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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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This dataset comprises a vector shapefile of the Puerto Rico geologic map from Bawiec et al. (1999), clipped to study areas in the Lares, Utuado, and Naranjito municipalities, with a modified basal contact of the Tertiary Lares Limestone (Tla) re-mapped using a lidar-derived digital elevation model (DEM) (USGS, 2018). The limestone unit of interest forms a prominent break in slope with the underlying geologic units, and this break in slope was mapped as the Tla basal contact. Only the southern contact of the Tla unit was modified. References: Bawiec, W.J., ed., 1999, Geology, geochemistry, geophysics, mineral occurrences and mineral resource assessment for the Commonwealth of Puerto Rico: U.S. Geological Survey...
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Water surface elevations within seven Willamette River off-channel features (OCF; alcoves and side channels) were measured using submerged pressure transducers. Transducers were installed from late May through mid-October, 2016, when discharge of the Willamette River was between approximately 5,500 and 45,000 cubic feet per second at Salem, Oregon (USGS gage 14191000) and 3,500 to 17,500 cubic feet per second at Harrisburg, Oregon (USGS gage 14166000). Pressure transducer sensor depth was measured at all seven sites. For five of the sites, pressure transducer sensor depths were converted to water surface elevations by surveying the water surface at each transducer with a real-time kinematic global positioning system...
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This digital elevation model provides a tool for calibrating tsunami risk to observations of the 1945 Makran tsunami in Karachi Harbour. The DEM bathymetry is derived from soundings made mainly during the first eight years after the tsunami. Although deficient in portraying intertidal backwaters and upland topography, the DEM accurately depicts the sheltered setting of one of the two tide gauges that recorded the 1945 tsunami.
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The files consist of two types: tabulated data files and graphical map files. Data files consist of six .csv files, representing six experiment dates (2016_06_14, 2016_16_15, 2016_18_15, 2016_16_21, 2016_16_22, 2016_16_23). Each of these files contains multiple columns of data, with each column representing either a time measurement or the value of a physical quantity measured at that time (e.g., flow depth, pore pressure, normal stress, etc.). Map files consist of six .pdf files, each representing an experiment date listed above. The maps show the thickness of the sediment deposited onto the runout pad after each experiment. Sediment thickness was determined using photogrammetery software from Adam Technology.
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Lidar data was collected on 24 and 25 May 2017 at the USGS debris-flow flume to monitor two gate-release debris flow experiments. A static prism of sediment was emplaced behind a gate at the top of the flume. Water was added via sprinklers to the surface and also via pipes to the subsurface, in order to saturate the sediment mass. The sediment mass moved down the flume as a debris flow when the gate was opened. Lidar data were collected from a Riegl VZ-400 terrestrial laser scanner to capture the mass failure. The laser scanner was modified, so that rather than scanning in a 360 degree motion, as it is designed, it only scanned a narrow swath (approximately 1 mm) along the full profile of the constructed sediment...
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Geomorphometry for Streams and Floodplains in the Chesapeake and Delaware Watersheds was generated as part of the project Quantifying Floodplain Ecological Processes and Ecosystem Services in the Delaware River Watershed funded through the William Penn Foundation' Delaware Watershed Research fund. This dataset contains geomorphometry for streams and floodplains in the Chesapeake and Delaware River watersheds. Geomorphometry is a quantitative representation of landscape surface form (e.g., channel width and depth) obtained from digital elevation models (DEMs). The dataset contains geomorphometry derived from running 3-m DEMs through the Floodplain and Channel Evaluation Tool (FACET) version 0.1.0. FACET generates...
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Geomorphometry for Streams and Floodplains in the Chesapeake and Delaware Watersheds was generated as part of the project Quantifying Floodplain Ecological Processes and Ecosystem Services in the Delaware River Watershed funded through the William Penn Foundation' Delaware Watershed Research fund. This dataset contains geomorphometry for streams and floodplains in the Chesapeake and Delaware River watersheds. Geomorphometry is a quantitative representation of landscape surface form (e.g., channel width and depth) obtained from digital elevation models (DEMs). The dataset contains geomorphometry derived from running 3-m DEMs through the Floodplain and Channel Evaluation Tool (FACET) version 0.1.0. FACET generates...
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Geomorphometry for Streams and Floodplains in the Chesapeake and Delaware Watersheds was generated as part of the project Quantifying Floodplain Ecological Processes and Ecosystem Services in the Delaware River Watershed funded through the William Penn Foundation' Delaware Watershed Research fund. This dataset contains geomorphometry for streams and floodplains in the Chesapeake and Delaware River watersheds. Geomorphometry is a quantitative representation of landscape surface form (e.g., channel width and depth) obtained from digital elevation models (DEMs). The dataset contains geomorphometry derived from running 3-m DEMs through the Floodplain and Channel Evaluation Tool (FACET) version 0.1.0. FACET generates...
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Geomorphometry for Streams and Floodplains in the Chesapeake and Delaware Watersheds was generated as part of the project Quantifying Floodplain Ecological Processes and Ecosystem Services in the Delaware River Watershed funded through the William Penn Foundation' Delaware Watershed Research fund. This dataset contains geomorphometry for streams and floodplains in the Chesapeake and Delaware River watersheds. Geomorphometry is a quantitative representation of landscape surface form (e.g., channel width and depth) obtained from digital elevation models (DEMs). The dataset contains geomorphometry derived from running 3-m DEMs through the Floodplain and Channel Evaluation Tool (FACET) version 0.1.0. FACET generates...
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As part of a collaborative study with the City of Raleigh, North Carolina, the U.S. Geological Survey developed a suite of high-resolution lidar-derived raster datasets for the Greater Raleigh Area, North Carolina, using repeat lidar data from the years 2013, 2015, and 2022. These datasets include raster representations of digital elevation models (DEMs), DEM of difference, the ten most common geomorphons (i.e. geomorphologic feature), lidar point density, and positive topographic openness. Raster footprints vary by year based on extent of lidar data collection. All files are available as Cloud Optimized GeoTIFF, meaning they are formatted to work on the cloud or can be directly downloaded. These metrics have been...
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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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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...
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 Sensor data from debris-flow experiments conducted in June, 2016, at the USGS debris-flow flume, HJ Andrews Experimental Forest, Blue River, Oregon Water surface elevations recorded by submerged water level loggers in off-channel features of the middle and upper Willamette River, Oregon, Summer, 2016 Bathymetric and topographic grid intended for simulations of the 1945 Makran tsunami in Karachi Harbour 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 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 DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Cape Lookout, NC, 2014 points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: 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 Data from in-situ displacement monitoring, Slumgullion landslide, Hinsdale County, Colorado Lidar data for gate release experiment at the USGS Debris-Flow Flume 24 and 25 May 2017 Geomorphometry for Hydrologic Unit 0205020506 (FACET version 0.1.0) Geomorphometry for Hydrologic Unit 02040205 (FACET version 0.1.0) Geomorphometry for Hydrologic Unit 02040103 (FACET version 0.1.0) Geomorphometry for Hydrologic Unit 02040201 (FACET version 0.1.0) Geo-referenced orthophotographs of the Snow River, Alaska, acquired September 1, 2018 Lidar-derived rasters of point density, elevation, and geomorphological features for 2013, 2015, and 2022 for the Greater Raleigh Area, North Carolina Modified basal contact of the Tertiary Lares Limestone in the vicinity of Utuado, Puerto Rico, USA, derived from USGS Open-File Report 98-038 Sensor data from debris-flow experiments conducted in June, 2016, at the USGS debris-flow flume, HJ Andrews Experimental Forest, Blue River, Oregon Lidar data for gate release experiment at the USGS Debris-Flow Flume 24 and 25 May 2017 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 SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Cedar Island, VA, 2012–2013 Bathymetric and topographic grid intended for simulations of the 1945 Makran tsunami in Karachi Harbour 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 Lidar-derived rasters of point density, elevation, and geomorphological features for 2013, 2015, and 2022 for the Greater Raleigh Area, North Carolina DCpts, DTpts, SLpts: Dune crest, dune toe, and mean high water shoreline positions: Cape Lookout, NC, 2014 Modified basal contact of the Tertiary Lares Limestone in the vicinity of Utuado, Puerto Rico, USA, derived from USGS Open-File Report 98-038 Geomorphometry for Hydrologic Unit 0205020506 (FACET version 0.1.0) Geomorphometry for Hydrologic Unit 02040205 (FACET version 0.1.0) Geomorphometry for Hydrologic Unit 02040103 (FACET version 0.1.0) Geomorphometry for Hydrologic Unit 02040201 (FACET version 0.1.0)