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

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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 - Maximum Change Likelihood
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 - Maximum Change Likelihood: data release DOI:10.5066/P96A2Q5X, U.S. Geological Survey, Coastal and Marine Hazards and Resources 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, Coastal and Marine Hazards and Resources Program, 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: -77.5279
    East_Bounding_Coordinate: -66.8837
    North_Bounding_Coordinate: 45.1930
    South_Bounding_Coordinate: 36.5149
  3. What does it look like?
    https://www.sciencebase.gov/catalog/file/get/6197cb8dd34eb622f692ee19?name=MaxCCL_Graphic.jpg (JPEG)
    Outer Cape Cod with Maximum CCL 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 Mercator_1SP.
      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?
    USGS_CCL_MaximumCCL_2022.tif
    Raster geospatial data file. (Source: Producer defined)
    OID
    Internal object identifier. (Source: Producer defined) Sequential unique whole numbers that are automatically generated.
    Value
    Unique numeric values contained in each raster cell that represents change likelihood. Definitions for change likelihood can be found in the associated Data Report - Section 1.2.1. (Source: U.S. Geological Survey)
    ValueDefinition
    0No CCL value assigned
    1Extremely unlikely to change
    2Very unlikely to change
    3Unlikely to change
    4Somewhat unlikely to change
    5Mostly uncertain to slightly unlikely to change
    6Mostly uncertain to slightly likely to change
    7Somewhat likely to change
    8Likely to change
    9Very likely to change
    10Extremely likely to change
    Count
    Number of raster cells with this value. (Source: Producer defined)
    Range of values
    Minimum:146803.0
    Maximum:365003781.0

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 Maximum Change Likelihood is the combination of supervised learning outcomes from the Fabric, Perpetual, and Event hazards. Each 10 mpp raster cell is assigned a value between 1 and 10 that is an estimate of change likelihood, where 1 is low and 10 is high, based on an ordinal scale. 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?
    PerpetualHazardsCompilation (source 1 of 3)
    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 - Perpetual Hazard Compilation.

    Online Links:

    Type_of_Source_Media: Digital and/or Hardcopy
    Source_Contribution: Perpetual Hazards data
    EventHazardsCompilation (source 2 of 3)
    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 - Event Hazard Compilation.

    Online Links:

    Type_of_Source_Media: Digital and/or Hardcopy
    Source_Contribution: Event Hazards data
    Fabric (source 3 of 3)
    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 - 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:

    Type_of_Source_Media: Digital and/or Hardcopy
    Source_Contribution: Fabric dataset
  2. How were the data generated, processed, and modified?
    Date: 2021 (process 1 of 6)
    This step and all the subsequent steps were completed by Elizabeth A. Pendleton or Travis K. Sterne using ESRI ArcGIS Pro geospatial software. Any steps that mention the use of “tools” or “functions” refer to geoprocessing tools utilized in ArcGIS Pro. The steps described in detail below are computed on the domain defined by the fabric, event hazards composite, and perpetual hazards composite datasets, which can be found as complimentary data products in this data release. The data processed and included in this dataset has been clipped or modified to fit within the domain of the Northeast CCL study area. Classification schema in this dataset are defined by possible unique spatial combinations of the seven land cover types defined by the Fabric dataset and Hazard (both Event and Perpetual) datasets. Land cover types include rocky shores, hardened shorelines, developed, forest, marsh, unconsolidated shores, and tidal flats. Event hazards include high tide flooding, storm overwash, and wave power . Perpetual hazards include relative sea-level rise projections for 2030, storm recurrence interval, and short-term shoreline erosion rate. Unique combinations between fabric and hazards datasets can be enumerated to over 10,000 classes, and thus were binned according to user-defined criteria based on previous knowledge of landscape and hazard interaction. The final raster dataset (at end of step 4) presented here is the result of a supervised machine learning (Support Vector Machine (SVM)) landscape classification using training samples created by the user.
    Date: 2021 (process 2 of 6)
    Step 1: In preparation for supervised image classification, two sets of training samples were created, one for the dataset with fabric and perpetual hazards and one for the dataset with fabric and event hazards. For the fabric and perpetual hazards dataset, 30 classes that represented combinations of landscapes and hazards were created with a total of 607 samples created within the 30 classes. For the fabric and event hazards dataset, 25 classes that represented combinations of landscapes and hazards were created, and a total of 461 samples were created for the 25 classes. For a detailed explanation of classes, refer to the associated Data Report (Pendleton and others, 2023). Person who carried out this activity:
    Travis K Sterne
    U.S. Geological Survey, NORTHEAST REGION
    Geographer
    384 Woods Hole Road
    Woods Hole, MA
    US

    (508) 548 8700 x2219 (voice)
    tsterne@usgs.gov
    Data sources used in this process:
    • PerpetualHazardsCompilation
    • EventHazardsCompilation
    • Fabric
    Data sources produced in this process:
    • TrainingSamples
    Date: 2021 (process 3 of 6)
    Step 2: The Image Classification Wizard in ArcPro was used to process the SVM classification. Supervised, pixel-based parameters were applied in the configuration step, and the previously compiled training samples (generated in step 1) were used to train the model and generate two classified datasets, one for Event Hazards and one for Perpetual Hazards. The pixel values of the classified raster that was produced for each Hazard type from the execution of the SVM machine learning step were unique integers that indicated 1) which land cover type from the Fabric dataset would be affected (tens place) and 2) the CCL value which would be added to the existing one included in the Fabric dataset in subsequent steps (ones place). For a list of these values see tables in the complimentary CCL Data Report (Pendleton and others, 2023). Person who carried out this activity:
    Elizabeth A. Pendleton
    U.S. Geological Survey, NORTHEAST REGION
    Geologist
    384 Woods Hole Road
    Woods Hole, MA
    US

    (508) 548 8700 x2259 (voice)
    ependleton@usgs.gov
    Data sources used in this process:
    • TrainingSamples
    Data sources produced in this process:
    • EventHazardsOutclass
    • PerpHazardsOutclass
    Date: 2021 (process 4 of 6)
    Step 3: Each of the classified rasters created in the previous step were combined with the Fabric dataset. Using raster calculator, the estimated impact (0-3) of each respective hazard was added to the change likelihood value (CCL) assigned to the Fabric dataset. The resulting raster dataset ranged in value from 0 to 12, which was then normalized to a maximum value of 10 using raster calculator, where any cells with values greater than 10 were reclassified to a value of 10. Data sources used in this process:
    • EventHazardsOutclass
    • PerpHazardsOutclass
    • Fabric
    Data sources produced in this process:
    • EventHazardsOutputReclass
    • PerpHazardsOutputReclass
    Date: 2021 (process 5 of 6)
    Step 4: Finally, the two outputs from step 3 were mosaicked to a new raster (Mosaic to New Raster tool) using 'maximum' as the mosaic operator, in order to retain the maximum CCL value from the event and the perpetual raster outputs. This produced the Maximum Coastal Change Likelihood raster layer – the maximum predicted change likelihood between the Event and Perpetual Hazards scenarios created in the previous step. Data sources used in this process:
    • EventHazardsOutputReclass
    • PerpHazardsOutputReclass
    Data sources produced in this process:
    • MaxCCL
    Date: 2021 (process 6 of 6)
    Step 5: Raster calculator was used to identify areas where event and perpetual hazards are predicted to have a high (CCL of greater than 7) or low (CCL of 7 or lower) degree of impact. Areas where event hazards resulted in a CCL of greater than or equal to 8, but perpetual hazards resulted in a CCL lower than 8 were assigned a value of 1, areas where both event and perpetual hazards resulted in a CCL of greater than or equal to 6 were assigned a value of 2, areas where both event and perpetual hazard resulted in a CCL of less than 6 were assigned a values of 3, and areas where event hazards resulted in a CCL less than 6, but perpetual resulted in a CCL of greater than or equal to 6 were assigned a value of 4. The resulting geotiff raster included values ranging 1 to 4, each value indicating which type of hazard is most likely to cause significant change to the coastal landscape, if any. Data sources used in this process:
    • EventHazardsOutputReclass
    • PerpHazardsOutputReclass
    Data sources produced in this process:
    • CCLFourSquare
  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 1999-593, 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 a compilation of coastal hazards likely to be present in the coming decade based on previous empirical research and expert opinion. The final output generated is the expected outcome based on this information.
  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) and rectilinear conversion of the finite element climatological wave data. Likewise some data, like NOAA’s ESI and the shoreline change data were rasterized from a source vector, and there can be spatial inconsistencies associated with the rasterization of vector data. 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’s domain is defined by the z-values (elevation) domain of the Fabric dataset (of this publication), and as such has a horizontal positional uncertainty of up to 50 cm along the edge of the domain, which corresponds to +/- 10 meters MHW . However, this dataset has no explicit vertical depth values itself, and therefore there is no vertical position accuracy estimate except along the boundary of this dataset domain.
  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 areas where coastal change in the coming decade may be greatest. Existing gaps in coverage for this dataset are a result of data gaps in source information (fabric and hazards).
  5. How consistent are the relationships among the observations, including topology?
    All data were checked for accuracy during processing. Any inconsistencies in the final data product are artifacts of source data.

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: 19-Mar-2024
Metadata author:
U.S. Geological Survey
Attn: Elizabeth A. Pendleton
Geologist
384 Woods Hole Rd
Woods Hole, MA

(508) 457 2259 (voice)
whsc_data_contact@usgs.gov
Contact_Instructions:
The metadata contact email address is a generic address in the event the person is no longer with USGS. (updated on 20240319)
Metadata standard:
Content Standard for Digital Geospatial Metadata, FGDC-STD-001-1998 (FGDC-STD-001.1-1998)
This page is <https://cmgds.marine.usgs.gov/catalog/whcmsc/SB_data_release/DR_P96A2Q5X/USGS_CCL_MaximumCCL_2022.faq.html>
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