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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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This data release contains coastal wetland synthesis products for Massachusetts, developed in collaboration with the Massachusetts Office of Coastal Zone Management. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and tidal range are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the intent of providing Federal, State, and local managers with tools to estimate the vulnerability and ecosystem service potential of these wetlands....
Categories: Data; Types: Downloadable, Map Service, OGC WFS Layer, OGC WMS Layer, Shapefile; Tags: Buzzards Bay, Cape Cod, Cape Cod Bay, Cape Cod National Seashore, Danvers River, All tags...
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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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Note: The 2022 data release "Geospatial Characterization of Salt Marshes in Chesapeake Bay" incorporates the Blackwater region salt marsh dataset. (https://doi.org/10.5066/P997EJYB) This data release contains coastal wetland synthesis products for the geographic region of Blackwater salt marsh complex, Chesapeake Bay, Maryland. Metrics for resiliency, including unvegetated to vegetated ratio (UVVR), marsh elevation, and others, are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands...
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The Sandy Hook artificial reef, located on the sea floor offshore of Sandy Hook, New Jersey was built to create habitat for marine life. The reef was created by the placement of heavy materials on the sea floor; ninety-five percent of the material in the Sandy Hook reef is rock. In 2000, the U.S. Geological Survey surveyed the area using a Simrad EM1000 multibeam echosounder mounted on the Canadian Coast Guard (CCG) ship Frederick G. Creed. The purpose of this multibeam survey, done in cooperation with the U.S. Army Corps of Engineers when the Creed was in the New York region in April 2000, was to map the bathymetry and backscatter intensity of the sea floor in the area of the Sandy Hook artificial reef. The collected...
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The data in this release re-map the beach and nearshore environment at Head of the Meadow Beach in Truro, MA and provide updated environmental context for the 2020 CoastCam installation that looks out at the coast shared by beachgoers, shorebirds, seals, and sharks. This is related to the field activity 2022-015-FA and a collaboration with the National Park Service at Cape Cod National Seashore to monitor the region that falls within the field of view of CoastCam CACO-01, which are two video cameras aimed at the beach. In March and April 2023, U.S. Geological Survey and Woods Hole Oceanographic Institute (WHOI) scientists conducted field surveys to collect topographic and bathymetric data. Images of the beach for...
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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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These data map in high detail surficial cross-sections of North Core Banks, a barrier island in Cape Lookout National Seashore, NC, in October 2022. U.S. Geological Survey field efforts are part of an interagency agreement with the National Park Service to monitor the recovery of the island from Hurricanes Florence (2018) and Dorian (2019). Three sites of outwash, overwash, and pond formation were targeted for extensive vegetation ground-truthing, sediment samples, bathymetric mapping with a remote-controlled surface vehicle, and uncrewed aerial systems (UAS) flights to collect multispectral imagery. Five semi-permanent ground control points were also installed and surveyed to act as control for additional aerial...
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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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Coastal wetlands are major global carbon sinks, however, they are heterogeneous and dynamic ecosystems. To characterize spatial and temporal variability in a New England salt marsh, static chamber measurements of greenhouse gas (GHG) fluxes were compared among major plant-defined zones (high marsh dominated by Distichlis spicata and a zone of invasive Phragmites australis) during 2013 and 2014 growing seasons. Two sediment cores were collected in 2015 from the Phragmites zone to support previously reported core collections from the high marsh sites (Gonneea and others 2018). Collected cores were up to 70 cm in length with dry bulk density ranges from 0.04 to 0.33 grams per cubic centimeter and carbon content 22.4%...
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Transport of material in an estuary is important for water quality and hazards concern. We studied these processes in the Hudson River Estuary, located along the northeast coast of the U.S. using the COAWST numerical modeling system. A skill assessment of the COAWST model for the 3-D salinity structure of the estuary has been successfully studied in the past, and the present research extended that understanding to look at both physical and numerical mixing. The model grid extends from the south at the Battery, NY to the north in Troy, NY. The simulation is performed from March 25 to July 11, 2005 (111 days). For more information see: https://doi.org/10.5066/P95E8LAS.
Categories: Data; Types: Map Service, NetCDF OPeNDAP Service, OGC WMS Layer; Tags: CMG_Portal, Earth Science > Human Dimensions > Natural Hazards > Floods, Earth Science > Oceans > Marine Sediments >Sediment Transport, Earth Science > Oceans > Ocean Circulation > Ocean Currents, Earth Science > Oceans > Ocean Temperature > Potential Temperature, 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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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 Numerical model of salinity transport and mixing in the Hudson River Estuary 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 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 Elevation of marsh units in Blackwater salt marsh complex, Chesapeake Bay, Maryland Unvegetated to vegetated ratio of marsh units in Massachusetts salt marshes 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 Static chamber gas fluxes and carbon and nitrogen isotope content of age-dated sediment cores from a Phragmites wetland in Sage Lot Pond, Massachusetts, 2013-2015 Low-altitude aerial imagery collected from a Helikite at Head of the Meadow Beach, Truro, MA on March 10, 2023 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) Sediment sample analysis data from ponds to the beach on North Core Banks, NC in October 2022 Static chamber gas fluxes and carbon and nitrogen isotope content of age-dated sediment cores from a Phragmites wetland in Sage Lot Pond, Massachusetts, 2013-2015 Low-altitude aerial imagery collected from a Helikite at Head of the Meadow Beach, Truro, MA on March 10, 2023 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Fisherman Island, VA, 2014 Sediment sample analysis data from ponds to the beach on North Core Banks, NC in October 2022 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 Elevation of marsh units in Blackwater salt marsh complex, Chesapeake Bay, Maryland 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) Numerical model of salinity transport and mixing in the Hudson River Estuary Unvegetated to vegetated ratio of marsh units in Massachusetts salt marshes 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