This U.S. Geological Survey data release includes bare-earth digital elevation models (DEMs) that were produced by removing elevation bias in vegetated areas from structure-from-motion (SfM) data products for two sites on Dauphin Island, Alabama. These data were collected in the late fall of 2018 and spring of 2019. In addition to the bare-earth DEMs, this data release also includes vegetation masks, examples of model uncertainty, training data, prediction data, and validation data associated with this effort.

Vegetation and elevation survey data were collected in 4-square-meter quadrats via Real-Time Kinematic GPS from September 9, 2018 to April 17, 2019 on Dauphin Island, AL. Vegetation data included total percent herbaceous cover, percent cover by plant species, and mean height of vegetation within the quadrat. The percent cover by species was used to determine the dominant species for the plot.

Aerial imagery acquired with a small unmanned aircraft system (sUAS), in conjunction with surveyed ground control points (GCP) visible in the imagery, can be processed with structure-from-motion (SfM) photogrammetry techniques to produce high-resolution orthomosaics, three-dimensional (3D) point clouds and digital elevation models (DEMs). This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides UAS survey data products consisting of DEMs produced from imagery collected at Little Dauphin Island and Pelican Island, Alabama, from September 2018 to April 2019 in order to develop integrated models linking geomorphology, habitat characteristics, and non-breeding shorebird species usage. Photogrammetry software was used to perform SfM processing on low-altitude digital aerial imagery acquired with a 3DR Solo UAS quadcopter equipped with a Ricoh GR II digital camera and MicaSense RedEdge 3 multispectral camera, using surveyed temporary targets (black and white, 4-square checked pattern) distributed uniformly throughout the UAS flight operations area as GCPs. The following SfM products are produced for each UAS survey over the northern half of Little Dauphin Island and all of Pelican Island: * georeferenced red-green-blue (RGB) orthomosaic image with 5-centimeter (cm) resolution * georeferenced multispectral (MS) orthomosaic image with 5-cm resolution * DEM with 5-cm horizontal resolution * 3D RGB-colored point cloud All horizontal data are provided in the Universal Transverse Mercator (UTM) projected coordinate system, Zone 16 North (16N), referenced to the North American Datum of 1983 (NAD83(2011)), and elevation is referenced to the North American Vertical Datum of 1988 (NAVD88), GEOID12B.

Aerial imagery acquired with a small unmanned aircraft system (sUAS), in conjunction with surveyed ground control points (GCP) visible in the imagery, can be processed with structure-from-motion (SfM) photogrammetry techniques to produce high-resolution orthomosaics, three-dimensional (3D) point clouds and digital elevation models (DEMs). This dataset, prepared by the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC), provides UAS survey data products consisting of DEMs produced from imagery collected at Little Dauphin Island and Pelican Island, Alabama, from September 2018 to April 2019 in order to develop integrated models linking geomorphology, habitat characteristics, and non-breeding shorebird species usage. Photogrammetry software was used to perform SfM processing on low-altitude digital aerial imagery acquired with a 3DR Solo UAS quadcopter equipped with a Ricoh GR II digital camera and MicaSense RedEdge 3 multispectral camera, using surveyed temporary targets (black and white, 4-square checked pattern) distributed uniformly throughout the UAS flight operations area as GCPs. The following SfM products are produced for each UAS survey over the northern half of Little Dauphin Island and all of Pelican Island: * georeferenced red-green-blue (RGB) orthomosaic image with 5-centimeter (cm) resolution * georeferenced multispectral (MS) orthomosaic image with 5-cm resolution * DEM with 5-cm horizontal resolution * 3D RGB-colored point cloud All horizontal data are provided in the Universal Transverse Mercator (UTM) projected coordinate system, Zone 16 North (16N), referenced to the North American Datum of 1983 (NAD83(2011)), and elevation is referenced to the North American Vertical Datum of 1988 (NAVD88), GEOID12B.

These data map the beach and nearshore environment at Head of the Meadow Beach in Truro, MA, providing updated regional context for the 2019 CoastCam installation. CoastCam CACO-01 are two video cameras aimed at the beach that view the coast shared by beachgoers, shorebirds, seals, and sharks. These data were collected as part of field activity 2022-015-FA and a collaboration with the National Park Service at Cape Cod National Seashore to monitor the region. In March 2022, U.S. Geological Survey and Woods Hole Oceanographic Institute (WHOI) scientists conducted field surveys to re-map the field of view of the CoastCam. Aerial images of the beach for use in structure from motion were taken with a camera (Sony a6000) and a post-processed kinematic (PPK) system attached to a helium filled balloon-kite (Helikite). High-precision GPS targets (AeroPoints) and numbered black and white tarps were used as ground control points. Bathymetry was collected in the nearshore using a single-beam echosounder mounted on a surf capable self-righting electric autonomous surface vehicle. Agisoft Metashape (v. 1.8.1) was used to create a digital surface model with the collected imagery, which was merged with the bathymetry in MATLAB (v. 2020b) to create a continuous topobathy product.

A barrier island habitat prediction model was used to forecast barrier island habitats (for example, beach, dune, intertidal marsh, and woody vegetation) for Dauphin Island, Alabama, based on potential island configurations associated with a variety of restoration measures and varying future conditions of storminess and sea level (Enwright and others, 2020). This USGS data release contains five habitat model predictions from the aforementioned modeling effort. These include: (1) the contemporary period (that is, 2015); (2) with action Year 0 (that is, hypothetically, predicted habitat coverage in 2128 based on our sea-level change rate); (3) with action Year 10 (that is, predicted habitat coverage after ten years of morphodynamic modeling with simulated storms); (4) without action Year 0; and (5) without action Year 10. Additionally, this data release includes change maps that highlight changes over the decadal simulation (that is, Year 0 to Year 10) with and without action, respectively, along with the difference between Year 10 for the with and without the action simulation. For more information on the habitat model methodology and results, see the publication listed in the larger work section of this metadata (Enwright and others, 2020) and Enwright and others (in review).

These data map the beach and nearshore environment at Head of the Meadow Beach in Truro, MA, providing updated regional context for the 2019 CoastCam installation. CoastCam CACO-01 are two video cameras aimed at the beach that view the coast shared by beachgoers, shorebirds, seals, and sharks. These data were collected as part of field activity 2021-014-FA and a collaboration with the National Park Service at Cape Cod National Seashore to monitor the region. In February 2021, U.S. Geological Survey and Woods Hole Oceanographic Institute (WHOI) scientists conducted field surveys to re-map the field of view of the CoastCam. Aerial images of the beach for use in Structure from motion were taken with a camera (Ricoh GRII) and a post-processed kinematic (PPK) system attached to a helium powered balloon-kite (Helikite). High-precision GPS targets (AeroPoints) were used as ground control points. Bathymetry was collected in the nearshore using a single-beam echosounder mounted on a surf capable self-righting electric autonomous Small Surf Vehicle (SSV). Agisoft Metashape (v. 1.7.2) was used to create a digital elevation model with the collected imagery and this was merged with the bathymetry in MATLAB (v. 2020) to create a continuous topobathy product.

A global 1-km resolution land surface digital elevation model (DEM) grayscale hillshade. Derived from the SRTM30+ v11 dataset produced at Scripps Institution of Oceanography from United States Geological Survey (USGS) 30 arc-second SRTM30 gridded DEM data, itself a product of NASA's Shuttle Radar Topography Mission (SRTM). GTOPO30 data are used for high latitudes where SRTM data are not available.

A barrier island habitat prediction model was used to forecast barrier island habitats (for example, beach, dune, intertidal marsh, and woody vegetation) for Dauphin Island, Alabama, based on potential island configurations associated with a variety of restoration measures and varying future conditions of storminess and sea-levels. In this study, we loosely coupled a habitat model framework with decadal hydrodynamic geomorphic model outputs to forecast habitats for 2 potential future conditions related to storminess (that is, “medium” storminess and “high” storminess based on storm climatology data) and 4 sea-level scenarios (that is, a “low” increase in sea level 0.3 m by around 2030 and 2050 and 1.0 m by around 2070 and 2128). Here, storminess refers to decadal-scale variation in the frequency and magnitude of storms. These sea-level rise (SLR) scenarios followed two SLR curves the U.S. Army Corps of Engineers intermediate SLR curve (0.7 m by 2100) and high SLR curve (1.7 m by 2100). The hydrodynamic geomorphic modeling was quasi-static, using an elevated offshore water level to capture impacts of future sea-level increases, and as such did not account for the dynamic effects of rising sea levels. However, for intertidal marshes, it was important to factor in the timing of the SLR since the SLR rate is important for the ability of an intertidal marsh to keep pace with SLR. Thus, we used literature-based assumptions related to the rate of SLR to account for potential vertical accretion in intertidal marshes. This USGS data release contains comma separated values (CSV) files for predictor variables by tidal zone and spatially explicit raster-based habitat prediction results for the various island configurations assessed for this modeling effort. For more information on the habitat model methodology and results, see the publication listed in the larger work section of this metadata (Enwright and others, 2020).

A barrier island habitat prediction model was used to forecast barrier island habitats (for example, beach, dune, intertidal marsh, and woody vegetation) for Dauphin Island, Alabama, based on potential island configurations associated with a variety of restoration measures and varying future conditions of storminess and sea-levels. In this study, we loosely coupled a habitat model framework with decadal hydrodynamic geomorphic model outputs to forecast habitats for 2 potential future conditions related to storminess (that is, “medium” storminess and “high” storminess based on storm climatology data) and 4 sea-level scenarios (that is, a “low” increase in sea level 0.3 m by around 2030 and 2050 and 1.0 m by around 2070 and 2128). Here, storminess refers to decadal-scale variation in the frequency and magnitude of storms. These sea-level rise (SLR) scenarios followed two SLR curves the U.S. Army Corps of Engineers intermediate SLR curve (0.7 m by 2100) and high SLR curve (1.7 m by 2100). The hydrodynamic geomorphic modeling was quasi-static, using an elevated offshore water level to capture impacts of future sea-level increases, and as such did not account for the dynamic effects of rising sea levels. However, for intertidal marshes, it was important to factor in the timing of the SLR since the SLR rate is important for the ability of an intertidal marsh to keep pace with SLR. Thus, we used literature-based assumptions related to the rate of SLR to account for potential vertical accretion in intertidal marshes. This USGS data release contains comma separated values (CSV) files for predictor variables by tidal zone and spatially explicit raster-based habitat prediction results for the various island configurations assessed for this modeling effort. For more information on the habitat model methodology and results, see the publication listed in the larger work section of this metadata (Enwright and others, 2020).