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LandPro2009 is ARC's landuse/landcover GIS database for the 21-county Atlanta Region (Cherokee, Clayton, Cobb, DeKalb, Douglas, Fayette, Fulton, Gwinnett, Henry, Rockdale, the EPA non-attainment (8hr standard) counties of Carroll, Coweta, Barrow, Bartow, Forsyth, Hall, Newton, Paulding, Spalding and Walton and Dawson which will become a part of the 2010 Urbanized Area). LandPro2009 was created by on-screen photo-interpretation and digitizing of ortho-rectified aerial photography at a scale of 1:14,000. The primary source for this GIS database was 2009 true color imagery with 1.64-foot pixel resolution, provided by Aerials Express, Inc.
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The USDA, NASS Cropland Data Layer (CDL) is a raster, geo-referenced, crop-specific land cover data layer. The 2010 CDL has a ground resolution of 30 meters. The CDL is produced using satellite imagery from the Landsat 5 TM sensor, Landsat 7 ETM+ sensor, and the Indian Remote Sensing RESOURCESAT-1 (IRS-P6) Advanced Wide Field Sensor (AWiFS) collected during the current growing season.Some CDL states used additional satellite imagery and ancillary inputs to supplement and improve the classification. These additional sources can include the United States Geological Survey (USGS) National Elevation Dataset (NED), the USGS National Land Cover Dataset 2001 (NLCD 2001), and the National Aeronautics and Space Administration...
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A shapefile of the extent of irrigated agricultural fields which includes an attribute table of the irrigated acreage for the period between January and December 2020 was compiled for Alachua, Baker, Bradford, Columbia, Dixie, Gilchrist, Hamilton, Jefferson, Lafayette, Levy, Madison, Suwannee, Taylor, and Union Counties, Florida. These counties are fully or partially within the Suwannee River Water Management District boundaries. Attributes for each polygon that represents a field include a general or specific crop type, irrigation system, and primary water source for irrigation.
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A shapefile of the extent of irrigated agricultural fields which includes an attribute table of the irrigated acreage for the period between January and December 2020 was compiled for Lake, Marion, and Orange Counties, Florida. Attributes for each polygon that represents a field include a general or specific crop type, irrigation system, and primary water source for irrigation.
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The GIS shapefile and summary tables provide irrigated agricultural land-use for Citrus, Hernando, Pasco, and Sumter Counties, Florida through a cooperative project between the U.S Geological Survey (USGS) and the Florida Department of Agriculture and Consumer Services (FDACS), Office of Agricultural Water Policy. Information provided in the shapefile includes the location of irrigated land field verified for 2019, crop type, irrigation system type, and primary water source used in Citrus, Hernando, Pasco, and Sumter Counties, Florida. A map image of the shapefile is provided in the attachment.
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This data set consists of a detailed digital map of the areal extent of fields and a summary of the irrigated acreage for the 2018 growing season developed for Manatee County, Florida. Selected attribute data that include crop type, irrigation system, and primary water source were collected for each irrigated field.
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The dataset includes Land Use/Land Cover types throughout the Chenier Eco-Region in Southwest Louisiana. Using the 2015 National Aerial Imagery Program (NAIP) dataset (1m) as the basemap, E-Cognition image objects were derived from the multiresolution segmentation algorithm at 75 and 250 segments. Attempts to refine the data training methods using E-cognition, to extrapolate automating categories of this information to the entire map resulted with exceedingly low accuracy. Therefore, a raster was produced by piecing together several data resources, which provide reliable data for specific LandUse/LandCover (LULC) categories. This process involved stitching together more reliable sources for specific categories to...
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The public ownership layer shows land ownership/management for public entities, Federal, Tribal, State, and Local. The base linework was derived mostly from the BLM Landlines (Public Land Survey & Jurisdiction) layer and from ODF ownership layer. This base linework was enhanced using lines derived from the Government Corners Database (GCDB)
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These data were released prior to the October 1, 2016 effective date for the USGS’s policy dictating the review, approval, and release of scientific data as referenced in USGS Survey Manual Chapter 502.8 Fundamental Science Practices: Review and Approval of Scientific Data for Release. Estimates of area and aerial extent of land-use categories are an essential component for computing the water budget of the High Plains aquifer. These raster land-use land class data represent yearly simulated future land use for the High Plains from 2009 to 2050 These data were developed using the FOREcasting SCEnarios (FORE-SCE) of future land cover model (Sohl and others, 2007; Sohl and Sayler 2008) for two (A2 and B2) of the...
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These data were released prior to the October 1, 2016 effective date for the USGS’s policy dictating the review, approval, and release of scientific data as referenced in USGS Survey Manual Chapter 502.8 Fundamental Science Practices: Review and Approval of Scientific Data for Release. Estimates of land use categories are an essential component for computing the water budget of the High Plains aquifer. These raster land-use data represent yearly estimated land use for the High Plains from 1949 to 2008. These data were developed using the FOREcasting SCEnarios of future land cover (FORE-SCE) model (Sohl and others, 2007) and then processed using a Geographic Information System (GIS). The GIS software used to process...
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This dataset is the third (circa 2013) in a series of three 1-kilometer land use land cover (LULC) time-periods datasets (1975, 2000, and 2013) aids in monitoring change in West Africa’s land resources. To monitor and map these changes, a 26 general LULC class system was used. The classification system that was developed was primarily inspired by the “Yangambi Classification” (Trochain, 1957). This fairly broad class system for LULC was used because the classes can be readily identified on Landsat satellite imagery. A visual photo-interpretation approach was used to identify and map the LULC classes represented on Landsat images. The Rapid Land Cover Mapper (RLCM) was used to facilitate the photo-interpretation...
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This raster file represents classification and mapping results for priority area 1 of the Cody Region and Yellowstone National Park land cover remote sensing project. Extensive field collected reference data describing the range of plant communities and habitat types comprising the Bighorn Basin have been analyzed to produce a classification of land cover types based on the Wyoming Game and Fish Department (WGFD) Wildlife Observation System (WOS). Corresponding land cover classes were subsequently spatially modeled using a non-parametric Classification and Regression Tree (CART) algorithm that integrated both spectral data from Landsat Thematic Mapper satellite imagery and a variety of ancillary environmental data...
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To accurately estimate agricultural water use or to project future water demands, a detailed inventory of current irrigated crop acreage is needed at a high level of resolution. In many Florida counties this kind of detailed high-resolution inventory is not available. A detailed digital map and summary of irrigated acreage during the 2015 growing season was developed for 13 of the 15 counties that compose the Suwannee River Water Management District. The irrigated areas were delineated using land-use data, orthoimagery, water management district consumptive water-use permits, and digitized agricultural landuse maps developed by the Florida Department of Agriculture and Consumer Services, Florida Statewide Agricultural...
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This data set consists of a detailed digital map of the areal extent of fields and a summary of the irrigated acreage for the 2018 growing season developed for Hillsborough County, Florida. Selected attribute data that include crop type, irrigation system, and primary water source were collected for each irrigated field.
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A detailed table summarizing the irrigated acreage by crop type and irrigation system was developed for the 2018 growing season (November 2017 and September 2018) for Hardee County, Florida. The acreage totals presented are derived from the results of a cooperative project that mapped and field-verified irrigated fields in Hardee County, Florida. In addition, the table provides acreage totals by crop type published by the U.S. Department of Commerce for 1978 and 1982 and the U.S. Department of Agriculture for years between 1987 and 2012.
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A detailed inventory of irrigated crop acreage is not available at the level of resolution needed to accurately estimate agricultural water-use or to project future water demands in many Florida counties. A detailed summary table of irrigated acreage for the 2016 growing season was developed by the U.S. Geological Survey (USGS) for Santa Rosa County, Florida, and includes totals of acreage by irrigation system. It also provides a comparison between published acreage totals for previous years by the U.S. Department of Agriculture (USDA) Census of Agriculture and those determined from the 2016 inventory. The irrigated acreage estimated by the USGS for the 2016 inventory was 560 acres.
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The Landsat-based Irrigation Dataset (LANID) uses a random-forest machine-learning model with greenness and vegetative indices, climate data, and crop masks to identify irrigated crops (Xie and others, 2021, Xie and Lark, 2021). Separate western US and eastern US methods are used to train and validate the model. Annual LANID maps for 2018-20 were created using the same techniques in Xie and others, 2021, and Xie and Lark, 2021.
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The dataset includes Land Use/Land Cover types throughout the Chenier Eco-Region in Southwest Louisiana. Using the 2015 NAIP dataset (1m) as the basemap, E-Cognition image objects were derived from the multiresolution segmentation algorithm at 75 and 250 segments. Attempts to refine the data training methods using E-cognition, to extrapolate automating categories of this information to the entire map resulted with exceedingly low accuracy. Therefore, a raster was produced by piecing together several data resources, which provide reliable data for specific LULC categories. This process involved stitching together more reliable sources for specific categories to apply to higher resolution (75) segmentation product....
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These data were released prior to the October 1, 2016 effective date for the USGS’s policy dictating the review, approval, and release of scientific data as referenced in USGS Survey Manual Chapter 502.8 Fundamental Science Practices: Review and Approval of Scientific Data for Release. This data set depicts land use and land cover from the 1970s and 1980s and has been previously published by the U.S. Geological Survey (USGS) in other file formats. This version has been reformatted to other file formats and includes minor edits applied by the U.S. Environmental Protection Agency (USEPA) and USGS scientists. This data set was developed to meet the needs of the USGS National Water-Quality Assessment (NAWQA) Program.
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These data were released prior to the October 1, 2016 effective date for the USGS’s policy dictating the review, approval, and release of scientific data as referenced in USGS Survey Manual Chapter 502.8 Fundamental Science Practices: Review and Approval of Scientific Data for Release. This data set depicts land use and land cover from the 1970s and 1980s and has been previously published by the U.S. Geological Survey (USGS) in other file formats. This version has been reformatted to other file formats and includes minor edits applied by the U.S. Environmental Protection Agency (USEPA) and USGS scientists. This data set was developed to meet the needs of the USGS National Water-Quality Assessment (NAWQA) Program.


map background search result map search result map Land Cover Mapping Database for the Cody Region and Absaroka Front - 30 meter Oregon Public Ownership Land Use - Greater Atlanta Region USDA-NASS, 2010 Cropland Data Layer, Nevada Table 2 (Report Appendix 2).  Reported and inventoried irrigated acreage by crop and irrigation system type for the individual counties in the Suwannee River Water Management District, 1974-2015 High resolution landcover for the Western Gulf Coastal Plain of Louisiana Grassland priority rankings model for the Western Gulf Coastal Plain of Louisiana Table 2. Reported and inventoried crop and irrigated acreage in Santa Rosa County, Florida, 1974–2016. GIS shapefile: Manatee County, Florida irrigated agricultural land-use for the 2018 growing season GIS shapefile: Hillsborough County, Florida irrigated agricultural land-use for the 2018 growing season Table: Reported and inventoried crop and irrigated acreage in Hardee County, Florida, 1978-2018 GIS shapefile and related summary data describing irrigated agricultural land-use in Citrus, Hernando, Pasco, and Sumter Counties, Florida for 2019 Gambia Land Use Land Cover 2013 GIS Shapefile of Irrigated Agricultural Acreage for Lake, Marion, and Orange Counties, Florida in 2020 GIS shapefile of Irrigated Agricultural Acreage within the Suwannee River Water Management District in 2020 Enhanced Historical Land-Use and Land-Cover Data Sets of the U.S. Geological Survey: polygon format files Enhanced Historical Land-Use and Land-Cover Data Sets of the U.S. Geological Survey: raster format files DS-777 Annual Model-Backcasted Land-Use/Land-Cover Rasters from 1949 to 2008 for the High Plains Aquifer in Parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming DS-777 Annual Model-Forecasted Land-Use/Land-Cover Rasters from 2009 to 2050 for the A2 Climate Scenario for the High Plains Aquifer in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming LANID: Landsat-based irrigation dataset for CONUS 2018-20 Table 2. Reported and inventoried crop and irrigated acreage in Santa Rosa County, Florida, 1974–2016. Table: Reported and inventoried crop and irrigated acreage in Hardee County, Florida, 1978-2018 GIS shapefile: Manatee County, Florida irrigated agricultural land-use for the 2018 growing season GIS shapefile: Hillsborough County, Florida irrigated agricultural land-use for the 2018 growing season GIS shapefile and related summary data describing irrigated agricultural land-use in Citrus, Hernando, Pasco, and Sumter Counties, Florida for 2019 GIS Shapefile of Irrigated Agricultural Acreage for Lake, Marion, and Orange Counties, Florida in 2020 Land Cover Mapping Database for the Cody Region and Absaroka Front - 30 meter Gambia Land Use Land Cover 2013 Grassland priority rankings model for the Western Gulf Coastal Plain of Louisiana High resolution landcover for the Western Gulf Coastal Plain of Louisiana Land Use - Greater Atlanta Region GIS shapefile of Irrigated Agricultural Acreage within the Suwannee River Water Management District in 2020 Table 2 (Report Appendix 2).  Reported and inventoried irrigated acreage by crop and irrigation system type for the individual counties in the Suwannee River Water Management District, 1974-2015 Oregon Public Ownership USDA-NASS, 2010 Cropland Data Layer, Nevada DS-777 Annual Model-Forecasted Land-Use/Land-Cover Rasters from 2009 to 2050 for the A2 Climate Scenario for the High Plains Aquifer in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming DS-777 Annual Model-Backcasted Land-Use/Land-Cover Rasters from 1949 to 2008 for the High Plains Aquifer in Parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming LANID: Landsat-based irrigation dataset for CONUS 2018-20 Enhanced Historical Land-Use and Land-Cover Data Sets of the U.S. Geological Survey: polygon format files Enhanced Historical Land-Use and Land-Cover Data Sets of the U.S. Geological Survey: raster format files