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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 Digital Shoreline Analysis System software to compute their 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, represent...
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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 Digital Shoreline Analysis System software to compute their 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, represent...
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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...
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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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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...
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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...
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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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Two marine geological surveys were conducted in Long Island Sound, Connecticut and New York, in fall 2017 and spring 2018 by the U.S. Geological Survey, University of Connecticut, and University of New Haven through the Long Island Sound Mapping and Research Collaborative. Sea-floor images and videos were collected at 210 sampling sites within the survey area, and surficial sediment samples were collected at 179 of the sites. The sediment data and the observations from the images and videos are used to identify sediment texture and sea-floor habitats.
Categories: Data; Types: Downloadable, Map Service, OGC WFS Layer, OGC WMS Layer, Shapefile; Tags: Atlantic Ocean, Beckman Coulter Multisizer 3, CMHRP, CSV, Coastal and Marine Hazards and Resources Program, All tags...
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In spring and summer 2017, the U.S. Geological Survey’s Gas Hydrates Project conducted two cruises aboard the research vessel Hugh R. Sharp to explore the geology, chemistry, ecology, physics, and oceanography of sea-floor methane seeps and water column gas plumes on the northern U.S. Atlantic margin between the Baltimore and Keller Canyons. Split-beam and multibeam echo sounders and a chirp subbottom profiler were deployed during the cruises to map water column backscatter, sea-floor bathymetry and backscatter, and subsurface stratigraphy associated with known and undiscovered sea-floor methane seeps. The first cruise, known as the Interagency Mission for Methane Research on Seafloor Seeps and designated as field...
Categories: Data; Types: Downloadable, Map Service, OGC WFS Layer, OGC WMS Layer, Shapefile; Tags: Accomac Canyon, Applied Acoustics, Atlantic Margin, Atlantic Ocean, Baltimore Canyon, All tags...
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Coastal resources are increasingly impacted by erosion, extreme weather events, sea-level rise, tidal flooding, and other potential hazards related to climate change. These hazards have varying impacts on coastal landscapes due to the numerous geologic, oceanographic, ecological, and socioeconomic factors that exist at a given location. Here, an assessment framework is introduced that synthesizes existing datasets describing the variability of the landscape and hazards that may act on it to evaluate the likelihood of coastal change along the U.S coastline within the coming decade. The pilot study, conducted in the Northeastern U.S. (Maine to Virginia), is comprised of datasets derived from a variety of federal,...
Categories: Data; Types: Downloadable, GeoTIFF, Map Service, Raster; Tags: Acadia National Park, ArcGIS Pro, Arcpy, Autoclassification, Automation, All tags...
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Geologic structure and isopach maps were constructed by interpreting over 19,890 trackline kilometers of co-located multichannel boomer, sparker and chirp seismic reflection profiles from the continental shelf of the Delmarva Peninsula, including Maryland and Virginia state waters. In this region, Brothers and others (2020) interpret 12 seismic units and 11 regional unconformities. They interpret the infilled channels as Late Tertiary and Quaternary courses of the Susquehanna, Potomac, Rappahannock, York and James Rivers and tributaries, in addition to a broad drainage system. These regional unconformities form a composite unconformity interpreted as the Quaternary-Tertiary (Q-T) unconformity. A depth to Tertiary...
Categories: Data; Types: Downloadable, GeoTIFF, Map Service, Raster; Tags: 32-bit GeoTIFF, Applied Acoustics S-Boom Source, Assateague Island, Assateague Island National Seashore, Atlantic Ocean, All tags...


map background search result map search result map DisMOSH, Cost, MOSHShoreline: Distance to foraging areas for piping plovers (foraging shoreline, cost mask, and least-cost path distance): Cedar Island, VA, 2012–2013 ElevMHW: Elevation adjusted to local mean high water: Cedar Island, VA, 2014 DisOcean: Distance to the ocean: Monomoy Island, MA, 2014 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Assawoman Island, VA, 2014 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Fisherman Island, VA, 2014 ElevMHW: Elevation adjusted to local mean high water: Parramore 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: Parramore Island, VA, 2014 DisOcean: Distance to the ocean: Smith Island, VA, 2014 DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Wreck Island, VA, 2014 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Wreck Island, VA, 2014 Location and grain-size analysis results of sediment samples collected in Long Island Sound, Connecticut and New York, in fall 2017 and spring 2018 by the U.S. Geological Survey, University of Connecticut, and University of New Haven during field activities 2017-056-FA and 2018-018-FA (simplified point shapefile and CSV files) Ultra-short baseline - navigation points and tracklines for Applied Acoustics EasyTrack Nexus 2 USBL data collected for ROV Global Explorer during USGS field activity 2017-001-FA 2015 Mean High Water Shorelines of the Puerto Rico Coast used in Shoreline Change Analysis 2016 NOAA Mean High Water Shorelines of the Puerto Rico coast used in Shoreline Change Analysis Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Event Hazards Depth to Quaternary regional unconformities offshore of the Delmarva Peninsula, including Maryland and Virginia state waters Intersects for the Northern California coastal region generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0 Long-term shoreline change rates for the Southern California coastal region using the Digital Shoreline Analysis System version 5.0 2017 lidar-derived mean high water shoreline for the southern coast of North Carolina from Cape Lookout to Cape Fear (NCsouth) Bias feature containing proxy-datum bias information to be used in the Digital Shoreline Analysis System for the southern coast of North Carolina from Cape Lookout to Cape Fear (NCsouth) DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Wreck Island, VA, 2014 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Wreck Island, VA, 2014 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Fisherman Island, VA, 2014 ElevMHW: Elevation adjusted to local mean high water: Cedar Island, VA, 2014 DisMOSH, Cost, MOSHShoreline: Distance to foraging areas for piping plovers (foraging shoreline, cost mask, and least-cost path distance): Cedar Island, VA, 2012–2013 DisOcean: Distance to the ocean: Smith Island, VA, 2014 ElevMHW: Elevation adjusted to local mean high water: Parramore Island, VA, 2014 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Assawoman Island, VA, 2014 Location and grain-size analysis results of sediment samples collected in Long Island Sound, Connecticut and New York, in fall 2017 and spring 2018 by the U.S. Geological Survey, University of Connecticut, and University of New Haven during field activities 2017-056-FA and 2018-018-FA (simplified point shapefile and CSV files) Ultra-short baseline - navigation points and tracklines for Applied Acoustics EasyTrack Nexus 2 USBL data collected for ROV Global Explorer during USGS field activity 2017-001-FA 2016 NOAA Mean High Water Shorelines of the Puerto Rico coast used in Shoreline Change Analysis 2015 Mean High Water Shorelines of the Puerto Rico Coast used in Shoreline Change Analysis 2017 lidar-derived mean high water shoreline for the southern coast of North Carolina from Cape Lookout to Cape Fear (NCsouth) Bias feature containing proxy-datum bias information to be used in the Digital Shoreline Analysis System for the southern coast of North Carolina from Cape Lookout to Cape Fear (NCsouth) Depth to Quaternary regional unconformities offshore of the Delmarva Peninsula, including Maryland and Virginia state waters Intersects for the Northern California coastal region generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0 Long-term shoreline change rates for the Southern California coastal region using the Digital Shoreline Analysis System version 5.0 Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Event Hazards