Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Fabric Dataset

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Frequently anticipated questions:

What does this data set describe?

Title:
Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Fabric Dataset
Abstract:
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.
  1. How might this data set be cited?
    Sterne, Travis K., Pendleton, Elizabeth A., Lentz, Erika E., and Henderson, Rachel E., 20230228, Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Fabric Dataset: data release DOI:10.5066/P96A2Q5X, U.S. Geological Survey, Coastal and Marine Geology Program, Woods Hole Coastal and Marine Science Center, Woods Hole, MA.

    Online Links:

    This is part of the following larger work.

    Sterne, Travis K., Pendleton, Elizabeth A., Lentz, Erika E., and Henderson, Rachel E., 2023, Coastal Change Likelihood in the U.S. Northeast Region — Maine to Virginia: data release DOI:10.5066/P96A2Q5X, U.S. Geological Survey, Reston, VA.

    Online Links:

    Other_Citation_Details:
    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.
  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -77.527891
    East_Bounding_Coordinate: -66.883630
    North_Bounding_Coordinate: 45.192996
    South_Bounding_Coordinate: 36.514857
  3. What does it look like?
    https://www.sciencebase.gov/catalog/file/get/61781f88d34e4c6b7fe2a444?name=Fabric_Graphic.jpg (JPEG)
    Outer Cape Cod with land cover type as defined in the coastal fabric data layer
  4. Does the data set describe conditions during a particular time period?
    Beginning_Date: 2010
    Ending_Date: 2021
    Currentness_Reference:
    ground condition of source data
  5. What is the general form of this data set?
    Geospatial_Data_Presentation_Form: raster digital data
  6. How does the data set represent geographic features?
    1. How are geographic features stored in the data set?
      This is a Raster data set. It contains the following raster data types:
      • Dimensions 128025 x 118491 x 1, type Grid Cell
    2. What coordinate system is used to represent geographic features?
      The map projection used is WGS 1984 Web Mercator (auxiliary sphere).
      Projection parameters:
      False_Easting: 0.0
      False_Northing: 0.0
      Latitude_of_Projection_Origin: 0.0
      Longitude_of_Central_Meridian: 0.0
      Standard_Parallel: 0.0
      Standard_Parallel: 0.0
      Planar coordinates are encoded using row and column
      Abscissae (x-coordinates) are specified to the nearest 10.0
      Ordinates (y-coordinates) are specified to the nearest 10.0
      Planar coordinates are specified in meters
      The horizontal datum used is WGS_1984.
      The ellipsoid used is WGS 84.
      The semi-major axis of the ellipsoid used is 6378137.0.
      The flattening of the ellipsoid used is 1/298.257223563.
  7. How does the data set describe geographic features?
    NEStates_Fabric.tif
    Attribute table (.dbf) (Source: Producer Defined)
    OID_
    Internal identifier number (Source: 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. (Source: U.S. Geological Survey)
    ValueDefinition
    1000Aquatic land cover type. No other landscape characteristics are associated with this land cover type in Version 1 of CCL.
    2000Rocky Shore land cover type. No other landscape characteristics are associated with this land cover type in Version 1 of CCL.
    3000Hardened Shore land cover type with "high" dune height OR no dune height data present. Thresholds for dune height are defined in Process Step 6.
    3100Hardened 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.
    3200Hardened 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.
    4000Developed land cover type with "high" elevation and "high" slope as defined in process Step 8.
    4001Developed land cover type with "high" elevation and "low" slope as defined in process Step 8.
    4002Developed land cover type with "low" elevation and "high" slope as defined in Process Step 8.
    4003Developed land cover type with "low" elevation and "low" slope as defined in Process Step 8.
    5000Forest land cover type with "high" elevation and "high" slope as defined in Process Step 8 for developed areas.
    5001Forest land cover type with "high" elevation and "low" slope as defined in Process Step 8 for developed areas.
    5002Forest land cover type with "low" elevation and "high" slope as defined in Process Step 8 for developed areas.
    5003Forest land cover type with "low" elevation and "low" slope as defined in Process Step 8 for developed areas.
    6000Marsh land cover type with "high" elevation and "low" UVVR as defined in Process Step 7.
    6010Marsh land cover type with "high" elevation and "moderate" UVVR as defined in Process Step 7.
    6020Marsh land cover type with "low" elevation and "low" UVVR as defined in Process Step 7.
    6030Marsh land cover type with "low" elevation and "moderate" UVVR as defined in Process Step 7.
    6040Marsh land cover type with "high" elevation and "high" UVVR as defined in Process Step 7.
    6050Marsh land cover type with "low" elevation and "high" UVVR as defined in Process Step 7.
    6060Area 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.
    7000Unconsolidated shore land cover type with "high" dune height OR no dune height data present. Thresholds for dune height are defined in Process Step 6.
    7100Unconsolidated 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.
    7200Unconsolidated 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.
    8000Tidal Flats land cover type with "high" dune height OR no dune height data present. Thresholds for dune height are defined in Process Step 6.
    8100Tidal 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.
    Count
    Number of cells containing this specific value. (Source: ESRI)
    Range of values
    Minimum:47
    Maximum:364060780
    Land_Cover
    Land cover type as defined by the reclassified land cover data layer created in process steps 2 and 3. (Source: Producer Defined)
    ValueDefinition
    AquaticIncludes areas with land cover types "Open Water", "Palustrine Aquatic Bed", and "Estuarine Aquatic Bed" under NOAA CCAP data.
    Rocky ShoreIncludes 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.
    Hardened ShoreIncludes areas with attributes "GENERALIZED_ESI_TYPE > 1:Armored" under NOAA ESI data.
    DevelopedIncludes areas with land cover type "Developed High Intensity", "Developed Medium Intensity", "Developed Low Intensity", "Developed Open Space", and "Cultivated Crops" under NOAA CCAP data.
    ForestIncludes areas with land cover types "Pasture/Hay", "Grassland/Herbaceous", "Deciduous Forest", "Evergreen Forest", "Mixed Forest", and "Shrub/Scrub" under NOAA CCAP data.
    MarshIncludes 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.
    Unconsolidated ShoreIncludes areas with land cover types "Unconsolidated Shore" and "Barren Land" under NOAA CCAP data and other landcover sources referred to in Process Step 2.
    Tidal FlatsIncludes areas with attributes "7: Exposed Tidal Flats" and "9A: Sheltered Tidal Flats" under NOAA ESI line and polygon data.
    Dune_SCR
    Low dune elevation and long-term shoreline change rates. The result of process Step 6. (Source: Producer Defined)
    ValueDefinition
    N/AIndicates that low dune elevation or shoreline change rates are not applicable to the land cover type described in Land_Cover.
    HighDunes/NoDataIndicates 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.
    LowDunes_OR_SCRIndicates the presence of either dune elevation below 3 meters (low) or long-term shoreline change.
    LowDunes_AND_SCRIndicates the presence of both low dune elevation and long-term shoreline change.
    Elev_UVVR
    Elevation and UVVR characteristics. (Source: Producer Defined)
    ValueDefinition
    N/AIndicates that UVVR is not applicable to the land cover type defined in Land_Cover.
    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.
    High_ModSee definition for "High_Low"
    Low_LowSee definition for "High_Low"
    Low_ModSee definition for "High_Low"
    High_HighSee definition for "High_Low"
    Low_HighSee definition for "High_Low"
    DataInsufficientSalt 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.
    Elev_Slope
    Elevation and Slope characteristics for developed and forested land cover type. (Source: Producer Defined)
    ValueDefinition
    N/AIndicates that the elevation and slope metric is not applicable to the land cover type defined in Land_Cover.
    High_HighThe 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.
    High_LowSee definition for High_High.
    Low_HighSee definition for High_High.
    Low_LowSee definition for High_High.
    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. (Source: Producer Defined)
    ValueDefinition
    0No estimate of changeability
    1Consolidated landscapes; nearly incapable of measurable change due to decadal-scale coastal processes
    2Consolidated structures built to resist coastal processes, nearly incapable of change.
    3Consolidated 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
    4Unconsolidated forested environments that are capable of change; typically afforded some protection due to setback, elevation, and vegetation.
    5Unconsolidated wetland environments that are capable of change, afforded some protection to no protection from coastal hazards/processes.
    6Unconsolidated bluff and beach environments that are capable of change, afforded some to no protection from coastal processes.
    7Unconsolidated environments that are capable of change, afforded some to no protection from hazards and frequently submerged or flooded.
    8An environment (listed above) that has associated measured change data that increases the estimated change likelihood value.
    9An environment (listed above) that has associated measured change data that increases the estimated change likelihood value.
    Entity_and_Attribute_Overview:
    This section describes the attribute information associated with the raster dataset. Please review the individual attribute descriptions for detailed information.
    Entity_and_Attribute_Detail_Citation: U.S. Geological Survey - ScienceBase

Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
    • Travis K. Sterne
    • Elizabeth A. Pendleton
    • Erika E. Lentz
    • Rachel E. Henderson
  2. Who also contributed to the data set?
  3. To whom should users address questions about the data?
    U.S. Geological Survey
    Attn: Travis K Sterne
    384 Woods Hole Rd
    Woods Hole, MA

    (508) 548 8700 x2219 (voice)
    tsterne@usgs.gov

Why was the data set created?

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.

How was the data set created?

  1. From what previous works were the data drawn?
    NOAA SLR Topo (source 1 of 23)
    Office for Coastal Management, 2016, NOAA Office for Coastal Management Sea Level Rise Data: 1-10ft Seal Level Rise Inundation Extent.

    Online Links:

    Type_of_Source_Media: Digital and/or Hardcopy
    Source_Contribution: Elevation
    CONED (source 2 of 23)
    Danielson, Jeffrey, and Tyler, Dean, 2018, Coastal National Elevation Database.

    Online Links:

    Type_of_Source_Media: Digital and/or Hardcopy
    Source_Contribution: Elevation
    CZM Topobathy (source 3 of 23)
    Andrews, B.D., Baldwin, W.E., Sampson, D.W., and Schwab, W.C., 20191227, Continuous bathymetry and elevation models of the Massachusetts coastal zone and continental shelf: data release DOI:10.5066/F72806T7, U.S. Geological Survey, Reston, VA.

    Online Links:

    Type_of_Source_Media: Digital and/or Hardcopy
    Source_Contribution: Elevation
    CCAP (source 4 of 23)
    NOAA Office for Coastal Management, 20161003, C-CAP Derived Land Cover BETA.

    Online Links:

    Type_of_Source_Media: Digital and/or Hardcopy
    Source_Contribution: Land Cover
    UVVR (source 5 of 23)
    Couvillion, B.R., Ganju, N.K., and Defne, Z., 20210205, An Unvegetated to Vegetated Ratio (UVVR) for coastal wetlands of the Conterminous United States (2014-2018): data release DOI:10.5066/P97DQXZP, U.S. Geological Survey, Reston, VA.

    Online Links:

    Other_Citation_Details:
    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.
    Type_of_Source_Media: Digital and/or Hardcopy
    Source_Contribution: Marsh Vegetation Cover
    Shoreline Change - MA (source 6 of 23)
    Himmelstoss, E.A., Farris, A.S., Weber, K.M., and Henderson, R.E., 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: data release DOI:10.5066/P9RRBEYK, U.S. Geoloigcal Survey, Reston, VA.

    Online Links:

    Other_Citation_Details:
    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.
    Type_of_Source_Media: Digital and/or Hardcopy
    Source_Contribution: Shoreline Change Rates
    Shoreline Change - Not MA (source 7 of 23)
    Himmelstoss, E.A., Kratzmann, M., Hapke, C., Thieler, E.R., and List, J., 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: Open-File Report 2010-1119, U.S. Geological Survey, Reston, VA.

    Online Links:

    Type_of_Source_Media: Digital and/or Hardcopy
    Source_Contribution: Shoreline Change Rates
    Vdatum (source 8 of 23)
    Service, National Ocean, 2021, VDatum.

    Online Links:

    Type_of_Source_Media: Digital and/or Hardcopy
    Source_Contribution: MHW elevation conversion
    PCOI (source 9 of 23)
    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: data release DOI:10.5066/P99ILAB9, U.S. Geological Survey, Reston, VA.

    Online Links:

    Other_Citation_Details:
    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.
    Type_of_Source_Media: Digital and/or Hardcopy
    Source_Contribution: Dune Height, probability of over-wash
    HUC (source 10 of 23)
    U.S. Geological Survey, 2020, Watershed Boundary Dataset: National Hydrography Dataset.

    Online Links:

    Type_of_Source_Media: Digital and/or Hardcopy
    Source_Contribution: Watershed boundaries
    UVVR Metrics 1 (source 11 of 23)
    Defne, Z., Aretxabaleta, A.L., Ganju, N.K., Kalra, T.S., Jones, D.K., and Smith, K.E.L., 20200130, A geospatially resolved wetland vulnerability index: Synthesis of physical drivers.

    Online Links:

    Type_of_Source_Media: Digital and/or Hardcopy
    Source_Contribution: UVVR metrics
    UVVR Metrics 2 (source 12 of 23)
    Ganju, N.K., Defne, Z., Kirwan, M.L., Fagherazzi, S., D'Alpaos, A., and Carniello, L., 20170123, Spatially integrative metrics reveal hidden vulnerability of microtidal salt marshes.

    Online Links:

    Type_of_Source_Media: Digital and/or Hardcopy
    Source_Contribution: UVVR metrics
    ESI_1 (source 13 of 23)
    Office of Response and Restoration, 2017, National Environmental Sensitivity Index Shoreline: GULF/ATLANTIC ESI, PACIFIC ESI: ESIL (ESI Shoreline Types - Lines)..

    Online Links:

    Type_of_Source_Media: Digital and/or Hardcopy
    Source_Contribution: Land Cover
    ESI_2 (source 14 of 23)
    Office of Response and Restoration, 2016, Maine and New Hampshire 2016 ESI Polygons.

    Online Links:

    Other_Citation_Details: NOAA National Centers for Environmental Information
    Type_of_Source_Media: Digital and/or Hardcopy
    Source_Contribution: Land Cover
    ESI_3 (source 15 of 23)
    Office of Response and Restoration, 2016, Massachusetts and Rhode Island 2016 ESIP (ESI Shoreline Types - Polygons).

    Online Links:

    Other_Citation_Details: NOAA National Centers for Environmental Information
    Type_of_Source_Media: Digital and/or Hardcopy
    Source_Contribution: Land Cover
    ESI_4 (source 16 of 23)
    Office of Response and Restoration, 2016, Long Island Sound 2016 ESI POLYGONS.

    Online Links:

    Other_Citation_Details: NOAA National Centers for Environmental Information
    Type_of_Source_Media: Digital and/or Hardcopy
    Source_Contribution: Land Cover
    ESI_5 (source 17 of 23)
    Office of Response and Restoration, 2016, NY/NJ Metro Area, Hudson River, and South Long Island 2016 ESIP (Environmental Sensitivity Index Polygons).

    Online Links:

    Other_Citation_Details: NOAA National Centers for Environmental Information
    Type_of_Source_Media: Digital and/or Hardcopy
    Source_Contribution: Land Cover
    ESI_6 (source 18 of 23)
    Office of Response and Restoration, 2014, Delaware / New Jersey / Pennsylvania 2014 ESI Polygons.

    Online Links:

    Other_Citation_Details: NOAA National Centers for Environmental Information
    Type_of_Source_Media: Digital and/or Hardcopy
    Source_Contribution: Land Cover
    ESI_7 (source 19 of 23)
    Office of Response and Restoration, 2016, Chesapeake Bay and the Outer Coasts of Maryland and Virginia 2016 ESI Polygons.

    Online Links:

    Other_Citation_Details: NOAA National Centers for Environmental Information
    Type_of_Source_Media: Digital and/or Hardcopy
    Source_Contribution: Land Cover
    NWI (source 20 of 23)
    U.S. Fish and Wildlife Service, 2020, National Wetlands Inventory.

    Online Links:

    Other_Citation_Details:
    U.S. Department of the Interior, Fish and Wildlife Service, Washington, D.C.
    Type_of_Source_Media: Digital and/or Hardcopy
    Source_Contribution: Salt Marsh Extent
    NJ Land Cover (source 21 of 23)
    New Jersey Department of Environmental Protection Bureau of GIS, 20190128, Land Use/Land Cover of New Jersey 2015.

    Online Links:

    Type_of_Source_Media: Digital and/or Hardcopy
    Source_Contribution: Land Cover
    VA Land Cover (source 22 of 23)
    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),, and Worldview Solutions, Inc., 2020, 1-meter Virginia Land Cover.

    Online Links:

    Type_of_Source_Media: Digital and/or Hardcopy
    Source_Contribution: Land Cover
    Chesapeake Conservancy (source 23 of 23)
    Conservancy, Chesapeake, 2018, Landcover Data Project 2013/2014.

    Online Links:

    Type_of_Source_Media: Digital and/or Hardcopy
    Source_Contribution: Land Cover
  2. How were the data generated, processed, and modified?
    Date: 2021 (process 1 of 10)
    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. Data sources used in this process:
    • NOAA SLR Topo
    • CONED
    • CZM Topobathy
    • Vdatum
    Data sources produced in this process:
    • Elevation Mosaic
    Date: 2021 (process 2 of 10)
    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. Data sources used in this process:
    • CCAP
    • VGIN
    • Chesapeake Bay Conservation
    • NJDEP
    • Elevation Mosaic
    Data sources produced in this process:
    • Tree 1
    Date: 2021 (process 3 of 10)
    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. Data sources used in this process:
    • ESI_1
    • ESI_1
    • ESI_2
    • ESI_3
    • ESI_4
    • ESI_5
    • ESI_6
    • ESI_7
    • Tree 1
    Data sources produced in this process:
    • Tree 2
    Date: 2021 (process 4 of 10)
    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. Data sources used in this process:
    • Shoreline Change - MA
    • Shoreline Change - Not MA
    • Tree 2
    Data sources produced in this process:
    • Shoreline Change
    Date: 2021 (process 5 of 10)
    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. Data sources used in this process:
    • Tree 2
    • PCOI
    Data sources produced in this process:
    • Low Dunes
    Date: 2021 (process 6 of 10)
    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. Data sources used in this process:
    • Tree 2
    Data sources produced in this process:
    • Tree 3
    Date: 2021 (process 7 of 10)
    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. Data sources used in this process:
    • Tree 3
    • UVVR
    • HUC
    • Elevation Mosaic
    • UVVR Metrics 1
    • UVVR Metrics 2
    • NWI
    Data sources produced in this process:
    • Tree 4
    Date: 2021 (process 8 of 10)
    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. Data sources used in this process:
    • Tree 4
    • Elevation Mosaic
    Data sources produced in this process:
    • Tree 5
    • Slope
    Date: 2021 (process 9 of 10)
    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. Data sources used in this process:
    • Tree 5
    • Elevation Mosaic
    • Slope
    Data sources produced in this process:
    • Tree 6
    Date: 2021 (process 10 of 10)
    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. Data sources used in this process:
    • Tree 6
    Data sources produced in this process:
    • Tree 7
  3. What similar or related data should the user be aware of?
    Thieler, E.R., and Hammar-Klose, E.S., 1999, National assessment of coastal vulnerability to sea-level rise; U.S. Atlantic Coast: Open-File Report 1999-595, U.S. Geological Survey, Reston, VA.

    Online Links:

    Pendleton, Elizabeth A., Lentz, Erika E., Sterne, Travis K., and Henderson, Rachel E., 2023, Development and application of a coastal change likelihood assessment for the northeast region, Maine to Virginia: Data Report 1169, U.S. Geological Survey, Reston, VA.

    Online Links:

    Other_Citation_Details:
    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)

How reliable are the data; what problems remain in the data set?

  1. How well have the observations been checked?
    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.
  2. How accurate are the geographic locations?
    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.
  3. How accurate are the heights or depths?
    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.
  4. Where are the gaps in the data? What is missing?
    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.
  5. How consistent are the relationships among the observations, including topology?
    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)

How can someone get a copy of the data set?

Are there legal restrictions on access or use of the data?
Access_Constraints None. Please see 'Distribution Info' for details.
Use_Constraints 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.
  1. Who distributes the data set? (Distributor 1 of 1)
    U.S. Geological Survey - ScienceBase
    Denver Federal Center, Building 810, Mail Stop 302
    Denver, CO
    US

    1-888-275-8747 (voice)
    sciencebase@usgs.gov
  2. What's the catalog number I need to order this data set? 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.
  3. What legal disclaimers am I supposed to read?
    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.
  4. How can I download or order the data?

Who wrote the metadata?

Dates:
Last modified: 28-Feb-2023
Metadata author:
U.S. Geological Survey
Attn: Elizabeth A Pendleton
Geologist
384 Woods Hole Rd
Woods Hole, MA

(508) 457 2259 (voice)
ependleton@usgs.gov
Metadata standard:
Content Standard for Digital Geospatial Metadata, FGDC-STD-001-1998 (FGDC-STD-001-1998)
This page is <https://cmgds.marine.usgs.gov/catalog/whcmsc/SB_data_release/DR_P96A2Q5X/USGS_CCL_Fabric_2022.faq.html>
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