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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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The U.S. Geological Survey, Woods Hole Coastal and Marine Science Center in cooperation with the University of Maine mapped approximately 50 square kilometers of the seafloor within Belfast Bay, Maine. Three geophysical surveys conducted in 2006, 2008 and 2009 collected swath bathymetric (2006 and 2008) and chirp seismic reflection profile data (2006 and 2009). The project characterized the spatial, morphological and subsurface variability of the Belfast Bay, Maine pockmark field. Pockmarks are large seafloor depressions that are associated with seabed fluid escape.
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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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In September 2018, the U.S. Geological Survey, in collaboration with the U.S. Army Corps of Engineers, conducted high-resolution geophysical mapping and sediment sampling to determine the distribution of historical mine tailings on the floor of Lake Superior. Large amounts of waste material from copper mining, locally known as “stamp sands,” were dumped into the lake in the early 20th century, with wide-reaching consequences that have continued into the present. Mapping was focused offshore of the town of Gay on the Keweenaw Peninsula of Michigan, where ongoing erosion and re-deposition of the stamp sands has buried miles of native, white-sand beaches. Stamp sands are also encroaching onto Buffalo Reef, a large...
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In summer 2018, the U.S. Geological Survey partnered with the U.S Department of Energy and the Bureau of Ocean Energy Management to conduct the Mid-Atlantic Resources Imaging Experiment (MATRIX) as part of the U.S. Geological Survey Gas Hydrates Project. The field program objectives were to acquire high-resolution 2-dimensional multichannel seismic-reflection and split-beam echosounder data along the U.S Atlantic margin between North Carolina and New Jersey to determine the distribution of methane gas hydrates in below-sea floor sediments and investigate potential connections between gas hydrate dynamics and sea floor methane seepage. MATRIX field work was carried out between August 8 and August 28, 2018 on the...
Categories: Data, Data Release - In Progress; Types: Downloadable, Map Service, OGC WFS Layer, OGC WMS Layer, Shapefile; Tags: Atlantic Ocean, BOEM, Bureau of Ocean Energy Management, CMHRP, Cape Hatteras, 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...
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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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, CMGP, 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...
Categories: Data; Types: Downloadable, GeoTIFF, Map Service, OGC WFS Layer, OGC WMS Layer, Raster, Shapefile; Tags: Assawoman Island, Assawoman Island, Atlantic Ocean, Barrier Island, Bayesian Network, 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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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...
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: Edwin B. Forsythe NWR, NJ, 2012 points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Edwin B. Forsythe NWR, NJ, 2013–2014 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Fire Island, NY, 2010 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Fire Island, NY, 2012 shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Fire Island, NY, 2012 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Rockaway Peninsula, NY, 2010 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Monomoy Island, MA, 2014 points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Assawoman 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: Fisherman Island, VA, 2014 shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Metompkin Island, VA, 2014 shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Myrtle Island, VA, 2014 Seismic Reflection, EdgeTech SB-424 Chirp tracklines collected in the vicinity of Buffalo Reef, Michigan, within Lake Superior during USGS field activity 2018-043-FA, (Esri polyline shapefile, GCS WGS 84) Split-beam Echo Sounder and Navigation Data Collected Using a Simrad EK80 Wide Band Tranceiver and ES38-10 Transducer During the Mid-Atlantic Resource Imaging Experiment (MATRIX), USGS Field Activity 2018-002-FA. Seismic reflection-tracklines, shotpoints, and profile images collected in Belfast Bay, Maine using an EdgeTech SB-424 subbottom profiler during USGS field activities 2006-024-FA and 2009-037-FA (Esri polyline, and point shapefiles, WGS 84, and JPEG images) 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 Intersects for the region of the Elizabeth Islands, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1 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 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: 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: Fisherman Island, VA, 2014 shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Metompkin Island, VA, 2014 Seismic Reflection, EdgeTech SB-424 Chirp tracklines collected in the vicinity of Buffalo Reef, Michigan, within Lake Superior during USGS field activity 2018-043-FA, (Esri polyline shapefile, GCS WGS 84) points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Assawoman Island, VA, 2014 Intersects for the region of the Elizabeth Islands, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1 Development: Development delineation: Edwin B. Forsythe NWR, NJ, 2012 shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Fire Island, NY, 2012 points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Edwin B. Forsythe NWR, NJ, 2013–2014 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Fire Island, NY, 2010 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Fire Island, NY, 2012 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 Intersects for the Southern California coastal region generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.0 Split-beam Echo Sounder and Navigation Data Collected Using a Simrad EK80 Wide Band Tranceiver and ES38-10 Transducer During the Mid-Atlantic Resource Imaging Experiment (MATRIX), USGS Field Activity 2018-002-FA.