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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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Layered geospatial PDF 7.5 Minute Quadrangle Map. Layers of geospatial data include orthoimagery, roads, grids, geographic names, elevation contours, hydrography, and other selected map features. This map is derived from GIS (geospatial information system) data. It represents a repackaging of GIS data in traditional map form, not creation of new information. The geospatial data in this map are from selected National Map data holdings and other government sources.
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Layered geospatial PDF 7.5 Minute Quadrangle Map. Layers of geospatial data include orthoimagery, roads, grids, geographic names, elevation contours, hydrography, and other selected map features. This map is derived from GIS (geospatial information system) data. It represents a repackaging of GIS data in traditional map form, not creation of new information. The geospatial data in this map are from selected National Map data holdings and other government sources.
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Layered geospatial PDF 7.5 Minute Quadrangle Map. Layers of geospatial data include orthoimagery, roads, grids, geographic names, elevation contours, hydrography, and other selected map features. This map is derived from GIS (geospatial information system) data. It represents a repackaging of GIS data in traditional map form, not creation of new information. The geospatial data in this map are from selected National Map data holdings and other government sources.
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Layered geospatial PDF 7.5 Minute Quadrangle Map. Layers of geospatial data include orthoimagery, roads, grids, geographic names, elevation contours, hydrography, and other selected map features. This map is derived from GIS (geospatial information system) data. It represents a repackaging of GIS data in traditional map form, not creation of new information. The geospatial data in this map are from selected National Map data holdings and other government sources.
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This data set includes the relative production scenarios for eight (8) grass species based on linear models from Epstein, et al. (1998). We selected two indicator species for each community: shortgrass prairie: blue grama (Bouteloua gracilis; BOGR) and buffalo grass (Bouteloua dactyloides; BODA); mixedgrass prairie: sideoats grama (Bouteloua curtipendula; BOCU) and little bluestem (Schizachyrium scoparium; SCSC); tallgrass prairie: big bluestem (Andropogon gerardii; ANGE) and Indiangrass (Sorghastrum nutans; SONU); and semiarid grasslands: black grama (Bouteloua eriopoda; BOER) and tobosagrass (Pleuraphis mutica; PLMU). Soil texture (percent by weight) came from the Earth Systems Science Center (2008) which provided...
This data set includes the relative production scenarios for bufflaograss [0.72(Temp) - 0.12(Precip) - 0.04(Sand) + 3.08]; this is the model from Epstein, et al. (1998). Soil texture (percent by weight) came from the Earth Systems Science Center (2008) which provided processed soils data from NRCS (gSSURGO), mean annual temperature (Celsius) and/or mean annual precipitation (millimeters) came from contemporary (1981 - 2010) estimates (Maurer et al. 2002) or a GCM. Global Climate Models (GCM) providing scenarios included: warmer-wetter scenario (CESM1-BGC, RCP4.5, Neale et al., 2010), warmer drier scenario (GISS-E2-R, RCP4.5, Schmidt, 2014), hotter-wetter scenario (Miroc-ESM, RCP8.5, Watanabe et al., 2011), and hotter-drier...
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Airborne electromagnetic (AEM), magnetic, and radiometric data were acquired in late February to early March 2018 along 2,364 line-kilometers in the Shellmound, Mississippi study area. Data were acquired by CGG Canada Services, Ltd. with three different helicopter-borne sensors: the CGG Canada Services, Ltd. RESOLVE frequency-domain AEM instrument that is used to map subsurface geologic structure at depths up to 100 meters, depending on the subsurface resistivity; a Scintrex CS-3 cesium vapor magnetometer that detects changes in deep (hundreds of meters to kilometers) geologic structure based on variations in the magnetic properties of different formations; and a Radiation Solutions RS-500 spectrometer that detects...
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Layered geospatial PDF 7.5 Minute Quadrangle Map. Layers of geospatial data include orthoimagery, roads, grids, geographic names, elevation contours, hydrography, and other selected map features. This map is derived from GIS (geospatial information system) data. It represents a repackaging of GIS data in traditional map form, not creation of new information. The geospatial data in this map are from selected National Map data holdings and other government sources.
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Layered geospatial PDF 7.5 Minute Quadrangle Map. Layers of geospatial data include orthoimagery, roads, grids, geographic names, elevation contours, hydrography, and other selected map features. This map is derived from GIS (geospatial information system) data. It represents a repackaging of GIS data in traditional map form, not creation of new information. The geospatial data in this map are from selected National Map data holdings and other government sources.
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Layered geospatial PDF 7.5 Minute Quadrangle Map. Layers of geospatial data include orthoimagery, roads, grids, geographic names, elevation contours, hydrography, and other selected map features. This map is derived from GIS (geospatial information system) data. It represents a repackaging of GIS data in traditional map form, not creation of new information. The geospatial data in this map are from selected National Map data holdings and other government sources.
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Layered geospatial PDF 7.5 Minute Quadrangle Map. Layers of geospatial data include orthoimagery, roads, grids, geographic names, elevation contours, hydrography, and other selected map features. This map is derived from GIS (geospatial information system) data. It represents a repackaging of GIS data in traditional map form, not creation of new information. The geospatial data in this map are from selected National Map data holdings and other government sources.
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Layered geospatial PDF 7.5 Minute Quadrangle Map. Layers of geospatial data include orthoimagery, roads, grids, geographic names, elevation contours, hydrography, and other selected map features. This map is derived from GIS (geospatial information system) data. It represents a repackaging of GIS data in traditional map form, not creation of new information. The geospatial data in this map are from selected National Map data holdings and other government sources.


map background search result map search result map USGS Topo Map Vector Data (Vector) 18015 Grandfather Mountain, North Carolina 20190912 for 7.5 x 7.5 minute FileGDB 10.1 USGS Topo Map Vector Data (Vector) 18659 Grover, Pennsylvania 20190920 for 7.5 x 7.5 minute FileGDB 10.1 USGS Topo Map Vector Data (Vector) 18476 Greenvine, Texas 20190725 for 7.5 x 7.5 minute FileGDB 10.1 USGS Topo Map Vector Data (Vector) 19977 Hedges, Kentucky 20190830 for 7.5 x 7.5 minute FileGDB 10.1 USGS Topo Map Vector Data (Vector) 20034 Hell Roaring Creek, Texas 20190723 for 7.5 x 7.5 minute FileGDB 10.1 USGS Topo Map Vector Data (Vector) 20119 Henriette, Minnesota 20190903 for 7.5 x 7.5 minute FileGDB 10.1 Airborne EM, magnetic, and radiometric survey data Potential productivity and change estimates for eight grassland species to evaluate vulnerability to climate change in the southern Great Plains USGS US Topo 7.5-minute map for Amherst, TX 2019 USGS US Topo 7.5-minute map for Baileyville, TX 2019 USGS US Topo 7.5-minute map for Barwise, TX 2019 USGS US Topo 7.5-minute map for Boothe, TX 2019 USGS US Topo 7.5-minute map for Cactus Creek, TX 2019 Digital elevation model (DEM) of beach topography of Lake Superior at Minnesota Point, Duluth, MN, August 2019 USGS US Topo 7.5-minute map for Rocky Reef, MT 2020 USGS US Topo 7.5-minute map for Chignik A-2 NW, AK 2020 USGS Topo Map Vector Data (Vector) 76194 Sarles OE N, North Dakota 20200722 for 7.5 x 7.5 minute Shapefile USGS US Topo 7.5-minute map for Climax South, GA 2020 USGS US Topo 7.5-minute map for McRae NW, GA 2020 Digital elevation model (DEM) of beach topography of Lake Superior at Minnesota Point, Duluth, MN, August 2019 USGS Topo Map Vector Data (Vector) 18015 Grandfather Mountain, North Carolina 20190912 for 7.5 x 7.5 minute FileGDB 10.1 USGS Topo Map Vector Data (Vector) 18659 Grover, Pennsylvania 20190920 for 7.5 x 7.5 minute FileGDB 10.1 USGS Topo Map Vector Data (Vector) 18476 Greenvine, Texas 20190725 for 7.5 x 7.5 minute FileGDB 10.1 USGS Topo Map Vector Data (Vector) 19977 Hedges, Kentucky 20190830 for 7.5 x 7.5 minute FileGDB 10.1 USGS Topo Map Vector Data (Vector) 20034 Hell Roaring Creek, Texas 20190723 for 7.5 x 7.5 minute FileGDB 10.1 USGS Topo Map Vector Data (Vector) 20119 Henriette, Minnesota 20190903 for 7.5 x 7.5 minute FileGDB 10.1 USGS US Topo 7.5-minute map for Amherst, TX 2019 USGS US Topo 7.5-minute map for Baileyville, TX 2019 USGS US Topo 7.5-minute map for Barwise, TX 2019 USGS US Topo 7.5-minute map for Boothe, TX 2019 USGS US Topo 7.5-minute map for Cactus Creek, TX 2019 USGS US Topo 7.5-minute map for Climax South, GA 2020 USGS US Topo 7.5-minute map for McRae NW, GA 2020 Airborne EM, magnetic, and radiometric survey data Potential productivity and change estimates for eight grassland species to evaluate vulnerability to climate change in the southern Great Plains