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This dataset is comprised of three files containing northing, easting, and elevation ("XYZ") information for light detection and ranging (LiDAR) data representing beach topography and sonar data representing near-shore bathymetry of Lake Superior at Minnesota Point, Duluth, Minnesota. The point data is the same as that in LAS (industry-standard binary format for storing large point clouds) files that were used to create a digital elevation model (DEM) of the approximately 5.9 square kilometer (2.3 square mile) surveyed area. LiDAR data were collected August 10, 2019 using a boat-mounted Optech ILRIS scanner and methodology similar to that described by Huizinga and Wagner (2019). Multi-beam sonar data were collected...
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wy_lvl7_coarsescale: Wyoming hierarchical cluster level 7 (coarse-scale) for Greater sage-grouse We developed a hierarchical clustering approach that identifies biologically relevant landscape units that can 1) be used as a long-term population monitoring framework, 2) be repeated across the Greater sage-grouse range, 3) be used to track the outcomes of local and regional populations by comparing population changes across scales, and 4) be used to inform where to best spatially target studies that identify the processes and mechanisms causing population trends to change among spatial scales. The spatial variability in the amount and quality of habitat resources can affect local population success and result in different...
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This dataset describes the Survey Data collected for the Planning Assistance to the States (PAS) study along Little Sugar Creek and selected tributaries, near Bella Vista, Arkansas, and Pineville, Missouri, December 2019. Little Sugar Creek is a tributary to the Elk River in Missouri that commences in Benton County, Arkansas and terminates in McDonald County, Missouri. The stream headwaters are located southeast of Garfield, Arkansas. Little Sugar Creek flows through Bella Vista, Arkansas, and runs north to its confluence with the Big Sugar Creek just south of Pineville, Missouri where it forms the Elk River. Browning Creek, Blowing Spring Creek, Spanker Creek and McKisic Creek are all tributaries to the Little...
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This data release contains the boundaries of assessment units and input data for the assessment of continuous oil and gas resources in the Delle Phosphatic Member of the Mississippian Woodman Formation, western Utah, eastern Nevada and southeastern Idaho. The Assessment Unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The Assessment Unit is defined within the context of the higher-level Total Petroleum System. The Assessment Unit is shown herein as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the province and incorporates a set of known or postulated oil and (or) gas accumulations sharing similar...
Aerial images in the vicinity of USGS gaging station #07094500 Arkansas River at Parkdale, Colorado were collected on March 20-22, 2018, using Unmanned Aircraft Systems (UAS, or "drones"). Data were processed using structure-from-motion analysis to generate a three-dimensional point cloud that identifies pixels from multiple images representing the same object and calculates the x, y, and z coordinates of that object/pixel. The point cloud was processed to create a digital surface model of the site. Finally, source images were stitched together based on shared pixels and orthogonally adjusted to create a high resolution (approximately 2 cm pixel size) orthoimage for the study area. The orthomosaic image captures...
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wy_lvl2_finescale: Wyoming hierarchical cluster level 2 (fine-scale) for Greater sage-grouse We developed a hierarchical clustering approach that identifies biologically relevant landscape units that can 1) be used as a long-term population monitoring framework, 2) be repeated across the Greater sage-grouse range, 3) be used to track the outcomes of local and regional populations by comparing population changes across scales, and 4) be used to inform where to best spatially target studies that identify the processes and mechanisms causing population trends to change among spatial scales. The spatial variability in the amount and quality of habitat resources can affect local population success and result in different...
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This dataset is a digital elevation model (DEM) of the beach topography of Lake Superior at Minnesota Point, Duluth, Minnesota. The DEM has a 1-meter (m; 3.28084 foot [ft]) cell size and was created from a LAS (industry-standard binary format for storing large point clouds) dataset of terrestrial light detection and ranging (LiDAR) data with an average point spacing of 0.137 m (0.45 ft). LiDAR data were collected August 10, 2019 using a boat-mounted Optech ILRIS scanner and methodology similar to that described by Huizinga and Wagner (2019). References: Huizinga, R.J. and Wagner, D.M., 2019, Erosion monitoring along selected bank locations of the Coosa River in Alabama using terrestrial light detection and ranging...
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This data release contains the boundaries of assessment units and input data for the assessment of undiscovered gas hydrate resources on the north slope of Alaska. The Assessment Unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The Assessment Unit is defined within the context of the higher-level Total Petroleum System. The Assessment Unit is shown herein as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the province and incorporates a set of known or postulated oil and (or) gas accumulations sharing similar geologic, geographic, and temporal properties within the Total Petroleum System, such as...
Categories: Data; Types: Downloadable, Map Service, OGC WFS Layer, OGC WMS Layer, Shapefile; Tags: Assessment Unit, Continuous Assessment Unit, Earth Science, Economic geology, Energy Resources, All tags...
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The U.S. Geological Survey (USGS) collected over 1,840 physical property measurements on selected plutons in the Great Basin, primarily in California and Nevada. Data include station identifier, geographic coordinates, rock type, density, magnetic susceptibility, remanent magnetization, declination, and inclination where available. Data are presented in ASCII format and include density and magnetic property data in pluton_data.csv, a data dictionary describing the data fields in data_dictionary.csv, and a rock data dictionary listing rock types in rock_dictionary.csv. Preliminary results and interpretation were described by Ponce and others (2010) and some samples are from Sikora and others (1991). References: Ponce,...
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The 2018 KÄ«lauea eruption and caldera collapse generated intense cycles of seismicity tied to repeated large seismic (Mw ~5) collapse events associated with magma withdrawal from beneath the summit. To gain insight into the underlying dynamics and aid eruption response, we applied waveform-based earthquake detection and double-difference location as the eruption unfolded. Here, we augment these rapid results by grouping events based on patterns of correlation-derived phase polarities across the network. From April 29 to August 6, bracketing the eruption, we used ~2800 events cataloged by the Hawaiian Volcano Observatory to detect and precisely locate 44,000+ earthquakes. Resulting hypocentroids resolve complex,...
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The Best Management Practices Statistical Estimator (BMPSE) was developed by the U.S. Geological Survey (USGS), in cooperation with the Federal Highway Administration (FHWA) Office of Project Delivery and Environmental Review to provide planning-level information about the performance of structural best management practices for decision makers, planners, and highway engineers to assess and mitigate possible adverse effects of highway and urban runoff on the Nation's receiving waters (Granato 2013, 2014; Granato and others, 2021a,b). The BMPSE was used to calculate statistics and create input files for fitting the trapezoidal distribution to data from studies documenting the performance of individual structural stormwater...
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This data release contains the boundaries of assessment of undiscovered continuous tight-gas resources in the Mesaverde Group and Wasatch Formation, Uinta-Piceance Province, Utah and Colorado. The Assessment Unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The Assessment Unit is defined within the context of the higher-level Total Petroleum System. The Assessment Unit is shown herein as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the province and incorporates a set of known or postulated oil and (or) gas accumulations sharing similar geologic, geographic, and temporal properties within the Total...
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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...


map background search result map search result map Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 2 (Wyoming), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 7 (Wyoming), Interim 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 USGS National and Global Oil and Gas Assessment Project-Northern Alaska Province, Gas Hydrate Assessment Unit Boundaries and Assessment Input Data Forms USGS National and Global Oil and Gas Assessment Project - Piceance and Uinta Basins, Mesaverde Group Tight Gas Assessment Unit Boundaries and Assessment Input Data Forms High resolution earthquake catalogs from the 2018 Kilauea eruption sequence Digital elevation model (DEM) of beach topography of Lake Superior at Minnesota Point, Duluth, MN, August 2019 XYZ files of LiDAR and sonar data collected at Lake Superior at Minnesota Point, Duluth, MN, August 2019 Survey Data Collection for the Planning Assistance to the States Study along Little Sugar Creek and Selected Tributaries near Bella Vista, Arkansas, and Pineville, Missouri, December 2019 Density and magnetic properties of selected plutons (granitoids) in the Great Basin, parts of Arizona, California, Idaho, Nevada, Oregon, and Utah USGS National and Global Oil and Gas Assessment Project-Eastern Great Basin Province, Delle Phosphatic Member Shale Assessment Unit Boundaries and Assessment Input Data Forms Best Management Practices Statistical Estimator (BMPSE) Version 1.2.0 Orthorectified Mosaic Photograph of a Portion of the Arkansas River at Parkdale, Colorado, March, 2018 Orthorectified Mosaic Photograph of a Portion of the Arkansas River at Parkdale, Colorado, March, 2018 Digital elevation model (DEM) of beach topography of Lake Superior at Minnesota Point, Duluth, MN, August 2019 XYZ files of LiDAR and sonar data collected at Lake Superior at Minnesota Point, Duluth, MN, August 2019 High resolution earthquake catalogs from the 2018 Kilauea eruption sequence SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Cedar Island, VA, 2012–2013 Survey Data Collection for the Planning Assistance to the States Study along Little Sugar Creek and Selected Tributaries near Bella Vista, Arkansas, and Pineville, Missouri, December 2019 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 ElevMHW: Elevation adjusted to local mean high water: Fire Island, NY, 2014 USGS National and Global Oil and Gas Assessment Project - Piceance and Uinta Basins, Mesaverde Group Tight Gas Assessment Unit Boundaries and Assessment Input Data Forms USGS National and Global Oil and Gas Assessment Project-Eastern Great Basin Province, Delle Phosphatic Member Shale Assessment Unit Boundaries and Assessment Input Data Forms Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 2 (Wyoming), Interim Hierarchically nested and biologically relevant monitoring frameworks for Greater Sage-grouse, 2019, Cluster Level 7 (Wyoming), Interim USGS National and Global Oil and Gas Assessment Project-Northern Alaska Province, Gas Hydrate Assessment Unit Boundaries and Assessment Input Data Forms Density and magnetic properties of selected plutons (granitoids) in the Great Basin, parts of Arizona, California, Idaho, Nevada, Oregon, and Utah Best Management Practices Statistical Estimator (BMPSE) Version 1.2.0