The U.S. Geological Survey (USGS) maintains shoreline positions for the United States coasts from both older sources, such as aerial photos or topographic surveys, as well as contemporary sources like lidar point clouds and digital elevation models (DEMs). These shorelines are compiled and analyzed in the Digital Shoreline Analysis System (DSAS) software to compute rates of change. It is useful to keep a record of historical shoreline positions as a method of monitoring change over time to identify areas most susceptible to erosion or accretion. These data can help coastal managers understand which areas of the coast are vulnerable. This data release and other associated products represent an expansion of the USGS national-scale shoreline database to include Puerto Rico and its islands, Vieques and Culebra. The United States Geological Survey (USGS) in cooperation with the Coastal Research and Planning Institute of Puerto Rico (CoRePI, part of the Graduate School of Planning at the University of Puerto Rico, Rio Piedras Campus) has derived and compiled a database of historical shoreline positions using a variety of methods. These shorelines are used to measure the rate of shoreline change over time.

The U.S. Geological Survey (USGS) maintains shoreline positions for the United States coasts from both older sources, such as aerial photos or topographic surveys, as well as contemporary sources like lidar point clouds and digital elevation models (DEMs). These shorelines are compiled and analyzed in the Digital Shoreline Analysis System (DSAS) software to compute rates of change. It is useful to keep a record of historical shoreline positions as a method of monitoring change over time to identify areas most susceptible to erosion or accretion. These data can help coastal managers understand which areas of the coast are vulnerable. This data release and other associated products represent an expansion of the USGS national-scale shoreline database to include Puerto Rico and its islands, Vieques and Culebra. The United States Geological Survey (USGS) in cooperation with the Coastal Research and Planning Institute of Puerto Rico (CoRePI, part of the Graduate School of Planning at the University of Puerto Rico, Rio Piedras Campus) has derived and compiled a database of historical shoreline positions using a variety of methods. These shorelines are used to measure the rate of shoreline change over time.

The U.S. Geological Survey (USGS) maintains shoreline positions for the United States coasts from both older sources, such as aerial photos or topographic surveys, as well as contemporary sources like lidar point clouds and digital elevation models (DEMs). These shorelines are compiled and analyzed in the Digital Shoreline Analysis System (DSAS) software to compute rates of change. It is useful to keep a record of historical shoreline positions as a method of monitoring change over time to identify areas most susceptible to erosion or accretion. These data can help coastal managers understand which areas of the coast are vulnerable. This data release and other associated products represent an expansion of the USGS national-scale shoreline database to include Puerto Rico and its islands, Vieques and Culebra. The United States Geological Survey (USGS) in cooperation with the Coastal Research and Planning Institute of Puerto Rico (CoRePI, part of the Graduate School of Planning at the University of Puerto Rico, Rio Piedras Campus) has derived and compiled a database of historical shoreline positions using a variety of methods. These shorelines are used to measure the rate of shoreline change over time.

The U.S. Geological Survey (USGS) maintains shoreline positions for the United States coasts from both older sources, such as aerial photos or topographic surveys, as well as contemporary sources like lidar point clouds and digital elevation models (DEMs). These shorelines are compiled and analyzed in the Digital Shoreline Analysis System (DSAS) software to compute rates of change. It is useful to keep a record of historical shoreline positions as a method of monitoring change over time to identify areas most susceptible to erosion or accretion. These data can help coastal managers understand which areas of the coast are vulnerable. This data release and other associated products represent an expansion of the USGS national-scale shoreline database to include Puerto Rico and its islands, Vieques and Culebra. The United States Geological Survey (USGS) in cooperation with the Coastal Research and Planning Institute of Puerto Rico (CoRePI, part of the Graduate School of Planning at the University of Puerto Rico, Rio Piedras Campus) has derived and compiled a database of historical shoreline positions using a variety of methods. These shorelines are used to measure the rate of shoreline change over time.

During Hurricane Irma, Florida and Georgia experienced substantial impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses from hurricanes result in increased vulnerability of coastal regions, including densely populated areas. Erosion may put critical infrastructure at risk of future flooding and may cause economic loss. The U.S. Geological Survey (USGS) Coastal and Marine Hazards Resources Program is working to assess shoreline erosion along the southeast U.S. coastline and analyze its implications for future vulnerability.

During Hurricane Irma, Florida and Georgia experienced substantial impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses from hurricanes result in increased vulnerability of coastal regions, including densely populated areas. Erosion may put critical infrastructure at risk of future flooding and may cause economic loss. The U.S. Geological Survey (USGS) Coastal and Marine Hazards Resources Program is working to assess shoreline erosion along the southeast U.S. coastline and analyze its implications for future vulnerability.

This data release contains foreshore slopes for primarily open-ocean sandy beaches along the East Coast of the United States (Maine through Florida). The slopes were calculated while extracting shoreline position from lidar point cloud data collected between 1997 and 2018. The shoreline positions have been previously published, but the slopes have not. An along-shore reference baseline was defined, and then 20-meter spaced cross-shore beach transects were created perpendicular to the baseline. All data points within 1 meter (along-shore) of each transect were associated with that transect. For each transect, the points on the foreshore were identified, and a linear regression was fit through the foreshore points. Beach slope was defined as the slope of the regression. The regression was evaluated at the elevation of mean high water (MHW) to yield the cross-shore location of the shoreline. In areas where more than one lidar survey is available, the slopes from each survey are provided. While most of the slopes are for sandy beaches, there are some slope data from rocky headlands and other steeper beaches. The slope data files (slopeData_EastCoast.csv and slopeData_EastCoast.shp) contain beach slope, the location at which the beach slope data was calculated (the shoreline position), and the estimated uncertainty of the shoreline position. The reference line data files (referenceLine_EastCoast.csv and referenceLine_EastCoast.shp) contain information about the reference baseline, the cross-shore transects, and the MHW values used to estimate the shoreline location. Both file types *.csv (ascii files containing comma separated values) and *.shp (binary files supported by Esri known as shapefiles) contain the same information. Both file types are provided as a convenience to the user.

The U.S. Geological Survey (USGS), in cooperation with the National Marine Sanctuary Program of the National Oceanic and Atmospheric Administration (NOAA), has conducted seabed mapping and related research in the Stellwagen Bank National Marine Sanctuary (SBNMS) region since 1993. The interpretive datasets and source information presented here are for quadrangle 5, which is one of 18 similarly sized segments of the 3,700 square kilometer (km2) SBNMS region. The seabed of the SBNMS region is a glaciated terrain that is topographically and texturally diverse. Quadrangle 5 includes the shallow, rippled, coarse-grained sandy crest and upper eastern and western flanks of southern Stellwagen Bank, its fine-grained sandy lower western flank, and the muddy seabed in Stellwagen Basin. Water depths range from <25 m on the bank crest to ~100 m in the basin. The data presented here for quadrangle 5 are the foundation for Scientific Investigations Map 3515 (Valentine and Cross, 2024), which presents maps of seabed topography, ruggedness, backscatter intensity, distribution of geologic substrates, sediment mobility, distribution of fine- and coarse-grained sand, and substrate mud content. The maps of quadrangle 5 show the distribution of substrates across the southern part of Stellwagen Bank and the adjacent basins. Bathymetric and seabed backscatter imagery, photographs, video, and grain-size analyses were used to create the geologic interpretations presented here and have been reprocessed and released in segments to supports these interpretations. For the quadrangle 5 interpretations, data from 729 stations were analyzed, including 620 sediment samples. The seabed geology map of quadrangle 5 shows the distribution of 20 substrate types ranging from boulder ridges to mobile and rippled sand, to mud. Substrate types are defined or inferred through sediment grain-size composition, surface morphology, sediment layering, the mobility or immobility of substrate surfaces, and water depth range. Scientific Investigations Map 3515 portrays the major geological elements (substrates, topographic features, processes) of environments within quadrangle 5. It is intended to be a basis for the study of sediment transport processes that affect a shallow, offshore bank, for the study of the ecological requirements of invertebrate and vertebrate species that use these substrates, and to support seabed management in the region.

This data release contains reference baselines for primarily open-ocean sandy beaches along the west coast of the United States (California, Oregon and Washington). The slopes were calculated while extracting shoreline position from lidar point cloud data collected between 2002 and 2011. The shoreline positions have been previously published, but the slopes have not. A reference baseline was defined and then evenly-spaced cross-shore beach transects were created. Then all data points within 1 meter of each transect were associated with each transect. Next, it was determined which points were one the foreshore, and then a linear regression was fit through the foreshore points. Beach slope was defined as the slope of the regression. Finally, the regression was evaluated at the elevation of Mean High Water (MHW) to yield the location of the shoreline. In some areas there was more than one lidar survey available; in these areas the slopes from each survey are provided. While most of the slopes are for sandy beaches, there is some slope data from rocky headlands and other steeper beaches. These data files (referenceLine_WestCoast.csv and referenceLine_WestCoast.shp) contain information about the reference baseline, the cross-shore transects, and the Mean High Water values used to estimate the shoreline. The accompanying data files (slopeData_WestCoast.csv and slopeData_WestCoast.shp) contain the slope data. The csv and shapefiles contain the same information, both file types are provided as a convenience to the user.

This data release contains foreshore slopes for primarily open-ocean sandy beaches along the west coast of the United States (California, Oregon and Washington). The slopes were calculated while extracting shoreline position from lidar point cloud data collected between 2002 and 2011. The shoreline positions have been previously published, but the slopes have not. A reference baseline was defined and then evenly-spaced cross-shore beach transects were created. Then all data points within 1 meter of each transect were associated with each transect. Next, it was determined which points were one the foreshore, and then a linear regression was fit through the foreshore points. Beach slope was defined as the slope of the regression. Finally, the regression was evaluated at the elevation of Mean High Water (MHW) to yield the location of the shoreline. In some areas there was more than one lidar survey available; in these areas the slopes from each survey are provided. While most of the slopes are for sandy beaches, there is some slope data from rocky headlands and other steeper beaches. These data files (slopeData_WestCoast.csv and slopeData_WestCoast.shp) contain beach slope, the location the beach slope data was calculated (the shoreline position), and the estimated uncertainty of the shoreline position. The accompanying data files (referenceLine_WestCoast.csv and referenceLine_WestCoast.shp) contain information about the reference baseline, the cross shore transects, and the MHW values used to estimate the shoreline location. The csv and shapefiles contain the same information, both file types are provided as a convenience to the user.

This data release contains foreshore slopes for primarily open-ocean sandy beaches along the United States portion of the Gulf of Mexico (Texas through Florida). The slopes were calculated while extracting shoreline position from lidar point cloud data collected between 2001 and 2018. The shoreline positions have been previously published, but the slopes have not. An alongshore reference baseline was defined, and then 20-meter spaced cross-shore beach transects were created perpendicular to the baseline. All data points within 1 meter (alongshore) of each transect were associated with that transect. For each transect, the points on the foreshore were identified, and a linear regression was fit through the foreshore points. Beach slope was defined as the slope of the regression. The regression was evaluated at the elevation of mean high water (MHW) to yield the cross-shore location of the shoreline. In areas where more than one lidar survey is available, the slopes from each survey are provided. Most of the slopes are for sandy beaches, but some transects cross seawalls or other structures that cause steeper slopes. The slope data files (slopeData_GulfCoast.csv and slopeData_GulfCoast.shp) contain beach slope, the location at which the beach slope data were calculated (the shoreline position), and the estimated uncertainty of the shoreline position. The reference line data files (referenceLine_GulfCoast.csv and referenceLine_GulfCoast.shp) contain information about the reference baseline, the cross-shore transects, and the MHW values used to estimate the shoreline location. Both file types *.csv (ascii files containing comma separated values) and *.shp (binary files supported by Esri known as shapefiles) contain the same information. Both file types are provided as a convenience to the user.

The U.S. Geological Survey (USGS), in cooperation with the National Marine Sanctuary Program of the National Oceanic and Atmospheric Administration (NOAA), has conducted seabed mapping and related research in the Stellwagen Bank National Marine Sanctuary (SBNMS) region since 1993. The interpretive datasets and source information presented here are for quadrangle 2, which is one of 18 similarly-sized quadrangles that comprise the 3,700 square kilometer (km2) SBNMS region. The seabed of the SBNMS region is a glaciated terrain that is topographically and texturally diverse. Mapping of quadrangle 2 shows the distribution of substrates across the southwestern part of Stellwagen Bank, in Stellwagen Basin to the west and southwest of the bank, in Little Stellwagen Basin, and in the western part of Race Point Channel to the south of the bank. Water depths range from ~19 m on the bank crest to ~64 m in Stellwagen Basin. The previously unpublished data provided in this data release in conjunction with previously published bathymetric and seabed backscatter imagery, photographs, video, and grain-size analyses for quadrangle 2 are the foundation for Scientific Investigations Map 3530 (Valentine and Cross, 2024), which presents maps of seabed topography, ruggedness, backscatter intensity, distribution of geologic substrates, sediment mobility, distribution of fine- and coarse-grained sand, and substrate mud content. For the quadrangle 2 interpretations, data from 733 ground validation stations were analyzed, including 656 sediment samples. The seabed geology of quadrangle 2 comprises 19 substrate types ranging from boulder ridges to mobile and rippled sand to mud. Not all of these substrates are mappable as individual polygons. Substrate types are defined or inferred by sediment grain-size composition, surface morphology, sediment layering, the mobility or immobility of substrate surfaces, and water depth range. Scientific Investigations Map 3530 portrays the major geological elements (substrates, topographic features, processes) of environments within quadrangle 2. It is intended to be a basis for the study of sediment transport processes that affect a shallow, offshore bank and adjacent basins, for the study of the ecological requirements of invertebrate and vertebrate species that use these substrates, and to support seabed management in the region.

The U.S. Geological Survey (USGS) maintains shoreline positions for the United States coasts from various historical sources, such as aerial photographs or topographic surveys, and contemporary sources, such as lidar-point clouds and digital elevation models. Shorelines are compiled in a geographic informaitoin system (GIS) and analyzed in the USGS Digital Shoreline Analysis System (DSAS) software to calculate rates of change. Keeping a record of historical shoreline positions is an effective method to monitor change over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers understand which areas of the coast are vulnerable to change. This data release, and other associated products, represents an expansion of the USGS national-scale shoreline database to include Long Island Sound (LIS) covering coastal areas in New York and Connecticut. The shoreline positions and shoreline change rates provide actionable information to homeowners, coastal communities, and managers of public and private properties to improve resiliency for coastal hazards in Long Island Sound.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along open-ocean sandy shores of the conterminous United States and parts of Alaska and Hawaii as part of the Coastal Change Hazards programmatic focus, formerly the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind this national scale project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner. In this release, three new tidal datum-based mean high water (MHW) shorelines extracted from 2009/2010/2011, 2015, and 2016 lidar elevation data are included in the analysis (coverage not necessarily continuous statewide). The full range of shoreline data is 1852 to 2016. The proxy-datum bias correction has been applied on a transect-by-transect basis to reconcile offsets between the MHW shorelines and proxy-based HWL shorelines for the entire California coastal region which is divided into three subregions: Northern California (NorCal), Central California (CenCal), and Southern California (SoCal). In the previous report (Hapke et al., 2006), the proxy-datum bias correction was only applied to regional shoreline averages. This shoreline change update for California reports proxy-datum bias corrected rates when that information was computed while extracting shoreline positions from lidar data. In areas where the methods for delineating shorelines did not include computing bias correction values, the rates are reported without that correction. The proxy-datum bias concept is explained further in Ruggiero and List (2009) and in the process steps of the metadata file associated with the transect rates.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along open-ocean sandy shores of the conterminous United States and parts of Alaska and Hawaii as part of the Coastal Change Hazards programmatic focus, formerly the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind this national scale project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner. In this release, three new tidal datum-based mean high water (MHW) shorelines extracted from 2009/2010/2011, 2015, and 2016 lidar elevation data are included in the analysis (coverage not necessarily continuous statewide). The full range of shoreline data is 1852 to 2016. The proxy-datum bias correction has been applied on a transect-by-transect basis to reconcile offsets between the MHW shorelines and proxy-based HWL shorelines for the entire California coastal region which is divided into three subregions: Northern California (NorCal), Central California (CenCal), and Southern California (SoCal). In the previous report (Hapke et al., 2006), the proxy-datum bias correction was only applied to regional shoreline averages. This shoreline change update for California reports proxy-datum bias corrected rates when that information was computed while extracting shoreline positions from lidar data. In areas where the methods for delineating shorelines did not include computing bias correction values, the rates are reported without that correction. The proxy-datum bias concept is explained further in Ruggiero and List (2009) and in the process steps of the metadata file associated with the transect rates.

Sandy ocean beaches are a popular recreational destination, often surrounded by communities containing valuable real estate. Development is on the rise despite the fact that coastal infrastructure is subjected to flooding and erosion. As a result, there is an increased demand for accurate information regarding past and present shoreline changes. To meet these national needs, the Coastal and Marine Geology Program of the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data along open-ocean sandy shores of the conterminous United States and parts of Alaska and Hawaii as part of the Coastal Change Hazards programmatic focus, formerly the National Assessment of Shoreline Change project. There is no widely accepted standard for analyzing shoreline change. Existing shoreline data measurements and rate calculation methods vary from study to study and prevent combining results into state-wide or regional assessments. The impetus behind this national scale project was to develop a standardized method of measuring changes in shoreline position that is consistent from coast to coast. The goal was to facilitate the process of periodically and systematically updating the results in an internally consistent manner. In this release, three new tidal datum-based mean high water (MHW) shorelines extracted from 2009/2010/2011, 2015, and 2016 lidar elevation data are included in the analysis (coverage not necessarily continuous statewide). The full range of shoreline data is 1852 to 2016. The proxy-datum bias correction has been applied on a transect-by-transect basis to reconcile offsets between the MHW shorelines and proxy-based HWL shorelines for the entire California coastal region which is divided into three subregions: Northern California (NorCal), Central California (CenCal), and Southern California (SoCal). In the previous report (Hapke et al., 2006), the proxy-datum bias correction was only applied to regional shoreline averages. This shoreline change update for California reports proxy-datum bias corrected rates when that information was computed while extracting shoreline positions from lidar data. In areas where the methods for delineating shorelines did not include computing bias correction values, the rates are reported without that correction. The proxy-datum bias concept is explained further in Ruggiero and List (2009) and in the process steps of the metadata file associated with the transect rates.

During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The U.S. Geological Survey (USGS) Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion along the southeast US coastline and implications for vulnerability to future storms.

During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The U.S. Geological Survey (USGS) Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion along the southeast US coastline and implications for vulnerability to future storms.

During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The U.S. Geological Survey (USGS) Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion along the southeast US coastline and implications for vulnerability to future storms.

During Hurricane Irma in September 2017, Florida and Georgia experienced significant impacts to beaches, dunes, barrier islands, and coral reefs. Extensive erosion and coral losses result in increased immediate and long-term hazards to shorelines that include densely populated regions. These hazards put critical infrastructure at risk to future flooding and erosion and may cause economic losses. The U.S. Geological Survey (USGS) Coastal and Marine Hazards Resources Program (CMHRP) is assessing hurricane-induced coastal erosion along the southeast US coastline and implications for vulnerability to future storms.

The U.S. Geological Survey (USGS), in cooperation with the National Marine Sanctuary Program of the National Oceanic and Atmospheric Administration (NOAA), has conducted seabed mapping and related research in the Stellwagen Bank National Marine Sanctuary (SBNMS) region since 1993. The interpretive datasets and source information presented here are for quadrangle 3, which is one of 18 similarly-sized quadrangles that comprise the 3,700 square kilometer (km2) SBNMS region. The seabed of SBNMS is a glaciated terrain that is topographically and texturally diverse. Mapping of quadrangle 3 shows the distribution of substrates on the southeastern part of Stellwagen Bank, on adjacent banks and basins in deeper water to the east, in the eastern part of Race Point Channel to the south of the bank, and on the northern slope of Outer Cape Cod. Water depths range from ~25 m on the bank crest to ~135 m east of South Ninety Bank which lies off the eastern margin of Stellwagen Bank. The data presented here for quadrangle 3 are the foundation for Scientific Investigations Map 3544 (Valentine and Cross, 2026), which presents maps of seabed topography, ruggedness, backscatter intensity, distribution of geologic substrates, sediment mobility, distribution of fine- and coarse-grained sand, and substrate mud content. For the quadrangle 3 interpretation, data from 309 ground validation stations were analyzed, including 279 sediment samples. The geologic substrate maps of quadrangle 3 show the distribution of 21 substrates that represent a wide range of textures, such as rippled sand, immobile sand, immobile muddy sand, sand that partially veneers gravel, and boulder ridges. Not all of these substrates can be mapped as individual polygons as some scattered deposits are not coherent units mappable at the given scale. Substrate types are defined or inferred by sediment grain-size composition, surface morphology, sediment layering, the mobility or immobility of substrate surfaces, and water depth range. Scientific Investigations Map 3544 portrays the major geological elements (substrates, topographic features, processes) of environments within quadrangle 3. It is intended to be a basis for the study of sediment transport processes that affect a shallow, offshore bank and adjacent basins, for the study of the ecological requirements of invertebrate and vertebrate species that use these substrates, and to support seabed management in the region.