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The Barrier Island Comprehensive Monitoring (BICM) program was developed by Louisiana’s Coastal Protection and Restoration Authority (CPRA) and is implemented as a component of the System Wide Assessment and Monitoring Program (SWAMP). The program uses both historical data and contemporary data collections to assess and monitor changes in the aerial and subaqueous extent of islands, habitat types, sediment texture and geotechnical properties, environmental processes, and vegetation composition. Examples of BICM datasets include still and video aerial photography for documenting shoreline changes, shoreline positions, habitat mapping, land change analyses, light detection and ranging (lidar) surveys for topographic...
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The Barrier Island Comprehensive Monitoring (BICM) program was developed by Louisiana’s Coastal Protection and Restoration Authority (CPRA) and is implemented as a component of the System Wide Assessment and Monitoring Program (SWAMP). The program uses both historical data and contemporary data collections to assess and monitor changes in the aerial and subaqueous extent of islands, habitat types, sediment texture and geotechnical properties, environmental processes, and vegetation composition. Examples of BICM datasets include still and video aerial photography for documenting shoreline changes, shoreline positions, habitat mapping, land change analyses, light detection and ranging (lidar) surveys for topographic...
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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...
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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 dataset consists of point cloud data collected in 2016 and 2017 of the lower and upper Scenic Drive landslide locations in La Honda, California. Point cloud data were collected in 2016 to establish baseline for movement detection of past landslides. Point cloud data were collected in 2017 adjacent and upslope of 2016 data to document a newly formed landslide. The data were collected with a Riegl VZ400 Terrestrial Laser Scanner and georeferenced using a Leica Viva GS15 survey grade GPS. The data are delivered as georeferenced (NAD83 UTM zone 10N ellipsoid) classified point clouds, 5 cm resolution digital elevation models, and a text file of surveyed GPS control points. The included files are: LH2017_Jan.laz...
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The State of Alaska Division of Geological & Geophysical Surveys acquired photogrammetric digital surface models (DSMs) and co-registered orthorectified aerial images (orthoimages) for the west coast of Alaska in support of coastal vulnerability mapping efforts. This report is a summary of the data collected over 26 developed areas along approximately 3,500 km of coastline in the Bering Sea, Norton Sound, and Yukon-Kuskokwim Delta regions (fig. 1). Aerial photographs were collected between July 31 and September 6, 2015, and processed using Structure from- Motion (SfM) photogrammetry techniques. Ground control points (GCPs) and checkpoints were collected in support of these data products during a Global Navigation...
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In advance of design, permitting, and construction of a pipeline to deliver North Slope natural gas to out-of-state customers and Alaska communities, the Division of Geological & Geophysical Surveys (DGGS) has acquired lidar (light detection and ranging) data along proposed pipeline routes, nearby areas of infrastructure, and regions where significant geologic hazards have been identified. Lidar data will serve multiple purposes, but have primarily been collected to (1) evaluate active faulting, slope instability, thaw settlement, erosion, and other engineering constraints along proposed pipeline routes, and (2) provide a base layer for the state-federal GIS database that will be used to evaluate permit applications...
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In advance of design, permitting, and construction of a pipeline to deliver North Slope natural gas to out-of-state customers and Alaska communities, the Division of Geological & Geophysical Surveys (DGGS) has acquired lidar (Light Detection and Ranging) data along proposed pipeline routes, nearby areas of infrastructure, and regions where significant geologic hazards have been identified. Lidar data will serve multiple purposes, but have primarily been collected to (1) evaluate active faulting, slope instability, thaw settlement, erosion, and other engineering constraints along proposed pipeline routes, and (2) provide a base layer for the state-federal GIS database that will be used to evaluate permit applications...
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In advance of design, permitting, and construction of a pipeline to deliver North Slope natural gas to out-of-state customers and Alaska communities, the Division of Geological & Geophysical Surveys (DGGS) has acquired lidar (light detection and ranging) data along proposed pipeline routes, nearby areas of infrastructure, and regions where significant geologic hazards have been identified. Lidar data will serve multiple purposes, but have primarily been collected to (1) evaluate active faulting, slope instability, thaw settlement, erosion, and other engineering constraints along proposed pipeline routes, and (2) provide a base layer for the state-federal GIS database that will be used to evaluate permit applications...
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In advance of design, permitting, and construction of a pipeline to deliver North Slope natural gas to out-of-state customers and Alaska communities, the Division of Geological & Geophysical Surveys (DGGS) has acquired lidar (light detection and ranging) data along proposed pipeline routes, nearby areas of infrastructure, and regions where significant geologic hazards have been identified. Lidar data will serve multiple purposes, but have primarily been collected to (1) evaluate active faulting, slope instability, thaw settlement, erosion, and other engineering constraints along proposed pipeline routes, and (2) provide a base layer for the state-federal GIS database that will be used to evaluate permit applications...
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In advance of design, permitting, and construction of a pipeline to deliver North Slope natural gas to out-of-state customers and Alaska communities, the Division of Geological & Geophysical Surveys (DGGS) has acquired lidar (Light Detection and Ranging) data along proposed pipeline routes, nearby areas of infrastructure, and regions where significant geologic hazards have been identified. Lidar data will serve multiple purposes, but have primarily been collected to (1) evaluate active faulting, slope instability, thaw settlement, erosion, and other engineering constraints along proposed pipeline routes, and (2) provide a base layer for the state-federal GIS database that will be used to evaluate permit applications...
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In advance of design, permitting, and construction of a pipeline to deliver North Slope natural gas to out-of-state customers and Alaska communities, the Division of Geological & Geophysical Surveys (DGGS) has acquired lidar (Light Detection and Ranging) data along proposed pipeline routes, nearby areas of infrastructure, and regions where significant geologic hazards have been identified. Lidar data will serve multiple purposes, but have primarily been collected to (1) evaluate active faulting, slope instability, thaw settlement, erosion, and other engineering constraints along proposed pipeline routes, and (2) provide a base layer for the state-federal GIS database that will be used to evaluate permit applications...
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In advance of design, permitting, and construction of a pipeline to deliver North Slope natural gas to out-of-state customers and Alaska communities, the Division of Geological & Geophysical Surveys (DGGS) has acquired lidar (Light Detection and Ranging) data along proposed pipeline routes, nearby areas of infrastructure, and regions where significant geologic hazards have been identified. Lidar data will serve multiple purposes, but have primarily been collected to (1) evaluate active faulting, slope instability, thaw settlement, erosion, and other engineering constraints along proposed pipeline routes, and (2) provide a base layer for the state-federal GIS database that will be used to evaluate permit applications...
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In advance of design, permitting, and construction of a pipeline to deliver North Slope natural gas to out-of-state customers and Alaska communities, the Division of Geological & Geophysical Surveys (DGGS) has acquired lidar (Light Detection and Ranging) data along proposed pipeline routes, nearby areas of infrastructure, and regions where significant geologic hazards have been identified. Lidar data will serve multiple purposes, but have primarily been collected to (1) evaluate active faulting, slope instability, thaw settlement, erosion, and other engineering constraints along proposed pipeline routes, and (2) provide a base layer for the state-federal GIS database that will be used to evaluate permit applications...


map background search result map search result map Terrestrial lidar data from the 2017 Upper Scenic Drive Landslide, La Honda, California: classified point cloud and gridded elevation data from 2016-2017 High-resolution lidar data for infrastructure corridors, Tanacross Quadrangle, Alaska High-resolution lidar data for infrastructure corridors, Fairbanks Quadrangle, Alaska High-resolution lidar data for infrastructure corridors, Fairbanks Quadrangle, Alaska High-resolution lidar data for infrastructure corridors, Bettles Quadrangle, Alaska High-resolution lidar data for infrastructure corridors, Tyonek Quadrangle, Alaska High-resolution lidar data for infrastructure corridors, Talkeetna Quadrangle, Alaska High-resolution lidar data for infrastructure corridors, Healy Quadrangle, Alaska High-resolution lidar data for infrastructure corridors, Gulkana Quadrangle, Alaska Photogrammetric digital surface models and orthoimagery for 26 coastal communities of western Alaska Louisiana Barrier Island Comprehensive Monitoring Program – 2008-2016 habitat change, Modern Delta Region Louisiana Barrier Island Comprehensive Monitoring Program – 2008 habitat map, East Chenier Region SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Cedar Island, VA, 2012–2013 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, 2010 DisOcean: Distance to the ocean: Edwin B. Forsythe NWR, NJ, 2012 DisMOSH, Cost, MOSHShoreline: Distance to foraging areas for piping plovers (foraging shoreline, cost mask, and least-cost path distance): Edwin B. Forsythe NWR, NJ, 2013–2014 DisOcean: Distance to the ocean: Edwin B. Forsythe NWR, NJ, 2014 ElevMHW: Elevation adjusted to local mean high water: Fire Island, NY, 2014 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Rockaway Peninsula, NY, 2010–2011 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Rockaway Peninsula, NY, 2012 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Cedar Island, VA, 2012–2013 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Rockaway Peninsula, NY, 2012 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Rockaway Peninsula, NY, 2010–2011 DisOcean: Distance to the ocean: Edwin B. Forsythe NWR, NJ, 2012 DisMOSH, Cost, MOSHShoreline: Distance to foraging areas for piping plovers (foraging shoreline, cost mask, and least-cost path distance): Edwin B. Forsythe NWR, NJ, 2013–2014 DisOcean: Distance to the ocean: 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: Edwin B. Forsythe NWR, NJ, 2010 Louisiana Barrier Island Comprehensive Monitoring Program – 2008 habitat map, East Chenier Region High-resolution lidar data for infrastructure corridors, Tanacross Quadrangle, Alaska High-resolution lidar data for infrastructure corridors, Fairbanks Quadrangle, Alaska High-resolution lidar data for infrastructure corridors, Fairbanks Quadrangle, Alaska High-resolution lidar data for infrastructure corridors, Bettles Quadrangle, Alaska High-resolution lidar data for infrastructure corridors, Tyonek Quadrangle, Alaska High-resolution lidar data for infrastructure corridors, Talkeetna Quadrangle, Alaska High-resolution lidar data for infrastructure corridors, Healy Quadrangle, Alaska High-resolution lidar data for infrastructure corridors, Gulkana Quadrangle, Alaska Photogrammetric digital surface models and orthoimagery for 26 coastal communities of western Alaska