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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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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...
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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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Cassiterite (SnO2), a main ore mineral in tin deposits, was collected by multiple Russian geologists or obtained from museum collections in both the USA and Russia and dated at the U.S. Geological Survey. The dated samples represent four different mining districts spanning the entire country from the village of Pitkäranta in the west (31° E Longitude) to the Merekskoe Deposit in the Russian Far East (134°E Longitude). The samples were recovered from a variety of host deposit types that range from the Proterozoic to Phanerozoic. Cassiterite (in the form of mounted loose grains) was prepared and analyzed for direct age dating on a laser ablation inductively coupled plasma mass spectrometer (LA-ICP-MS) system at the...
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The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast by compiling a database of historical (mid 1800's-1989) shoreline positions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-2009 color aerial orthoimagery and 2007 topographic lidar datasets obtained...
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The data release for the geologic terranes of the Hailey 1 x 2 degrees quadrangle and the western part of the Idaho Falls 1 x 2 degrees quadrangle, south-central Idaho is a Geologic Map Schema (GeMS)-compliant version that updates the GIS files for the geologic map published in U.S. Geological Survey (USGS) Bulletin 2064-A (Worl and Johnson, 1995). The updated digital data present the attribute tables and geospatial features (lines and polygons) in the format that meets GeMS requirements. This data release presents the geologic map as shown on the plate and captured in geospatial data for the published map. Minor errors, such as mistakes in line decoration or differences between the digital data and the map image,...
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The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using 2008-2009 color aerial orthoimagery and 2007 topographic lidar datasets obtained from NOAA's Ocean Service, Coastal...
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The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using 2008-2009 color aerial orthoimagery and 2007 topographic lidar datasets obtained from NOAA's Ocean Service, Coastal...
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The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using 2008-2009 color aerial orthoimagery and 2007 topographic lidar datasets obtained from NOAA's Ocean Service, Coastal...
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The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using 2008-2009 color aerial orthoimagery and 2007 topographic lidar datasets obtained from NOAA's Ocean Service, Coastal...
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Time series data of water surface elevation and wave height were acquired at ten locations for 517 days (in three separate deployments) off the north coast of Roi-Namur Island, Kwajalein Atoll, Marshall Islands, in support of a study on the coastal circulation patterns and the transformation of surface waves over the coral reefs. The relative placement of sensors on the reefs were as follows: ROI13W1 and ROI13E1 – fore reef ROI13W2 and ROI13E2 – outer reef flat ROI13W1 and ROI13E1 – middle reef flat ROI13W1 and ROI13E1 – inner reef flat


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 Development: Development delineation: Edwin B. Forsythe NWR, NJ, 2010 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 Roi-Namur Island, Marshall Islands, wave and water level data, 2013-2015 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 Density and magnetic properties of selected plutons (granitoids) in the Great Basin, parts of Arizona, California, Idaho, Nevada, Oregon, and Utah GIS Data (ver. 2) for Geologic Terranes of the Hailey 1 x 2 Degrees Quadrangle and the Western Part of the Idaho Falls 1 x 2 Degrees Quadrangle, South-Central Idaho Intersects for the coastal region around Boston, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1 Intersects for coastal region of Buzzards Bay, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1 Long-term and short-term shoreline change rates for the region of Buzzards Bay, Massachusetts, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1 Intersects for coastal region of Cape Cod Bay, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1 Baselines for Outer Cape Cod, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1 Pb-Pb and U-Pb data of Proterozoic to Phanerozoic cassiterite deposits in Russia Roi-Namur Island, Marshall Islands, wave and water level data, 2013-2015 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 Intersects for the coastal region around Boston, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1 Development: Development delineation: 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 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 Intersects for coastal region of Buzzards Bay, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1 Long-term and short-term shoreline change rates for the region of Buzzards Bay, Massachusetts, calculated with and without the proxy-datum bias using the Digital Shoreline Analysis System version 5.1 Intersects for coastal region of Cape Cod Bay, Massachusetts, generated to calculate shoreline change rates using the Digital Shoreline Analysis System version 5.1 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 GIS Data (ver. 2) for Geologic Terranes of the Hailey 1 x 2 Degrees Quadrangle and the Western Part of the Idaho Falls 1 x 2 Degrees Quadrangle, South-Central Idaho 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 Pb-Pb and U-Pb data of Proterozoic to Phanerozoic cassiterite deposits in Russia