Reefense projects. This shapefile represents a line dataset of field activity number (FAN) 2023-310-FA chirp tracklines. |
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Unprocessed aerial imagery from 4-5 November 2020 CZU-fire survey of Central California.
This is a set of 11776 near-nadir aerial photogrammetric images and their derivatives, collected from CZU fire with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, preceded by the ... |
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Unprocessed aerial imagery from 26 January 2017 landslides survey of Central California.
This is a set of 4889 oblique aerial photogrammetric images and their derivatives, collected from San Francisco Bay area with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Unprocessed aerial imagery from 23 February 2017 landslides survey of Central California.
This is a set of 5954 oblique aerial photogrammetric images and their derivatives, collected from San Francisco Bay area with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, ... |
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Archive of Digitized Analog Boomer Seismic Reflection Data Collected from the Northern Gulf of Mexico: Intersea 1980
The U.S. Geological Survey (USGS) Coastal and Marine Hazards and Resources Program (CMHRP) has actively collected geophysical and sedimentological data in the northern Gulf of Mexico for several decades, including shallow subsurface data in the form of high-resolution seismic reflection profiles (HRSP). Prior to the mid-1990s most HRSP data were collected in analog format as paper rolls of continuous profiles up to 25 meters (m) long. As part of the National Geological and Geophysical Data Preservation ... |
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2023-310-FA_sol: Digital Chirp Subbottom Profile Start of Line Data Collected During USGS Field Activity Number 2023-310-FA Offshore of Kailua, Hawaii, May 2023
From May 7-13, 2022, the U.S. Geological Survey (USGS) conducted a geologic assessment, including bathymetric mapping, near Kailua, Hawaii in support of efforts to construct an artificial coral reef offshore of Marine Corps Base Hawaii (MCBH). Geophysical data were collected as part of the Coastal Sediment Availability and Flux and Defense Advanced Research Protection Agency (DARPA) Reefense projects. Geophysical data were collected as part of the Coastal Sediment Availability and Flux and Defense Advanced ... |
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2023-310-FA_shots: Digital Chirp Subbottom Profile Shotpoint Data Collected During USGS Field Activity Number 2023-310-FA Offshore of Kailua, Hawaii, May 2023
From May 7-13, 2023, the U.S. Geological Survey (USGS) conducted a geologic assessment, including bathymetric mapping, near Kailua, Hawaii in support of efforts to construct an artificial coral reef offshore of Marine Corps Base Hawaii (MCBH). Geophysical data were collected as part of the Coastal Sediment Availability and Flux and Defense Advanced Research Protection Agency (DARPA) Reefense projects. This shapefile represents a point dataset of field activity number (FAN) 2023-310-FA chirp subbottom ... |
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Archive of Chirp Subbottom Profile, Imagery, and Geospatial Data Collected in May 2023 from Oahu, Hawaii
As part of the Coastal Sediment Availability and Flux and Defense Advanced Research Protection Agency (DARPA) Reefense projects, scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) conducted a nearshore geophysical survey to map the shoreface and inner shelf, as well as characterizing stratigraphy near Oahu, Hawaii (HI) May 7-13, 2023. The purpose of this study was to conduct a geologic assessment (including bathymetric mapping) near Fort Hase Beach, ... |
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Exposure potential of marsh units to environmental health stressors in Connecticut salt marshes
This data release contains coastal wetland synthesis products for the state of Connecticut. Metrics for resiliency, including the unvegetated to vegetated ratio (UVVR), marsh elevation, tidal range, wave power, and exposure potential to environmental health stressors are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change ... |
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Land-Cover Data Derived from Landsat Satellite Imagery, Assateague Island to Metompkin Island, Maryland and Virginia, 1985 and 2015
This U.S. Geological Survey (USGS) data release includes geospatial datasets that were created to analyze wetland changes along the Virginia and Maryland Atlantic coasts between 1984 and 2015. Wetland change was determined by assessing two metrics: wetland persistence and land-cover switching. Because seasonal water levels, beach width, and vegetation differences can affect change analyses, only images acquired during the spring (March, April, and May) were included in the wetland-change metrics (N=10). ... |
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Photographs of piston and gravity cores collected in September 2019 offshore of south-central California (USGS FAN 2019-642-FA)
This dataset includes photographs of 39 piston and gravity cores that were collected as part of a groundtruthing survey in September 2019 aboard the R/V Bold Horizon. This dataset is one of several collected as part of the Bureau of Ocean Energy Management (BOEM)-funded California Deepwater Investigations and Groundtruthing (Cal DIG I) project. The purpose of the study is to assess shallow geohazards, benthic habitats, and thereby the potential for alternative energy infrastructure (namely floating wind ... |
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Multi-Sensor Core Logger (MSCL) data of piston and gravity cores collected in September 2019 offshore of south-central California (USGS FAN 2019-642-FA)
This dataset includes multi-sensor core logger (MSCL) data for 39 piston and gravity cores that were collected as part of a groundtruthing survey in September 2019 aboard the R/V Bold Horizon. This dataset is one of several collected as part of the Bureau of Ocean Energy Management (BOEM)-funded California Deepwater Investigations and Groundtruthing (Cal DIG I) project. The purpose of the study is to assess shallow geohazards, benthic habitats, and thereby the potential for alternative energy infrastructure ... |
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Lifespan of marsh units in New York salt marshes
Lifespan of salt marshes in New York are calculated using conceptual marsh units defined by Defne and Ganju (2018) and Welk and others (2019, 2020a, 2020b, 2020c). The lifespan calculation is based on estimated sediment supply and sea-level rise (SLR) predictions after Ganju and others (2020). Sea level predictions are local estimates which correspond to the 0.3, 0.5, and 1.0 meter increase in Global Mean Sea Level (GMSL) scenarios by 2100 from Sweet and others (2022). The U.S. Geological Survey has been ... |
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Elevation point clouds of the coast of Alaska from Icy Cape to Cape Prince of Wales, 2016
This part of the data release presents georeferenced elevation point clouds spanning the ocean shoreline of Alaska from Icy Cape to Cape Prince of Wales. Aerial images were collected, and data were processed, by Fairbanks Fodar (https://www.fairbanksfodar.com) in Fairbanks, Alaska, for the U.S. Geological Survey. The aerial images, from which the point clouds were derived, were collected in 2016 between August 29 and September 4 and extend from the shoreline to 400-4000 meters inland. The aerial images were ... |
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Orthoimagery of the coast of Alaska from Icy Cape to Cape Prince of Wales, 2016
This part of the data release presents orthoimagery spanning the ocean shoreline of Alaska from Icy Cape to Cape Prince of Wales. Aerial images were collected, and data were processed, by Fairbanks Fodar (https://www.fairbanksfodar.com) in Fairbanks, Alaska, for the U.S. Geological Survey. The aerial images, from which the orthoimages were created, were collected in 2016 between August 29 and September 4 and extend from the shoreline to 400-4000 meters inland. The aerial images were collected with precise ... |
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PCMSC PlaneCam – Field data from periodic and event-response surveys of the U.S. West Coast.
This is an ongoing collection of aerial oblique and near-nadir images, ancillary data, and derivatives, from aerial surveys of coastal and near-coastal environments with a crewed light aircraft using the "PCMSC PlaneCam," a mounted fixed-lens DSLR camera with an attached consumer-grade GPS for time-keeping and approximate position, and a Global Navigation Satellite System (GNSS) for precise positioning. Data are collected and produced primarily for coastal monitoring using structure-from-motion ... |
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Digital elevation models of the coast of Alaska from Icy Cape to Cape Prince of Wales, 2016
This part of the data release presents digital elevation models (DEMs) spanning the ocean shoreline of Alaska from Icy Cape to Cape Prince of Wales. Aerial images were collected, and data were processed, by Fairbanks Fodar (https://www.fairbanksfodar.com) in Fairbanks, Alaska, for the U.S. Geological Survey. The aerial images, from which the DEMs were created, were collected in 2016 between August 29 and September 4 and extend from the shoreline to 400-4000 meters inland. The aerial images were collected ... |
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2015-330-FA_trkln: Digital Chirp Subbottom Profile Trackline Data Collected During USGS Field Activity Number 2015-330-FA Offshore of Dauphin Island, Alabama, September 2015
From September 16 through 23, 2015, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport offshore of Dauphin Island, Alabama. Geophysical data were collected as part of the Alabama Barrier Island Restoration Feasibility Study. This shapefile represents a line dataset of field activity number (FAN) 2015-330-FA chirp tracklines. |
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Salish Sea water level validation simulations: 2017-2020
Simulations of water levels in the Salish Sea over the period October 1, 2016 to September 30, 2020 were conducted to validate the Salish Sea hydrodynamic model. The model accounts for sea level position, tides, remote sea-level anomalies, local winds and storm surge and stream flows as they affect water density. Comparison of modeled and measured water levels showed the model predicts extreme water levels at NOAA and USGS tide gage stations within 0.15 m. Model inputs and outputs of time-series forcing and ... |
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Salish Sea water level simulation projections: 2016-2099
Simulations of the period 2016-2099 were conducted using the Salish Sea hydrodynamic model to evaluate extreme water levels associated with anticipated changes in sea level and climate forcing. The model projections accounting for sea level position, tides, remote sea-level anomalies, local winds and storm surge and stream flows as they affect water density. Dynamically downscaled Weather Research and Forecasting (WRF) CMIP5 GFDL wind and atmospheric pressure fields were prescribed over the model open ... |
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Salish Sea water level hindcast simulations: 1985-2015
Simulatations of water levels in the Salish Sea for a continuous hindcast of the period October 1, 1985, to September 30, 2015 were conducted to evaluate the utility and skill of a sea-level anomaly predictor and to develop extreme water level estimates accounting for decadal climate variability. The model accounts for sea level position, tides, remote sea-level anomalies, local winds and storm surge and stream flows as they affect water density. Comparison of modeled and measured water levels showed the ... |
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Modeled and Observed Weekly Mean Wave Height for Validation of a Wave Exposure Model of Grand Bay, Mississippi
Coastal marshes are highly dynamic and ecologically important ecosystems that are subject to pervasive and often harmful disturbances, including shoreline erosion. Shoreline erosion can result in an overall loss of coastal marsh, particularly in estuaries with moderate- or high-wave energy. Not only can waves be important physical drivers of shoreline change, they can also influence shore-proximal vertical accretion through sediment delivery. For these reason, estimates of wave energy can provide a ... |
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2015-330-FA_sol: Digital Chirp Subbottom Profile Start of Line Data Collected During USGS Field Activity Number 2015-330-FA Offshore of Dauphin Island, Alabama, September 2015
From September 16 through 23, 2015, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport offshore of Dauphin Island, Alabama. Geophysical data were collected as part of the Alabama Barrier Island Restoration Feasibility Study. This shapefile represents a point dataset of field activity number (FAN) 2015-330-FA chirp subbottom profile start of trackline locations. |
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2015-330-FA_shots: Digital Chirp Subbottom Profile Shotpoint Data Collected During USGS Field Activity Number 2015-330-FA Offshore of Dauphin Island, Alabama, September 2015
From September 16 through 23, 2015, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport offshore of Dauphin Island, Alabama. Geophysical data were collected as part of the Alabama Barrier Island Restoration Feasibility Study. This shapefile represents a point dataset of field activity number (FAN) 2015-330-FA chirp subbottom profile 1,000-shot-interval locations. |
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Archive of Chirp Subbottom Profile, Imagery, and Geospatial Data Collected in 2015 Offshore of Dauphin Island, Alabama
From September 16 through 23, 2015, researchers from the U.S. Geological Survey (USGS) conducted an offshore geophysical survey to map the shoreface and determine Holocene stratigraphy near Dauphin Island, Alabama (AL). The Alabama Barrier Island Restoration Feasibility Study project objective includes the investigation of nearshore geologic controls on surface morphology. This publication serves as an archive of high-resolution chirp subbottom trace data, survey trackline map, navigation files, geographic ... |
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2017 USGS Lidar: Chenier Plain, LA Point Cloud files with Orthometric Vertical Datum North American Vertical Datum of 1988 (NAVD88) using GEOID12B
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2001 Gulf Coast USGS ... |
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2013 USACE NAE Topobathy Lidar: Maine Point Cloud files with Orthometric Vertical Datum North American Vertical Datum of 1988 (NAVD88) using GEOID12B
The Storm-Induced Coastal Change Hazards component of the National Assessment of Coastal Change Hazards project focuses on understanding the magnitude and variability of extreme storm impacts on sandy beaches. Lidar-derived beach morphologic features such as dune crest, toe and shoreline help define the vulnerability of the beach to storm impacts. This dataset defines the elevation and position of the seaward-most dune crest and toe and the mean high water shoreline derived from the 2001 Gulf Coast USGS ... |
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Digital surface models of Pea Island National Wildlife Refuge DUNEX Site, North Carolina in September and October 2021
The data in this part of the release are digital surface models (DSMs) that characterize the beach at the USGS DUring Nearshore Event eXperiment (DUNEX) site on Pea Island National Wildlife Refuge, NC. DUNEX is a multi-agency, academic, and non-governmental organization collaborative community experiment designed to study nearshore coastal processes during storm events. USGS participation in DUNEX will contribute new measurements and models that will increase our understanding of storm impacts to coastal ... |
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Summary statistics for the central Beaufort Sea coast, Alaska
A nested spectral wave model (Simulating Waves WAves Nearshore [SWAN]; Booij and others, 1999) was deployed for the central Beaufort Sea coast of Alaska to simulate waves for the period from 1979 to 2019. Results in the form of spatial summary statistics, describing wave parameters, wind speed and sea-ice area cover for the intermediate grid (see Overview Image on main page of data release), are provided. Further information can be found in Nederhoff and others (2021). |
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Wave model grids and bathymetry for the central Beaufort Sea coast, Alaska
The required grid and bathymetry files to run a nested spectral wave model (Simulating Waves WAves Nearshore [SWAN]; Booij and others, 1999) for the central Beaufort Sea coast of Alaska are provided. A three-level SWAN nesting grid with grid resolutions of 5000 meters, 1000 meters, and 200 meters for the overall, intermediate and detail grids, respectively (see included Browse Graphic) has been developed. For this purpose, available local bathymetry (Coastal Frontiers Corporation, 2014; Kasper and others, ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Parramore Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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Unprocessed aerial imagery from 23 January 2018 Thomas-fire survey of Southern California.
This is a set of 4838 oblique aerial photogrammetric images and their derivatives, collected from Montecito with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, preceded by the ... |
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Unprocessed aerial imagery from 19 April 2023 thomas-fire survey of Southern California.
This is a set of 3086 vertical aerial photogrammetric images and their derivatives, collected from Montecito with a fixed-lens digital camera from a crewed light aircraft, for processing using structure-from-motion photogrammetry and machine learning to study coastal geomorphic processes at high temporal and spatial resolution. JPG files in each folder follow the following naming convention: {CAM###}_{YYYYMMDDHHMMSS_ss}.jpg, where {CAM###} is the last 3 digits of the camera serial number, preceded by the ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Myrtle Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Monomoy Island, MA, 2013-2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Metompkin Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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Archive of digitized analog boomer seismic reflection data collected during USGS Cruise Kit Jones 92-1 along the Florida Shelf, July 1992
The U.S. Geological Survey (USGS) Coastal and Marine Geology Program has actively collected geophysical and sedimentological data in the northern Gulf of Mexico for several decades, including shallow subsurface data in the form of high-resolution seismic reflection profiles (HRSP). Prior to the mid-1990s most HRSP data were collected in analog format as paper rolls of continuous profiles up to 25 meters long. As part of the National Geological and Geophysical Data Preservation Program (NGGDPP; https:/ ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Fisherman Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Cobb Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Cape Lookout, NC, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Cape Hatteras, NC, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Assawoman Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Assateague Island, MD & VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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Time Series of Structure-from-Motion Products - Point Clouds: Little Dauphin Island and Pelican Island, Alabama, September 2018 to April 2019
Aerial imagery acquired with a small unmanned aircraft system (sUAS), in conjunction with surveyed ground control points (GCP) visible in the imagery, can be processed with structure-from-motion (SfM) photogrammetry techniques to produce high-resolution orthomosaics, three-dimensional (3D) point clouds and digital elevation models (DEMs). This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides UAS survey data products consisting of ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Initial_Elevations_N.txt)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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ST1_Final_DEM_metadata: Dauphin Island Decadal Forecast Evolution Model Inputs and Results: Final DEMs without restoration alternative for storminess bins (ST1-ST4) and sea level rise scenarios (SL1-SL3).
The model input and output of topography and bathymetry values resulting from forecast simulations of coupled modeling scenarios occurring between 2015 and 2025 at Dauphin Island, Alabama, and described in U.S. Geological Survey (USGS) Open-File Report 2020–1001 (https://doi.org/10.3133/ofr20201001), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Mickey and others (2020). |
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ST2_Final_DEM_metadata: Dauphin Island Decadal Forecast Evolution Model Inputs and Results: Final DEMs without restoration measures for storminess bins (ST1-ST4) and sea level rise scenarios (SL1-SL3).
The model input and output of topography and bathymetry values resulting from forecast simulations of coupled modeling scenarios occurring between 2015 and 2025 at Dauphin Island, Alabama, and described in U.S. Geological Survey (USGS) Open-File Report 2020–1001 (https://doi.org/10.3133/ofr20201001), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Mickey and others (2020). |
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ST3_Final_DEM_metadata: Dauphin Island Decadal Forecast Evolution Model Inputs and Results: Final DEMs without restoration alternative for storminess bins (ST1-ST4) and sea level rise scenarios (SL1-SL3).
The model input and output of topography and bathymetry values resulting from forecast simulations of coupled modeling scenarios occurring between 2015 and 2025 at Dauphin Island, Alabama, and described in U.S. Geological Survey (USGS) Open-File Report 2020–1001 (https://doi.org/10.3133/ofr20201001), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Mickey and others (2020). |
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ST4_Final_DEM_metadata: Dauphin Island Decadal Forecast Evolution Model Inputs and Results: Final DEMs without restoration alternative for storminess bins (ST1-ST4) and sea level rise scenarios (SL1-SL3).
The model input and output of topography and bathymetry values resulting from forecast simulations of coupled modeling scenarios occurring between 2015 and 2025 at Dauphin Island, Alabama, and described in U.S. Geological Survey (USGS) Open-File Report 2020–1001 (https://doi.org/10.3133/ofr20201001), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Mickey and others (2020). |
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USGS CoastCam at DUNEX: Intrinsic and Extrinsic Calibration Data (Camera 1)
Two digital video cameras were temporarily installed at the U.S. Fish and Wildlife Service (USFWS) Pea Island National Wildlife Refuge (PINWR) in North Carolina (NC), as part of the DUring Nearshore Event eXperiment (DUNEX). DUNEX was a collaborative community-led experiment that took place in the fall of 2021 along the Outer Banks of NC, with the goal of improving the understanding, observational techniques, and predictive capabilities for extreme storm processes and impacts within the coastal environment. ... |
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Lifespan of marsh units in Connecticut salt marshes
The lifespans of salt marshes in Connecticut are calculated based on estimated sediment supply and sea-level rise (SLR) predictions, following the methodology of Ganju and others (2020). The salt marsh delineations are from Ackerman and others (2023). The SLR predictions are local estimates corresponding to increases of 0.3, 0.5 and 1.0 meter in global mean sea level (GMSL) by 2100, as projected by Sweet and others (2022). This work has been a part of the USGS’s effort to expand the national assessment of ... |
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Lifespan of marsh units in Eastern Shore of Virginia salt marshes
The lifespans of salt marshes in Atlantic-facing Eastern Shore of Virginia are calculated based on estimated sediment supply and sea-level rise (SLR) predictions, following the methodology of Ganju and others (2020). The salt marsh delineations are from Ackerman and others (2023). The SLR predictions are local estimates corresponding to increases of 0.3, 0.5 and 1.0 meter in global mean sea level (GMSL) by 2100, as projected by Sweet and others (2022). This work has been a part of the USGS’s effort to ... |
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Conceptual marsh units of Blackwater salt marsh complex, Chesapeake Bay, Maryland
This data release contains coastal wetland synthesis products for the geographic region of Blackwater, Chesapeake Bay, Maryland. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and others, are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to ... |
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Unvegetated to vegetated ratio of marsh units in north shore Long Island salt marsh complex, New York
This data release contains coastal wetland synthesis products for the geographic region of north shore Long Island, New York. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been ... |
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Core descriptions and sedimentologic data from vibracores collected in 2021 from Central Florida Gulf Coast Barrier Islands
In 2021, scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS SPCMSC) conducted ground penetrating radar (GPR) and sediment sampling surveys on barrier islands located along the central Florida Gulf Coast (CFGC), Pinellas County, Florida (FL). This study investigated the past evolution of the CFGC from field sites at Anclote Keys, Caladesi and Honeymoon Islands, and Fort DeSoto to quantify changes that occurred along these barrier systems prior to the 20th ... |
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Archive of Chirp Sub-Bottom Profile, Imagery, and Navigational Data Collected During USGS Field Activity Numbers 2021-326-FA and 2022-326-FA in 2021 and 2022 from Duck, North Carolina
In June/December 2021 and July 2022, the U.S. Geological Survey (USGS) and U.S. Army Corps of Engineers, Engineer Research and Development Center (USACE-ERDC) conducted repeat, nearshore geologic assessments, including bathymetric mapping, near Duck, North Carolina (NC). This work was performed in support of efforts to map the shoreface, characterize stratigraphy, and investigate changes in seafloor elevations near the USACE Field Research Facility and to measure the co-evolution of the morphology and ... |
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Geospatial Navigational Data Associated with Chirp Sub-Bottom Profiles Collected During USGS Field Activity Numbers 2021-326-FA and 2022-326-FA in 2021 and 2022 from Duck, North Carolina
In June/December 2021 and July 2022, the U.S. Geological Survey (USGS) and U.S. Army Corps of Engineers, Engineer Research and Development Center (USACE-ERDC) conducted repeat, nearshore geologic assessments, including bathymetric mapping, near Duck, North Carolina (NC). This work was performed in support of efforts to map the shoreface, characterize stratigraphy, and investigate changes in seafloor elevations near the USACE Field Research Facility and to measure the co-evolution of the morphology and ... |
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Archive of Chirp Sub-Bottom Profile, Imagery, and Navigational Data Collected in June and July 2014 from Fire Island, New York
During June 15-23 and July 10-12, 2014, the U.S. Geological Survey (USGS) conducted a nearshore geologic assessment, including bathymetric mapping, along Fire Island, New York (NY). This work was conducted in support of efforts to map the shoreface, characterize stratigraphy, and investigate changes in seafloor elevations near Fire Island, NY to assess the impacts of Hurricane Sandy to the area in October 2012. Geophysical data were collected as part of the Hurricane Sandy Supplemental Project (GS2-2B). The ... |
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Exposure potential of marsh units to environmental health stressors in north shore Long Island salt marsh complex, New York
This data release contains coastal wetland synthesis products for the geographic region of north shore Long Island, New York. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been ... |
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Geospatial Navigational Data Associated with Chirp Sub-Bottom Profiles Collected During USGS Field Activity Number 2014-303-FA in June and July 2014 from Fire Island, New York
During June 15-23 and July 10-12, 2014, the U.S. Geological Survey (USGS) conducted a nearshore geologic assessment, including bathymetric mapping, along Fire Island, New York (NY). This work was conducted in support of efforts to map the shoreface, characterize stratigraphy, and investigate changes in seafloor elevations near Fire Island, NY to assess the impacts of Hurricane Sandy to the area in October 2012. Geophysical data were collected as part of the Hurricane Sandy Supplemental Project (GS2-2B). The ... |
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Mean tidal range in marsh units of Assateague Island National Seashore and Chincoteague Bay, Maryland and Virginia
Biomass production is positively correlated with mean tidal range in salt marshes along the Atlantic coast of the United States of America. Recent studies support the idea that enhanced stability of the marshes can be attributed to increased vegetative growth due to increased tidal range. This dataset displays the spatial variation of mean tidal range (i.e. Mean Range of Tides, MN) in the Assateague Island National Seashore and Chincoteague Bay based on conceptual marsh units defined by Defne and Ganju ... |
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Unvegetated to vegetated ratio of marsh units in Hudson Valley and New York City salt marsh complex, New York
This data release contains coastal wetland synthesis products for the geographic region of Hudson Valley and New York City, New York. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey ... |
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Exposure potential of marsh units to environmental health stressors in Hudson Valley and New York City salt marsh complex, New York
This data release contains coastal wetland synthesis products for the geographic region of Hudson Valley and New York City, New York. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey ... |
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2022-334-FA_trkln: Digital Chirp Subbottom Profile Trackline Data Collected During USGS Field Activity Number 2022-334-FA Offshore of Boca Chica Key, Florida, November 2022
From November 8-13, 2022, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport offshore of Boca Chica Key, Florida (FL). Geophysical data were collected as part of the Coastal Sediment Availability and Flux and Defense Advanced Research Protection Agency (DARPA) Reefense projects. This shapefile represents a line dataset of field activity number (FAN) 2022-334-FA chirp tracklines. |
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Laboratory Observations of Oscillatory Flow Over Sand Ripples: Image Metadata
These data comprise laboratory observations of oscillatory flows over mobile sand ripples. The data were collected January 6-7, 2016, in the small-oscillatory flow tunnel (S-OFT) in the Sediment Dynamics Laboratory at the U.S. Naval Research Laboratory (NRL), Stennis Space Center, Mississippi (MS), while Donya Frank-Gilchrist was a National Research Council post-doctoral fellow there. The flow tunnel has a 2-m long acrylic test section which was filled with coarse quartz sand. A piston and flywheel were ... |
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Laboratory Observations of Oscillatory Flow Over Sand Ripples: Velocity Metadata
These data comprise laboratory observations of oscillatory flows over mobile sand ripples. The data were collected January 6-7, 2016, in the small-oscillatory flow tunnel (S-OFT) in the Sediment Dynamics Laboratory at the U.S. Naval Research Laboratory (NRL), Stennis Space Center, Mississippi (MS), while Donya Frank-Gilchrist was a National Research Council post-doctoral fellow there. The flow tunnel has a 2-m long acrylic test section which was filled with coarse quartz sand. A piston and flywheel were ... |
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Mean tidal range of marsh units in north shore Long Island salt marsh complex, New York
This data release contains coastal wetland synthesis products for the geographic region of north shore Long Island, New York. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been ... |
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AllCases_Final_Bed_Elevations: Model Sensitivity to Sediment Parameters and Bed Composition in Delft3D: Model Output
The sensitivity to sediment parameterization and initial bed configuration on sediment transport processes and morphological evolution are assessed through process-based numerical modeling. Six sensitivity cases using a previously validated model for Dauphin Island, Alabama) are modeled using Delft3D (developed by Deltares) to understand impacts on bed level morphology, barrier island evolution, and sediment fluxes. Delft3D model output of suspended and bedload sediment fluxes, and final bed levels data are ... |
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AllCases_Sediment_Fluxes: Model Sensitivity to Sediment Parameters and Bed Composition in Delft3D: Model Output
The sensitivity to sediment parameterization and initial bed configuration on sediment transport processes and morphological evolution are assessed through process-based numerical modeling. Six sensitivity cases using a previously validated model for Dauphin Island, Alabama were modeled using Delft3D (developed by Deltares) to understand impacts on bed level morphology, barrier island evolution, and sediment fluxes. Delft3D model output of suspended and bedload sediment fluxes, and final bed levels data are ... |
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2022-334-FA_sol: Digital Chirp Subbottom Profile Start of Line Data Collected During USGS Field Activity Number 2022-334-FA Offshore of Boca Chica Key, Florida, November 2022
From November 8-13, 2022, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport offshore of Boca Chica Key, Florida (FL). Geophysical data were collected as part of the Coastal Sediment Availability and Flux and Defense Advanced Research Protection Agency (DARPA) Reefense projects. This shapefile represents a point dataset of field activity number (FAN) 2022-334-FA chirp subbottom profile start of trackline ... |
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Georeferenced Scans of National Oceanic and Atmospheric Administration (NOAA) T-Sheets Collected Along the New Jersey Coastline from 1839-1875
Historical shoreline surveys were conducted by the National Ocean Service (NOS), dating back to the early 1800s. The maps resulting from these surveys, often called t-sheets, provide a reference of historical shoreline position that can be compared to modern data to identify shoreline change. The t-sheets are stored at the National Archives and many have been scanned by the National Oceanic and Atmospheric Administration (NOAA) and are available on the NOAA Shoreline Web site (http://www.shoreline.noaa.gov ... |
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Mean tidal range of marsh units in Hudson Valley and New York City salt marsh complex, New York
This data release contains coastal wetland synthesis products for the geographic region of Hudson Valley and New York City, New York. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey ... |
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Upland boundary lines, points, and transects with rates for the Point Aux Chenes and Grand Bay Estuaries in Mississippi and Alabama from 1848 to 2022
This dataset represents a compilation of vector upland boundary lines, upland boundary points, and transects with rates for the Point Aux Chenes and Grand Bay estuaries (Mississippi and Alabama) for 1848, 1957/1958 (henceforth referred to as 1957), and 2019/2022 (henceforth referred to as 2022). Upland data were obtained from multiple data sources, including the National Oceanic and Atmospheric Administration (NOAA) topographic sheets (t-sheets) and WorldView 2 satellite imagery. Regardless of the source, ... |
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Elevation of marsh units in Hudson Valley and New York City salt marsh complex, New York
This data release contains coastal wetland synthesis products for the geographic region of Hudson Valley and New York City, New York. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey ... |
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Sedimentologic Data from Vibracores Collected in 2023 from St. Andrew Bay, Florida
In April 2023, scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) collected 11 sediment vibracores within East Bay, St. Andrew Bay, Florida (FL). Sediment vibracore and lithology data in this data release provide assessments on the composition and age of sediments below the seafloor. |
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Porewater chloride and sulfate concentrations from piston and gravity cores collected in September 2019 offshore of south-central California (USGS FAN 2019-642-FA)
This dataset includes concentrations chloride and sulfate in porewater from piston and gravity cores collected in September 2019 offshore of south-central California aboard the R/V Bold Horizon. This dataset is one of several collected as part of the Bureau of Ocean Energy Management (BOEM)-funded California Deepwater Investigations and Groundtruthing (Cal DIG I) project. The purpose of the study is to assess shallow geohazards, benthic habitats, and thereby the potential for alternative energy ... |
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Location and depth data for vibracores collected during a Monterey Bay Aquarium Research Institute cruise in February 2019 offshore of south-central California (USGS FAN 2019-603-FA)
This dataset includes the location and depth information for 49 vibracores that were collected by the Monterey Bay Aquarium Research Institute (MBARI) in February 2019 aboard the R/V Western Flyer using the remotely operated vehicle (ROV) Doc Ricketts. The collection of these cores was funded entirely by MBARI, and the cores have been donated to the U.S. Geological Survey (USGS). The cores were collected in collaboration with the USGS and the Bureau of Ocean Energy Management (BOEM) and are located in the ... |
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Photographs of vibracores collected during a Monterey Bay Aquarium Research Institute cruise in February 2019 offshore of south-central California (USGS FAN 2019-603-FA)
This dataset includes photographs of 49 vibracores that were collected by the Monterey Bay Aquarium Research Institute (MBARI) in February 2019 aboard the R/V Western Flyer using the remotely operated vehicle (ROV) Doc Ricketts. The collection of these cores was funded entirely by MBARI, and the cores have been donated to the U.S. Geological Survey (USGS). The cores were collected in collaboration with the USGS and the Bureau of Ocean Energy Management (BOEM) and are located in the same study area as the ... |
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Location data for vibracores collected during a Monterey Bay Aquarium Research Institute cruise in November 2019 offshore of south-central California (USGS FAN 2019-667-FA)
This dataset includes the location information for 49 vibracores that were collected by the Monterey Bay Aquarium Research Institute (MBARI) in November 2019 aboard the R/V Western Flyer using the remotely operated vehicle (ROV) Doc Ricketts. The collection of these cores was funded entirely by MBARI, and the cores have been donated to the U.S. Geological Survey (USGS). The cores were collected in collaboration with the USGS and the Bureau of Ocean Energy Management (BOEM) and are located in the same study ... |
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Photographs of vibracores collected during a Monterey Bay Aquarium Research Institute cruise in November 2019 offshore of south-central California (USGS FAN 2019-667-FA)
This dataset includes photographs of 49 vibracores that were collected by the Monterey Bay Aquarium Research Institute (MBARI) in November 2019 aboard the R/V Western Flyer using the remotely operated vehicle (ROV) Doc Ricketts. The collection of these cores was funded entirely by MBARI, and the cores have been donated to the U.S. Geological Survey (USGS). The cores were collected in collaboration with the USGS and the Bureau of Ocean Energy Management (BOEM) and are located in the same study area as the ... |
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Water level and velocity measurements from the 2012 University of Western Australia Fringing Reef Experiment (UWAFRE)
This data release contains water level and velocity measurements from wave runup experiments performed in a laboratory flume setting. Wave-driven water level variability (and runup at the shoreline) is a significant cause of coastal flooding induced by storms. Wave runup is challenging to predict, particularly along tropical coral reef-fringed coastlines due to the steep bathymetric profiles and large bottom roughness generated by reef organisms. The 2012 University of Western Australia Fringing Reef ... |
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Conceptual marsh units of Hudson Valley and New York City salt marsh complex, New York
This data release contains coastal wetland synthesis products for the geographic region of Hudson Valley and New York City, New York. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey ... |
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Elevation of marsh units in north shore Long Island salt marsh complex, New York
This data release contains coastal wetland synthesis products for the geographic region of north shore Long Island, New York. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been ... |
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Conceptual marsh units of north shore Long Island salt marsh complex, New York
This data release contains coastal wetland synthesis products for the geographic region of north shore Long Island, New York. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been ... |
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Conceptual marsh units for Plum Island Estuary and Parker River salt marsh complex, Massachusetts
The salt marsh complex of Plum Island Estuary and Parker River (PIEPR) was delineated to smaller, conceptual marsh units by geoprocessing of surface elevation data. Flow accumulation based on the relative elevation of each location was used to determine the ridge lines that separate each marsh unit while the surface slope was used to automatically assign each unit a drainage point, where water is expected to drain through. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. ... |
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2022-334-FA_shots: Digital Chirp Subbottom Profile Shotpoint Data Collected During USGS Field Activity Number 2022-334-FA Offshore of Boca Chica Key, Florida, November 2022
From November 8-13, 2022, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport offshore of Boca Chica Key, Florida (FL). Geophysical data were collected as part of the Coastal Sediment Availability and Flux and Defense Advanced Research Protection Agency (DARPA) Reefense projects. This shapefile represents a point dataset of field activity number (FAN) 2022-334-FA chirp subbottom profile 1,000-shot-interval ... |
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Hydrodynamic and Sediment Transport Model Application for OSAT3 Guidance: Locations of convergences in the maximum alongshore current
The U.S. Geological Survey has developed a method for estimating the mobility and potential alongshore transport of heavier-than-water sand and oil agglomerates (tarballs or surface residual balls, SRBs). During the Deepwater Horizon spill, some oil that reached the surf zone of the northern Gulf of Mexico mixed with suspended sediment and sank to form sub-tidal mats. If not removed, these mats can break apart to form SRBs and subsequently re-oil the beach. A method was developed for estimating SRB ... |
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Lifespan of marsh units in Assateague Island National Seashore and Chincoteague Bay, Maryland and Virginia
The sediment-based lifespan of salt marsh units in Assateague Island National Seashore (ASIS) and Chincoteague Bay is shown for conceptual marsh units defined by Defne and Ganju (2018). The lifespan represents the timescale by which the current sediment mass within a marsh parcel can no longer compensate for sediment export and deficits induced by sea-level rise. The lifespan calculation is based on vegetated cover, marsh elevation, sediment supply, and sea-level rise (SLR) predictions after Ganju and ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Wreck Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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Hydrodynamic and Sediment Transport Model Application for OSAT3 Guidance: Locations of decelerations in the direction of flow in the maximum alongshore current
The U.S. Geological Survey has developed a method for estimating the mobility and potential alongshore transport of heavier-than-water sand and oil agglomerates (tarballs or surface residual balls, SRBs). During the Deepwater Horizon spill, some oil that reached the surf zone of the northern Gulf of Mexico mixed with suspended sediment and sank to form sub-tidal mats. If not removed, these mats can break apart to form SRBs and subsequently re-oil the beach. A method was developed for estimating SRB ... |
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Hydrodynamic and Sediment Transport Model Application for OSAT3 Guidance: Ratio of wave- and current-induced shear stress to critical values for oil-sand ball and sediment mobilization
The U.S. Geological Survey has developed a method for estimating the mobility and potential alongshore transport of heavier-than-water sand and oil agglomerates (tarballs or surface residual balls, SRBs). During the Deepwater Horizon spill, some oil that reached the surf zone of the northern Gulf of Mexico mixed with suspended sediment and sank to form sub-tidal mats. If not removed, these mats can break apart to form SRBs and subsequently re-oil the beach. A method was developed for estimating SRB ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Smith Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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Hydrodynamic and Sediment Transport Model Application for OSAT3 Guidance: Surf-zone integrated alongshore potential flux for oil-sand balls
The U.S. Geological Survey has developed a method for estimating the mobility and potential alongshore transport of heavier-than-water sand and oil agglomerates (tarballs or surface residual balls, SRBs). During the Deepwater Horizon spill, some oil that reached the surf zone of the northern Gulf of Mexico mixed with suspended sediment and sank to form sub-tidal mats. If not removed, these mats can break apart to form SRBs and subsequently re-oil the beach. A method was developed for estimating SRB ... |
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Hydrodynamic and Sediment Transport Model Application for OSAT3 Guidance: wave direction
The U.S. Geological Survey has developed a method for estimating the mobility and potential alongshore transport of heavier-than-water sand and oil agglomerates (tarballs or surface residual balls, SRBs). During the Deepwater Horizon spill, some oil that reached the surf zone of the northern Gulf of Mexico mixed with suspended sediment and sank to form sub-tidal mats. If not removed, these mats can break apart to form SRBs and subsequently re-oil the beach. A method was developed for estimating SRB ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Ship Shoal Island, VA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Rhode Island National Wildlife Refuge, RI, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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Hydrodynamic and Sediment Transport Model Application for OSAT3 Guidance: Significant wave height
The U.S. Geological Survey has developed a method for estimating the mobility and potential alongshore transport of heavier-than-water sand and oil agglomerates (tarballs or surface residual balls, SRBs). During the Deepwater Horizon spill, some oil that reached the surf zone of the northern Gulf of Mexico mixed with suspended sediment and sank to form sub-tidal mats. If not removed, these mats can break apart to form SRBs and subsequently re-oil the beach. A method was developed for estimating SRB ... |
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Hydrodynamic and Sediment Transport Model Application for OSAT3 Guidance: peak wave period
The U.S. Geological Survey has developed a method for estimating the mobility and potential alongshore transport of heavier-than-water sand and oil agglomerates (tarballs or surface residual balls, SRBs). During the Deepwater Horizon spill, some oil that reached the surf zone of the northern Gulf of Mexico mixed with suspended sediment and sank to form sub-tidal mats. If not removed, these mats can break apart to form SRBs and subsequently re-oil the beach. A method was developed for estimating SRB ... |
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Scenarios_Grid
The U.S. Geological Survey has developed a method for estimating the mobility and potential alongshore transport of heavier-than-water sand and oil agglomerates (tarballs or surface residual balls, SRBs). During the Deepwater Horizon spill, some oil that reached the surf zone of the northern Gulf of Mexico mixed with suspended sediment and sank to form sub-tidal mats. If not removed, these mats can break apart to form SRBs and subsequently re-oil the beach. A method was developed for estimating SRB ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Parker River, MA, 2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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Tidal_Grid
The U.S. Geological Survey has developed a method for estimating the mobility and potential alongshore transport of heavier-than-water sand and oil agglomerates (tarballs or surface residual balls, SRBs). During the Deepwater Horizon spill, some oil that reached the surf zone of the northern Gulf of Mexico mixed with suspended sediment and sank to form sub-tidal mats. If not removed, these mats can break apart to form SRBs and subsequently re-oil the beach. A method was developed for estimating SRB ... |
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Hydrodynamic and Sediment Transport Model Application for OSAT3 Guidance: Ratio of the wave- and current-induced shear stress to the critical value for oil-tar balls and sediment mobilization over a tidal cycle
The U.S. Geological Survey has developed a method for estimating the mobility and potential alongshore transport of heavier-than-water sand and oil agglomerates (tarballs or surface residual balls, SRBs). During the Deepwater Horizon spill, some oil that reached the surf zone of the northern Gulf of Mexico mixed with suspended sediment and sank to form sub-tidal mats. If not removed, these mats can break apart to form SRBs and subsequently re-oil the beach. A method was developed for estimating SRB ... |
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Elevation of marsh units in Assateague Island National Seashore and Chincoteague Bay, Maryland and Virginia
Elevation distribution in the Assateague Island National Seashore (ASIS) salt marsh complex and Chincoteague Bay is given in terms of mean elevation of conceptual marsh units defined by Defne and Ganju (2018). The elevation data is based on the 1-meter resolution Coastal National Elevation Database (CoNED). Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal ... |
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Unvegetated to vegetated marsh ratio in Jamaica Bay to western Great South Bay salt marsh complex, New York
This data release contains coastal wetland synthesis products for the geographic region from Jamaica Bay to western Great South Bay, located in southeastern New York State. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy ... |
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Hydrodynamic and Sediment Transport Model Application for OSAT3 Guidance: Ratio of the wave- and current-induced shear stress to the critical value for oil-tar balls and sediment mobilization weighted by probability of wave scenario occurrence
The U.S. Geological Survey has developed a method for estimating the mobility and potential alongshore transport of heavier-than-water sand and oil agglomerates (tarballs or surface residual balls, SRBs). During the Deepwater Horizon spill, some oil that reached the surf zone of the northern Gulf of Mexico mixed with suspended sediment and sank to form sub-tidal mats. If not removed, these mats can break apart to form SRBs and subsequently re-oil the beach. A method was developed for estimating SRB ... |
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Hydrodynamic and Sediment Transport Model Application for OSAT3 Guidance: Surf-zone integrated alongshore potential flux for oil-sand balls of varying sizes weighted by probability of wave scenario occurrence
The U.S. Geological Survey has developed a method for estimating the mobility and potential alongshore transport of heavier-than-water sand and oil agglomerates (tarballs or surface residual balls, SRBs). During the Deepwater Horizon spill, some oil that reached the surf zone of the northern Gulf of Mexico mixed with suspended sediment and sank to form sub-tidal mats. If not removed, these mats can break apart to form SRBs and subsequently re-oil the beach. A method was developed for estimating SRB ... |
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Mean tidal range in marsh units of Jamaica Bay to western Great South Bay salt marsh complex, New York
This data release contains coastal wetland synthesis products for the geographic region from Jamaica Bay to western Great South Bay, located in southeastern New York State. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy ... |
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HATTERAS_INDEX - Hatteras Island, North Carolina (geographic, WGS84)
The shoreline of Cape Hatteras, North Carolina, is experiencing long-term coastal erosion. In order to better understand and monitor the changing coastline, historical aerial imagery is used to map shoreline change. For the area of Hatteras Island from Cape Point to Oregon Inlet, fourteen aerial datasets from 1978-2002 were scanned and georeferenced for use in a Geographic Information System (GIS). Shoreline positions (high water line) were digitized from georeferenced imagery. The shoreline vectors were ... |
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Unvegetated to vegetated marsh ratio in Fire Island National Seashore and central Great South Bay salt marsh complex, New York
Unvegetated to vegetated marsh ratio (UVVR) in the Fire Island National Seashore and central Great South Bay salt marsh complex, is computed based on conceptual marsh units defined by Defne and Ganju (2018). UVVR was calculated based on U.S. Department of Agriculture National Agriculture Imagery Program (NAIP) 1-meter resolution imagery. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been expanding national assessment of coastal change hazards and ... |
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Elevation of marsh units in Jamaica Bay to western Great South Bay salt marsh complex, New York
This data release contains coastal wetland synthesis products for the geographic region from Jamaica Bay to western Great South Bay, located in southeastern New York State. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy ... |
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Geomorphic habitat units derived from 2009 aerial imagery and elevation data for the Elwha River estuary, Washington
Estuary geomorphic units delineated at a scale of 1:1500 using a combination of (a) 11 September 2009 1 meter resolution NAIP aerial imagery; and (b) elevation-colored and hillshaded digital elevation models from USGS backpack/jetski topobathy surveys (17 September 2009) for areas < MHHW and aerial lidar surveys (4-6 April 2009) for elevations > MHHW. |
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Geomorphic habitat units derived from 2011 aerial imagery and elevation data for the Elwha River estuary, Washington
Estuary geomorphic units delineated at a scale of 1:1500 using a combination of (a) 03 September 2011* 0.3 meter resolution Microsoft/Digital Globe aerial imagery; and (b) elevation-colored and hillshaded digital elevation models from USGS backpack/jetski topobathy surveys (25 August 2011) for areas < MHHW and aerial lidar surveys (13-15 April 2012) for elevations > MHHW. *Image date of 3-Sep-11 corrected in metadata. During product generation the imagery date was believed to be 8-25-2011, as reported by ... |
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Geomorphic habitat units derived from 2012 aerial imagery and elevation data for the Elwha River estuary, Washington
Estuary geomorphic units delineated at a scale of 1:1500 using a combination of (a) 30 August 2012 0.15 meter resolution NPS Elwha PlaneCam aerial imagery; and (b) elevation-colored and hillshaded digital elevation models from USGS backpack/jetski topobathy surveys (28 August 2012) for areas < MHHW and aerial lidar surveys (17 October 2012) for elevations > MHHW. |
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Geomorphic habitat units derived from 2013 aerial imagery and elevation data for the Elwha River estuary, Washington
Estuary geomorphic units delineated at a scale of 1:1500 using a combination of (a) 26 August 2013 0.15 meter resolution NPS Elwha PlaneCam aerial imagery; and (b) elevation-colored and hillshaded digital elevation models from USGS backpack/jetski topobathy surveys (16 September 2013) for areas < MHHW and aerial lidar surveys (17 October 2012) supplemented with NPS Elwha PlaneCam SfM photogrammetry data (19 September 2013) for elevations > MHHW. |
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Geomorphic habitat units derived from 2014 aerial imagery and elevation data for the Elwha River estuary, Washington
Estuary geomorphic units delineated at a scale of 1:1500 using a combination of (a) 28 August 2014 0.15 meter resolution NPS Elwha PlaneCam aerial imagery; and (b) elevation-colored and hillshaded digital elevation models from USGS backpack/jetski topobathy surveys (5-8 September 2014) for areas < MHHW and aerial lidar surveys (7 November 2014) supplemented with NPS Elwha PlaneCam SfM photogrammetry data (30 September 2014) for elevations > MHHW. |
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Vegetation habitat units derived from 2009 aerial imagery and field data for the Elwha River estuary, Washington
Estuary vegetation cover delineated from 11 September 2009 1-meter-resolution NAIP aerial imagery at a scale of 1:1500. |
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Vegetation habitat units derived from 2011 aerial imagery and field data for the Elwha River estuary, Washington
Estuary vegetation cover delineated from 3 September 2011* 0.3-meter-resolution aerial imagery (Microsoft/Digital Globe) at a scale of 1:1500. *Image date of 3-Sep corrected in metadata. During product generation the imagery date was believed to be 8-25-2011, as reported by DigitalGlobe reseller. |
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Vegetation habitat units derived from 2012 aerial imagery and field data for the Elwha River estuary, Washington
Estuary vegetation cover delineated from 30 August 2012 0.15-meter-resolution NPS Elwha PlaneCam aerial imagery at a scale of 1:1500. |
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Vegetation habitat units derived from 2013 aerial imagery and field data for the Elwha River estuary, Washington
Estuary vegetation cover delineated from 26 August 2013 0.15-meter-resolution NPS Elwha PlaneCam aerial imagery at a scale of 1:1500. |
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Vegetation habitat units derived from 2014 aerial imagery and field data for the Elwha River estuary, Washington
Estuary vegetation cover delineated from 28 August 2014 0.15-meter-resolution NPS Elwha PlaneCam aerial imagery at a scale of 1:1500. |
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Atlantic and Gulf coast sandy coastline topo-bathy profile and characteristic database
Seamless topographic-bathymetric (topo-bathy) profiles and their derived morphologic characteristics were developed for sandy coastlines along the Atlantic and Gulf coasts of the United States. As such, the rocky coasts of Maine, the coral reefs in southern Florida and the Keys, and the marsh coasts in the Mississippi Delta and the Florida Big Bend region and are not included in this dataset. Topographic light detection and ranging (lidar) data (dune crest, dune toe, and shorelines) from Doran and others ... |
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Dynamically downscaled future wave projections from SWAN model results for the main Hawaiian Islands
Projected wave climate trends from WAVEWATCH3 model output were used as input for nearshore wave models (for example, SWAN) for the main Hawaiian Islands to derive data and statistical measures (mean and top 5 percent values) of wave height, wave period, and wave direction for the recent past (1996-2005) and future projections (2026-2045 and 2085-2100). Three-hourly global climate model (GCM) wind speed and wind direction output from four different GCMs provided by the Coupled Model Inter-Comparison Project ... |
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HyCReWW database: A hybrid coral reef wave and water level metamodel
We developed the HyCReWW metamodel to predict wave run-up under a wide range of coral reef morphometric and offshore forcing characteristics. Due to the complexity and high dimensionality of the problem, we assumed an idealized one-dimensional reef profile, characterized by seven primary parameters. XBeach Non-Hydrostatic was chosen to create the synthetic dataset and Radial Basis Functions implemented in Matlab were chosen for interpolation. Results demonstrate the applicability of the metamodel to obtain ... |
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Dauphin Island Decadal Forecast Evolution Model Inputs and Results: Final DEMs with restoration alternative 2 that extends Pelican Island simulated with ST2_SL1 and ST3_SL3 scenarios
The model input and output of topography and bathymetry values resulting from forecast simulations of coupled modeling scenarios occurring between 2015 and 2025 at Dauphin Island, Alabama, and described in U.S. Geological Survey (USGS) Open-File Report 2020–1001 (https://doi.org/10.3133/ofr20201001), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Mickey and others (2020). |
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Polygon shapefiles attributed with morphometric information for barrier islands and spits located along the north coast of Alaska between Cape Beaufort and the U.S.-Canadian border, 1947 to 2019
A suite of morphological metrics were derived from existing shoreline and elevation datasets for barrier islands and spits located along the north-slope coast of Alaska between Cape Beaufort and the U.S.-Canadian border. This dataset includes barrier polygons attributed with morphological metrics from five time periods: 1950s, 1980s, 2000s, 2010s, and 2020s. |
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Dauphin Island Decadal Forecast Evolution Model Inputs and Results: Final DEMs with restoration alternative 3 that extends Pelican Island simulated with ST2_SL1 and ST3_SL3 scenarios
The model input and output of topography and bathymetry values resulting from forecast simulations of coupled modeling scenarios occurring between 2015 and 2025 at Dauphin Island, Alabama, and described in U.S. Geological Survey (USGS) Open-File Report 2020–1001 (https://doi.org/10.3133/ofr20201001), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Mickey and others (2020). |
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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 1967-10-18
Presented here is a point cloud produced by the U.S. Geological Survey (USGS) from historical U.S. Air Force vertical aerial imagery, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was downloaded from USGS Eros Data Center and processed using structure-from-motion ... |
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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2017-03-08
Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion ... |
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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2017-05-19
Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point Cloud Coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion ... |
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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2017-05-27
Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion ... |
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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2017-06-13
Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion ... |
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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2017-06-26
Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion ... |
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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2017-10-12
Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using a UAS-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. The point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Ricoh GR camera in DNG format and processed using structure-from-motion photogrammetry ... |
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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2017-12-07
Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using a UAS-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. Point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Ricoh GR camera in DNG format and processed using structure-from-motion photogrammetry with ... |
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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2017-12-21
Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. Point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion ... |
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Structure-from-motion point cloud of Mud Creek, Big Sur, California, 2018-01-29
Presented here is a point cloud collected by the U.S. Geological Survey (USGS) using an oblique plane-mounted camera system, covering the area of the Mud Creek landslide on California State Route 1 (SR1), Mud Creek, Big Sur, California. Point cloud is referenced to previously published lidar data and contains RGB information as well as XYZ. Point cloud coordinates are in NAD83 UTM Zone 10 meters. Imagery was collected with a Nikon D800 camera in RAW format and processed using structure-from-motion ... |
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Dauphin Island Decadal Forecast Evolution Model Inputs and Results: Final DEMs with restoration alternative 4 that extends Pelican Island simulated with ST2_SL1 and ST3_SL3 scenarios
The model input and output of topography and bathymetry values resulting from forecast simulations of coupled modeling scenarios occurring between 2015 and 2025 at Dauphin Island, Alabama, and described in U.S. Geological Survey (USGS) Open-File Report 2020–1001 (https://doi.org/10.3133/ofr20201001), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Mickey and others (2020). |
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Model input and output files for modeling surface gravity waves on a schematized ancient lake on Mars
This portion of the data release presents a wave model application developed to simulate wind generated surface gravity waves on an ancient lake on Mars. The phase-averaged wave model, SWAN, was applied within the Delft3D modeling system (Deltares, 2018) with reduced gravity and a range of atmospheric densities and wind speeds to simulate potential conditions that could generate wind waves on Mars. The data release includes model input files for simulations with three different atmospheric densities, ... |
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Tabulated wave parameter results from modeling surface gravity waves on a schematized ancient lake on Mars
This portion of the data release presents tabulated wave parameter results derived from simulations of wind generated surface gravity waves on an ancient lake on Mars. The phase-averaged wave model, SWAN, was applied within the Delft3D modeling system (Deltares, 2018) with reduced gravity and a range of atmospheric densities and wind speeds to simulate potential conditions that could generate wind waves on Mars. |
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Coral reef profiles for wave-runup prediction
This data release includes representative cluster profiles (RCPs) from a large (>24,000) selection of coral reef topobathymetric cross-shore profiles (Scott and others, 2020). We used statistics, machine learning, and numerical modelling to develop the set of RCPs, which can be used to accurately represent the shoreline hydrodynamics of a large variety of coral reef-lined coasts around the globe. In two stages, the data were reduced by clustering cross-shore profiles based on morphology and hydrodynamic ... |
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Dauphin Island Decadal Forecast Evolution Model Inputs and Results: Final DEMs with restoration alternative 5 that extends Pelican Island simulated with ST2_SL1 and ST3_SL3 scenarios
The model input and output of topography and bathymetry values resulting from forecast simulations of coupled modeling scenarios occurring between 2015 and 2025 at Dauphin Island, Alabama, and described in U.S. Geological Survey (USGS) Open-File Report 2020–1001 (https://doi.org/10.3133/ofr20201001), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Mickey and others (2020). |
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Dauphin Island Decadal Forecast Evolution Model Inputs and Results: Final DEMs with restoration alternative 6 that extends Pelican Island simulated with ST2_SL1 and ST3_SL3 scenarios
The model input and output of topography and bathymetry values resulting from forecast simulations of coupled modeling scenarios occurring between 2015 and 2025 at Dauphin Island, Alabama, and described in U.S. Geological Survey (USGS) Open-File Report 2020–1001 (https://doi.org/10.3133/ofr20201001), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Mickey and others (2020). |
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Conceptual marsh units for Jamaica Bay to western Great South Bay salt marsh complex, New York
This data release contains coastal wetland synthesis products for the geographic region from Jamaica Bay to western Great South Bay, located in southeastern New York State. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and mean tidal range, are calculated for smaller units delineated from a Digital Elevation Model, providing the spatial variability of physical factors that influence wetland health. Through scientific efforts initiated with the Hurricane Sandy ... |
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Dauphin Island Decadal Forecast Evolution Model Inputs and Results: Final DEMs with restoration alternative 7 that extends Pelican Island simulated with ST2_SL1 and ST3_SL3 scenarios
The model input and output of topography and bathymetry values resulting from forecast simulations of coupled modeling scenarios occurring between 2015 and 2025 at Dauphin Island, Alabama, and described in U.S. Geological Survey (USGS) Open-File Report 2020–1001 (https://doi.org/10.3133/ofr20201001), are provided here. For further information regarding model input generation and visualization of model output topography and bathymetry, refer to Mickey and others (2020). |
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Unvegetated to vegetated marsh ratio in Assateague Island National Seashore and Chincoteague Bay, Maryland and Virginia
Unvegetated to vegetated marsh ratio (UVVR) in the Assateague Island National Seashore and Chincoteague Bay is computed based on conceptual marsh units defined by Defne and Ganju (2018). UVVR was calculated based on U.S. Department of Agriculture National Agriculture Imagery Program (NAIP) 1-meter resolution imagery. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to ... |
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Unvegetated to vegetated marsh ratio in Cape Cod National Seashore salt marsh complex, Massachusetts
Unvegetated to vegetated marsh ratio (UVVR) in the Cape Cod National Seashore (CACO) salt marsh complex and approximal wetlands is computed based on conceptual marsh units defined by Defne and Ganju (2019). UVVR was calculated based on U.S. Department of Agriculture National Agriculture Imagery Program (NAIP) 1-meter resolution imagery. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been expanding national assessment of coastal change hazards and ... |
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Flooding extent polygons for modelled wave-driven water levels in Florida with and without projected coral reef degradation
This data release presents projected flooding extent polygon (flood masks) shapefiles based on wave-driven total water levels for the State Florida (the Florida Peninsula and the Florida Keys). There are 12 associated flood mask shapefiles: one for each of four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years), the current scenario (base) and each of the degradation scenarios (Mean Elevation and Mean Erosion). |
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Projected flooding extents and depths based on 10-, 50-, 100-, and 500-year wave-energy return periods for the State of Florida before and after Hurricanes Irma and Maria due to the storms' damage to the coral reefs
This part of the data release presents projected flooding extent polygon shapefiles based on wave-driven total water levels for the State Florida (the Florida Peninsula and the Florida Keys). There are eight associated flood mask and flood depth shapefiles: one for each of four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years), the pre-storm scenario (base) and the post-storm scenarios. |
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Projected flooding extents and depths based on 10-, 50-, 100-, and 500-year wave-energy return periods for the Commonwealth of Puerto Rico before and after Hurricanes Irma and Maria due to the storms' damage to the coral reefs
This part of the data release presents projected flooding extent polygon (flood masks) shapefiles based on wave-driven total water levels for Commonwealth of Puerto Rico. There are eight associated flood mask and flood depth shapefiles: one for each of four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years), the pre-storm scenario (base) and the post-storm scenarios. |
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Hydrodynamic model of the lower Columbia River, Oregon and Washington, 2017-2020
A three-dimensional hydrodynamic model of the lower Columbia River (LCR) was constructed using the Delft3D Flexible Mesh (DFM) modeling suite to simulate water levels, flow, and seabed stresses for time period of January 1, 2017 to April 20, 2020. This data release describes the construction and validation of the model application and provides input files suitable to run the model on Delft3D Flexible Mesh software version 2021.01. |
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Hydrodynamic and sediment transport model of San Francisco Bay, California, Nov-Dec 2014
A three-dimensional hydrodynamic and sediment transport model of San Pablo and Suisun Bays was constructed using the Delft3D4 (D3D) modeling suite (Deltares, 2021a) to simulate water levels, flow, waves, and suspended sediment for time period of Nov 1 to Dec 31, 2014. This data release describes the construction and validation of the model application and provides input files suitable to run the model on D3D software version 4.04.01. |
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Unvegetated to vegetated ratio of marsh units in Chesapeake Bay salt marshes
This data release contains coastal wetland synthesis products for Chesapeake Bay. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and tidal range are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the intent of providing ... |
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Weekly Wind Speed and Frequency for a Wave Exposure Model of Grand Bay, Mississippi
Coastal marshes are highly dynamic and ecologically important ecosystems that are subject to pervasive and often harmful disturbances, including shoreline erosion. Shoreline erosion can result in an overall loss of coastal marsh, particularly in estuaries with moderate- or high-wave energy. Not only can waves be important physical drivers of shoreline change, they can also influence shore-proximal vertical accretion through sediment delivery. For these reason, estimates of wave energy can provide a ... |
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Mean tidal range of marsh units in Chesapeake Bay salt marshes
This data release contains coastal wetland synthesis products for Chesapeake Bay. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and tidal range are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the intent of providing ... |
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Archive of Chirp Subbottom Profile, Imagery, and Geospatial Data Collected in 2022 from Boca Chica Key, Florida
As part of the Coastal Sediment Availability and Flux and Defense Advanced Research Protection Agency (DARPA) Reefense projects, scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) conducted a nearshore geophysical survey at the nearshore ledge offshore of Boca Chica Key, Florida (FL) November 8-13, 2022. The objective of the project was to collect bathymetric maps and conduct a geologic assessment of the nearshore ledge off Boca Chica Key in support ... |
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Mean tidal range in marsh units of Cape Cod National Seashore salt marsh complex, Massachusetts
Biomass production is positively correlated with mean tidal range in salt marshes along the Atlantic coast of the United States of America. Recent studies support the idea that enhanced stability of the marshes can be attributed to increased vegetative growth due to increased tidal range. This dataset displays the spatial variation of mean tidal range (i.e. Mean Range of Tides, MN) in the Cape Cod National Seashore (CACO) salt marsh complex and approximal wetlands based on conceptual marsh units defined by ... |
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Elevation of marsh units in Chesapeake Bay salt marshes
This data release contains coastal wetland synthesis products for Chesapeake Bay. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and tidal range are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the intent of providing ... |
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Conceptual marsh units of Chesapeake Bay salt marshes
This data release contains coastal wetland synthesis products for Chesapeake Bay. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and tidal range are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the intent of providing ... |
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Projected flood extent polygons and flood depth points based on 10-, 50-, 100-, and 500-year wave-energy return periods, with and without coral reefs, for American Samoa (the islands of Tutuila, Ofu-Olosega, and Tau)
This part of the data release presents projected flooding extent polygon (flood masks) and flooding depth points (flood points) shapefiles based on wave-driven total water levels for American Samoa (the islands of Tutuila, Ofu-Olosega, and Tau). For each island there are 8 associated flood mask and flood depth shapefiles: one for each of four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years) and both with (wrf) and without (worf) the presence of coral reefs. Flooding depth point data ... |
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Projected flood extent polygons and flood depth points based on 10-, 50-, 100-, and 500-year wave-energy return periods, with and without coral reefs, for the Commonwealth of the Northern Mariana Islands (the islands of Saipan and Tinian)
This part of the data release presents projected flooding extent polygon (flood masks) and flooding depth points (flood points) shapefiles based on wave-driven total water levels for Commonwealth of the Northern Mariana Islands (the islands of Saipan and Tinian). For each island there are 8 associated flood mask and flood depth shapefiles: one for each of four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years) and both with (wrf) and without (worf) the presence of coral reefs. |
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Projected flood extent polygons and flood depth points based on 10-, 50-, 100-, and 500-year wave-energy return periods, with and without coral reefs, for the State Florida (the Florida Peninsula and the Florida Keys)
This part of the data release presents projected flooding extent polygon (flood masks) and flooding depth points (flood points) shapefiles based on wave-driven total water levels for the State Florida (the Florida Peninsula and the Florida Keys). For each island there are 8 associated flood mask and flood depth shapefiles: one for each of four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years) and both with (wrf) and without (worf) the presence of coral reefs. Flooding depth point data ... |
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Projected flood extent polygons and flood depth points based on 10-, 50-, 100-, and 500-year wave-energy return periods, with and without coral reefs, for the Territory of Guam
This part of the data release presents projected flooding extent polygon (flood masks) and flooding depth points (flood points) shapefiles based on wave-driven total water levels for the Territory of Guam. There are 8 associated flood mask and flood depth shapefiles: one for each of four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years) and both with (wrf) and without (worf) the presence of coral reefs. Flooding depth point data are also presented as a comma-separated value (.csv) ... |
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Projected flood extent polygons and flood depth points based on 10-, 50-, 100-, and 500-year wave-energy return periods, with and without coral reefs, for the State of Hawaii (the islands of Hawaii, Kahoolawe, Kauai, Lanai, Maui, Molokai, Niihau, and Oahu)
This part of the data release presents projected flooding extent polygon (flood masks) and flooding depth points (flood points) shapefiles based on wave-driven total water levels for the State of Hawaii (the islands of Hawaii, Kahoolawe, Kauai, Lanai, Maui, Molokai, Niihau, and Oahu). For each island there are 8 associated flood mask and flood depth shapefiles: one for each of four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years) and both with (wrf) and without (worf) the presence of ... |
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Projected flood extent polygons and flood depth points based on 10-, 50-, 100-, and 500-year wave-energy return periods, with and without coral reefs, for the Territory of Puerto Rico (the islands of Culebra, Puerto Rico, and Vieques)
This part of the data release presents projected flooding extent polygon (flood masks) and flooding depth points (flood points) shapefiles based on wave-driven total water levels for the Territory of Puerto Rico (the islands of Culebra, Puerto Rico, and Vieques). For each island there are 8 associated flood mask and flood depth shapefiles: one for each four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years) and both with (wrf) and without (worf) the presence of coral reefs. Flooding ... |
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Projected flooding extents and depths based on 10-, 50-, 100-, and 500-year wave-energy return periods, with and without coral reefs, for the States of Hawaii and Florida, the Territories of Guam, American Samoa, Puerto Rico, and the U.S. Virgin Islands, and the Commonwealth of the Northern Mariana Islands
This data release provides flooding extent polygons (flood masks) and depth values (flood points) based on wave-driven total water levels for 22 locations within the States of Hawaii and Florida, the Territories of Guam, American Samoa, Puerto Rico, and the U.S. Virgin Islands, and the Commonwealth of the Northern Mariana Islands. For each of the 22 locations there are eight associated flood mask polygons and flood depth point files: one for each four nearshore wave energy return periods (rp; 10-, 50-, 100- ... |
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Projected flood extent polygons and flood depth points based on 10-, 50-, 100-, and 500-year wave-energy return periods, with and without coral reefs, for the Territory of the U.S. Virgin Islands (the islands of Saint Croix, Saint John, and Saint Thomas)
This part of the data release presents projected flooding extent polygon (flood masks) and flooding depth points (flood points) shapefiles based on wave-driven total water levels for the Territory of the U.S. Virgin Islands (the islands of Saint Croix, Saint John, and Saint Thomas). For each island there are 8 associated flood mask and flood depth shapefiles: one for each four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years) and both with (wrf) and without (worf) the presence of ... |
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Elevation of marsh units in Plum Island Estuary and Parker River salt marsh complex, Massachusetts
This data release provides elevation distribution in the Plum Island Estuary and Parker River (PIEPR) salt marsh complex. Elevation distribution was calculated in terms of mean elevation of conceptual marsh units defined by Defne and Ganju (2018). The elevation data was based on the 1-meter gridded Digital Elevation Model and supplemented by 1-meter resampled 1/9 arc-second resolution National Elevation Data, where data gaps exist. Through scientific efforts initiated with the Hurricane Sandy Science Plan, ... |
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Modeled effects of depth and semidiurnal temperature fluctuations on predictions of year that coral reef locations reach annual severe bleaching for various global climate model projections
Using global climate model projections of sea-surface temperature at coral reef sites, we modeled the effects of depth and exposure to semidiurnal temperature fluctuations to examine how these effects may alter the projected year of annual severe bleaching for coral reef sites globally. Here we present the first global maps of the effects these processes have on bleaching projections for three IPCC-AR5 emissions scenarios. |
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Physics-based numerical model simulations of wave propagation over and around theoretical atoll and island morphologies for sea-level rise scenarios
Schematic atoll models with varying theoretical morphologies were used to evaluate the relative control of individual morphological parameters on alongshore transport gradients. Here we present physics-based numerical SWAN model results of incident wave transformations for a range of atoll and island morphologies and sea-level rise scenarios. Model results are presented in NetCDF format, accompanied by a README text file that lists the parameters used in each model run. These data accompany the following ... |
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Hydrodynamic model of the San Francisco Bay and Delta, California
A one- and two-dimensional hydrodynamic model of the San Francisco Bay and Delta was constructed using the Delft3D Flexible Mesh Suite (Delft3D FM; Kernkamp and others, 2011; https://www.deltares.nl/en/software/delft3d-flexible-mesh-suite/) to simulate still water levels. Required model input files are provided to run the model for the time period from October 1, 2018, to April 30, 2019. This data release describes the construction and validation of the model application and provides input files suitable to ... |
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Unvegetated to vegetated ratio of marsh units in Massachusetts salt marshes
This data release contains coastal wetland synthesis products for Massachusetts, developed in collaboration with the Massachusetts Office of Coastal Zone Management. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and tidal range are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal ... |
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Projected flooding extents and depths based on 10-, 50-, 100-, and 500-year wave-energy return periods for the State of Florida for current and potentially restored coral reefs
This part of the data release presents projected flooding extent polygon (flood masks) shapefiles based on wave-driven total water levels for the State Florida (the Florida Peninsula and the Florida Keys). There are 16 associated flood mask and flood depth shapefiles: one for each of four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years), the current scenario (base) and each of the restoration scenarios (structural_25, structural_05, and ecological_25). |
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Projected flooding extents and depths based on 10-, 50-, 100-, and 500-year wave-energy return periods for the Commonwealth of Puerto Rico for current and potentially restored coral reefs
This part of the data release presents projected flooding extent polygon (flood masks) shapefiles based on wave-driven total water levels for the Commonwealth of Puerto Rico. There are 16 associated flood mask and flood depth shapefiles: one for each of four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years), the current scenario (base) and each of the restoration scenarios (structural_25, structural_05, and ecological_25). |
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Projected flooding extents and depths based on 10-, 50-, 100-, and 500-year wave-energy return periods for the Territory of the U.S. Virgin Islands for current and potentially restored coral reefs
This part of the data release presents projected flooding extent polygon (flood masks) shapefiles based on wave-driven total water levels for the Territory of the U.S. Virgin Islands. There are 16 associated flood mask and flood depth shapefiles: one for each of four nearshore wave energy return periods (rp; 10-, 50-, 100-, and 500-years), the current scenario (base) and each of the restoration scenarios (structural_25, structural_05, and ecological_25). |
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Model parameter input files to study three-dimensional flow over coral reef spur-and-groove morphology
This data set consists of physics-based Delft3D-FLOW and SWAN hydrodynamic models input files used to study the wave-induced 3D flow over spur-and-groove (SAG) formations. SAG are a common and impressive characteristic of coral reefs. They are composed of a series of submerged shore-normal coral ridges (spurs) separated by shore-normal patches of sediment (grooves) on the fore reef of coral reef environments. Although their existence and geometrical properties are well documented, the literature concerning ... |
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Mean tidal range in marsh units of Fire Island National Seashore and central Great South Bay salt marsh complex, New York
Biomass production is positively correlated with mean tidal range in salt marshes along the Atlantic coast of the United States of America. Recent studies support the idea that enhanced stability of the marshes can be attributed to increased vegetative growth due to increased tidal range. This dataset displays the spatial variation of mean tidal range (i.e. Mean Range of Tides, MN) in the Fire Island National Seashore and central Great South Bay salt marsh complex, based on conceptual marsh units defined ... |
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Mean tidal range of marsh units in Massachusetts salt marshes
This data release contains coastal wetland synthesis products for Massachusetts, developed in collaboration with the Massachusetts Office of Coastal Zone Management. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and tidal range are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal ... |
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Unvegetated to vegetated marsh ratio in Plum Island Estuary and Parker River salt marsh complex, Massachusetts
Unvegetated to vegetated marsh ratio (UVVR) in the Plum Island Estuary and Parker River (PIEPR) salt marsh complex was computed based on conceptual marsh units defined by Defne and Ganju (2018). UVVR was calculated based on U.S. Department of Agriculture National Agriculture Imagery Program (NAIP) 1-meter resolution imagery. Through scientific efforts initiated with the Hurricane Sandy Science Plan, the U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_114_Elevations_NA)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_114_Elevations_N)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_134_Elevations_NA)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_134_Elevations_N)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
Info |
Elevation of marsh units in Massachusetts salt marshes
This data release contains coastal wetland synthesis products for Massachusetts, developed in collaboration with the Massachusetts Office of Coastal Zone Management. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and tidal range, are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_152_Elevations_NA)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_152_Elevations_N)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_155_Elevations_NA)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
Info |
Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_155_Elevations_N)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
Info |
Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_158_Elevations_NA)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
Info |
Conceptual marsh units of Massachusetts salt marshes
This data release contains coastal wetland synthesis products for Massachusetts, developed in collaboration with the Massachusetts Office of Coastal Zone Management. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and tidal range are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal ... |
Info |
Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_158_Elevations_N)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
Info |
Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_186_Elevations_NA)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
Info |
Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_186_Elevations_N)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_191_Elevations_NA)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
Info |
Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_191_Elevations_N)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
Info |
2013-14 Massachusetts Lidar-Derived Dune Toe Point Data
This data release of dune metrics for the Massachusetts coast is part of a 2018 update to the Massachusetts Shoreline Change Project. Because of continued coastal population growth and the increased threat of coastal erosion, the Massachusetts Office of Coastal Zone Management (CZM) launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. Maps of historic shoreline locations from the mid-1800s to 1978 were produced from multiple data sources, and in 2001, a 1994 shoreline ... |
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Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_23_Elevations_NA)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
Info |
Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_23_Elevations_N)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
Info |
Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_257_Elevations_NA)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
Info |
Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_257_Elevations_N)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
Info |
Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_4_Elevations_NA)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
Info |
2013-14 Massachusetts Lidar-Derived Dune Crest Point Data
This data release of dune metrics for the Massachusetts coast is part of a 2018 update to the Massachusetts Shoreline Change Project. Because of continued coastal population growth and the increased threat of coastal erosion, the Massachusetts Office of Coastal Zone Management (CZM) launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. Maps of historic shoreline locations from the mid-1800s to 1978 were produced from multiple data sources, and in 2001, a 1994 shoreline ... |
Info |
Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_4_Elevations_N)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
Info |
Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_71_Elevations_NA)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
Info |
Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_71_Elevations_N)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
Info |
Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_95_Elevations_NA)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
Info |
Assessing the Effectiveness of Nourishment in Decadal Barrier Island Morphological Resilience: Model Inputs and Outputs (Storm_95_Elevations_N)
Using version 5527 of the XBeach numerical model (Roelvink and others, 2009), barrier island morphological change was simulated at Dauphin Island, Alabama (AL), for a 30-year forecast of multiple storms and sea level rise, considering scenarios of no-action and beach and dune nourishment as described in Passeri and others (2021). The two-dimensional XBeach model can be applied to barrier islands to solve for time-dependent topography and bathymetry. The XBeach model setup requires the input of topographic ... |
Info |
Conceptual marsh units for Fire Island National Seashore and central Great South Bay salt marsh complex, New York
The salt marsh complex of Fire Island National Seashore (FIIS) and central Great South Bay was delineated to smaller, conceptual marsh units by geoprocessing of surface elevation data. Flow accumulation based on the relative elevation of each location is used to determine the ridge lines that separate each marsh unit while the surface slope is used to automatically assign each unit a drainage point, where water is expected to drain through. Through scientific efforts initiated with the Hurricane Sandy ... |
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Unvegetated to vegetated ratio of marsh units in Blackwater salt marsh complex, Chesapeake Bay, Maryland
This data release contains coastal wetland synthesis products for the geographic region of Blackwater salt marsh complex, Chesapeake Bay, Maryland. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and others, are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and ... |
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Elevation of marsh units in Blackwater salt marsh complex, Chesapeake Bay, Maryland
This data release contains coastal wetland synthesis products for the geographic region of Blackwater salt marsh complex, Chesapeake Bay, Maryland. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and others, are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and ... |
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2022-309-FA_trkln: Digital Chirp Subbottom Profile Trackline Data Collected During USGS Field Activity Number 2022-309-FA Offshore of Seven Mile Island, New Jersey, April and May 2022
From April 29 through May 2, 2022, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport offshore of Seven Mile Island, New Jersey. Geophysical data were collected as part of the Coastal Sediment Availability and Flux project. This shapefile represents a line dataset of field activity number (FAN) 2022-309-FA chirp tracklines. |
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Archive of Digitized Analog Boomer Seismic Reflection Data Collected from the Northern Gulf of Mexico: 1982, 1985, 1986, 1989, 1991, and 1992
The U.S. Geological Survey (USGS) Coastal and Marine Hazards and Resources Program (CMHRP) has actively collected geophysical and sedimentological data in the northern Gulf of Mexico for several decades, including shallow subsurface data in the form of high-resolution seismic reflection profiles (HRSP). Prior to the mid-1990s most HRSP data were collected in analog format as paper rolls of continuous profiles up to 25 meters long. As part of the National Geological and Geophysical Data Preservation Program ... |
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2022-309-FA_sol: Digital Chirp Subbottom Profile Start of Line Data Collected During USGS Field Activity Number 2022-309-FA Offshore of Seven Mile Island, New Jersey, April and May 2022
From April 29 through May 2, 2022, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport offshore of Seven Mile Island, New Jersey. Geophysical data were collected as part of the Coastal Sediment Availability and Flux project. This shapefile represents a point dataset of field activity number (FAN) 2022-309-FA chirp subbottom profile start of trackline locations. |
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Conceptual marsh units for Cape Cod National Seashore salt marsh complex, Massachusetts
The salt marsh complex of Cape Cod National Seashore (CACO), Massachusetts, USA and approximal wetlands were delineated to smaller, conceptual marsh units by geoprocessing of surface elevation data. Flow accumulation based on the relative elevation of each location is used to determine the ridge lines that separate each marsh unit while the surface slope is used to automatically assign each unit a drainage point, where water is expected to drain through. Through scientific efforts initiated with the ... |
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Archive of digitized analog boomer seismic reflection data collected during USGS Cruise USFHC in Mississippi Sound and Bay St. Louis, September 1989
The U.S. Geological Survey (USGS) Coastal and Marine Geology Program has actively collected geophysical and sedimentological data in the northern Gulf of Mexico for several decades, including shallow subsurface data in the form of high-resolution seismic reflection profiles (HRSP). Prior to the mid-1990s most HRSP data were collected in analog format as paper rolls of continuous profiles up to 25 meters long. As part of the National Geological and Geophysical Data Preservation Program (NGGDPP, https:/ ... |
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2022-309-FA_shots: Digital Chirp Subbottom Profile Shotpoint Data Collected During USGS Field Activity Number 2022-309-FA Offshore of Seven Mile Island, New Jersey, April and May 2022
From April 29 through May 2, 2022, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport offshore of Seven Mile Island, New Jersey. Geophysical data were collected as part of the Coastal Sediment Availability and Flux project. This shapefile represents a point dataset of field activity number (FAN) 2022-309-FA chirp subbottom profile 1,000-shot-interval locations. |
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Archive of Chirp Subbottom Profile, Imagery, and Geospatial Data Collected in 2022 from Seven Mile Island, New Jersey
From April 29 through May 2, 2022, researchers from the U.S. Geological Survey (USGS) conducted a nearshore geophysical survey to map the shoreface and inner shelf, as well as characterizing stratigraphy near Seven Mile Island, New Jersey (NJ). The Coastal Sediment Availability and Flux project objectives include understanding the morphologic evolution of the barrier island system on a variety of time scales (months to centuries) and resolving storm-related impacts, post-storm beach response, and recovery. ... |
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2022-312-FA_trkln: Digital Chirp Subbottom Profile Trackline Data Collected During USGS Field Activity Number 2022-312-FA Near Panama City, Florida, November 2022
From June 20-30, 2022, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport near Panama City, Florida (FL). Geophysical data were collected as part of the Coastal Sediment Availability and Flux and Defense Advanced Research Protection Agency (DARPA) Reefense projects. This shapefile represents a line dataset of field activity number (FAN) 2022-312-FA chirp tracklines. |
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2022-312-FA _sol: Digital Chirp Subbottom Profile Start of Line Data Collected During USGS Field Activity Number 2022-312-FA Near Panama City, Florida, June 2022
From June 20-30, 2022, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport near Panama City, Florida (FL). Geophysical data were collected as part of the Coastal Sediment Availability and Flux and Defense Advanced Research Protection Agency (DARPA) Reefense projects. This shapefile represents a point dataset of field activity number (FAN) 2022-312-FA chirp subbottom profile start of trackline locations. |
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Mean tidal range in marsh units of Plum Island Estuary and Parker River salt marsh complex, Massachusetts
Biomass production is positively correlated with mean tidal range in salt marshes along the Atlantic coast of the United States of America. Recent studies support the idea that enhanced stability of the marshes can be attributed to increased vegetative growth due to increased tidal range. This dataset displays the spatial variation of mean tidal range (i.e. Mean Range of Tides, MN) in the Plum Island Estuary and Parker River (PIEPR) salt marsh complex based on conceptual marsh units defined by Defne and ... |
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2022-312-FA_shots: Digital Chirp Subbottom Profile Shotpoint Data Collected During USGS Field Activity Number 2022-312-FA Near Panama City, Florida, November 2022
From June 20-30, 2022, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport near Panama City, Florida (FL). Geophysical data were collected as part of the Coastal Sediment Availability and Flux and Defense Advanced Research Protection Agency (DARPA) Reefense projects. This shapefile represents a point dataset of field activity number (FAN) 2022-312-FA chirp subbottom profile 1,000-shot-interval locations. |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Rockaway Peninsula, NY, 2013–2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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Archive of Chirp Subbottom Profile, Imagery, and Geospatial Data Collected in June 2022 Near Panama City, Florida
As part of the Coastal Sediment Availability and Flux and Defense Advanced Research Protection Agency (DARPA) Reefense projects, scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) conducted a nearshore geophysical survey to map back-barrier and lagoonal areas, as well as characterizing stratigraphy near Panama City, Florida (FL) in June 2022. The purpose of this study was to conduct a geologic assessment (including bathymetric mapping) of the environs ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Rockaway Peninsula, NY, 2012
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Rockaway Peninsula, NY, 2010–2011
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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2022-328-FA_trkln: Digital Chirp Subbottom Profile Trackline Data Collected During USGS Field Activity Number 2022-328-FA Offshore of Breton Island, Louisiana, August 2022
On August 5, 2022, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport offshore of Breton Island, Louisiana (LA). Geophysical data were collected as part of the Breton Island Post Construction Monitoring project. This shapefile represents a line dataset of field activity number (FAN) 2022-328-FA chirp tracklines. |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Fire Island, NY, 2014–2015
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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2022-328-FA_sol: Digital Chirp Subbottom Profile Start of Line Data Collected During USGS Field Activity Number 2022-328-FA Offshore of Breton Island, Louisiana, August 2022
On August 5, 2022, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport offshore of Breton Island, Louisiana (LA). Geophysical data were collected as part of the Breton Island Post Construction Monitoring project. This shapefile represents a point dataset of field activity number (FAN) 2022-328-FA chirp subbottom profile start of trackline locations. |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Fire Island, NY, 2012
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Fire Island, NY, 2010–2011
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
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Orthomosaics of Pea Island National Wildlife Refuge DUNEX Site, North Carolina in September and October 2021
The data in this part of the release are orthomosaics that characterize the beach at the USGS DUring Nearshore Event eXperiment (DUNEX) site on Pea Island National Wildlife Refuge, NC. During September and October 2021, USGS and Woods Hole Oceanographic Institute (WHOI) scientists conducted multiple field surveys to collect a topobathy elevation time series. Images of the beach for use in structure from motion were taken with a camera attached to a helium filled balloon-kite (Helikite). Agisoft Metashape (v ... |
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SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Edwin B. Forsythe NWR, NJ, 2013–2014
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
Info |
2022-328-FA_shots: Digital Chirp Subbottom Profile Shotpoint Data Collected During USGS Field Activity Number 2022-328-FA Offshore of Breton Island, Louisiana, August 2022
On August 5, 2022, the U.S. Geological Survey (USGS) conducted geophysical surveys to investigate the geologic controls on barrier island evolution and sediment transport offshore of Breton Island, Louisiana (LA). Geophysical data were collected as part of the Breton Island Post Construction Monitoring project. This shapefile represents a point dataset of field activity number (FAN) 2022-328-FA chirp subbottom profile 1,000-shot-interval locations. |
Info |
SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Edwin B. Forsythe NWR, NJ, 2012
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
Info |
SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Edwin B. Forsythe NWR, NJ, 2010
Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly ... |
Info |