Metadata: Identification_Information: Citation: Citation_Information: Originator: Travis K. Sterne Originator: Elizabeth A. Pendleton Originator: Erika E. Lentz Originator: Rachel E. Henderson Publication_Date: 20230228 Title: Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Maximum Change Likelihood Edition: 1.0 Geospatial_Data_Presentation_Form: raster digital data Series_Information: Series_Name: data release Issue_Identification: DOI:10.5066/P96A2Q5X Publication_Information: Publication_Place: Woods Hole Coastal and Marine Science Center, Woods Hole, MA Publisher: U.S. Geological Survey, Coastal and Marine Hazards and Resources Program Online_Linkage: https://doi.org/10.5066/P96A2Q5X Online_Linkage: https://www.sciencebase.gov/catalog/item/6197cb8dd34eb622f692ee19 Larger_Work_Citation: Citation_Information: Originator: Travis K. Sterne Originator: Elizabeth A. Pendleton Originator: Erika E. Lentz Originator: Rachel E. Henderson Publication_Date: 2023 Title: Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia Geospatial_Data_Presentation_Form: raster digital data Series_Information: Series_Name: data release Issue_Identification: DOI:10.5066/P96A2Q5X Publication_Information: Publication_Place: Reston, VA Publisher: U.S. Geological Survey, Coastal and Marine Hazards and Resources Program 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. Online_Linkage: https://doi.org/10.5066/P96A2Q5X Online_Linkage: https://www.sciencebase.gov/catalog/item/6197cb8dd34eb622f692ee19 Description: 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. Purpose: 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. Time_Period_of_Content: Time_Period_Information: Range_of_Dates/Times: Beginning_Date: 2010 Ending_Date: 2021 Currentness_Reference: ground condition of source data Status: Progress: Complete Maintenance_and_Update_Frequency: None planned Spatial_Domain: Bounding_Coordinates: West_Bounding_Coordinate: -77.5279 East_Bounding_Coordinate: -66.8837 North_Bounding_Coordinate: 45.1930 South_Bounding_Coordinate: 36.5149 Keywords: Theme: Theme_Keyword_Thesaurus: ISO 19115 Topic Category Theme_Keyword: geoscientificInformation Theme_Keyword: oceans Theme: Theme_Keyword_Thesaurus: None Theme_Keyword: U.S. Geological Survey Theme_Keyword: USGS Theme_Keyword: Coastal and Marine Hazards Mission Area Theme_Keyword: Woods Hole Coastal and Marine Science Center Theme_Keyword: Coastal Fabric Theme_Keyword: Elevation Theme_Keyword: Interpretation Theme_Keyword: Bathymetry Theme_Keyword: Landcover Theme_Keyword: Land Cover Theme_Keyword: Topography Theme_Keyword: UVVR Theme_Keyword: Unvegetated-Vegetated Ratio Theme_Keyword: Shoreline Change Theme_Keyword: Coastal Hazards Theme_Keyword: High Tide Flooding Theme_Keyword: Storm Recurrence Theme_Keyword: Wave Power Theme_Keyword: Storm Overwash Theme_Keyword: Sea Level Rise Theme_Keyword: Coastal Change Hazard Assessment Theme_Keyword: Coastal Vulnerability Index Theme_Keyword: Machine Learning Theme_Keyword: Autoclassification Theme_Keyword: Automation Theme_Keyword: Arcpy Theme_Keyword: ArcGIS Pro Theme_Keyword: Support Vector Machine Theme_Keyword: Training Samples Theme_Keyword: Supervised Classification Theme_Keyword: Decision Tree Framework Theme_Keyword: scientific interpretation Theme_Keyword: land use and land cover Theme: Theme_Keyword_Thesaurus: USGS Thesaurus Theme_Keyword: marine geology Theme_Keyword: coastal processes Theme_Keyword: sea-level change Theme_Keyword: topography Theme_Keyword: hazards Theme: Theme_Keyword_Thesaurus: USGS Metadata Identifier Theme_Keyword: USGS:6197cb8dd34eb622f692ee19 Place: Place_Keyword_Thesaurus: None Place_Keyword: Northeast US Place_Keyword: Maine Place_Keyword: New Hampshire Place_Keyword: Massachusetts Place_Keyword: Rhode Island Place_Keyword: Connecticut Place_Keyword: New York Place_Keyword: New Jersey Place_Keyword: Delaware Place_Keyword: Maryland Place_Keyword: Virginia Place_Keyword: St. Croix Island International Historic Site Place_Keyword: Acadia National Park Place_Keyword: Gateway National Recreation Area Place_Keyword: George Washington Birthplace National Monument Place_Keyword: Cape Cod National Seashore 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. Point_of_Contact: Contact_Information: Contact_Organization_Primary: Contact_Organization: U.S. Geological Survey Contact_Person: Travis K Sterne Contact_Address: Address_Type: Mailing and Physical Address: 384 Woods Hole Rd City: Woods Hole State_or_Province: MA Postal_Code: 02543 Contact_Voice_Telephone: (508) 548 8700 x2219 Contact_Electronic_Mail_Address: tsterne@usgs.gov Browse_Graphic: Browse_Graphic_File_Name: https://www.sciencebase.gov/catalog/file/get/6197cb8dd34eb622f692ee19?name=MaxCCL_Graphic.jpg Browse_Graphic_File_Description: Outer Cape Cod with Maximum CCL data layer Browse_Graphic_File_Type: JPEG Native_Data_Set_Environment: Version 6.2 (Build 9200) ; Esri ArcGIS 10.6.0.8321 Cross_Reference: Citation_Information: Originator: Thieler, E.R. Originator: Hammar-Klose, E.S. Publication_Date: 1999 Title: National assessment of coastal vulnerability to sea-level rise; U.S. Atlantic Coast Geospatial_Data_Presentation_Form: vector digital data Series_Information: Series_Name: Open-File Report Issue_Identification: 1999-593 Publication_Information: Publication_Place: Reston, VA Publisher: U.S. Geological Survey Online_Linkage: https://doi.org/10.3133/ofr99593 Online_Linkage: https://pubs.usgs.gov/of/1999/of99-593/ Cross_Reference: Citation_Information: Originator: Elizabeth A. Pendleton Originator: Erika E. Lentz Originator: Travis K. Sterne Originator: Rachel E. Henderson Publication_Date: 2023 Title: Development and application of a coastal change likelihood assessment for the northeast region, Maine to Virginia Series_Information: Series_Name: Data Report Issue_Identification: 1169 Publication_Information: Publication_Place: Reston, VA Publisher: U.S. Geological Survey 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) Online_Linkage: https://doi.org/10.3133/dr1169 Online_Linkage: https://pubs.er.usgs.gov/publication/dr1169 Data_Quality_Information: Attribute_Accuracy: Attribute_Accuracy_Report: 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. Logical_Consistency_Report: All data were checked for accuracy during processing. Any inconsistencies in the final data product are artifacts of source data. Completeness_Report: 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). Positional_Accuracy: Horizontal_Positional_Accuracy: Horizontal_Positional_Accuracy_Report: 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. Vertical_Positional_Accuracy: Vertical_Positional_Accuracy_Report: 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. Lineage: Source_Information: Source_Citation: Citation_Information: Originator: Travis K. Sterne Originator: Elizabeth A. Pendleton Originator: Erika E. Lentz Originator: Rachel E. Henderson Publication_Date: 2023 Title: Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Perpetual Hazard Compilation Geospatial_Data_Presentation_Form: raster digital data Online_Linkage: https://doi.org/10.5066/P96A2Q5X Online_Linkage: https://www.sciencebase.gov/catalog/item/61783250d34e4c6b7fe2a4a2 Type_of_Source_Media: Digital and/or Hardcopy Source_Time_Period_of_Content: Time_Period_Information: Range_of_Dates/Times: Beginning_Date: 2010 Ending_Date: 2021 Source_Currentness_Reference: ground condition Source_Citation_Abbreviation: PerpetualHazardsCompilation Source_Contribution: Perpetual Hazards data Source_Information: Source_Citation: Citation_Information: Originator: Travis K. Sterne Originator: Elizabeth A. Pendleton Originator: Erika E. Lentz Originator: Rachel E. Henderson Publication_Date: 2023 Title: Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Event Hazard Compilation Geospatial_Data_Presentation_Form: raster digital data Online_Linkage: https://doi.org/10.5066/P96A2Q5X Online_Linkage: https://www.sciencebase.gov/catalog/item/61783250d34e4c6b7fe2a4a2 Type_of_Source_Media: Digital and/or Hardcopy Source_Time_Period_of_Content: Time_Period_Information: Range_of_Dates/Times: Beginning_Date: 2010 Ending_Date: 2021 Source_Currentness_Reference: ground condition Source_Citation_Abbreviation: EventHazardsCompilation Source_Contribution: Event Hazards data Source_Information: Source_Citation: Citation_Information: Originator: Travis K. Sterne Originator: Elizabeth A. Pendleton Originator: Erika E. Lentz Originator: Rachel E. Henderson Publication_Date: 2023 Title: Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Fabric Dataset Geospatial_Data_Presentation_Form: raster digital data Series_Information: Series_Name: data release Issue_Identification: DOI:10.5066/P96A2Q5X Publication_Information: Publication_Place: Woods Hole Coastal and Marine Science Center, Woods Hole, MA Publisher: U.S. Geological Survey, Coastal and Marine Geology Program Online_Linkage: https://doi.org/10.5066/P96A2Q5X Online_Linkage: https://www.sciencebase.gov/catalog/item/61781f88d34e4c6b7fe2a444 Type_of_Source_Media: Digital and/or Hardcopy Source_Time_Period_of_Content: Time_Period_Information: Range_of_Dates/Times: Beginning_Date: 2010 Ending_Date: 2021 Source_Currentness_Reference: ground condition Source_Citation_Abbreviation: Fabric Source_Contribution: Fabric dataset Process_Step: Process_Description: 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. Process_Date: 2021 Process_Step: Process_Description: 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). Source_Used_Citation_Abbreviation: PerpetualHazardsCompilation Source_Used_Citation_Abbreviation: EventHazardsCompilation Source_Used_Citation_Abbreviation: Fabric Process_Date: 2021 Source_Produced_Citation_Abbreviation: TrainingSamples Process_Contact: Contact_Information: Contact_Person_Primary: Contact_Person: Travis K Sterne Contact_Organization: U.S. Geological Survey, NORTHEAST REGION Contact_Position: Geographer Contact_Address: Address_Type: mailing address Address: 384 Woods Hole Road City: Woods Hole State_or_Province: MA Postal_Code: 02543 Country: US Contact_Voice_Telephone: (508) 548 8700 x2219 Contact_Electronic_Mail_Address: tsterne@usgs.gov Process_Step: Process_Description: 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). Source_Used_Citation_Abbreviation: TrainingSamples Process_Date: 2021 Source_Produced_Citation_Abbreviation: EventHazardsOutclass Source_Produced_Citation_Abbreviation: PerpHazardsOutclass Process_Contact: Contact_Information: Contact_Person_Primary: Contact_Person: Elizabeth A. Pendleton Contact_Organization: U.S. Geological Survey, NORTHEAST REGION Contact_Position: Geologist Contact_Address: Address_Type: mailing address Address: 384 Woods Hole Road City: Woods Hole State_or_Province: MA Postal_Code: 02543 Country: US Contact_Voice_Telephone: (508) 548 8700 x2259 Contact_Electronic_Mail_Address: ependleton@usgs.gov Process_Step: Process_Description: 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. Source_Used_Citation_Abbreviation: EventHazardsOutclass Source_Used_Citation_Abbreviation: PerpHazardsOutclass Source_Used_Citation_Abbreviation: Fabric Process_Date: 2021 Source_Produced_Citation_Abbreviation: EventHazardsOutputReclass Source_Produced_Citation_Abbreviation: PerpHazardsOutputReclass Process_Step: Process_Description: 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. Source_Used_Citation_Abbreviation: EventHazardsOutputReclass Source_Used_Citation_Abbreviation: PerpHazardsOutputReclass Process_Date: 2021 Source_Produced_Citation_Abbreviation: MaxCCL Process_Step: Process_Description: 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. Source_Used_Citation_Abbreviation: EventHazardsOutputReclass Source_Used_Citation_Abbreviation: PerpHazardsOutputReclass Process_Date: 2021 Source_Produced_Citation_Abbreviation: CCLFourSquare Spatial_Data_Organization_Information: Direct_Spatial_Reference_Method: Raster Raster_Object_Information: Raster_Object_Type: Grid Cell Row_Count: 128025 Column_Count: 118491 Vertical_Count: 1 Spatial_Reference_Information: Horizontal_Coordinate_System_Definition: Planar: Map_Projection: Map_Projection_Name: Mercator_1SP Map_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_Coordinate_Information: Planar_Coordinate_Encoding_Method: row and column Coordinate_Representation: Abscissa_Resolution: 10.0 Ordinate_Resolution: 10.0 Planar_Distance_Units: meters Geodetic_Model: Horizontal_Datum_Name: WGS_1984 Ellipsoid_Name: WGS 84 Semi-major_Axis: 6378137.0 Denominator_of_Flattening_Ratio: 298.257223563 Entity_and_Attribute_Information: Detailed_Description: Entity_Type: Entity_Type_Label: USGS_CCL_MaximumCCL_2022.tif Entity_Type_Definition: Raster geospatial data file. Entity_Type_Definition_Source: Producer defined Attribute: Attribute_Label: OID Attribute_Definition: Internal object identifier. Attribute_Definition_Source: Producer defined Attribute_Domain_Values: Unrepresentable_Domain: Sequential unique whole numbers that are automatically generated. Attribute: Attribute_Label: Value Attribute_Definition: 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. Attribute_Definition_Source: U.S. Geological Survey Attribute_Domain_Values: Enumerated_Domain: Enumerated_Domain_Value: 0 Enumerated_Domain_Value_Definition: No CCL value assigned Enumerated_Domain_Value_Definition_Source: U.S. Geological Survey Attribute_Domain_Values: Enumerated_Domain: Enumerated_Domain_Value: 1 Enumerated_Domain_Value_Definition: Extremely unlikely to change Enumerated_Domain_Value_Definition_Source: Producer defined Attribute_Domain_Values: Enumerated_Domain: Enumerated_Domain_Value: 2 Enumerated_Domain_Value_Definition: Very unlikely to change Enumerated_Domain_Value_Definition_Source: U.S. Geological Survey Attribute_Domain_Values: Enumerated_Domain: Enumerated_Domain_Value: 3 Enumerated_Domain_Value_Definition: Unlikely to change Enumerated_Domain_Value_Definition_Source: U.S. Geological Survey Attribute_Domain_Values: Enumerated_Domain: Enumerated_Domain_Value: 4 Enumerated_Domain_Value_Definition: Somewhat unlikely to change Enumerated_Domain_Value_Definition_Source: U.S. Geological Survey Attribute_Domain_Values: Enumerated_Domain: Enumerated_Domain_Value: 5 Enumerated_Domain_Value_Definition: Mostly uncertain to slightly unlikely to change Enumerated_Domain_Value_Definition_Source: U.S. Geological Survey Attribute_Domain_Values: Enumerated_Domain: Enumerated_Domain_Value: 6 Enumerated_Domain_Value_Definition: Mostly uncertain to slightly likely to change Enumerated_Domain_Value_Definition_Source: U.S. Geological Survey Attribute_Domain_Values: Enumerated_Domain: Enumerated_Domain_Value: 7 Enumerated_Domain_Value_Definition: Somewhat likely to change Enumerated_Domain_Value_Definition_Source: U.S. Geological Survey Attribute_Domain_Values: Enumerated_Domain: Enumerated_Domain_Value: 8 Enumerated_Domain_Value_Definition: Likely to change Enumerated_Domain_Value_Definition_Source: Producer defined Attribute_Domain_Values: Enumerated_Domain: Enumerated_Domain_Value: 9 Enumerated_Domain_Value_Definition: Very likely to change Enumerated_Domain_Value_Definition_Source: U.S. Geological Survey Attribute_Domain_Values: Enumerated_Domain: Enumerated_Domain_Value: 10 Enumerated_Domain_Value_Definition: Extremely likely to change Enumerated_Domain_Value_Definition_Source: U.S. Geological Survey Attribute: Attribute_Label: Count Attribute_Definition: Number of raster cells with this value. Attribute_Definition_Source: Producer defined Attribute_Domain_Values: Range_Domain: Range_Domain_Minimum: 146803.0 Range_Domain_Maximum: 365003781.0 Distribution_Information: Distributor: Contact_Information: Contact_Organization_Primary: Contact_Organization: U.S. Geological Survey - ScienceBase Contact_Address: Address_Type: mailing and physical address Address: Denver Federal Center, Building 810, Mail Stop 302 City: Denver State_or_Province: CO Postal_Code: 80225 Country: US Contact_Voice_Telephone: 1-888-275-8747 Contact_Electronic_Mail_Address: sciencebase@usgs.gov Resource_Description: 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. Distribution_Liability: 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. Standard_Order_Process: Digital_Form: Digital_Transfer_Information: Format_Name: GeoTIFF Format_Version_Number: ESRI ArcGIS Pro v2.6.3 Transfer_Size: 259 Digital_Transfer_Option: Online_Option: Computer_Contact_Information: Network_Address: Network_Resource_Name: https://doi.org/10.5066/P96A2Q5X Network_Resource_Name: https://www.sciencebase.gov/catalog/file/get/6197cb8dd34eb622f692ee19 Network_Resource_Name: https://www.sciencebase.gov/catalog/item/6197cb8dd34eb622f692ee19 Fees: None Metadata_Reference_Information: Metadata_Date: 20230228 Metadata_Contact: Contact_Information: Contact_Organization_Primary: Contact_Organization: U.S. Geological Survey Contact_Person: Elizabeth A. Pendleton Contact_Position: Geologist Contact_Address: Address_Type: Mailing and Physical Address: 384 Woods Hole Rd City: Woods Hole State_or_Province: MA Postal_Code: 02543 Contact_Voice_Telephone: (508) 457 2259 Contact_Electronic_Mail_Address: ependleton@usgs.gov Metadata_Standard_Name: Content Standard for Digital Geospatial Metadata, FGDC-STD-001-1998 Metadata_Standard_Version: FGDC-STD-001.1-1998