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These data are a qualitatively derived interpretive polygon shapefile defining surficial sediment type and distribution, and geomorphology, for nearly 1,400 square kilometers of sea floor on the inner-continental shelf from Fenwick Island, Maryland to Fisherman’s Island, Virginia, USA. These data are classified according to Barnhardt and others (1998) bottom-type classification system, which was modified to highlight changes in secondary sediment-types such as mud and gravel across this primarily sandy shelf. Most of the geophysical and sample data used to create this interpretive layer were collected as part of the Linking Coastal Processes and Vulnerability: Assateague Island Regional Study project (GS2-2C), supported...
Categories: Data; Types: Downloadable, Map Service, OGC WFS Layer, OGC WMS Layer, Shapefile; Tags: Assateague Island, Assateague Island National Seashore, Assawoman Island, Atlantic Ocean, Backscatter, All tags...
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The San Juan Bay Estuary, Puerto Rico, contains mangrove forests that store significant amounts of organic carbon in soils and biomass. There is a strong urbanization gradient across the estuary, from the highly urbanized and clogged Caño Martin Peña in the western part of the estuary, a series of lagoons in the center of the estuary, and a tropical forest reserve (Piñones) in the easternmost part with limited urbanization. We collected sediment cores to determine carbon burial rates and vertical sediment accretion from five sites in the San Juan Bay Estuary. Cores were radiometrically-dated using lead-210 and the Plum age model. Sites had soil C burial rates ranging from 50 grams per meter squared per year (g m-2...
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The U.S. Geological Survey, in cooperation with the Massachusetts Office of Coastal Zone Management compiled Massachusetts vector shorelines into an updated dataset for the Office’s Shoreline Change Project. The Shoreline Change Project started in 1989 to identify erosion-prone areas of the Massachusetts coast by compiling a database of historical shoreline positions. Trends of shoreline position over long- and short-term timescales provide information to landowners, managers, and potential buyers about possible future changes to costal resources and infrastructure. This updated dataset strengthens the understanding of shoreline position change in Massachusetts. It includes U.S. Geological Survey vector shorelines...
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This product provides spatial variations in wave thrust along shorelines in Massachusetts and Rhode Island. Natural features of relevance along the State coast are salt marshes. In recent times, marshes have been eroding primarily through lateral erosion. Wave thrust represents a metric of wave attack acting on marsh edges. The wave thrust is calculated as the vertical integral of the dynamic pressure of waves. This product uses a consistent methodology with sufficient spatial resolution to include the distinct features of each marsh system. Waves under different climatological wind forcing conditions were simulated using the coupled ADCIRC/SWAN model system. The estuarine and bay areas are resolved with horizontal...
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The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using 2008-2009 color aerial orthoimagery and 2007 topographic lidar datasets obtained from NOAA's Ocean Service, Coastal...
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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 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. Shoreline positions were compiled prior to and following Hurricane Irma along the sandy shorelines of the Gulf of Mexico and Atlantic...
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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 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. Shoreline positions were compiled prior to and following Hurricane Irma along the sandy shorelines of the Gulf of Mexico and Atlantic...
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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 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. Shoreline positions were compiled prior to and following Hurricane Irma along the sandy shorelines of the Gulf of Mexico and Atlantic...
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The U.S. Geological Survey (USGS) maintains shoreline positions for the United States' coasts from both older sources, such as aerial photographs or topographic surveys, and contemporary sources, such as lidar-point clouds and digital elevation models. These shorelines are compiled and analyzed in the USGS Digital Shoreline Analysis System (DSAS), version 5.1 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...
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The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This data release includes one new mean high water (MHW) shoreline extracted from lidar data collected in 2017 for the entire coastal region of North Carolina which is divided into four subregions: northern North Carolina...
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In coastal areas of the United States, where water and land interface in complex and dynamic ways, it is common to find concentrated residential and commercial development. These coastal areas often contain various landholdings managed by Federal, State, and local municipal authorities for public recreation and conservation. These areas are frequently subjected to a range of natural hazards, which include flooding and coastal erosion. In response, the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data to calculate rates of shoreline change along the conterminous coast of the United States, and select coastlines of Alaska and Hawaii, as part of the Coastal Change Hazards priority...
Categories: Data; Types: Downloadable, Map Service, OGC WFS Layer, OGC WMS Layer, Shapefile; Tags: Baseline, CMGP, California, CenCal, Central California, All tags...
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In coastal areas of the United States, where water and land interface in complex and dynamic ways, it is common to find concentrated residential and commercial development. These coastal areas often contain various landholdings managed by Federal, State, and local municipal authorities for public recreation and conservation. These areas are frequently subjected to a range of natural hazards, which include flooding and coastal erosion. In response, the U.S. Geological Survey (USGS) is compiling existing reliable historical shoreline data to calculate rates of shoreline change along the conterminous coast of the United States, and select coastlines of Alaska and Hawaii, as part of the Coastal Change Hazards priority...
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The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This data release includes one new mean high water (MHW) shoreline extracted from lidar data collected in 2017 for the entire coastal region of North Carolina which is divided into four subregions: northern North Carolina...
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The lifespans of salt marshes in Atlantic-facing Eastern Shore of Virginia are calculated based on estimated sediment supply and sea-level rise (SLR) predictions, following the methodology of Ganju and others (2020). The salt marsh delineations are from Ackerman and others (2023). The SLR predictions are local estimates corresponding to increases of 0.3, 0.5 and 1.0 meter in global mean sea level (GMSL) by 2100, as projected by Sweet and others (2022). This work has been a part of the USGS’s effort to expand the national assessment of coastal change hazards and forecast products to coastal wetlands. The aim is to equip federal, state and local managers with tools to estimate the vulnerability and ecosystem service...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
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Understanding how sea-level rise will affect coastal landforms and the species and habitats they support is critical for crafting approaches that balance the needs of humans and native species. Given this increasing need to forecast sea-level rise effects on barrier islands in the near and long terms, we are developing Bayesian networks to evaluate and to forecast the cascading effects of sea-level rise on shoreline change, barrier island state, and piping plover habitat availability. We use publicly available data products, such as lidar, orthophotography, and geomorphic feature sets derived from those, to extract metrics of barrier island characteristics at consistent sampling distances. The metrics are then incorporated...
Categories: Data; Types: Downloadable, GeoTIFF, Map Service, OGC WFS Layer, OGC WMS Layer, Raster, Shapefile; Tags: Atlantic Ocean, Barrier Island, Bayesian Network, CMHRP, Coastal Erosion, All tags...


map background search result map search result map Sediment Texture and Geomorphology of the Sea Floor from Fenwick Island, Maryland to Fisherman's Island, Virginia Development: Development delineation: Fire Island, NY, 2010–2011 Development: Development delineation: Fire Island, NY, 2014–2015 Development: Development delineation: Rockaway Peninsula, NY, 2010–2011 shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Cobb Island, VA, 2014 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Myrtle Island, VA, 2014 points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Myrtle Island, VA, 2014 Collection, analysis, and age-dating of sediment cores from mangrove wetlands in San Juan Bay Estuary, Puerto Rico, 2016 2018 mean high water shoreline of the coast of MA used in shoreline change analysis Long-term and short-term shoreline change rates for Outer Cape Cod, Massachusetts, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1 Wave thrust values at point locations along the shorelines of Massachusetts and Rhode Island Baseline for the coast of Puerto Rico's main island generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1 (ver. 2.0, March 2023) Long-term shoreline change rates for the Florida east coast (FLec) coastal region using the Digital Shoreline Analysis System version 5 Short-term shoreline change rates for the Florida east coast (FLec) coastal region using the Digital Shoreline Analysis System version 5 Long-term shoreline change rates for the Florida panhandle (FLph) coastal region using the Digital Shoreline Analysis System version 5 Shorelines of the Central California coastal region (1852-2016) used in shoreline change analysis Intersects for the Southern California coastal region generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0 Long and short-term shoreline intersect points for the western coast of North Carolina (NCwest), calculated using the Digital Shoreline Analysis System version 5.1 2017 lidar-derived mean high water shoreline for the coast of North Carolina from Cape Hatteras to Cape Lookout (NCcentral) Lifespan of marsh units in Eastern Shore of Virginia salt marshes DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Myrtle Island, VA, 2014 Collection, analysis, and age-dating of sediment cores from mangrove wetlands in San Juan Bay Estuary, Puerto Rico, 2016 shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Cobb Island, VA, 2014 points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Myrtle Island, VA, 2014 Development: Development delineation: Fire Island, NY, 2014–2015 Development: Development delineation: Fire Island, NY, 2010–2011 Long-term and short-term shoreline change rates for Outer Cape Cod, Massachusetts, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1 Lifespan of marsh units in Eastern Shore of Virginia salt marshes 2017 lidar-derived mean high water shoreline for the coast of North Carolina from Cape Hatteras to Cape Lookout (NCcentral) Baseline for the coast of Puerto Rico's main island generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1 (ver. 2.0, March 2023) Sediment Texture and Geomorphology of the Sea Floor from Fenwick Island, Maryland to Fisherman's Island, Virginia 2018 mean high water shoreline of the coast of MA used in shoreline change analysis Long-term shoreline change rates for the Florida panhandle (FLph) coastal region using the Digital Shoreline Analysis System version 5 Wave thrust values at point locations along the shorelines of Massachusetts and Rhode Island Intersects for the Southern California coastal region generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0 Short-term shoreline change rates for the Florida east coast (FLec) coastal region using the Digital Shoreline Analysis System version 5 Long-term shoreline change rates for the Florida east coast (FLec) coastal region using the Digital Shoreline Analysis System version 5 Shorelines of the Central California coastal region (1852-2016) used in shoreline change analysis