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This dataset is based on U.S. Geological Survey (USGS) resource assessments for “undiscovered” natural gas liquid resources, which are resources that have not yet been extensively proven by drilling (USGS 2014). Individual resource assessments describe the amount of petroleum resources in units with similar geologic features. We quantified the density of natural gas liquid resources by adding together the amounts in spatially overlapping assessment units and dividing these totals by polygon areas. Since assessments for geologic areas used in this analysis were completed at various times, the certainty related to these values is likely to vary according to geologic unit. USGS [U.S. Geological Survey]. 2014. Energy...
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This dataset is based on U.S. Geological Survey (USGS) resource assessments for “undiscovered” oil resources, which are resources that have not yet been extensively proven by drilling (USGS 2014). Individual resource assessments describe the amount of petroleum resources in units with similar geologic features. We focused on the amount of undiscovered continuous oil because technological advances have made exploitation of continuous resources increasingly profitable and large amounts remain undeveloped in comparison with conventional resources. We quantified the density of continuous oil resources by adding together the amounts in spatially overlapping assessment units and dividing these totals by polygon areas....
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This dataset “Broad-scale assessment of biophysical features in Colorado: Soil salinity using electrical conductance” presents information extracted from the Natural Resources Conservation Service (NRCS) gridded surface soils geographic database (gSSURGO). Fields retained and presented here include map unit (MU) codes and component (COMP) codes that may be used to reference records in the original, NRCS, data. Soil salinity is typically measured and evaluated based on electrical conductance (EC), and values presented here include the representative value for the map unit component (ECR) and the highest estimated value (ECH). Soils with high salinity can affect the composition of vegetation and can limit production...
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Hillshade of lidar-derived, bare earth digital elevation model, with 235-degree azimuth and 20-degree sun angle, 0.25m resolution, depicting earthquake effects following the August 24, 2014 South Napa Earthquake.
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Five principal components are used to represent the climate variation in an original set of 12 composite climate variables reflecting complex precipitation and temperature gradients. The dataset provides coverage for future climate (defined as the 2040-2070 normal period) under the RCP4.5 emission scenarios. Climate variables were chosen based on their known influence on local adaptation in plants, and include: mean annual temperature, summer maximum temperature, winter minimum temperature, annual temperature range, temperature seasonality (coefficient of variation in monthly average temperatures), mean annual precipitation, winter precipitation, summer precipitation, proportion of summer precipitation, precipitation...
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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This raster represents a continuous surface of sage-grouse habitat suitability index (HSI) values for northeastern California. HSIs were calculated for spring (mid-March to June), summer (July to mid-October), and winter (November to March) sage-grouse seasons, and then multiplied together to create this composite dataset.
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Five principal components are used to represent the climate variation in an original set of 12 composite climate variables reflecting complex precipitation and temperature gradients. The dataset provides coverage for future climate (defined as the 2040-2070 normal period) under the RCP8.5 emission scenarios. Climate variables were chosen based on their known influence on local adaptation in plants, and include: mean annual temperature, summer maximum temperature, winter minimum temperature, annual temperature range, temperature seasonality (coefficient of variation in monthly average temperatures), mean annual precipitation, winter precipitation, summer precipitation, proportion of summer precipitation, precipitation...
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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, Cape Cod, 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...
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...
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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...


map background search result map search result map Hillshade raster (235-degree azimuth, 20-degree sun angle) derived from lidar data collected after the August 24, 2014 South Napa earthquake Undiscovered Continuous Oil, Colorado Plateau Undiscovered Natural Gas Liquids Colorado Plateau Broad-scale assessment of biophysical features in Colorado: Soil salinity using electrical conductance Principal components of climate variation in the Desert Southwest for the future time period 2040-2070 (RCP 4.5) Composite Habitat Suitability Index Raster Dataset SupClas, GeoSet, SubType, VegDen, VegType: Categorical landcover rasters (landcover, geomorphic setting, substrate type, vegetation density, and vegetation type): Cedar Island, VA, 2012–2013 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 Principal components of climate variation in the Desert Southwest for the future time period 2040-2070 (RCP 8.5) points, transects, beach width: Barrier island geomorphology and shorebird habitat metrics at 50-m alongshore transects and 5-m cross-shore points: Monomoy Island, MA, 2013-2014 ElevMHW: Elevation adjusted to local mean high water: Rhode Island National Wildlife Refuge, RI, 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 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 Orthorectified Mosaic Photograph of a Portion of the Arkansas River at Parkdale, Colorado, March, 2018 Vegetation classification model (Veg) for basin A1 Digital terrain model (DTM) for basin B1 Final surface model (SRF) for basin B1 Orthorectified Mosaic Photograph of a Portion of the Arkansas River at Parkdale, Colorado, March, 2018 Vegetation classification model (Veg) for basin A1 Digital terrain model (DTM) for basin B1 Final surface model (SRF) for basin B1 DisOcean: Distance to the ocean: Wreck 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 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: Smith Island, VA, 2014 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 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 ElevMHW: Elevation adjusted to local mean high water: Rhode Island National Wildlife Refuge, RI, 2014 Composite Habitat Suitability Index Raster Dataset Broad-scale assessment of biophysical features in Colorado: Soil salinity using electrical conductance Undiscovered Continuous Oil, Colorado Plateau Undiscovered Natural Gas Liquids Colorado Plateau Principal components of climate variation in the Desert Southwest for the future time period 2040-2070 (RCP 4.5) Principal components of climate variation in the Desert Southwest for the future time period 2040-2070 (RCP 8.5)