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Investigations of coastal change and coastal resources often require continuous elevation profiles from the seafloor to coastal terrestrial landscapes. Differences in elevation data collection in the terrestrial and marine environments result in separate elevation products that may not share a vertical datum. This data release contains the assimilation of multiple elevation products into a continuous digital elevation model at a resolution of 3-arcseconds (approximately 90 meters) from the terrestrial landscape to the seafloor for the contiguous U.S., focused on the coastal interface. All datasets were converted to a consistent horizontal datum, the North American Datum of 1983, but the native vertical datum for each dataset was not adjusted. Artifacts in the source elevation products were replaced with other available elevation products when possible, corrected using various spatial tools, or otherwise marked for future correction. This data release contains the assimilation of multiple elevation products into a continuous digital elevation model at a resolution of 3-arcseconds (approximately 90 meters) from the terrestrial landscape to the seafloor for the contiguous U.S. that were constructed using this shapefile.

Dauphin Island, Alabama is a barrier island located in the Gulf of Mexico that supports local residence, tourism, commercial infrastructure, and the historical Fort Gaines. During the past decade the island has been impacted by several major hurricanes (Ivan, 2004; Katrina, 2005; Isaac 2012). Storms along with sea level rise, presents a continued threat to island stability. State and federal managers are taking a scientific investigative approach to identify the best options available to formulate and implement a long-term plan to properly restore Dauphin Island and provide resilience against future storms and sea-level rise. Island morphology, including current bathymetry data, is one of several aspects being investigated and funded through a grant from National Fish and Wildlife Foundation Gulf Environmental Benefit Fund. In August 2015, the United States Geological Survey Saint Petersburg Coastal and Marine Science Center (USGS SPCMSC) in cooperation with the United States Army Corps of Engineers (USACE) and the state of Alabama conducted bathymetric surveys of the nearshore waters surrounding Dauphin Island. This data release provides 1,165-line kilometers (km) of processed single-beam bathymetry (SBB) data collected by the USGS SPCMSC in August 2015 (Field Activity Number [FAN] 2015-326-FA). Data were acquired aboard 4 separate survey vessels; the RV Sallenger (subFAN, 15BIM10), the RV Jabba Jaw (subFAN, 15BIM11), the RV Shark (subFAN, 15BIM12), and the RV Chum (subFAN, 15BIM13). The data are provided in three datums: 1) the International Terrestrial Reference Frame of 2000 (ITRF00), ellipsoid height (-47.04 meters [m] to -29.36 m); 2) the North American Datum of 1983, realization of CORS96 (NAD83 (CORS96)) horizontal, and the North American Vertical Datum 1988 (NAVD88) vertical (-0.24 m to -17.33 m); and 3) the NAD83 (CORS96) horizontal, and Mean Lower Low Water (MLLW) vertical (-0.12 m to -17.93 m). Additional files include trackline shapefiles, digital and handwritten Field Activity Collection Systems (FACS) logs, a comprehensive 50-meter Digital Elevation Model (DEM), and formal Federal Geographic Data Committee (FGDC) metadata.

Aerial_WDL_Shorelines.zip features digitized historic shorelines for the Dauphin Island coastline from October 1940 to November 2015. This dataset contains 10 Wet Dry Line (WDL) shorelines separated into 58 shoreline segments alongshore Dauphin Island, AL. The individual sections are divided according to location along the island and shoreline type: open-ocean, back-barrier, marsh shoreline. Imagery of Dauphin Island, Alabama was acquired from several sources including the United States Geological Survey (USGS), the National Agriculture Imagery Program (NAIP), the United States Department of Agriculture's Farm Service Agency (USDA, FSA), and the University of Alabama. These images were downloaded directly from the source's website or received as a hard copy via mail. Using ArcMap 10.3.1, the imagery was used to delineate and digitize historical shorelines at the wet-dry line along sandy beaches and the mean high water line where vegetation indicated. These shorelines were digitized for use in long-term shoreline and wetland analyses and physical change assessments. Shorelines for all 10 dates were compiled into a database for use with the Digital Shoreline Analysis System (DSAS; Thieler and others, 2009) to quantify rates of shoreline change over the 1940-2015 time period. The migration of shorelines through time is presented as the linear regression rate (LRR) in the associated back-barrier and open ocean transect files, which are also included in the USGS data release (https://coastal.er.usgs.gov/data-release/provisional/ip086178/).

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 2015 Mississippi coastal shorelines were originally extracted from 2015 Landsat imagery and published within United States Geological Survey (USGS) Open-File Report (OFR) 2015-1179 (https://doi.org/10.3133/ofr20151179). Shoreline files for Ship, Horn, and Petit Bois Islands were merged to a single shapefile and spatially adjusted using 2015/2016 USGS bathymetric survey tracklines (Dewitt and others, 2017) to more closely match island shoreline positions during USGS surveys.

This shapefile consists of Dauphin Island, AL shorelines extracted from lidar data collected from November 1998 to January 2014. This dataset contains 14 Mean High Water (MHW) shorelines separated into 37 shoreline segments alongshore Dauphin Island, AL. The individual sections are divided according to location along the island and shoreline type: open ocean, back-barrier, marsh shoreline.

Raw lidar point data was converted to a gridded surface, from which a contour of the operational MHW shoreline (0.24 m North American Vertical Datum of 1988 [NAVD 88]; Weber and others, 2005) was identified and extracted. This produced a continuous MHW shoreline for each of the lidar datasets from 1998 – 2014.

Shorelines for all 14 dates were compiled into a database for use with the Digital Shoreline Analysis System (DSAS; Thieler and others, 2009) to quantify rates of shoreline change over the 1998-2014 time period. The migration of shorelines through time is presented as the linear regression rate (LRR) in the associated transect files (https://coastal.er.usgs.gov/data-release/provisional/ip086178/).