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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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This imagery dataset consists of 3-meter resolution, lidar-derived imagery of the Carlisle 30 x 60 minute quadrangle in Pennsylvania. The source data used to construct this imagery consists of 1-meter resolution lidar-derived digital elevation models (DEMs). The lidar source data were compiled from different acquisitions published between 2019 and 2020 and downloaded from the USGS National Map TNM Download. The data were processed using geographic information systems (GIS) software. The data is projected in WGS 1984 Web Mercator. This representation illustrates the terrain as a hillshade with contrast adjusted to highlight local relief according to a topographic position index (TPI) calculation.
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This imagery dataset consists of 3-meter resolution, lidar-derived imagery of the Front Royal 30 x 60 minute quadrangle in West Virginia and Virginia. The source data used to construct this imagery consists of 1-meter lidar-derived digital elevation models (DEMs) and lidar point cloud (LPC). The lidar source data were compiled from different acquisitions published between 2014 and 2021. The data were processed using geographic information systems (GIS) software. The data is projected in WGS 1984 Web Mercator. This representation illustrates the terrain as a hillshade with contrast adjusted to highlight local relief according to a topographic position index (TPI) calculation.
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This imagery dataset consists of 3-meter resolution, lidar-derived imagery of the Roanoke 30 x 60 minute quadrangle in Virginia. It also covers a part of the Appalachian Basin Province. The source data used to construct this imagery consists of 1-meter resolution lidar-derived digital elevation models (DEMs). The lidar source data were compiled from different acquisitions published between 2017 and 2021 and downloaded from the USGS National Map TNM Download. The data were processed using geographic information systems (GIS) software. The data spatial reference is the WGS 1984 geographic coordinate system. This representation illustrates the terrain as a hillshade with contrast adjusted to highlight local relief...
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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 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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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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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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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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This imagery dataset consists of 3-meter resolution, lidar-derived imagery of the Radford 30 x 60 minute quadrangle in Virginia. It also covers a part of the Appalachian Basin Province. The source data used to construct this imagery consists of 1-meter resolution lidar-derived digital elevation models (DEMs). The lidar source data were compiled from different acquisitions published between 2018 and 2020 and downloaded from the USGS National Map TNM Download. The data were processed using geographic information systems (GIS) software. The data is projected in WGS 1984 Web Mercator. This representation illustrates the terrain as a hillshade with contrast adjusted to highlight local relief according to a topographic...
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This imagery dataset consists of 3-meter resolution, lidar-derived imagery of the Beckley 30 x 60 minute quadrangle in West Virginia, Virginia and Kentucky. The source data used to construct this imagery consists of 1-meter resolution lidar-derived digital elevation models (DEMs). The lidar source data were compiled from different acquisitions published between 2020 and 2022. The data were processed using geographic information systems (GIS) software. The data is projected in WGS 1984 Web Mercator. This representation illustrates the terrain as a hillshade with contrast adjusted to highlight local relief according to a topographic position index (TPI) calculation.
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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...
thumbnail
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...
thumbnail
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...
thumbnail
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...


map background search result map search result map ElevMHW: Elevation adjusted to local mean high water: Cedar Island, VA, 2012 ElevMHW: Elevation adjusted to local mean high water: Cedar Island, VA, 2014 ElevMHW: Elevation adjusted to local mean high water: Edwin B. Forsythe NWR, NJ, 2010 ElevMHW: Elevation adjusted to local mean high water: Edwin B. Forsythe NWR, NJ, 2012 ElevMHW: Elevation adjusted to local mean high water: Fire Island, NY, 2014 ElevMHW: Elevation adjusted to local mean high water: Rockaway Peninsula, NY, 2010 ElevMHW: Elevation adjusted to local mean high water: Rockaway Peninsula, NY, 2012 ElevMHW: Elevation adjusted to local mean high water: Rhode Island National Wildlife Refuge, RI, 2014 ElevMHW: Elevation adjusted to local mean high water: Assateague Island, MD & VA, 2014 ElevMHW: Elevation adjusted to local mean high water: Assawoman Island, VA, 2014 ElevMHW: Elevation adjusted to local mean high water: Fisherman Island, VA, 2014 ElevMHW: Elevation adjusted to local mean high water: Parramore Island, VA, 2014 Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Event Hazards Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Maximum Change Likelihood Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Hazard Impact Type Enhanced Terrain Imagery of the Carlisle 30 x 60 Minute Quadrangle from Lidar-Derived Elevation Models at 3-Meter Resolution Enhanced Terrain Imagery of the Radford 30 x 60 Minute Quadrangle from Lidar-Derived Elevation Models at 3-Meter Resolution Enhanced Terrain Imagery of the Roanoke 30 x 60 Minute Quadrangle from Lidar-Derived Elevation Models at 3-Meter Resolution Enhanced Terrain Imagery of the Beckley 30 x 60 Minute Quadrangle from Lidar-Derived Elevation Models at 3-Meter Resolution Enhanced Terrain Imagery of the Front Royal 30 x 60 Minute Quadrangle from Lidar-Derived Elevation Models at 3-Meter Resolution ElevMHW: Elevation adjusted to local mean high water: Cedar Island, VA, 2014 ElevMHW: Elevation adjusted to local mean high water: Cedar Island, VA, 2012 ElevMHW: Elevation adjusted to local mean high water: Parramore Island, VA, 2014 ElevMHW: Elevation adjusted to local mean high water: Assawoman Island, VA, 2014 ElevMHW: Elevation adjusted to local mean high water: Rockaway Peninsula, NY, 2010 ElevMHW: Elevation adjusted to local mean high water: Rockaway Peninsula, NY, 2012 ElevMHW: Elevation adjusted to local mean high water: Edwin B. Forsythe NWR, NJ, 2012 ElevMHW: Elevation adjusted to local mean high water: Edwin B. Forsythe NWR, NJ, 2010 ElevMHW: Elevation adjusted to local mean high water: Fire Island, NY, 2014 ElevMHW: Elevation adjusted to local mean high water: Assateague Island, MD & VA, 2014 ElevMHW: Elevation adjusted to local mean high water: Rhode Island National Wildlife Refuge, RI, 2014 Enhanced Terrain Imagery of the Radford 30 x 60 Minute Quadrangle from Lidar-Derived Elevation Models at 3-Meter Resolution Enhanced Terrain Imagery of the Roanoke 30 x 60 Minute Quadrangle from Lidar-Derived Elevation Models at 3-Meter Resolution Enhanced Terrain Imagery of the Front Royal 30 x 60 Minute Quadrangle from Lidar-Derived Elevation Models at 3-Meter Resolution Enhanced Terrain Imagery of the Carlisle 30 x 60 Minute Quadrangle from Lidar-Derived Elevation Models at 3-Meter Resolution Enhanced Terrain Imagery of the Beckley 30 x 60 Minute Quadrangle from Lidar-Derived Elevation Models at 3-Meter Resolution Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Maximum Change Likelihood Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Hazard Impact Type Coastal Change Likelihood in the U.S. Northeast Region: Maine to Virginia - Event Hazards