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This dataset includes one vector shapefile delineating the position of the shorelines at Barter Island, Alaska spanning seven decades, between the years 1947 and 2020. Shoreline positions delineated from a combination of aerial photography, declassified satellite photography, and very-high resolution satellite imagery can be used to quantify the movement of the shoreline through time. These data were used to calculate rates of change every 10 meters alongshore using the Digital Shoreline Analysis System (DSAS) version 5.0. DSAS uses a measurement baseline method to calculate rate-of-change statistics. Transects are cast from the reference baseline to intersect each shoreline, establishing measurement points used...
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This imagery dataset consists of 3-meter resolution, lidar-derived imagery of the Reading 30 x 60 minute quadrangle in Pennsylvania and parts New Jersey. It also covers part of the Delaware River Basin. The source data used to construct this imagery consists of 1-meter and 3-meter resolution Lidar-derived digital elevation models (DEMs). The lidar source data were compiled from different acquisitions published between 2013 and 2018 from the U.S. Department of Agriculture (USDA) and the US Geological Survey (USGS). The data were processed using geographic information systems (GIS) software. The data are projected in North America Datum (NAD) UTM Zone 18 North. This representation illustrates the terrain as a hillshade...
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This imagery dataset consists of 3-meter resolution, lidar-derived imagery of the Lewisburg 30 x 60 minute quadrangle in Virginia and West Virginia. 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 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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We used the 2010 historical imagery of the Escalante River, Utah in ArcGIS to quantify channel area and average width and quantify woody riparian vegetation cover in two reaches of the river. Reach 1 was approximately 15 river kilometers (rkms) long and located between Sand and Boulder creeks within Grand Staircase Escalante National Monument. Reach 2 was approximately 16 rkms in length, extending from the Glen Canyon National Recreation Area boundary to just upstream of Choprock Canyon. We delineated the extent of active channel. Active channel was defined as the portion of the channel free of vegetation. We also delineated fluvial geomorphic features such as point bars, mid-channel bars, lateral bars and floodplain....
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In February 2017, the Grand River Dam Authority filed to relicense the Pensacola Hydroelectric Project with the Federal Energy Regulatory Commission. The predominant feature of the Pensacola Hydroelectric Project is Pensacola Dam, which impounds Grand Lake O’ the Cherokees (locally called Grand Lake) in northeastern Oklahoma. Identification of information gaps and assessment of project effects on stakeholders are central aspects of the Federal Energy Regulatory Commission relicensing process. Some upstream stakeholders have expressed concerns about the dynamics of sedimentation and flood flows in the transition zone between major rivers and Grand Lake O’ the Cherokees. To relicense the Pensacola Hydroelectric Project...
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Dr. Richard Janda of the USGS began a channel monitoring program in Redwood Creek in northern coastal California in 1973. The USGS continued this work through 2013, when the Research Geologist, Dr. Mary Madej retired. This effort produced 40 years of channel change data in rivers that were disrupted by severe erosion following timber harvest of old-growth redwood forests, a portion of the program's data (plus 1953 data) has been preserved in this data release. Original field surveys documented bank erosion, aggradation, and degradation at 60 cross-sectional transects at annual or biannual timesteps. Three river reaches also have long-term longitudinal channel bed surveys which document the distribution and development...
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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...
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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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Geophysical and geological survey data were collected off Town Neck Beach in Sandwich, Massachusetts, in May and July 2016. Approximately 130 linear kilometers of subbottom (seismic-reflection) and 234-kilohertz interferometric sonar (bathymetric and backscatter) data were collected along with sediment samples, sea floor photographs, and (or) video at 26 sites within the geophysical survey area. Sediment grab samples were collected at 19 of the 26 sampling sites and video and (or) photographic imagery of the sea floor were taken at all 26 sites. These survey data are used to characterize the sea floor by identifying sediment-texture, seabed morphology, and underlying geologic structure and stratigraphy. Data collected...
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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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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...


map background search result map search result map River Channel Survey Data, Redwood Creek, California, 1953-2013 Bathymetric surveys of the Neosho River, Spring River, and Elk River, northeastern Oklahoma and southwestern Missouri, 2016–17 A polygon shapefile of bottomland vegetation cover and geomorphic features of the Escalante River, Utah mapped from 2010 aerial imagery Survey lines along which seismic reflection data were collected in 2016 by the U.S. Geological Survey off Town Neck Beach Sandwich, Massachusetts during field activity 2016-017-FA 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: Edwin B. Forsythe NWR, NJ, 2014 shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Edwin B. Forsythe NWR, NJ, 2014 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, 2010–2011 shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Rockaway Peninsula, NY, 2012 DisOcean: Distance to the ocean: Coast Guard Beach, MA, 2014 DisOcean: Distance to the ocean: Rhode Island National Wildlife Refuge, RI, 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: Myrtle Island, VA, 2014 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: 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 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 Historical shoreline positions at Barter Island, Alaska for the years spanning 1947 to 2020 Enhanced Terrain Imagery of the Reading 30 x 60 Minute Quadrangle from Lidar-Derived Elevation Models at 3-Meter Resolution Enhanced Terrain Imagery of the Lewisburg 30 x 60 Minute Quadrangle from Lidar-Derived Elevation Models at 3-Meter Resolution Survey lines along which seismic reflection data were collected in 2016 by the U.S. Geological Survey off Town Neck Beach Sandwich, Massachusetts during field activity 2016-017-FA 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 DisOcean: Distance to the ocean: Coast Guard Beach, MA, 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: Cedar Island, VA, 2010 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Parramore Island, VA, 2014 shoreline, inletLines: Shoreline polygons and tidal inlet delineations: 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, 2010–2011 shoreline, inletLines: Shoreline polygons and tidal inlet delineations: Edwin B. Forsythe NWR, NJ, 2014 DisOcean: Distance to the ocean: Rhode Island National Wildlife Refuge, RI, 2014 River Channel Survey Data, Redwood Creek, California, 1953-2013 Bathymetric surveys of the Neosho River, Spring River, and Elk River, northeastern Oklahoma and southwestern Missouri, 2016–17 Enhanced Terrain Imagery of the Lewisburg 30 x 60 Minute Quadrangle from Lidar-Derived Elevation Models at 3-Meter Resolution Enhanced Terrain Imagery of the Reading 30 x 60 Minute Quadrangle from Lidar-Derived Elevation Models at 3-Meter Resolution