The overall goal of the project was to systematically gather and quantify seafloor mapping data needs within the Southeast US study region (estuary to Exclusive Economic Zone (EEZ) of North Carolina, South Carolina, and Georgia). The results identify locations where stakeholder interests overlap with other organizations, leading to improved coordination of data needs, and leveraging collective resources to meet these shared goals. Already, priority areas identified by this study are being used by NOAA to focus planned fiscal year 2021 seafloor mapping missions. The web mapping application incorporating these results can be found here: https://noaa.maps.arcgis.com/home/item.html?id=04cdd2a68c4f427f893f2042f326dc80Spatial information on the arrangement of geological features, habitats and living marine resources on the seabed are often the foundation for decision-making in ecosystem management and ocean planning. Collecting information on the seabed depths and geomorphology is an expensive operation requiring airborne platforms like satellites, planes or drones, or small vessels to large research ships. Coordinating these data needs and data collection efforts will better leverage collective resources and meet shared goals. To help enable this coordination, in 2020 the National Oceanic and Atmospheric Administration (NOAA) National Centers for Coastal Ocean Science (NCCOS) developed a spatial framework, process, and online application to identify common data collection priorities for seafloor mapping, sampling, and visual surveys along shore and offshore of the Southeast United States (North Carolina, South Carolina, and Georgia).Twenty-five representatives from federal and state agencies, academic institutions, and non-governmental conservation groups, designated seafloor mapping priorities using an online prioritization tool. Participants allocated virtual coins across 5x5 km grid cells to denote their organization’s regions of seafloor mapping needs. Grid cells with more coins were higher priorities than cells with fewer coins. Participants also reported why these locations were important and what data types were needed. Results were analyzed and mapped using statistical techniques to identify significant relationships between priorities, reasons for those priorities and data needs. Several common areas of interest were identified in the spatially explicit analysis of the responses. Nearshore surfzone along Georgia, South Carolina, and North Carolina were highlighted by several agencies and organizations interested in sediment and sand resources as well as potential for rocky reef habitats. Inshore estuarine areas were highlighted by state agencies and conservation groups interested in monitoring change in managed areas like National Estuarine Reserves. On the outer continental shelf, areas near Blake Plateau off South Carolina and the continental shelf break off North Carolina were identified by federal agencies and conservation organizations as areas of sensitive habitats or historically significantly shipwrecks and maritime resources.The seafloor mapping prioritization approach described in the Buckel et al. (2021) report associated with these data provides recommendations to organizations charged with mapping the seabed for navigation and commerce as well as resource assessments and management. Already, the priority areas identified in this exercise are being used by NOAA to focus planned seafloor mapping missions. Furthermore, the outcomes from this regional exercise contribute into a National Mapping Prioritization under the lead of NOAA to coordinate mapping activities across the entire US EEZ. Together, these quantitative seafloor mapping prioritization approaches will enable improved coordination and more efficient allocation of resources needed to conduct seafloor mapping providing data to support environmental stewardship, safe navigation and commerce.

From the site: “The Geologic Atlas of the United States is a set of 227 folios published by the U.S. Geological Survey between 1894 and 1945. Each folio includes both topographic and geologic maps for each quad represented in that folio, as well as description of the basic and economic geology of the area. The Geologic Atlas collection is maintained by the Map & GIS Library. The repository interface with integrated Yahoo! Maps was developed by the Digital Initiatives -- Research & Technology group within the TAMU Libraries using the Manakin interface framework on top of the DSpace digital repository software. Additional files of each map are available for download for use in GIS or Google Earth. A tutorial is provided which describes how to download theses files.”

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .

From the site: “The Geologic Atlas of the United States is a set of 227 folios published by the U.S. Geological Survey between 1894 and 1945. Each folio includes both topographic and geologic maps for each quad represented in that folio, as well as description of the basic and economic geology of the area. The Geologic Atlas collection is maintained by the Map & GIS Library. The repository interface with integrated Yahoo! Maps was developed by the Digital Initiatives -- Research & Technology group within the TAMU Libraries using the Manakin interface framework on top of the DSpace digital repository software. Additional files of each map are available for download for use in GIS or Google Earth. A tutorial is provided which describes how to download theses files.”

The U.S. Geological Survey South Atlantic Water Science Center, in cooperation with the South Carolina Department of Transportation, implemented a South Carolina StreamStats application in 2018. This shapefile dataset contains vector lines representing streams, rivers, and ditches that were used in preparing the underlying data for the South Carolina StreamStats application. Data were compiled from multiple sources, but principally represent lidar-derived linework from the South Carolina Department of Natural Resources and the South Carolina Lidar Consortium.The South Carolina hydrography lines were created from elevation rasters that ranged from 4 to 10 ft resolution, to produce a product of approximately 1:6,000-scale. Other sources include the 1:24,000 scale high resolution National Hydrography Dataset streamlines [for streamlines in Georgetown County (SC), NC, and GA] and the 1:4,800 scale local-resolution North Carolina Stream Mapping Project lines (mountain counties). These ...

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe's Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe's Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Raster data in both ArcGIS Grid and ERDAS Imagine format is available for download at http://gis1.usgs.gov/csas/gap/viewer/land_cover/Map.aspx Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS. In adition to the raster datasets the data is available in Web Mapping Services (WMS) format for each of the six NVC classification levels (Class, Subclass, Formation, Division, Macrogroup, Ecological System) at the following links. http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Class_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Subclass_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Formation_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Division_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Macrogroup_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_Ecological_Systems_Landuse/MapServer

The overall goal of the project was to systematically gather and quantify seafloor mapping data needs within the Southeast US study region (estuary to Exclusive Economic Zone (EEZ) of North Carolina, South Carolina, and Georgia). The results identify locations where stakeholder interests overlap with other organizations, leading to improved coordination of data needs, and leveraging collective resources to meet these shared goals. Already, priority areas identified by this study are being used by NOAA to focus planned fiscal year 2021 seafloor mapping missions. The web mapping application incorporating these results can be found here: https://noaa.maps.arcgis.com/home/item.html?id=04cdd2a68c4f427f893f2042f326dc80Spatial information on the arrangement of geological features, habitats and living marine resources on the seabed are often the foundation for decision-making in ecosystem management and ocean planning. Collecting information on the seabed depths and geomorphology is an expensive operation requiring airborne platforms like satellites, planes or drones, or small vessels to large research ships. Coordinating these data needs and data collection efforts will better leverage collective resources and meet shared goals. To help enable this coordination, in 2020 the National Oceanic and Atmospheric Administration (NOAA) National Centers for Coastal Ocean Science (NCCOS) developed a spatial framework, process, and online application to identify common data collection priorities for seafloor mapping, sampling, and visual surveys along shore and offshore of the Southeast United States (North Carolina, South Carolina, and Georgia).Twenty-five representatives from federal and state agencies, academic institutions, and non-governmental conservation groups, designated seafloor mapping priorities using an online prioritization tool. Participants allocated virtual coins across 5x5 km grid cells to denote their organization’s regions of seafloor mapping needs. Grid cells with more coins were higher priorities than cells with fewer coins. Participants also reported why these locations were important and what data types were needed. Results were analyzed and mapped using statistical techniques to identify significant relationships between priorities, reasons for those priorities and data needs. Several common areas of interest were identified in the spatially explicit analysis of the responses. Nearshore surfzone along Georgia, South Carolina, and North Carolina were highlighted by several agencies and organizations interested in sediment and sand resources as well as potential for rocky reef habitats. Inshore estuarine areas were highlighted by state agencies and conservation groups interested in monitoring change in managed areas like National Estuarine Reserves. On the outer continental shelf, areas near Blake Plateau off South Carolina and the continental shelf break off North Carolina were identified by federal agencies and conservation organizations as areas of sensitive habitats or historically significantly shipwrecks and maritime resources.The seafloor mapping prioritization approach described in the Buckel et al. (2021) report associated with these data provides recommendations to organizations charged with mapping the seabed for navigation and commerce as well as resource assessments and management. Already, the priority areas identified in this exercise are being used by NOAA to focus planned seafloor mapping missions. Furthermore, the outcomes from this regional exercise contribute into a National Mapping Prioritization under the lead of NOAA to coordinate mapping activities across the entire US EEZ. Together, these quantitative seafloor mapping prioritization approaches will enable improved coordination and more efficient allocation of resources needed to conduct seafloor mapping providing data to support environmental stewardship, safe navigation and commerce.

These data were automated to provide an accurate high-resolution historical shoreline of North Carolina and South Carolina suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://www.fisheries.noaa.gov/inport/item/39808

The overall goal of the project was to systematically gather and quantify seafloor mapping data needs within the Southeast US study region (estuary to Exclusive Economic Zone (EEZ) of North Carolina, South Carolina, and Georgia). The results identify locations where stakeholder interests overlap with other organizations, leading to improved coordination of data needs, and leveraging collective resources to meet these shared goals. Already, priority areas identified by this study are being used by NOAA to focus planned fiscal year 2021 seafloor mapping missions. The web mapping application incorporating these results can be found here: https://noaa.maps.arcgis.com/home/item.html?id=04cdd2a68c4f427f893f2042f326dc80Spatial information on the arrangement of geological features, habitats and living marine resources on the seabed are often the foundation for decision-making in ecosystem management and ocean planning. Collecting information on the seabed depths and geomorphology is an expensive operation requiring airborne platforms like satellites, planes or drones, or small vessels to large research ships. Coordinating these data needs and data collection efforts will better leverage collective resources and meet shared goals. To help enable this coordination, in 2020 the National Oceanic and Atmospheric Administration (NOAA) National Centers for Coastal Ocean Science (NCCOS) developed a spatial framework, process, and online application to identify common data collection priorities for seafloor mapping, sampling, and visual surveys along shore and offshore of the Southeast United States (North Carolina, South Carolina, and Georgia).Twenty-five representatives from federal and state agencies, academic institutions, and non-governmental conservation groups, designated seafloor mapping priorities using an online prioritization tool. Participants allocated virtual coins across 5x5 km grid cells to denote their organization’s regions of seafloor mapping needs. Grid cells with more coins were higher priorities than cells with fewer coins. Participants also reported why these locations were important and what data types were needed. Results were analyzed and mapped using statistical techniques to identify significant relationships between priorities, reasons for those priorities and data needs. Several common areas of interest were identified in the spatially explicit analysis of the responses. Nearshore surfzone along Georgia, South Carolina, and North Carolina were highlighted by several agencies and organizations interested in sediment and sand resources as well as potential for rocky reef habitats. Inshore estuarine areas were highlighted by state agencies and conservation groups interested in monitoring change in managed areas like National Estuarine Reserves. On the outer continental shelf, areas near Blake Plateau off South Carolina and the continental shelf break off North Carolina were identified by federal agencies and conservation organizations as areas of sensitive habitats or historically significantly shipwrecks and maritime resources.The seafloor mapping prioritization approach described in the Buckel et al. (2021) report associated with these data provides recommendations to organizations charged with mapping the seabed for navigation and commerce as well as resource assessments and management. Already, the priority areas identified in this exercise are being used by NOAA to focus planned seafloor mapping missions. Furthermore, the outcomes from this regional exercise contribute into a National Mapping Prioritization under the lead of NOAA to coordinate mapping activities across the entire US EEZ. Together, these quantitative seafloor mapping prioritization approaches will enable improved coordination and more efficient allocation of resources needed to conduct seafloor mapping providing data to support environmental stewardship, safe navigation and commerce.

These data were automated to provide an accurate high-resolution historical shoreline of Long, South Carolina to Winnabow, North Carolina suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://inport.nmfs.noaa.gov/inport/item/39808

In 2018 Amy Almond, a DFP intern, worked on the project "Coastal Impoundment Mapping in the Southeast". An impoundment is defined as an area within which water levels are actively managed to benefit wetland-dependent wildlife.The goal was to create a comprehensive GIS map layer of impoundments within the Southern Atlantic Coastal Plain. Amy contacted managers of National Wildlife Refuges and state-managed lands within the range (NC, SC, GA, FL) for information about impoundments on their lands.The information provided by the project will help determine potential locations to manage for Black Rails or to work with partners to create new habitat. This information will also benefit other waterbirds and waterfowl since their habitat overlaps.In order to obtain the impoundment information, Amy emailed each of the land managers with a short data request. She asked them to send any GIS shapefiles they had of impounded areas on their lands and sent a Google Form questionnaire to ask specific questions about how they manage these areas, like primary species management, vegetation, and water depth.Most of the land managers responded to the Google Form we created to collect descriptive management data. Some folks sent an email or management plan document, which did not contain the same information as the Google Form.Total impoundments: 750Total impoundment acreage: 121,129.52Total impoundment hectares: 49,019.37Federal ImpoundmentsRefuge Complexes: 15Refuges: 63Impoundments: 329Acres: 63,977Hectares: 25,890.57 State ImpoundmentsStates: 4State-managed lands: 48Impoundments: 421Acres: 57,152.52Hectares: 23,128.8 North Carolina ImpoundmentsImpoundments: 143Acres: 19,699.88Hectares: 7,972.26 South Carolina ImpoundmentsImpoundments: 322Acres: 50,451.69Hectares: 20,597.08 Georgia ImpoundmentsImpoundments: 93Acres: 7,116.85Hectares: 2,880.08Florida ImpoundmentsImpoundments: 192Acres: 43,861.1Hectares: 17,749.96

Beach erosion is a chronic problem along most open-ocean shores of the United States. As coastal populations continue to grow, and community infrastructures are threatened by erosion, there is increased demand for accurate information regarding past and present shoreline changes. There is also need for a comprehensive analysis of shoreline movement that is regionally consistent. To meet these national needs, the USGS National Assessment of Shoreline Change Project has collected and analyzed a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data.

This dataset consists of short-term (~30 years) shoreline change rates. Rate calculations were computed using the Digital Shoreline Analysis System (DSAS), an ArcGIS extension developed by the U.S. Geological Survey. Short-term rates of shoreline change were calculated using an end-point rate method based on available shorelines to provide an approximately 30-yr short-term rate. A reference baseline was used as the originating point for the orthogonal transects cast by the DSAS software. The transects intersect each shoreline establishing measurement points, which are then used to calculate short-term rates.

To make these results more accessible to the public and other agencies, the USGS created this web service. This web service was created utilizing ESRI ArcServer. This service meets open geospatial consortium standards.

The data compilation used to derive the shoreline change rates is available in a service with the title USGS Map service: National Shoreline Change - Historic Shorelines by State. The reference baseline used to derive the shoreline change rates is available in a service with the title USGS Map service: National Shoreline Change - Offshore Baseline. The locations of the transects used in the change rate calculation are available in a service with the title USGS Map service: National Shoreline Change - Intersection Points.

The geographic information system (GIS) data layers from this web service are cataloged by state for ease of access.

NOAA's National Geophysical Data Center (NGDC) is building high-resolution digital elevation models (DEMs) for select U.S. coastal regions. These integrated bathymetric-topographic DEMs are used to support tsunami forecasting and modeling efforts at the NOAA Center for Tsunami Research, Pacific Marine Environmental Laboratory (PMEL). The DEMs are part of the tsunami forecast system SIFT (Short-term Inundation Forecasting for Tsunamis) currently being developed by PMEL for the NOAA Tsunami Warning Centers, and are used in the MOST (Method of Splitting Tsunami) model developed by PMEL to simulate tsunami generation, propagation, and inundation. Bathymetric, topographic, and shoreline data used in DEM compilation are obtained from various sources, including NGDC, the U.S. National Ocean Service (NOS), the U.S. Geological Survey (USGS), the U.S. Army Corps of Engineers (USACE), the Federal Emergency Management Agency (FEMA), and other federal, state, and local government agencies, academic institutions, and private companies. DEMs are referenced to the vertical tidal datum of Mean High Water (MHW) and horizontal datum of World Geodetic System 1984 (WGS84). Grid spacings for the DEMs range from 1/3 arc-second (~10 meters) to 3 arc-seconds (~90 meters).The DEM Global Mosaic is an image service providing access to bathymetric/topographic digital elevation models stewarded at NOAA's National Centers for Environmental Information (NCEI), along with the global GEBCO_2014 grid: http://www.gebco.net/data_and_products/gridded_bathymetry_data. NCEI builds and distributes high-resolution, coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. They can be used for modeling of coastal processes (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness. This service is a general-purpose global, seamless bathymetry/topography mosaic. It combines DEMs from a variety of near sea-level vertical datums, such as mean high water (MHW), mean sea level (MSL), and North American Vertical Datum of 1988 (NAVD88). Elevation values have been rounded to the nearest meter, with DEM cell sizes going down to 1 arc-second. Higher-resolution DEMs, with greater elevation precision, are available in the companion NAVD88: http://noaa.maps.arcgis.com/home/item.html?id=e9ba2e7afb7d46cd878b34aa3bfce042 and MHW: http://noaa.maps.arcgis.com/home/item.html?id=3bc7611c1d904a5eaf90ecbec88fa799 mosaics. By default, the DEMs are drawn in order of cell size, with higher-resolution grids displayed on top of lower-resolution grids. If overlapping DEMs have the same resolution, the newer one is shown. Please see NCEI's corresponding DEM Footprints map service: http://noaa.maps.arcgis.com/home/item.html?id=d41f39c8a6684c54b62c8f1ab731d5ad for polygon footprints and more information about the individual DEMs used to create this composite view. In this visualization, the elevations/depths are displayed using this color ramp: http://gis.ngdc.noaa.gov/viewers/images/dem_color_scale.png.A map service showing the location and coverage of land and seafloor digital elevation models (DEMs) available from NOAA's National Centers for Environmental Information (NCEI). NCEI builds and distributes high-resolution, coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. They can be used for modeling of coastal processes (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness. Layers available in the map service: Layers 1-4: DEMs by Category (includes various DEMs, both hosted at NCEI, and elsewhere on the web); Layers 6-11: NCEI DEM Projects (DEMs hosted at NCEI, color-coded by project); Layer 12: All NCEI Bathymetry DEMs (All bathymetry or bathy-topo DEMs hosted at NCEI).This is an image service providing access to bathymetric/topographic digital elevation models stewarded at NOAA's National Centers for Environmental Information (NCEI), with vertical units referenced to mean high water (MHW). NCEI builds and distributes high-resolution, coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. They can be used for modeling of coastal processes (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness. This service provides data from many individual DEMs combined together as a mosaic. By default, the rasters are drawn in order of cell size, with higher-resolution grids displayed on top of lower-resolution grids. If overlapping DEMs have the same resolution, the newer one is shown. Alternatively, a single DEM or group of DEMs can be isolated using a filter/definition query or using the 'Lock Raster 'mosaic method in ArcMap. This is one of three services displaying collections of DEMs that are referenced to common vertical datums: North American Vertical Datum of 1988 (NAVD88): http://noaa.maps.arcgis.com/home/item.html?id=e9ba2e7afb7d46cd878b34aa3bfce042, Mean High Water (MHW): http://noaa.maps.arcgis.com/home/item.html?id=3bc7611c1d904a5eaf90ecbec88fa799, and Mean Higher High Water: http://noaa.maps.arcgis.com/home/item.html?id=9471f8d4f43e48109de6275522856696. In addition, the DEM Global Mosaic is a general-purpose global, seamless bathymetry/topography mosaic containing all the DEMs together. Two services are available: http://noaa.maps.arcgis.com/home/item.html?id=c876e3c96a8642ab8557646a3b4fa0ff Elevation Values: http://noaa.maps.arcgis.com/home/item.html?id=c876e3c96a8642ab8557646a3b4fa0ff and Color Shaded Relief: http://noaa.maps.arcgis.com/home/item.html?id=feb3c625dc094112bb5281c17679c769. Please see the corresponding DEM Footprints map service: http://noaa.maps.arcgis.com/home/item.html?id=d41f39c8a6684c54b62c8f1ab731d5ad for polygon footprints and more information about the individual DEMs used to create this composite view. This service has several server-side functions available. These can be selected in the ArcGIS Online layer using 'Image Display ', or in ArcMap under 'Processing Templates '. None: The default. Provides elevation/depth values in meters relative to the NAVD88 vertical datum. ColorHillshade: An elevation-tinted hillshade visualization. The depths are displayed using this color ramp: http://gis.ngdc.noaa.gov/viewers/images/dem_color_scale.png. GrayscaleHillshade: A simple grayscale hillshade visualization. SlopeMapRGB: Slope in degrees, visualized using these colors: http://downloads.esri.com/esri_content_doc/landscape/SlopeMapLegend_V7b.png. SlopeNumericValues: Slope in degrees, returning the actual numeric values. AspectMapRGB: Orientation of the terrain (0-360 degrees), visualized using these colors: http://downloads.esri.com/esri_content_doc/landscape/AspectMapLegendPie_V7b.png. AspectNumericValues: Aspect in degrees, returning the actual numeric values.

This data set, 3 of 5 archived at the NODC, contains digital orthophotography. The data set presents information that represented conditions for the specified Digital Orthophoto Quarter Quads (DOQQ) regions of interest as specified by PhotoScience Task No. 01012C0053 for coastal areas of South Carolina. The project area was selected specifically to cover those sections of the South Carolina coastal critical zone where oysters had historically been mapped by SC Department of Natural Resources, Marine Division. Additional data from this collection is archived at the NODC under accession numbers 0084621, 0084749, 0084751, 0084752.

The extent of the DOQQs for the project area ranges from the Hilton Head area in the southern part of South Carolina to the Myrtle Beach area in the northern part of the state. The digital orthophotos in this series have a theoretical ground resolution of 0.25 meter. The digital orthophotos are 4-band in nature (red, green, blue, near infrared) and are delivered as flown in four-band .img file format with associated .png, .jpg, and indexing files. The four-band imagery is delivered in mosaics equaling one eighth of a DOQ. All data were captured during specific imaging windows per contract. The total DOQQ area is approximately 1,527 square miles.

These data were automated to provide an accurate high-resolution historical shoreline of North and South Santee River to Bull Bay, SC suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://inport.nmfs.noaa.gov/inport/item/39808

COASTSPAN sampling conducted by the South Carolina Department of Natural Resources in 2017 took place in both nearshore and estuarine waters along the South Carolina coast including: Bulls Bay, Charleston Harbor, North Edisto, Port Royal Sound, St. Helena Sound, and Winyah Bay.

This data set comprises the Environmental Sensitivity Index (ESI) maps for the shoreline of South Carolina. ESI data characterize coastal environments and wildlife by their sensitivity to spilled oil. The ESI data include information for three main components: shoreline habitats; sensitive biological resources; and human-use resources. This atlas was developed to be utilized within desktop GIS systems and contains GIS files and related D-base files. Associated files include MOSS (Multiple Overlay Statistical System) export files, .PDF maps, and detailed user guides and metadata.

The intertidal habitats were mapped during aerial and ground surveys conducted over the period from March to October 1995. The biological and human-use resources data were compiled by regional biologists in 1995. The dates for these data vary and range from historical to the 1990s.

These data were automated to provide an accurate high-resolution historical shoreline of Cooper River, East and West Branches, North of Charleston, South Carolina suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://www.fisheries.noaa.gov/inport/item/39808

There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the USGS has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Historical shoreline positions serve as easily understood features that can be used to describe the movement of beaches through time. These data are used to calculate rates of shoreline change for the U.S. Geological Survey's (USGS) National Assessment of Shoreline Change Project.

Each shoreline may represent a compilation of data from one or more sources for one or more dates provided by one or more agencies. Details regarding source are provided in the 'Data Quality Information' section of the individual shoreline metadata report.

To make this shoreline data more accessible to the public and other agencies, the USGS created this web service. This web service was created utilizing ESRI ArcServer. Vector shoreline layers were collected, organized by state, and symbology made consistent among similar data sets. This service meets open geospatial consortium standards.

The usSEABED database contains a compilation of published and previously unpublished sediment texture and other geologic data about the sea floor from diverse sources. The data were compiled using the dbSEABED system to bring assorted data together in a unified database. Maps display areas of different bottom types (carbonate, coral, shells, and terrigenous) and sediment classifications using the Folk and Shepard systems, based on either laboratory data or verbal records of observations. usSEABED information is a scientific foundation for the USGS Marine Aggregate Resources and Processes Assessment and Benthic Habitats projects. The usSEABED database includes data for sites in U.S. waters from rivers, lakes, estuaries, and from the beach to the continental shelf.

To make the usSEABED data more accessible to the public and other agencies, the USGS created this web service. This web service was created utilizing ESRI ArcServer. This service meets open geospatial consortium standards.

The geographic information system (GIS) data layers from this web service are cataloged by region for ease of access.