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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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The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates at 40-meter intervals 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 Services Center. This 2018 update includes two new mean high water (MHW) shorelines for the Massachusetts...
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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, Cape Lookout, 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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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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The natural resiliency of the New Jersey barrier island system, and the efficacy of management efforts to reduce vulnerability, depends on the ability of the system to recover and maintain equilibrium in response to storms and persistent coastal change. This resiliency is largely dependent on the availability of sand in the beach system. In an effort to better understand the system's sand budget and processes in which this system evolves, high-resolution geophysical mapping of the sea floor in Little Egg Inlet and along the southern end of Long Beach Island near Beach Haven, New Jersey was conducted from May 31 to June 10, 2018, followed by a sea floor sampling survey conducted from October 22 to 23, 2018, as part...
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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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...
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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 Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast by compiling a database of historical (mid 1800's-1989) shoreline positions. 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...
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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 Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast by compiling a database of historical (mid 1800's-1989) shoreline positions. 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...


map background search result map search result map SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Edwin B. Forsythe NWR, NJ, 2013–2014 points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Fire Island, NY, 2012 DisMOSH, Cost, MOSHShoreline: Distance to foraging areas for piping plovers (foraging shoreline, cost mask, and least-cost path distance): Fire Island, NY, 2014–2015 ElevMHW: Elevation adjusted to local mean high water: Rockaway Peninsula, NY, 2010 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Rockaway Peninsula, NY, 2012 points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Rockaway Peninsula, NY, 2012 DisOcean: Distance to the ocean: Parker River, MA, 2014 points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Cape Lookout, NC, 2014 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Rhode Island National Wildlife Refuge, RI, 2014 Development: Development delineation: Assateague Island, MD & VA, 2014 Development: Development delineation: Assawoman Island, VA, 2014 shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Assawoman Island, VA, 2014 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Smith Island, VA, 2014 Seismic Reflection, Boomer 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) High-resolution geophysical and geological data collected in Little Egg Inlet and offshore the southern end of Long Beach Island, NJ, during USGS Field Activities 2018-001-FA and 2018-049-FA Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for the north shore of Martha's Vineyard, MA Bathymetric data collected in the Belfast Bay, Maine pockmark field using a SWATHplus-M interferometric sonar in 2006 and 2008, by the U.S. Geological Survey (32-bit floating point raster, UTM 19 WGS 84, MLLW) Baseline for the Elizabeth Islands, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1 Intersects for coastal region of Martha's Vineyard, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1 Baselines for the coast of Nantucket, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1 Development: Development delineation: Assawoman Island, VA, 2014 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Smith Island, VA, 2014 Seismic Reflection, Boomer 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) DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Rockaway Peninsula, NY, 2012 Bathymetric data collected in the Belfast Bay, Maine pockmark field using a SWATHplus-M interferometric sonar in 2006 and 2008, by the U.S. Geological Survey (32-bit floating point raster, UTM 19 WGS 84, MLLW) shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Assawoman Island, VA, 2014 DisOcean: Distance to the ocean: Parker River, MA, 2014 ElevMHW: Elevation adjusted to local mean high water: Rockaway Peninsula, NY, 2010 points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Rockaway Peninsula, NY, 2012 Baseline for the Elizabeth Islands, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1 Uncertainty table for lidar-derived shorelines used when calculating rates in the Digital Shoreline Analysis System software for the north shore of Martha's Vineyard, MA High-resolution geophysical and geological data collected in Little Egg Inlet and offshore the southern end of Long Beach Island, NJ, during USGS Field Activities 2018-001-FA and 2018-049-FA Baselines for the coast of Nantucket, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1 Intersects for coastal region of Martha's Vineyard, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Edwin B. Forsythe NWR, NJ, 2013–2014 DisMOSH, Cost, MOSHShoreline: Distance to foraging areas for piping plovers (foraging shoreline, cost mask, and least-cost path distance): Fire Island, NY, 2014–2015 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Rhode Island National Wildlife Refuge, RI, 2014 points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Fire Island, NY, 2012 Development: Development delineation: Assateague Island, MD & VA, 2014 points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Cape Lookout, NC, 2014