Scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS SPCMSC) in St. Petersburg, Florida, conducted a bathymetric survey of Point Aux Chenes Bay and a small portion of Grand Bay, Mississippi/Alabama, from March 3-6, 2021. Efforts were supported by the Estuarine and MaRsh Geology project (EMRG), and the data described will provide baseline bathymetric information for future research investigating wetland/marsh evolution, sediment transport, and recent and long-term geomorphic change. The data will also support modeling of future changes in response to restoration efforts and storm impacts. During this study, bathymetry data were collected aboard two personal watercrafts (PWC) outfitted with single-beam echosounders.

The U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center conducted research to quantify bathymetric changes along the Florida Reef Tract (FRT) from Miami to Boca Chica Key, Florida. Changes in seafloor elevation were calculated from the 1930s to 2016 using digitized hydrographic sheet sounding data and light detection and ranging (lidar)-derived digital elevation models (DEMs) acquired by the National Oceanic and Atmospheric Administration (NOAA) in 2016 and 2017. Most of the elevation data from the 2016/2017 time period was collected during 2016, and, as an abbreviated naming convention, this time period was referred to as 2016. An elevation change analysis between the 1930s and 2016 data was performed to quantify and map historical impacts to seafloor elevation and to determine elevation-change statistics for 15 habitat types found within the study area along the FRT. Annual elevation-change rates were calculated for each elevation-change data point. Seafloor elevation-change along the FRT was projected 25, 50, 75 and 100 years from 2016 using these historical annual rates of elevation change. Water depth was projected 25, 50, 75 and 100 years from 2016 using historical rates of annual elevation change plus 2016 local sea level rise (SLR) data from NOAA. Data were collected under Florida Keys National Marine Sanctuary permit FKNMS-2016-068.

This dataset contains shoreline positions derived from available Landsat satellite imagery for five states (Delaware, Maryland, Viginia, Georgia, and Florida) along the U.S. Atlantic coast for the time period 1984 to 2021. An open-source toolbox, CoastSat (Vos and others, 2019a and 2019b), was used to classify coastal Landsat imagery and detect shorelines at the sub-pixel scale. Resulting shorelines are presented in KMZ format. Significant uncertainty is associated with the locations of shorelines in extremely dynamic regions, including at the locations of river mouths, tidal inlets, capes, and ends of spits. These data are readily viewable in Google Earth. For best display of results, it is recommended to turn off any 3D viewing. For technical users and researchers, data can be ingested into Global Mapper or QGIS for more detailed analysis. Similar shoreline positions for North Carolina and South Carolina are available from Barnard and others, 2023 at https://doi.org/10.5066/P9W91314.

This dataset contains shoreline positions derived from available Landsat satellite imagery for North Carolina and South Carolina for the time period of 1984 to 2021. Positions were determined using CoastSat (Vos and others, 2019a and 2019b), an open-source mapping toolbox, was used to classify coastal Landsat imagery and detect shorelines at the sub-pixel scale. To understand shoreline evolution in complex environments and operate long-term simulations illustrating potential shoreline positions in the next century (Vitousek and others, 2017, 2021), robust historical shoreline data is necessary. Satellite-derived shorelines (SDS) offer expansive shoreline observational data over large geographic and temporal scales. Resulting shorelines for the period of 1984-2021 are presented in KMZ format. Significant uncertainty is associated with the locations of shorelines in extremely dynamic regions, including at the locations of river mouths, tidal inlets, capes, and ends of spits. These data are readily viewable in Google Earth. For best display of results, it is recommended to turn off any 3D viewing. For technical users and researchers, data can be ingested into Global Mapper or QGIS for more detailed analysis.

To determine continued change to the shoreface morphology and evolution at Seven Mile Island, New Jersey, scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS SPCMSC) in St. Petersburg, Florida, conducted a single-beam bathymetric survey of Seven Mile Island, New Jersey, from April 29 - May 2, 2022. During this study, single-beam bathymetry data were collected using a personal watercraft (PWC) and a floating-towed-seismic sled. Both the PWC and the seismic sled were outfitted with high precision Global Navigation Satellite System (GNSS) receivers, motion reference units, and survey grade single-beam echosounders.

In February 2017, the United States Geological Survey Saint Petersburg Coastal and Marine Science Center (USGS SPCMSC) conducted multibeam and single-beam bathymetric surveys of the nearshore waters off Madeira Beach, Florida. These data were collected as part of a regional study designed to better understand coastal processes on barrier islands and sandy beaches. Results from this study will be incorporated with observations from other regional studies in order to validate operational water level and coastal change hazards models being expanded nationwide (National Assessment of Storm-Induced Coastal Change Hazards). The regional study area is Madeira Beach located on one of eleven barrier islands in Pinellas County in west-central Florida along the Gulf of Mexico. These barrier islands support highly developed and densely populated coastal communities, comprised of local residents, year-round tourist population, and undeveloped natural habitats within local and state parks. A measure of the nearshore bathymetry is useful for better understanding the interaction of the hydrodynamics and morphodynamics offshore and in the surf zone that ultimately control the beach response. This USGS data release provides processed multibeam bathymetry (MBES) and processed single-beam bathymetry (SBES) data collected under the USGS field activity number (FAN) 2017-305-FA. This FAN has four subfans each representing one research vessel (R/V): 17TST01 (R/V Sallenger), 17TST02 (R/V Jabba Jaw, a 22-foot shallow draft Twin Vee [TVEE]), 17TST03 (R/V Shark, a 12-foot Yamaha personal watercraft [SHRK]) and 17TST04 (R/V Chum, a 12-foot Yamaha personal watercraft [CHUM]). The point data (x,y,z) are provided in two datums: 1) the International Reference Frame of 2008 (ITRF08) and ellipsoid height; and 2) the North American Datum 1983 in the CORS96 realization (NAD83 (CORS96)) for the horizontal and the North American Vertical Datum 1988 (NAVD88) with respect to GEOID12B for the vertical. Additional files include a single-beam trackline shapefile (.shp) and a 20-meter (m) cell size single-beam digital elevation model (DEM, .tif). For further information regarding data collection and/or processing please see the metadata associated with this data release.

This dataset contains projections of shoreline change and uncertainty bands for future scenarios of sea-level rise (SLR). Scenarios include 25, 50, 75, 100, 150, 200, and 300 centimeters (cm) of SLR by the year 2100. Output for SLR of 0 cm is also included, reflective of conditions in 2005, in accordance with recent SLR projections and guidance from the National Oceanic and Atmospheric Administration (NOAA; see process steps).Projections were made using the Coastal Storm Modeling System - Coastal One-line Assimilated Simulation Tool (CoSMoS-COAST), a numerical model (described in Vitousek and others, 2017; 2021; 2023) run in an ensemble forced with global-to-local nested wave models and assimilated with satellite-derived shoreline (SDS) observations. Shoreline positions from models are generated at pre-determined cross-shore transects and output includes different cases covering important model behaviors (cases are described in process steps of metadata; see citations listed in the Cross References section for more details on the methodology and supporting information). This model shows change in shoreline positions along transects, considering sea level, wave conditions, along-shore/cross-shore sediment transport, long-term trends due to sediment supply, and estimated variability due to unresolved processes (as described in Vitousek and others, 2021). Variability associated with complex coastal processes (for example, beach cusps/undulations and shore-attached sandbars) are included via a noise parameter in a model, which is tuned using observations of shoreline change at each transect and run in an ensemble of 200 simulations; this approach allows for a representation of statistical variability in a model that is assimilated with sequences of noisy observations. The model synthesizes and improves upon numerous, well-established shoreline models in the scientific literature; processes and methods are described in this metadata (see lineage and process steps), but also described in more detail in Vitousek and others 2017, 2021, and 2023. KMZ data are readily viewable in Google Earth. For best display of results, it is recommended to turn off any 3D features or terrain. For technical users and researchers, shapefile and KMZ data can be ingested into geographic information system (GIS) software such as Global Mapper or QGIS.

This dataset contains projections of shoreline change and uncertainty bands for future scenarios of sea-level rise (SLR). Scenarios include 25, 50, 75, 100, 150, 200, and 300 centimeters (cm) of SLR by the year 2100. Output for SLR of 0 cm is also included, reflective of conditions in 2005, in accordance with recent SLR projections and guidance from the National Oceanic and Atmospheric Administration (NOAA; see process steps). Projections were made using the Coastal Storm Modeling System - Coastal One-line Assimilated Simulation Tool (CoSMoS-COAST), a numerical model (described in Vitousek and others, 2017; 2021; 2023) run in an ensemble forced with global-to-local nested wave models and assimilated with satellite-derived shoreline (SDS) observations. Shoreline positions from models are generated at pre-determined cross-shore transects and output includes different cases covering important model behaviors (cases are described in process steps of metadata; see citations listed in the Cross References section for more details on the methodology and supporting information). This model shows change in shoreline positions along transects, considering sea level, wave conditions, along-shore/cross-shore sediment transport, long-term trends due to sediment supply, and estimated variability due to unresolved processes (as described in Vitousek and others, 2021). Variability associated with complex coastal processes (for example, beach cusps/undulations and shore-attached sandbars) are included via a noise parameter in a model, which is tuned using observations of shoreline change at each transect and run in an ensemble of 200 simulations; this approach allows for a representation of statistical variability in a model that is assimilated with sequences of noisy observations. The model synthesizes and improves upon numerous, well-established shoreline models in the scientific literature; processes and methods are described in this metadata (see lineage and process steps), but also described in more detail in Vitousek and others 2017, 2021 and 2023. KMZ data are readily viewable in Google Earth. For best display of results, it is recommended to turn off any 3D features or terrain. For technical users and researchers, shapefile and KMZ data can be ingested into geographic information system (GIS) software such as Global Mapper or QGIS.

Scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS – SPCSMC), conducted a single-beam bathymetry survey within Point Aux Chenes Bay, Mississippi (MS), in June 2022 under the USGS Field Activity Number (FAN) 2022-320-FA. The data was collected from two personal watercrafts (PWC): research vessel (R/V) Shark (subFAN 22CCT09, WVR1) and R/V Chum (subFAN 22CCT10, WVR2). A re-survey of just the north and south subtidal reefs occurred in November 2022 (subFANs 22CCT11 and 22CCT12, respectively). Efforts were supported by the Coastal Marine Hazards Research Preogram (CMHRP) and the National Oceanic and Atmospheric Association (NOAA) Effects of Sea Level Rise (ESLR) Program in partnership with Grand Bay National Estuarine Research Reserve and Mississippi State University. The processed point data files (xyz) are released in two datums, the World Geodetic System of 1984 (WGS84 G2139) ellipsoid height referenced to the Universal Transverse Mercator (UTM) Zone 16 North (N); and the North American Datum of 1983 (NAD83 (2011)), North American Vertical Datum of 1988 (NAVD88) orthometric height with respect to GEOID12A. Additional data products include a single-beam trackline shapefile (.shp), a 10-meter (m) cell-size digital elevation model (DEM). and formal Federal Geographic Data Committee (FGDC) metadata. For consistency and comparison, the shoreline derivation method from Terrano and others (2021) utilized to derive the 2021 shoreline was replicated to derive the 2022 shoreline. Similarly, methods from Stalk and others (2021) utilized to create the 2021 10-meter DEM were replicated to create the 2022 10-m DEM included in this data release. For further information regarding data collection and/or processing methods, refer to DeWitt and others (2017) and Stalk and others (2021). For further information regarding data collection and/or processing methods, refer to DeWitt and others (2017) and Stalk and others (2021).

As part of the restoration monitoring component of the Deepwater Horizon early restoration project, scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS SPCMSC) conducted single-beam and multibeam bathymetry surveys around Breton Island, Louisiana (LA), from August 3-5, 2022, for Field Activity Number (FAN) 2022-328-FA. The purpose of data collection was to develop a baseline digital elevation model of the seafloor around Breton Island for comparison with both previous and future elevation assessments, and to evaluate elevation change following island restoration. The survey encompassed approximately 65 square kilometers of nearshore environment including the former Mississippi River to Gulf Outlet and submerged areas of South Breton Island. The single-beam bathymetry was acquired using two 12-foot personal watercrafts (PWCs) and a 20-foot Twin Vee. All vessels were outfitted with high precision Global Navigation Satellite System (GNSS) receivers, motion reference units, and survey grade single-beam echosounders. For further information regarding data collection and/or processing, please see the metadata associated with this data release. For additional information on post-processing steps please refer to DeWitt and others (2016) and Hansen and others (2017).

This dataset contains shoreline positions derived from available Landsat satellite imagery for four states (Texas, Louisiana, Mississippi, and Florida) along the U.S. Gulf coast for the time period 1984 to 2022. An open-source toolbox, CoastSat (Vos and others, 2019a and 2019b), was used to classify coastal Landsat imagery and detect shorelines at the sub-pixel scale. Resulting shorelines are presented in CSV format. Significant uncertainty is associated with the locations of shorelines in extremely dynamic regions, including at the locations of river mouths, tidal inlets, capes, and ends of spits. These data are readily viewable in a text or spreadsheet editor. For technical users and researchers, data can be ingested into Global Mapper or QGIS or similar for more detailed analysis. Similar shoreline positions for North Carolina and South Carolina are available from Barnard and others, 2023 at https://doi.org/10.5066/P9W91314.

Scientists from the U.S. Geological Survey (USGS) St. Petersburg Coastal and Marine Science Center (SPCMSC) conducted single-beam and multibeam bathymetry (Stalk and others, 2025) surveys around the northern Chandeleur Islands, Louisiana, from June 12 to 20 and from July 31 to August 9, 2023, as part of Field Activity Number (FAN) 2023-325-FA. The purpose of data collection was to measure submerged coastal elevations along the Chandeleur Islands, located in the Breton National Wildlife Refuge. Funded by the Extending Government Funding and Delivering Emergency Assistance Act (Public Law 117-43) enacted on September 30, 2021, these data, in combination with previous bathymetric data collected at the study area (Stalk and others, 2017; Stalk and others, 2020), can be used to quantify storm-related barrier island sediment redistribution following the 2020-2021 hurricane seasons. The survey encompassed approximately 760 square kilometers (km) of the gulf-side and sound-side nearshore environments around the northern Chandeleur Islands. The single-beam bathymetry was acquired using two 12-foot (ft) personal watercrafts and two boats (a 20-ft Twin Vee [TVEE] and a 17-ft Mako). All vessels were outfitted with high precision Global Navigation Satellite System receivers, motion reference units, and survey grade single-beam echosounders (SBES). Long Term Change (LTC) lines were collected by the TVEE as part of a comparative long term change analysis, but for this data release, LTC lines represent shore-perpendicular transects extending both offshore and soundward 3 km on either side of the island. Sub-bottom profile geophysical data were also collected during this FAN and are provided in Forde and others (2024).

A dataset of Landsat, Sentinel, and Planetscope satellite images of coastal shoreline regions, and corresponding semantic segmentations. The dataset consists of folders of images and label images. Label images are images where each pixel is given a discrete class by a human annotator, among the following classes: a) water, b) whitewater/surf, c) sediment, and d) other. These data are intended only to be used as a training and validation dataset for a machine learning based image segmentation model that is specifically designed for the task of coastal shoreline satellite image semantic segmentation.

A labeled dataset of Landsat, Sentinel, and Planetscope satellite visible-band images of coastal shoreline regions, consisting of folders of images that have been labeled as either suitable or unsuitable for shoreline detection using existing conventional approaches such as CoastSat (Vos and others, 2019) or CoastSeg (Fitzpatrick and others, 2024). These data are intended to be used as inputs to models that determine the suitability or otherwise of the image. These data are only to be used as a training and validation dataset for a machine learning model that is specifically designed for the task of determining the suitability of an image for the task of estimating the shoreline location.

A dataset of semantic segmentations of Landsat, Sentinel, and Planetscope satellite images of coastal shoreline regions, consisting of folders of images that have been labeled as either suitable or unsuitable for shoreline detection using existing conventional approaches such as CoastSat (Vos and others, 2019) or CoastSeg (Fitzpatrick and others, 2024). These data are intended only to be used as a training and validation dataset for a machine learning model that is specifically designed for the task of determining the suitability of a deep-learning-based image segmentation model output for the task of estimating the shoreline location. These data were used to train a Machine Learning model to recognize the quality of an image segmentation.

This dataset contains shoreline positions derived from available Landsat satellite imagery for two states (Oregon, and Washington) along the U.S. Pacific coast for the time period 1984 to 2023. An open-source toolbox, CoastSat (Vos and others, 2019a and 2019b), was used to classify coastal Landsat imagery and detect shorelines at the sub-pixel scale. Resulting shorelines are presented in CSV format. Significant uncertainty is associated with the locations of shorelines in extremely dynamic regions, including at the locations of river mouths, tidal inlets, capes, and ends of spits. These data are readily viewable in a text or spreadsheet editor. For technical users and researchers, data can be ingested into Global Mapper or QGIS or similar for more detailed analysis. Similar shoreline positions for North Carolina and South Carolina are available from Barnard and others, 2023 at https://doi.org/10.5066/P9W91314. Similar shoreline positions for Texas, Mississippi, Louisiana, and Florida are available from Buscombe and others, 2024 at https://doi.org/10.5066/P1WFZXDM.

The U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS SPCMSC) collected single beam echosounder (SBES) and differential global positioning system (DGPS) elevation data in the nearshore and beach environments of Wallops and Assawoman Islands, Virginia, in June 2024. This USGS data release includes the processed SBES and DGPS elevation point data (xyz) for Field Activity Number (FAN) 2024-310-FA. The SBES data were acquired using survey equipment mounted on personal watercrafts (PWCs) R/V Chum and R/V Shark, and the DGPS data were acquired using GPS antennas mounted on a utility terrain vehicle (UTV) or GPS backpack. Multibeam echosounder (MBES) and chirp seismic data were collected concurrently as part of FAN 2024-310-FA; those data are available as separate data releases (Forde and others, 2025; Bemelmans and others, 2025).

This dataset contains shoreline positions derived from available satellite imagery for multiple locations (Barter Island, Alaska; Elwha, Washington; Cape Cod, Massachusetts; Madeira Beach, Florida; and Rincon, Puerto Rico) across the United States for the time period 1984 to 2023. An open-source toolbox, CoastSeg (Fitzpatrick and others, 2024a; Fitzpatrick and others, 2024b), was used to classify coastal Landsat and Sentinel imagery and detect shorelines at the sub-pixel scale, using the CoastSat (Vos and others, 2019) methodology. Shorelines are derived for multiple slope values, representing the spatial and temporal variance of slope conditions at each site. Resulting shoreline positions are presented as discrete points in comma-separated value (CSV) format. Significant uncertainty is associated with the locations of shorelines in extremely dynamic regions at all sites, including at the locations of river mouths, tidal inlets, capes, ends of spits, and adjacent to wetlands at the Barter Island site. For technical users and researchers, data can be ingested into geospatial platforms (for example, QGIS or GlobalMapper) for more detailed analysis.

This dataset contains shorelines (as vectors, where vertices are positions determined along transects) derived from available satellite imagery for multiple locations (Barter Island, Alaska; Elwha, Washington; Cape Cod, Massachusetts; Madeira Beach, Florida; and Rincon, Puerto Rico) and associated settings used to derive the data across the United States for the time period 1984 to 2023. An open-source toolbox, CoastSeg (Fitzpatrick and others, 2024a; Fitzpatrick and others, 2024b), was used to classify coastal Landsat and Sentinel imagery and detect shorelines at the sub-pixel scale, using the CoastSat (Vos and others, 2019) methodology. Shorelines are derived for multiple slope values, representing the spatial and temporal variance of slope conditions at each site. Resulting shorelines from transect-based derivation are presented in GeoJSON format. Significant uncertainty is associated with the locations of shorelines in extremely dynamic regions at all sites, including at the locations of river mouths, tidal inlets, capes, ends of spits, and adjacent to wetlands at the Barter Island site. For technical users and researchers, data can be ingested into geospatial platforms (for example, QGIS or GlobalMapper) for more detailed analysis.

As part of the restoration monitoring component of the Deepwater Horizon early restoration project, scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS SPCMSC) conducted single-beam and multibeam bathymetry surveys around Breton Island, Louisiana (LA), from August 5-10, 2024, for Field Activity Number (FAN) 2024-320-FA. The purpose of data collection was to measure submerged elevations and develop a digital elevation model of the seafloor around Breton Island. These data were collected as part of the Louisiana Outer Coast Restoration Construction Monitoring project supported by funding from the Natural Resource Damage Assessment - Deepwater Horizon Restoration Activities and, in combination with both previous and planned future surveys, can be used to evaluate elevation change following island restoration. The survey area covered approximately 77 square kilometers (km^2) of nearshore environment surrounding Breton Island and extended into the adjacent portion of the former Mississippi River to Gulf Outlet (MRGO). The single-beam bathymetry was acquired using two 12-foot personal watercraft (PWC) and one 20-foot power catamaran. All vessels were outfitted with high precision Global Navigation Satellite System (GNSS) receivers, motion reference units, and survey grade single-beam echosounders (SBES). For additional information on post-processing steps, please refer to DeWitt and others (2016) and Hansen and others (2017). Multibeam echosounder (MBES) data including the backscatter component and chirp seismic sub-bottom data were collected concurrently as part of FAN 2024-320-FA; those data are available as separate data releases: Bemelmans and others (2025); Forde and others (2025), respectively.

In June 2025, the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS SPCMSC) participated in a multidisciplinary geoscientific field data collection at Midnight Pass and Milton Pass located along Florida s west-central coastline. These inlets were opened as a result of hurricanes Helene and Milton in 2024. Researchers with the Nearshore Extreme Events Reconnaissance Association (NEER, https://neerassociation.org/) conducted surveys of these inlets immediately following Hurricane Milton in 2024, and this data collected in 2025 is to support the continued monitoring of the evolution of the inlets (Stark and others, 2025). This metadata record describes the single-beam bathymetry survey conducted by the USGS-SPCMSC at Midnight Pass, Florida on June 25, 2025, under the USGS Field Activity Number (FAN) 2025-313-FA. The survey area encompassed the nearshore environment, the inlet, and the back bay waterway resulting in 3.94 square kilometers (km2) of coverage at Midnight Pass. The single-beam bathymetry was acquired using two 12-foot personal watercrafts (PWC), outfitted with high precision Global Navigation Satellite System (GNSS) receivers, motion reference units, and survey-grade single-beam echosounders (SBES). This data release provides point data (x,y,z) in two reference frames: 1) World Geodetic System 1984 (WGS84) realization G2296, Universal Transverse Mercator (UTM) zone 17 North (N) for the horizontal, and WGS84 (G2296) ellipsoidal height in meters for the vertical; and 2) North American Datum of 1983 (NAD83) realization of 2011 for the horizontal, and North American Vertical Datum 1988 (NAVD88) orthometric height in meters with respect to GEOID18 for the vertical. Additional files include single-beam trackline shapefiles (.shp). For further information regarding data collection and/or processing steps, please see the metadata associated with this data release.

In June 2025, the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center (USGS SPCMSC) participated in a multidisciplinary geoscientific field data collection at Midnight Pass and Milton Pass located along Florida's west-central coastline. These inlets were opened as a result of hurricanes Helene and Milton in 2024. Researchers with the Nearshore Extreme Events Reconnaissance Association (NEER, https://neerassociation.org/) conducted surveys of these inlets immediately following Hurricane Milton in 2024, and this data collected in 2025 is to support the continued monitoring of the evolution of the inlets (Stark and others, 2025). This metadata record describes the single-beam bathymetry surveys conducted by USGS SPCMSC at Milton Pass, Florida on June 26, 2025, under the USGS Field Activity Number (FAN) 2025-313-FA. The survey area encompassed the nearshore environment, the inlet, and the back bay waterway resulting in 3.41 square kilometers (km2) coverage at Milton Pass. The single-beam bathymetry was acquired using two 12-foot personal watercrafts (PWC), outfitted with high precision Global Navigation Satellite System (GNSS) receivers, motion reference units, and survey-grade single-beam echosounders (SBES). This data release provides point data (x, y, z) in two datums: 1) World Geodetic System 1984 (WGS84) realization G2296, Universal Transverse Mercator (UTM) zone 17 North (N) for the horizontal and WGS84 (G2296) ellipsoidal height in meters for the vertical; and 2) North American Datum of 1983 (NAD83) realization of 2011 for the horizontal, and North American Vertical Datum 1988 (NAVD88) orthometric height in meters with respect to GEOID18 for the vertical. Additional files include single-beam trackline shapefiles (.shp). For further information regarding data collection and/or processing steps, please see the metadata associated with this data release.