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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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We monitored displacement of the Slumgullion landslide located in Hinsdale County, Colorado. We measured displacement at the ground surface between 12 August 2011 and 10 October 2018, and in the subsurface between 4 September 2016 and 7 December 2016. Both types of data were acquired at irregular time intervals. Displacement at the ground surface was measured at locations within the upper, middle, and lower parts of the landslide using electronic cable extension transducers (extensometers) with stated ±0.7 mm accuracy (Extensometer_data.csv). Subsurface displacement was measured near the middle of the landslide using a 16-sensor array of 30.48-cm-long tilt sensors (inclinometer) installed within a PVC-cased borehole....
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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 data release consists of orthophotographs of the Snow River in Alaska acquired on September 1, 2018. The orthophotographs were produced from images obtained using a Hasselblad A6D-100C 100 megapixel digital mapping camera deployed within a pod mounted on the landing gear of a Robinson R44 helicopter. Images were acquired as the helicopter transited a series of flight lines designed to provide complete coverage, with ample overlap, of the study area along the Snow River. Also within the pod was an ATLANS GPS/Inertial Motion Unit (IMU) that recorded the position and orientation of the platform during the flight. This information was used to geo-reference the images by performing aerial triangulation and bundle...
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Chalk Cliffs, located 8 miles southwest of Buena Vista, Colorado, is one of the most active debris-flow areas in the state (U.S. Geological Survey). This "Child item" page includes videos of debris flows captured by one of the high-definition cameras at the monitoring site in Chalk Cliffs, CO. This camera (Wide-angle camera) is located near Station 1 on the opposite side of the basin with a broad view of the channel. The attached figure "station_and_camera_locations.png" provides an overview figure with the location of the three cameras and three stations along the channel. Video recording for all cameras is triggered using a rainfall threshold, derived from rainfall measurements from a rain gauge (Michel et al.,...
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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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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...
This dataset consists of an inventory of the locations of liquefaction-related phenomena triggered by the 7 January 2020 M6.4 Puerto Rico earthquake. The inventory is primarily based on field observations collected during post-earthquake reconnaissance conducted by the USGS and partners (Allstadt and others, 2020, Interactive Dashboard). Some additional locations were added based on reconnaissance reports by other groups (Miranda and others, 2020; Morales-Velez and others, 2020). We delineated 43 polygons of liquefaction areas and lateral spreading where we had sufficient evidence to do so (liquefaction_polygons_20210913.zip), but all outlines are approximate because liquefaction is primarily a subsurface process...
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Low-altitude (80-100 meters above ground level) Unmanned Aircraft Systems (UAS) imagery of Town Neck Beach in Sandwich, Massachusetts, were used in a structure-from-motion (SfM) photogrammetry workflow to create high-resolution topographic datasets. Imagery was collected at close to low tide on twelve days to observe changes in beach and dune morphology. Ground control points (GCPs), which are temporary targets on the ground located by using a real-time kinematic global navigation satellite system (RTK-GNSS) base station and rover, were used to constrain the SfM process. Photoscan (v. 1.2-1.4) was used to create a digital elevation model and orthomosaic for each data collection day. Collection of these data was...
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Low-altitude (80-100 meters above ground level) Unmanned Aircraft Systems (UAS) imagery of Town Neck Beach in Sandwich, Massachusetts, were used in a structure-from-motion (SfM) photogrammetry workflow to create high-resolution topographic datasets. Imagery was collected at close to low tide on twelve days to observe changes in beach and dune morphology. Ground control points (GCPs), which are temporary targets on the ground located by using a real-time kinematic global navigation satellite system (RTK-GNSS) base station and rover, were used to constrain the SfM process. Photoscan (v. 1.2-1.4) was used to create a digital elevation model and orthomosaic for each data collection day. Collection of these data was...
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This spreadsheet contains the results of open-bore auger samples collected by the U.S. Geological Survey in Maryland and Virginia for the Federal Highways Administration Turner-Fairbanks Highway Research Center. 19 sites were selected in 14 different locations across northern Virginia and southern Maryland by FBGC scientists to drill boreholes for the development of the TFHRC’s In-situ Scour Testing Device (ISTD) and to investigate the sedimentological properties of different layers of the near-surface soil column. Each worksheet in this excel workbook contains the detailed log information for one site, including its name, gps coordinates, surface elevation, borehole depth, information about the drilling rig used...


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): 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 ElevMHW: Elevation adjusted to local mean high water: 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: Metompkin Island, VA, 2014 DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Myrtle Island, VA, 2014 DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Smith Island, VA, 2014 DisOcean: Distance to the ocean: Wreck Island, VA, 2014 Data from in-situ displacement monitoring, Slumgullion landslide, Hinsdale County, Colorado Debris Flow Video Files for Wide Angle Camera (Station 1), Chalk Cliffs, Colorado, USA, 2016 Datasheets associated with borehole sampling of surficial sediments in Northern Virginia and Southern Maryland conducted by the U.S. Geological Survey for the Federal Highways Administration Turner-Fairbanks Research Center In Situ Scour Testing Device Geo-referenced orthophotographs of the Snow River, Alaska, acquired September 1, 2018 Town Neck Beach, Massachusetts, 10 cm 2016-2017 Digital Elevation Models Town Neck Beach, Massachusetts, 5 cm 2016-2017 Orthomosaics Debris Flow Video Files for Wide Angle Camera (Station 1), Chalk Cliffs, Colorado, USA, 2016 Town Neck Beach, Massachusetts, 10 cm 2016-2017 Digital Elevation Models DisOcean: Distance to the ocean: Wreck Island, VA, 2014 Town Neck Beach, Massachusetts, 5 cm 2016-2017 Orthomosaics DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Myrtle Island, VA, 2014 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters of landcover, geomorphic setting, substrate type, vegetation density, and vegetation type: Metompkin Island, VA, 2014 Geo-referenced orthophotographs of the Snow River, Alaska, acquired September 1, 2018 DisMOSH, Cost, MOSH_Shoreline: Distance to foraging areas for piping plovers including foraging shoreline, cost mask, and least-cost path distance: Smith Island, VA, 2014 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Cedar Island, VA, 2012–2013 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 ElevMHW: Elevation adjusted to local mean high water: Fire Island, NY, 2014 ElevMHW: Elevation adjusted to local mean high water: Rhode Island National Wildlife Refuge, RI, 2014 Datasheets associated with borehole sampling of surficial sediments in Northern Virginia and Southern Maryland conducted by the U.S. Geological Survey for the Federal Highways Administration Turner-Fairbanks Research Center In Situ Scour Testing Device