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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...
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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, Cedar Island, 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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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...
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This digital elevation model (DEM) for the Northern Atlantic Coastal Plain (NACP) from Long Island New York to northeastern North Carolina represents the elevation of the topographic and bathymetric surface at a uniform horizontal grid spacing of 100 feet and vertical units of 1 (integer) foot. The land-surface elevations are derived from U.S. Geological Survey 30-meter National Elevation Dataset (NED), and the bathymetric elevations are derived from 3 arc-second (90-meter-nominal) National Oceanic and Atmospheric Administration (NOAA) U.S. Coastal Relief Model (CRM). Horizontal coordinates are referenced to the North American Datum of 1983 (NAD 83) and vertical measurements are referenced to the North American...
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The datasets were generated for the Upper Choptank River Watershed in Maryland and Delaware. Depressions and the total stream network were derived from a lidar DEM collected during April–June 2003 (vertical accuracy root mean square error (RMSE) = 14.3 cm) and March–April 2006 (vertical accuracy RMSE = 18.5 cm) for Maryland (1 m resolution) and April 2007 (vertical accuracy RMSE = 18.5 cm) for Delaware (3 m resolution)). Three Landsat images were used to create a surface-water extent map from Landsat including Landsat-8 images collected on April 4, 2015 (p14r33) and April 11, 2015 (p15r33) and a Landsat-7 ETM+ image collected on April 12, 2015 (p14r33). The datasets were used with other previously published datasets...
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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, Raster; Tags: Atlantic Ocean, Barrier Island, CMHRP, Coastal Habitat, Coastal and Marine Hazards and Resources Program, 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...
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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Seabeach amaranth (Amaranthus pumilus) is a plant species that was once prevalent on beaches of the U.S. mid-Atlantic coast but is now listed as threatened by the U.S. Fish and Wildlife Service. For much of the 20th century, seabeach amaranth was absent from the mid-Atlantic coast and thought to be extinct, presumably as a result of increased development and recreational pressure. One region where there has been an effort to restore the seabeach amaranth population is Assateague Island National Seashore (ASIS), a National Park Service land holding located along the coasts of Maryland and Virginia. Here, the Natural Resources staff at ASIS planted seabeach amaranth cultivars for three growing seasons from 1999...


map background search result map search result map Digital Elevation Model (DEM) of Northern Atlantic Coastal Plain topography and bathymetry with a horizontal resolution of 100 feet and vertical unit of 1 integer foot Data release of the influence of data characteristics on detecting wetland/stream surface-water connections in the Delmarva Peninsula, Maryland and Delaware ElevMHW: Elevation adjusted to local mean high water: Cedar Island, VA, 2010 shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Cedar Island, VA, 2010 points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Cedar Island, VA, 2012–2013 DisOcean: Distance to the ocean: Cedar Island, VA, 2014 DisOcean: Distance to the ocean: Assateague Island, MD & VA, 2014 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Cobb Island, VA, 2014 shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Fisherman Island, VA, 2014 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Fisherman Island, VA, 2014 ElevMHW: Elevation adjusted to local mean high water: Metompkin Island, VA, 2014 ElevMHW: Elevation adjusted to local mean high water: Myrtle Island, VA, 2014 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Parramore Island, VA, 2014 DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Parramore Island, VA, 2014 shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Ship Shoal Island, VA, 2014 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Ship Shoal Island, VA, 2014 ElevMHW: Elevation adjusted to local mean high water: Wreck 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: Wreck Island, VA, 2014 Assateague Island Seabeach Amaranth Survey Data — 2001 to 2018 Thickness of Quaternary seismic stratigraphic units offshore of the Delmarva Peninsula, including Maryland and Virginia state waters shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Fisherman Island, VA, 2014 shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Ship Shoal Island, VA, 2014 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Ship Shoal Island, VA, 2014 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Fisherman Island, VA, 2014 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Cobb Island, VA, 2014 ElevMHW: Elevation adjusted to local mean high water: 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: Wreck Island, VA, 2014 ElevMHW: Elevation adjusted to local mean high water: Metompkin Island, VA, 2014 shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Cedar Island, VA, 2010 DisOcean: Distance to the ocean: Cedar Island, VA, 2014 ElevMHW: Elevation adjusted to local mean high water: Cedar Island, VA, 2010 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Parramore Island, VA, 2014 DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: 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: Cedar Island, VA, 2012–2013 Assateague Island Seabeach Amaranth Survey Data — 2001 to 2018 DisOcean: Distance to the ocean: Assateague Island, MD & VA, 2014 Data release of the influence of data characteristics on detecting wetland/stream surface-water connections in the Delmarva Peninsula, Maryland and Delaware Thickness of Quaternary seismic stratigraphic units offshore of the Delmarva Peninsula, including Maryland and Virginia state waters Digital Elevation Model (DEM) of Northern Atlantic Coastal Plain topography and bathymetry with a horizontal resolution of 100 feet and vertical unit of 1 integer foot