Travis K. Sterne
Elizabeth A. Pendleton
Erika E. Lentz
Rachel E. Henderson
20230228
Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Fabric Dataset
1.0
raster digital data
data release
DOI:10.5066/P96A2Q5X
Woods Hole Coastal and Marine Science Center, Woods Hole, MA
U.S. Geological Survey, Coastal and Marine Geology Program
https://doi.org/10.5066/P96A2Q5X
https://www.sciencebase.gov/catalog/item/61781f88d34e4c6b7fe2a444
Travis K. Sterne
Elizabeth A. Pendleton
Erika E. Lentz
Rachel E. Henderson
2023
Coastal Change Likelihood in the U.S. Northeast Region — Maine to Virginia
1
raster digital data
data release
DOI:10.5066/P96A2Q5X
Reston, VA
U.S. Geological Survey
Suggested citation: Sterne, T.K., Pendleton, E.A., Lentz, E.E., and Henderson, R.E., 2023, Coastal Change Likelihood in the U.S. Northeast Region — Maine to Virginia: U.S. Geological Survey data release, https://doi.org/10.5066/P96A2Q5X.
https://doi.org/10.5066/P96A2Q5X
https://www.sciencebase.gov/catalog/item/61781c1bd34e4c6b7fe2a425
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 likelihood of coastal change along the U.S coastline within the coming decade. The pilot study, conducted in the Northeastern U.S. (Maine to Virginia), is comprised of datasets derived from a variety of federal, state, and local sources. First, a decision tree-based dataset is built that describes the fabric or integrity of the coastal landscape and includes landcover, elevation, slope, long-term (>150 years) shoreline change trends, dune height, and marsh stability data. A second database was generated from coastal hazards, which are divided into event hazards (e.g., flooding, wave power, and probability of storm overwash) and persistent hazards (e.g., relative sea-level rise rate, short-term (about 30 years) shoreline erosion rate, and storm recurrence interval). The fabric dataset is then merged with the coastal hazards databases and a training dataset made up of hundreds of polygons is generated from the merged dataset to support a supervised learning classification. Results from this pilot study are location-specific at 10-meter resolution and are made up of four raster datasets that include (1) quantitative and qualitative information used to determine the resistance of the landscape to change, (2 & 3) the potential coastal hazards that act on it, (4) the machine learning output, or Coastal Change Likelihood (CCL), based on the cumulative effects of both fabric and hazards, and (5) an estimate of the hazard type (event or persistent) that is the likely to influence coastal change. Final outcomes are intended to be used as a first order planning tool to determine which areas of the coast may be more likely to change in response to future potential coastal hazards, and to examine elements and drivers that make change in a location more likely.
CCL is a first order planning tool that estimates the likelihood that an area of coast will experience change based on its inherit resistance to change, metrics associated with specific land cover types, and the hazards that impact a coast. The CCL Fabric dataset is a 10 mpp decision-tree-based assessment of land cover, elevation, slope, multi-decadal shoreline change trends, and marsh stability. Each raster cell is assigned a unique value based on the potential hazard scenario expected to occur in a given location. All relevant information pertaining to each grid cell is stored in the associated attribute table. This dataset covers the Northeast US coastline between +/- 10 meters elevation relative to mean high water (MHW) from Maine to Virginia.
2010
2021
ground condition of source data
None planned
-77.527891
-66.883630
45.192996
36.514857
ISO 19115 Topic Category
geoscientificInformation
oceans
None
U.S. Geological Survey
USGS
Coastal and Marine Hazards Mission Area
Woods Hole Coastal and Marine Science Center
Coastal Fabric
Elevation
Interpretation
Bathymetry
Landcover
Land Cover
Topography
UVVR
Unvegetated-Vegetated Ratio
Shoreline Change
Coastal Hazards
High Tide Flooding
Storm Recurrence
Wave Power
Storm Overwash
Sea Level Rise
Coastal Change Hazard Assessment
Coastal Vulnerability Index
Machine Learning
Autoclassification
Automation
Arcpy
ArcGIS Pro
Support Vector Machine
Training Samples
Supervised Classification
Decision Tree Framework
scientific interpretation
land use and land cover
USGS Thesaurus
marine geology
topography
coastal processes
hazards
sea-level change
USGS Metadata Identifier
USGS:61781f88d34e4c6b7fe2a444
None
Northeast US
Maine
New Hampshire
Massachusetts
Rhode Island
Connecticut
New York
New Jersey
Delaware
Maryland
Virginia
St. Croix Island International Historic Site
Acadia National Park
Gateway National Recreation Area
George Washington Birthplace National Monument
Cape Cod National Seashore
None. Please see 'Distribution Info' for details.
Not to be used for navigation. Public domain data from the U.S. Government are freely redistributable with proper metadata and source attribution. Please recognize the U.S. Geological Survey (USGS) as the source of this information. Additionally, there are limitations associated with coastal change hazard assessments. Although these data are published at a resolution of 10 mpp and are considered high resolution, the intended scale for use is around 1:24,000. Please read the associated Data Release (https://doi.org/10.3133/dr1169) for a list of caveats, applications, and use recommendations for these data.
U.S. Geological Survey
Travis K Sterne
mailing and physical
384 Woods Hole Rd
Woods Hole
MA
02543
(508) 548 8700 x2219
tsterne@usgs.gov
https://www.sciencebase.gov/catalog/file/get/61781f88d34e4c6b7fe2a444?name=Fabric_Graphic.jpg
Outer Cape Cod with land cover type as defined in the coastal fabric data layer
JPEG
Esri ArcGIS Pro 10.6.0.8321 2.6.3
Thieler, E.R.
Hammar-Klose, E.S.
1999
National assessment of coastal vulnerability to sea-level rise; U.S. Atlantic Coast
vector digital data
Open-File Report
1999-595
Reston, VA
U.S. Geological Survey
https://doi.org/10.3133/ofr99593
https://pubs.usgs.gov/of/1999/of99-593/
Elizabeth A. Pendleton
Erika E. Lentz
Travis K. Sterne
Rachel E. Henderson
2023
Development and application of a coastal change likelihood assessment for the northeast region, Maine to Virginia
Data Report
1169
Reston, VA
U.S. Geological Survey
Suggested citation: Pendleton, E.A., Lentz, E.E., Sterne, T.K., and Henderson, R.E., 2023, Development and application of a coastal change likelihood assessment for the northeast region, Maine to Virginia: U.S. Geological Survey Data Report 1169, 56 p., https://doi.org/10.3133/dr1169. The CCL data release (https://doi.org/10.5066/P96A2Q5X) is associated with the CCL Data Report (https://doi.org/10.3133/dr1169)
https://doi.org/10.3133/dr1169
https://pubs.er.usgs.gov/publication/dr1169
All data values represent relative likelihood for landscape change in response to coastal hazards based on previous empirical research and expert opinion. The final output generated is the expected outcome based on this information. Accuracy assessments were carried out on the landcover data used for this project to estimate the accuracy of land cover type, which determines the landscape's baseline CCL value.
All data were checked for accuracy during processing and an accuracy assessment was conducted on the composite landscape delineation (see associated data report), which suggests that the overall landscape classification is around eighty percent accurate with a Kappa coefficient of 0.77, which is considered ‘in substantial agreement’ with the source aerial imagery. Due to the large number of source datasets that have been combined and merged in this dataset, there are some known inconsistencies, and there may be others unlisted or yet unrealized. The source data often have incomplete extents, gaps, and inconsistencies, which primarily arise from the resolution, quality, density, format, date, source imagery, or spatial processing with other data sources. Some of the known issues are listed here.
Elevation inconsistencies: CONED elevation data were the preferred source of high-resolution seamless bathymetry and topography for the study area. However, CONED data north of Cape Ann, MA are not yet available. Additional DEM information was added to CONED to fill in gaps in northern MA, NH and ME. The offshore domain in ME and NH was approximated due to a lack of high resolution, seamless bathymetric data. The offshore domain is extended to the edge of the source CCAP landcover extent, but does not uniformly represent the –10-meter contour like areas where CONED data are the source elevation information
Landcover inconsistencies: 10 mpp Beta CCAP landcover data were the definitive source of landcover data wherever available. Alternative land use/land cover (LULC) data sources were used for New Jersey, Virginia, and Pennsylvania (along the Delaware River) where 10 mpp CCAP data are not available. The change in source landcover data created some seams along and within state boundaries, as well as some differences in landscape classification. For example, the New Jersey landcover source associates ‘recreational lands’ with ‘Urban’ landscapes, which is different than CCAP. This difference in classification algorithm causes parts of New Jersey to appear more developed compared to other states. Landcover data for the Aberdeen Proving Ground military installation are masked for security purposes and have been given a ‘NoData’ classification, which is the same as aquatic class. A change in imagery date in the MD CCAP data creates an unnatural boundary in the landcover data in part of MD.
Shoreline data inconsistencies: Three landscape classes in this study were derived from vector shoreline data (NOAA Environmental Sensitivity Index (ESI) datasets listed in the source data section). Both polyline and polygon datasets were rasterized and merged with the LULC source data. ESI shoreline classifications have some inconsistencies between the regional and national data products, and several of these can be seen in the landscape classification. For example, differences in the MD, VA and Chesapeake Bay ESI datasets may result in overlapping or misaligned features (such as seawalls). Spatial alignment issues arise from datum adjustments and shoreline source differences in the LULC data and the ESI data. Additionally, the rasterization of vector data can create irregularly or jagged shorelines when converted to pixels. Although the translation of these vector shoreline feature classifications is not perfect, the overall benefit of including the three additional landscape classes from vector sources (rocky, hardened, and tidal flats) in this study far exceeds the limitations of a coastal landscape classification without them.
Shoreline change data inconsistencies and completeness: The shoreline change rates associated with the unconsolidated shore landclass are more extensive for the state of MA than they are for other states in this study. Shoreline change data derived from the National Assessment of Shoreline change (see source listed below) are located along sandy, open-ocean-facing coasts. However, the MA shoreline change data often covers embayments and backbarrier shorelines. This difference in data availability results in more detailed assessment of the shoreline in MA compared to other states.
UVVR data resolution: The data from the Unvegetated to Vegetated Ratio (UVVR) are the only source fabric data that did not exist at 10 mpp or finer resolution. The 30 mpp UVVR product was used in this study to resolve the decision nodes associated with saltmarshes, and this affects the apparent resolution of salt marshes where UVVR was applied.
For a more complete explanation of how source data affect the results of the CCL dataset as a whole, see the discussion section of the associated data report (https://doi.org/10.3133/dr1169)
CCL is a model for coastal landscapes in the Northeast United States. All output is "clipped" to an elevation domain; this dataset represents change likelihood from +/- 10 meters (MHW) elevation, based on physical characteristics of the landscape, where data exist. Existing gaps in coverage for this dataset within this domain are a result of data gaps in source information. See the logical consistency report for a summary of source data limitations or inconsistencies.
Horizontal coordinate information is referenced to the World Geodetic System of 1984 (WGS 1984) in a Geographic Coordinate System or WGS 1984 Web Mercator (auxiliary sphere) in a Projected Coordinate System. Source data were resampled to 10 mpp for use. There may be resampling errors associated with coarsening (e.g. elevation data were resampled from 1 mpp to 10 mpp) or fining (UVVR were resampled from 30 mpp to 10 mpp) of the data. Likewise some data, like NOAA’s ESI and the shoreline type data for both hardened and rocky shores were rasterized from a source vector, and there can be spatial inconsistencies associated with the rasterization of vector data. Also, some areas, especially in part of MA, the ESI shoreline is slightly misaligned with the landcover data shoreline. The horizontal accuracy of this dataset is assumed to be better than +/- 30 meters, but dynamic coastal areas may experience much higher rates of change during storms, and horizontal offset at the shoreline maybe much higher (+/- 100 meters) in certain areas.
This dataset uses the Mean High Water (MHW) tidal datum. Source elevation data were transferred from North American Vertical Datum of 1988 (NAVD 88) or Mean High Water Level (MHW) to MHW using NOAA’s Vdatum (https://vdatum.noaa.gov/). Source CONED data have a theoretical root mean square error of 0.15 m (Danielson and Tyler, 2018). Andrews and others (2018) DEM for Massachusetts has a reported error of +/- 10 to 60cm. The NOAA SLR DEMs for land areas of ME and NH are not checked for accuracy but are in compliance with standards for FEMA Flood Hazard Mapping. The Gulf of ME DEM, which was used in the offshore areas of ME and NH varies in error according to original sources by which the data were collected from 1859 to 2010. In summary, these data may have vertical errors of 10 meters or more in offshore areas but are expected to be much higher on land. Vertical accuracy associated with the source data on land and the vertical transformation process are expected to be less than 0.5 m. Elevation data are used to define the domain of this dataset. Elevation and derivatives of the elevation grid (slope) are applied to certain landcover classes as well (marsh, developed, and forest), but there are no explicit elevation values within the attribute table of this grid.
Office for Coastal Management
2016
NOAA Office for Coastal Management Sea Level Rise Data: 1-10ft Seal Level Rise Inundation Extent
raster digital data
https://www.fisheries.noaa.gov/inport/item/48106
Digital and/or Hardcopy
2016
publication date - 2021
NOAA SLR Topo
Elevation
Jeffrey Danielson
Dean Tyler
2018
Coastal National Elevation Database
raster digital data
https://www.usgs.gov/core-science-systems/eros/coned
Digital and/or Hardcopy
2018
publication date
CONED
Elevation
B.D. Andrews
W.E. Baldwin
D.W. Sampson
W.C. Schwab
20191227
Continuous bathymetry and elevation models of the Massachusetts coastal zone and continental shelf
Version 3
raster digital data
data release
DOI:10.5066/F72806T7
Reston, VA
U.S. Geological Survey
https://doi.org/10.5066/F72806T7
Digital and/or Hardcopy
20191227
publication date
CZM Topobathy
Elevation
NOAA Office for Coastal Management
20161003
C-CAP Derived Land Cover BETA
raster digital data
https://coast.noaa.gov/digitalcoast/data/ccapderived.html
Digital and/or Hardcopy
20161003
publication date
CCAP
Land Cover
B.R. Couvillion
N.K. Ganju
Z. Defne
20210205
An Unvegetated to Vegetated Ratio (UVVR) for coastal wetlands of the Conterminous United States (2014-2018)
raster digital data
data release
DOI:10.5066/P97DQXZP
Reston, VA
U.S. Geological Survey
Suggested citation: Couvillion, B.R., Ganju, N.K., and Defne, Z., 2021, An Unvegetated to Vegetated Ratio (UVVR) for coastal wetlands of the Conterminous United States (2014-2018): U.S. Geological Survey data release, https://doi.org/10.5066/P97DQXZP.
https://doi.org/10.5066/P97DQXZP
https://www.sciencebase.gov/catalog/item/5fa18656d34e198cb793cba5
Digital and/or Hardcopy
20210205
ground condition
UVVR
Marsh Vegetation Cover
E.A. Himmelstoss
A.S. Farris
K.M. Weber
R.E. Henderson
20190807
Massachusetts Shoreline Change Project, 2018 Update: A GIS Compilation of Shoreline Change Rates Calculated Using Digital Shoreline Analysis System Version 5.0, With Supplementary Intersects and Baselines for Massachusetts
vector digital data
data release
DOI:10.5066/P9RRBEYK
Reston, VA
U.S. Geoloigcal Survey
Suggested citation: Himmelstoss, E.A., Farris, A.S., Weber, K.M., and Henderson, R.E., 2019, Massachusetts shoreline change project, 2018 update–A GIS compilation of shoreline change rates calculated using Digital Shoreline Analysis System version 5.0, with supplementary intersects and baselines for Massachusetts (ver. 2.0, August 2019): U.S. Geological Survey data release, https://doi.org/10.5066/P9RRBEYK.
https://doi.org/10.5066/P9RRBEYK
https://www.sciencebase.gov/catalog/item/5be5857ce4b0b3fc5cf8c6ca
Digital and/or Hardcopy
20190807
publication date
Shoreline Change - MA
Shoreline Change Rates
E.A. Himmelstoss
M. Kratzmann
C. Hapke
E.R. Thieler
J. List
2010
The national assessment of shoreline change: A GIS compilation of vector shorelines and associated shoreline change data for the New England and Mid-Atlantic Coasts: U.S. Geological Survey Open-File Report 2010–1119
vector digital data
Open-File Report
2010-1119
Reston, VA
U.S. Geological Survey
https://doi.org/10.3133/ofr20101119
https://pubs.usgs.gov/of/2010/1119/
Digital and/or Hardcopy
2010
publication date
Shoreline Change - Not MA
Shoreline Change Rates
National Ocean Service
2021
VDatum
Version 4.2
raster digital data
https://vdatum.noaa.gov
Digital and/or Hardcopy
2019
publication date
Vdatum
MHW elevation conversion
K.S. Doran
J.J. Birchler
M.W. Hardy
K.J. Bendik
J.M. Pardun
H.A. Locke
2020
National assessment of hurricane-induced coastal erosion hazards
Version 2.0, February 2021
vector digital data
data release
DOI:10.5066/P99ILAB9
Reston, VA
U.S. Geological Survey
Suggested citation: Doran, K.S., Birchler, J.J., Hardy, M.W., Bendik, K.J., Pardun, J.M., and Locke, H.A., 2020, National assessment of hurricane-induced coastal erosion hazards (ver. 2.0, February 2021): U.S. Geological Survey data release, https://doi.org/10.5066/P99ILAB9.
https://doi.org/10.5066/P99ILAB9
Digital and/or Hardcopy
20210218
publication date
PCOI
Dune Height, probability of over-wash
U.S. Geological Survey
2020
Watershed Boundary Dataset: National Hydrography Dataset
vector digital data
https://apps.nationalmap.gov/downloader/#/
Digital and/or Hardcopy
2020
publication date
HUC
Watershed boundaries
Z. Defne
A.L. Aretxabaleta
N.K. Ganju
T.S. Kalra
D.K. Jones
K.E.L. Smith
20200130
A geospatially resolved wetland vulnerability index: Synthesis of physical drivers
publication
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0228504
Digital and/or Hardcopy
20200130
publication date
UVVR Metrics 1
UVVR metrics
N.K. Ganju
Z. Defne
M.L. Kirwan
S. Fagherazzi
A. D'Alpaos
L. Carniello
20170123
Spatially integrative metrics reveal hidden vulnerability of microtidal salt marshes
publication
https://www.nature.com/articles/ncomms14156
Digital and/or Hardcopy
20170123
publication date
UVVR Metrics 2
UVVR metrics
Office of Response and Restoration
2017
National Environmental Sensitivity Index Shoreline: GULF/ATLANTIC ESI, PACIFIC ESI: ESIL (ESI Shoreline Types - Lines).
vector digital data
https://response.restoration.noaa.gov/esi_download
Digital and/or Hardcopy
2017
publication date
ESI_1
Land Cover
Office of Response and Restoration
2016
Maine and New Hampshire 2016 ESI Polygons
vector digital data
NOAA National Centers for Environmental Information
https://www.fisheries.noaa.gov/inport/item/40371
Digital and/or Hardcopy
2016
publication date
ESI_2
Land Cover
Office of Response and Restoration
2016
Massachusetts and Rhode Island 2016 ESIP (ESI Shoreline Types - Polygons)
vector digital data
NOAA National Centers for Environmental Information
https://www.fisheries.noaa.gov/inport/item/51698
Digital and/or Hardcopy
2016
publication date
ESI_3
Land Cover
Office of Response and Restoration
2016
Long Island Sound 2016 ESI POLYGONS
vector digital data
NOAA National Centers for Environmental Information
https://www.fisheries.noaa.gov/inport/item/40470
Digital and/or Hardcopy
2016
publication date
ESI_4
Land Cover
Office of Response and Restoration
2016
NY/NJ Metro Area, Hudson River, and South Long Island 2016 ESIP (Environmental Sensitivity Index Polygons)
vector digital data
NOAA National Centers for Environmental Information
https://www.fisheries.noaa.gov/inport/item/40466
Digital and/or Hardcopy
2016
publication date
ESI_5
Land Cover
Office of Response and Restoration
2014
Delaware / New Jersey / Pennsylvania 2014 ESI Polygons
vector digital data
NOAA National Centers for Environmental Information
https://www.fisheries.noaa.gov/inport/item/53861
Digital and/or Hardcopy
2014
publication date
ESI_6
Land Cover
Office of Response and Restoration
2016
Chesapeake Bay and the Outer Coasts of Maryland and Virginia 2016 ESI Polygons
vector digital data
NOAA National Centers for Environmental Information
https://www.fisheries.noaa.gov/inport/item/40644
Digital and/or Hardcopy
2016
publication date
ESI_7
Land Cover
U.S. Fish and Wildlife Service
2020
National Wetlands Inventory
vector digital data
U.S. Department of the Interior, Fish and Wildlife Service, Washington, D.C.
http://www.fws.gov/wetlands
Digital and/or Hardcopy
2020
publication date
NWI
Salt Marsh Extent
New Jersey Department of Environmental Protection Bureau of GIS
20190128
Land Use/Land Cover of New Jersey 2015
vector digital data
https://gisdata-njdep.opendata.arcgis.com/documents/6f76b90deda34cc98aec255e2defdb45/about
Digital and/or Hardcopy
2015
ground condition
NJ Land Cover
Land Cover
Virginia Geographic Information Network (VGIN)
Department of Conservation & Recreation (DCR)
Department of Environmental Quality (DEQ)
Virginia Department of Forestry (VDOF)
Tidal Marsh Inventory (TMI)
National Hydrography Dataset (NHD)
National Wetlands Inventory (NWI)
Virginia Economic Development Partnership (VEDP)
Virginia Department of Mines, Minerals, and Energy (DMME),
Worldview Solutions, Inc.
2020
1-meter Virginia Land Cover
raster digital data
https://vgin.maps.arcgis.com/home/item.html?id=6ae731623ff847df91df767877db0eae
Digital and/or Hardcopy
2011
2015
ground condition
VA Land Cover
Land Cover
Chesapeake Conservancy
2018
Landcover Data Project 2013/2014
13 class version
raster digital data
https://www.chesapeakeconservancy.org/conservation-innovation-center/high-resolution-data/land-cover-data-project/
Digital and/or Hardcopy
2013
2014
ground condition
Chesapeake Conservancy
Land Cover
All elevation data were re-sampled to 10-mpp resolution using the ESRI ArcGIS Pro Resample tool, converted to reflect elevation relative to mean high water (MHW; NAVD88) using NOAA Vdatum version 4.2 and consolidated into one mosaic dataset using ESRI ArcGIS Pro Mosaic to New Raster tool. This step created the domain (spatial extent) of all datasets in this publication (coastal fabric, coastal hazards, and coastal change likelihood).
This step and all of the following steps were completed by Travis K. Sterne using ESRI ArcGIS Pro Version 2.6.3 geospatial software. Steps 1 through 10 were automated using the Arcpy package for Python programming in a Jupyter Notebook environment. Any subsequent steps that mention the use of “tools” or “functions” refer to geoprocessing tools utilized in ArcGIS Pro.
The steps described in detail below define the project domain, then reclassify land cover data according to associated coastal metrics within the domain and apply an integer value that defines both land cover and relative ‘toughness’ of the landscape using a unique identifier value. The resultant raster (at end of step 10) is the outcome of a compounding raster decision matrix that is guided by both expert opinion and coastal change metrics.
NOAA SLR Topo
CONED
CZM Topobathy
Vdatum
2021
Elevation Mosaic
Step 2: All landcover raster datasets (CCAP, VGIN, Chesapeake Bay Conservation, and NJEP) were resampled to 10 mpp and, when necessary, simplified into a six-class classified raster dataset. This conversion was completed using the Reclassify tool. The values were reclassified as follows for a CCAP data source example: Developed High Intensity (2), Developed Medium Intensity (3), Developed Low Intensity (4), Developed Open Space (5), and Cultivated Crops (6), were all updated reclassified to DEVELOPED (4000); Deciduous Forest (9), Evergreen Forest (10), Mixed Forest (11), Pasture/Hay (7), Grassland/Herbaceous (8), Scrub/Shrub (12), were updated to FOREST (5000); , Palustrine Forested Wetland (13), Palustrine Scrub/Shrub Wetland (14), Palustrine. Emergent Wetland (15), Estuarine Forested Wetland (16), Estuarine Scrub/Shrub Wetland (17), and Estuarine Emergent Wetland (18), were updated to MARSH (6000); Unconsolidated Shore (19), and Barren Land (20), were updated to UNCONSOLIDATED SHORE (7000); Open Water (21), Palustrine Aquatic Bed (22), and Estuarine Aquatic Bed (23), were updated to AQUATIC (1000).
The NJDEP data was parsed according to the “TYPE15” attribute field and reclassified as follows: Urban and Agriculture were reclassified as DEVELOPED (4000). Barren Land was reclassified as UNCONSOLIDATED SHORE (7000). Forest land cover maintained the same classification name, though was assigned a value of 5000. Water was reclassified as AQUATIC (1000). Wetlands were reclassified as MARSH (6000).
The VGIN land cover dataset was reclassified as follows: 11-Hydro was reclassified to AQUATIC (1000). 21- Impervious (extracted), 22- Impervious (Local datasets), 61 – Harvested/Disturbed, and 82 – Cropland were reclassified to DEVELOPED (4000), 31 – Barren was reclassified to UNCONSOLIDATED SHORE (7000). 41 – Forest, 42 – Tree, 51 – Scrub/Shrub, 71 – TurfGrass, and 81 – Pasture were reclassified to FOREST (5000). 91 – Woody Wetlands and 92 – Emergent Wetlands were reclassified as MARSH (6000).
The Chesapeake Conservancy land cover data was reclassified as follows: 1 Water was reclassified as AQUATIC (1000). 2 Emergent Wetlands was reclassified as MARSH (6000). 3 Tree Canopy, 4 Shrubland, and 5 Low Vegetation were reclassified to FOREST (5000). 6 Barren was reclassified to UNCONSOLODATED SHORE (7000). 7 Structures, 8 Impervious Surfaces, 9 Impervious Roads, 10 Tree Canopy over Structures, 11 Tree Canopy over Impervious Surfaces, and 12 Tree Canopy over Impervious Roads were reclassified to DEVELOPED (4000). 13 Aberdeen Proving Ground was reclassified to AQUATIC (1000) given that this area exists as a data gap for several other data sources, and no process or analysis takes place for Aquatic land cover type in this dataset.
The resulting reclassified land cover datasets were was then mosaiced using the Mosaic to New Raster tool and clipped to the Elevation Mosaic using the Extract by Mask tool.
CCAP
VGIN
Chesapeake Bay Conservation
NJDEP
Elevation Mosaic
2021
Tree 1
Step 3: Tidal flat, hardened shores, and rocky shores were extracted from all ESI datasets using the Select by Attributes and Export Features functions and converted to raster format using the Feature to Raster tool with a 10-meter cell size. The attribute fields named “LANDWARD SHORETYPE” (line data) and “ESI_DESCRIPTION” (polygon data) were used to extract these land cover types in the following way: 1A Exposed Rocky Shores and 8A Sheltered Impermeable Rocky Shores were extracted and classified as ROCKY SHORES (2000). 6B Riprap, 1B Exposed Solid Man Made Structures, 6B Exposed Riprap, 8B Sheltered Solid Man Made Structures, and 8C Sheltered Riprap were extracted, converted, and classified as HARDENED SHORES (3000). ESI polygon data was used to extract Tidal Flat areas where data exist, and line data was used otherwise. 7A Exposed Tidal Flats and 9A Sheltered Tidal Flats areas were extracted, converted and classified as TIDAL FLATS (8000). All data extracted and converted from ESI feature data was mosaicked with Tree 1 using the Mosaic to New Raster tool.
ESI_1
ESI_1
ESI_2
ESI_3
ESI_4
ESI_5
ESI_6
ESI_7
Tree 1
2021
Tree 2
Step 4: Long-term shoreline change rates transect data were first vetted according to the 90th percentile confidence interval (CI) and the magnitude of calculated change. If the CI was greater than the magnitude of change, the transect was eliminated and not used in further processing. Transect data (Himmelstoss et al. , 2010 and Himmelstoss, Farris, & Weber, 2018) were then manually clipped to eliminate spatial overhang into inapplicable portions of the landscape such as back-bay areas of barrier islands using the Clip tool. Transects were then converted to raster format using the Feature to Raster tool and interpolated to the surface of the landscape using two low-pass smoothing filters using the Filter tool. This tool was utilized twice in order to fill in data gaps between the 50-meter-spaced transects. Given the relatively long uniform length for transects included in Himmelstoss et al. (2010), some transects, this analysis was confined to unconsolidated shore, marshes, and armored shore land cover type, while land cover type was unrestricted for transects included in Himmelstoss, Farris, and Weber (2018)..The resulting raster data layer was assigned a value of 100, and NoData otherwise.
Shoreline Change - MA
Shoreline Change - Not MA
Tree 2
2021
Shoreline Change
Step 5: Vector Dune crest height data from Doran and others (2021) was spatially joined to intersecting shoreline change transects using the Spatial Join tool from Himmelstoss and others (2010) and Himmelstoss, Farris, & Weber (2018). The result was then clipped and appended to the landscape used to create a new raster data layer using the same method described in Step 4. Cells where dune height was less than 3 meters were reclassified as 100, and “NoData” otherwise.
Tree 2
PCOI
2021
Low Dunes
Step 6: Unconsolidated shore, hardened shore, marsh, and tidal flat land cover type (Land Cover Mosaic) were analyzed according to overlapping Shoreline Change and Low Dune data. Raster calculator was then used to add both Shoreline Change and Low Dunes to the cell values of these land cover types in Tree 2. If cells in Tree 2 overlapped the presence of Shoreline Change or Low Dunes, a value of 100 was added to the existing cell value of Tree 2. If cells overlapped both Shoreline Change and Low Dunes, a value of 200 was added to Tree 2.
Tree 2
2021
Tree 3
Step 7: NWI vector polygon data classified as Estuarine and Marine Wetland under the WETLAND_TY attribute field was extracted and exported to its own feature data layer. This data layer was then used to clip out marsh land cover type in Tree 3 using the Extract by Mask tool, in order to isolate salt marsh areas to be analyzed according to elevation and UVVR.
The Zonal Statistics tool was used to determine the mean elevation of salt marsh areas within each HUC 12 unit. If the elevation of salt marsh pixels in the data layer was less than the mean, pixels were reclassified to a value of 0 (low) if their elevation is below the mean for their respective hydrologic unit, and 1 (high) otherwise using the Raster Calculator tool. For UVVR, several thresholds were applied according to Ganju and others (2017) that determine the lifespan of salt marshes. Both elevation and UVVR were considered in further reclassifying salt marsh pixels; the following conditions explain how these data were reclassified (condition, reclassified value):
High elevation/Low (< 0.15) UVVR, 0; High elevation/ Moderate (between 0.15 and 0.4) UVVR, 10; Low elevation/Low UVVR, 20 ; Low elevation/Moderate UVVR, 30; High elevation/High (> 0.4) UVVR, 40; Low elevation/High UVVR, 50. Areas classified as "marsh" under CCAP that were not covered by the extent of salt marsh according to NWI and/or salt marsh areas where UVVR data did not exist were reclassified to a value of 60 (data insufficient). Raster Calculator was used to add these values to Tree 3.
Tree 3
UVVR
HUC
Elevation Mosaic
UVVR Metrics 1
UVVR Metrics 2
NWI
2021
Tree 4
Step 8: Developed land cover type was analyzed for elevation and slope. A raster layer of landscape slope in percent rise was created using the Slope tool on the Elevation Mosaic. The Raster Calculator tool was then used to determine areas of low/high elevation and slope. Elevation below 2 meters relative to MHW is considered “low” elevation, and “high” otherwise. A slope of less than 6 percent-rise was considered “low”, and “high” otherwise. Threshold values were determined according to slope variance within the domain and expert elicitation, and the landscape was reclassified according to the following (condition, reclassified value): High elevation/high slope, 0; High elevation/ low slope, 1; Low elevation/ high slope, 2; Low elevation/ low slope, 3. Raster Calculator was used to add the reclassified values listed to Tree 4.
Tree 4
Elevation Mosaic
2021
Tree 5
Slope
Step 9: Forested land cover was also analyzed for elevation and slope. The same thresholds, methods, and values used in Step 8 were used in this analysis.
Tree 5
Elevation Mosaic
Slope
2021
Tree 6
Step 10: The dataset created at the end of Step 9 (Tree 6) includes integer values ranging from 1000 to 8200. Every unique value in this raster represents a possible environmental condition with a unique propensity to change in the coming decade according to published research and/or expert opinion. Each of these unique values was assigned a value between 0 and 10 in a new attribute field named “CCL”. Cell values of 1 represent areas unlikely to undergo any change in landform and 10 represent areas with a high likelihood to change. Further details on how this relative scale was applied can be found in the accompanying Data Report (Pendleton and others, 2023).
An attribute field was added for each of the metrics analyzed and used to reclassify this raster dataset. See the Entity and Attribute section for details.
Tree 6
2021
Tree 7
Raster
Grid Cell
128025
118491
1
WGS 1984 Web Mercator (auxiliary sphere)
0.0
0.0
0.0
0.0
0.0
0.0
row and column
10.0
10.0
meters
WGS_1984
WGS 84
6378137.0
298.257223563
NEStates_Fabric.tif
Attribute table (.dbf)
Producer Defined
OID_
Internal identifier number
ESRI
Sequential unique whole numbers that are automatically generated.
Value
Unique ID number. Each unique value represents a potential combination of landscape characteristics, which are described in detail throughout the process steps of this metadata document.
U.S. Geological Survey
1000
Aquatic land cover type. No other landscape characteristics are associated with this land cover type in Version 1 of CCL.
U.S. Geological Survey
2000
Rocky Shore land cover type. No other landscape characteristics are associated with this land cover type in Version 1 of CCL.
U.S. Geological Survey
3000
Hardened Shore land cover type with "high" dune height OR no dune height data present. Thresholds for dune height are defined in Process Step 6.
U.S. Geological Survey
3100
Hardened Shore land cover type with "low" dune height OR presence of long-term shoreline change. Thresholds for dune height and shoreline change metrics are defined in Process Step 6.
U.S. Geological Survey
3200
Hardened Shore land cover type with "low" dune height AND presence of long-term shoreline change. Thresholds for dune height and shoreline change metrics are defined in Process Step 6.
U.S. Geological Survey
4000
Developed land cover type with "high" elevation and "high" slope as defined in process Step 8.
U.S. Geological Survey
4001
Developed land cover type with "high" elevation and "low" slope as defined in process Step 8.
U.S. Geological Survey
4002
Developed land cover type with "low" elevation and "high" slope as defined in Process Step 8.
U.S. Geological Survey
4003
Developed land cover type with "low" elevation and "low" slope as defined in Process Step 8.
U.S. Geological Survey
5000
Forest land cover type with "high" elevation and "high" slope as defined in Process Step 8 for developed areas.
U.S. Geological Survey
5001
Forest land cover type with "high" elevation and "low" slope as defined in Process Step 8 for developed areas.
U.S. Geological Survey
5002
Forest land cover type with "low" elevation and "high" slope as defined in Process Step 8 for developed areas.
U.S. Geological Survey
5003
Forest land cover type with "low" elevation and "low" slope as defined in Process Step 8 for developed areas.
U.S. Geological Survey
6000
Marsh land cover type with "high" elevation and "low" UVVR as defined in Process Step 7.
U.S. Geological Survey
6010
Marsh land cover type with "high" elevation and "moderate" UVVR as defined in Process Step 7.
U.S. Geological Survey
6020
Marsh land cover type with "low" elevation and "low" UVVR as defined in Process Step 7.
U.S. Geological Survey
6030
Marsh land cover type with "low" elevation and "moderate" UVVR as defined in Process Step 7.
U.S. Geological Survey
6040
Marsh land cover type with "high" elevation and "high" UVVR as defined in Process Step 7.
U.S. Geological Survey
6050
Marsh land cover type with "low" elevation and "high" UVVR as defined in Process Step 7.
U.S. Geological Survey
6060
Area classified as Marsh according to NOAA C-CCAP or other land cover data source, though not classified as "salt marsh" according to NWI data source.
U.S. Geological Survey
7000
Unconsolidated shore land cover type with "high" dune height OR no dune height data present. Thresholds for dune height are defined in Process Step 6.
U.S. Geological Survey
7100
Unconsolidated shore land cover type with "low" dune height OR presence of long-term shoreline change. Thresholds for dune height and shoreline change metrics are defined in Process Step 6.
U.S. Geological Survey
7200
Unconsolidated shore land cover type with "low" dune height AND presence of long-term shoreline change. Thresholds for dune height and shoreline change metrics are defined in Process Step 6.
U.S. Geological Survey
8000
Tidal Flats land cover type with "high" dune height OR no dune height data present. Thresholds for dune height are defined in Process Step 6.
U.S. Geological Survey
8100
Tidal Flats land cover type with "low" dune height OR presence of long-term shoreline change. Thresholds for dune height and shoreline change metrics are defined in Process Step 6.
U.S. Geological Survey
Count
Number of cells containing this specific value.
ESRI
47
364060780
Land_Cover
Land cover type as defined by the reclassified land cover data layer created in process steps 2 and 3.
Producer Defined
Aquatic
Includes areas with land cover types "Open Water", "Palustrine Aquatic Bed", and "Estuarine Aquatic Bed" under NOAA CCAP data.
U.S. Geological Survey
Rocky Shore
Includes area with attributes "LANDWARD_SHORETYPE > 1A: Exposed Rocky Shores" and "8A: Sheltered, Impermeable, Rocky Shores" and "ESI_DESCRIPTION > 2A: Exposed Wave-Cut Platforms (Bedrock/Mud/Clay)" under NOAA ESI line and polygon data.
U.S. Geological Survey
Hardened Shore
Includes areas with attributes "GENERALIZED_ESI_TYPE > 1:Armored" under NOAA ESI data.
U.S. Geological Survey
Developed
Includes areas with land cover type "Developed High Intensity", "Developed Medium Intensity", "Developed Low Intensity", "Developed Open Space", and "Cultivated Crops" under NOAA CCAP data.
U.S. Geological Survey
Forest
Includes areas with land cover types "Pasture/Hay", "Grassland/Herbaceous", "Deciduous Forest", "Evergreen Forest", "Mixed Forest", and "Shrub/Scrub" under NOAA CCAP data.
U.S. Geological Survey
Marsh
Includes areas with land cover type "Palustrine Forested Wetland", "Palustrine Shrub/Scrub Wetland", "Palustrine Emergent Wetland", "Estuarine Forested Wetland", "Estuarine Shrub/Scrub Wetland", and "Estuarine Emergent Wetland" under NOAA CCAP data.
U.S. Geological Survey
Unconsolidated Shore
Includes areas with land cover types "Unconsolidated Shore" and "Barren Land" under NOAA CCAP data and other landcover sources referred to in Process Step 2.
U.S. Geological Survey
Tidal Flats
Includes areas with attributes "7: Exposed Tidal Flats" and "9A: Sheltered Tidal Flats" under NOAA ESI line and polygon data.
U.S. Geological Survey
Dune_SCR
Low dune elevation and long-term shoreline change rates. The result of process Step 6.
Producer Defined
N/A
Indicates that low dune elevation or shoreline change rates are not applicable to the land cover type described in Land_Cover.
U.S. Geological Survey
HighDunes/NoData
Indicates that either dune elevation is above the 3 meter3-meter threshold value in order to be considered "high" and/or no data exists for shoreline change rates.
U.S. Geological Survey
LowDunes_OR_SCR
Indicates the presence of either dune elevation below 3 meters (low) or long-term shoreline change.
U.S. Geological Survey
LowDunes_AND_SCR
Indicates the presence of both low dune elevation and long-term shoreline change.
U.S. Geological Survey
Elev_UVVR
Elevation and UVVR characteristics.
Producer Defined
N/A
Indicates that UVVR is not applicable to the land cover type defined in Land_Cover.
U.S. Geological Survey
High_Low
“High” or “Low” in the first position of this attribute indicates whether the elevation for a raster cell is above or below that of the mean for its respective hydrologic unit (HUC 12). “Low”, “Mod”, or “High” in the second position indicates the UVVR value for each cell. Definitions for specific UVVR thresholds (Low, Mod, and High) are described in detail in process Step 7. For example, "High_low" indicates an elevation above the mean elevation for the respective HUC 12 unit, and a low UVVR value.
U.S. Geological Survey
High_Mod
See definition for "High_Low"
U.S. Geological Survey
Low_Low
See definition for "High_Low"
U.S. Geological Survey
Low_Mod
See definition for "High_Low"
U.S. Geological Survey
High_High
See definition for "High_Low"
U.S. Geological Survey
Low_High
See definition for "High_Low"
U.S. Geological Survey
DataInsufficient
Salt marsh are where UVVR data does not exist OR area not classified as salt marsh by NWI but classified as wetland or marsh area in other land cover data sources.
U.S. Geological Survey
Elev_Slope
Elevation and Slope characteristics for developed and forested land cover type.
Producer Defined
N/A
Indicates that the elevation and slope metric is not applicable to the land cover type defined in Land_Cover.
U.S. Geological Survey
High_High
The term in the first position describes elevation and the second describes slope. Thresholds for both are defined in process Step 8. For example, “Low_High” indicates a raster cell with low elevation and high slope.
U.S. Geological Survey
High_Low
See definition for High_High.
U.S. Geological Survey
Low_High
See definition for High_High.
U.S. Geological Survey
Low_Low
See definition for High_High.
U.S. Geological Survey
Fab_RTC
A measure of the resistance of the landscapes to change (RTC) in response to coastal hazards, where low values are very resistant and high values have low resistance.
Producer Defined
0
No estimate of changeability
U.S. Geological Survey
1
Consolidated landscapes; nearly incapable of measurable change due to decadal-scale coastal processes
U.S. Geological Survey
2
Consolidated structures built to resist coastal processes, nearly incapable of change.
U.S. Geological Survey
3
Consolidated and Unconsolidated human-modified environments that are unlikely to change, but not built to resist coastal processes. Typically, afforded some protection due to setback and elevation
U.S. Geological Survey
4
Unconsolidated forested environments that are capable of change; typically afforded some protection due to setback, elevation, and vegetation.
U.S. Geological Survey
5
Unconsolidated wetland environments that are capable of change, afforded some protection to no protection from coastal hazards/processes.
U.S. Geological Survey
6
Unconsolidated bluff and beach environments that are capable of change, afforded some to no protection from coastal processes.
U.S. Geological Survey
7
Unconsolidated environments that are capable of change, afforded some to no protection from hazards and frequently submerged or flooded.
U.S. Geological Survey
8
An environment (listed above) that has associated measured change data that increases the estimated change likelihood value.
U.S. Geological Survey
9
An environment (listed above) that has associated measured change data that increases the estimated change likelihood value.
U.S. Geological Survey
This section describes the attribute information associated with the raster dataset. Please review the individual attribute descriptions for detailed information.
U.S. Geological Survey - ScienceBase
U.S. Geological Survey - ScienceBase
mailing and physical address
Denver Federal Center, Building 810, Mail Stop 302
Denver
CO
80225
US
1-888-275-8747
sciencebase@usgs.gov
This dataset contains the raster data layer (.tif) and associated files (.sld, .ovr, .cpg, and .dbf) needed to view and edit the information it contains, as well as the FGDC CSDGM metadata in XML format. The .sld is a Service Layer Definition file used by ScienceBase to display the data, the .ovr file contains the pyramids used by a GIS to display the data at different scales the .cpg file is for charactersets, and the .dbf is a dBASE table file used to store data attributes.
Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty.
GeoTIFF
ESRI ArcGIS Pro v2.6.3
453
https://doi.org/10.5066/P96A2Q5X
https://www.sciencebase.gov/catalog/file/get/61781f88d34e4c6b7fe2a444
https://www.sciencebase.gov/catalog/item/61781f88d34e4c6b7fe2a444
The first link is to the USGS publication page, the second link downloads all the data on the landing page, and the third link is to the dataset landing page.
None
20230228
U.S. Geological Survey
Elizabeth A Pendleton
Geologist
mailing and physical
384 Woods Hole Rd
Woods Hole
MA
02543
(508) 457 2259
ependleton@usgs.gov
Content Standard for Digital Geospatial Metadata, FGDC-STD-001-1998
FGDC-STD-001-1998