The U.S. Geological Survey (USGS), in partnership with several federal agencies, has now developed and released seven National Land Cover Database (NLCD) products: NLCD 1992, 2001, 2006, 2011, 2016, 2019, and 2021. Beginning with the 2016 release, land cover products were created for two-to-three-year intervals between 2001 and the most recent year. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. NLCD continues to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database. NLCD 2021 adds an additional year to the map products produced for NLCD 2019, with a streamlined compositing process for assembling and preprocessing Landsat imagery and geospatial ancillary datasets; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a theme-based post-classification protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and a scripted operational system. The overall accuracy of the 2019 Level I land cover was 91%. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2021 operational mapping (see https://doi.org/10.1080/15481603.2023.2181143 for the latest accuracy assessment publication). Questions about the NLCD 2021 land cover product can be directed to the NLCD 2021 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.

The U.S. Geological Survey (USGS), in association with the Multi-Resolution Land Characteristics (MRLC) Consortium, produces the National Land Cover Database (NLCD) for the United States. The MRLC, a consortium of federal agencies who coordinate and generate consistent and relevant land cover information at the national scale for a wide variety of environmental, land management, and modeling applications, have been providing the scientific community with detailed land cover products for more than 30 years. Over that time, NLCD has been one of the most widely used geospatial datasets in the U.S., serving as a basis for understanding the Nation’s landscapes in thousands of studies and applications, trusted by scientists, land managers, students, city planners, and many more as a definitive source of U.S. land cover. NLCD land cover suite is created through the classification of Landsat imagery and uses partner data from the MRLC Consortium to help refine many of the land cover classes. The classification system used by NLCD is modified from the Anderson Land Cover Classification System. The NLCD Class Legend and Description is maintained at https://www.mrlc.gov/data/legends/national-land-cover-database-class-legend-and-description. The land cover theme includes two separate products. The first is a standard land cover product suite that provides 16 land cover classes for the conterminous United States and Alaska only land cover types and is available at https://www.mrlc.gov/data. The second product suite, NLCD Land Cover Science Products, provides additional discrimination and land cover classes differentiating grass and shrub and regenerating forest regime from grass and shrub and rangeland setting and is available at https://www.mrlc.gov/nlcd-2021-science-research-products. The latest release of NLCD land cover spans the timeframe from 2001 to 2021 in 2 to 3-year intervals. These new products use a streamlined compositing process for assembling and preprocessing Landsat imagery and geospatial ancillary datasets; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a theme-based post-classification protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and a scripted operational system.

The U.S. Geological Survey (USGS), in association with the Multi-Resolution Land Characteristics (MRLC) Consortium, produces the National Land Cover Database (NLCD) for the United States. The MRLC, a consortium of federal agencies who coordinate and generate consistent and relevant land cover information at the national scale for a wide variety of environmental, land management, and modeling applications, have been providing the scientific community with detailed land cover products for more than 30 years. Over that time, NLCD has been one of the most widely used geospatial datasets in the U.S., serving as a basis for understanding the Nation’s landscapes in thousands of studies and applications, trusted by scientists, land managers, students, city planners, and many more as a definitive source of U.S. land cover. NLCD land cover suite is created through the classification of Landsat imagery and uses partner data from the MRLC Consortium to help refine many of the land cover classes. The classification system used by NLCD is modified from the Anderson Land Cover Classification System. The NLCD Class Legend and Description is maintained at https://www.mrlc.gov/data/legends/national-land-cover-database-class-legend-and-description. The land cover theme includes two separate products. The first is a standard land cover product suite that provides 16 land cover classes for the conterminous United States and Alaska only land cover types and is available at https://www.mrlc.gov/data. The second product suite, NLCD Land Cover Science Products, provides additional discrimination and land cover classes differentiating grass and shrub and regenerating forest regime from grass and shrub and rangeland setting and is available at https://www.mrlc.gov/nlcd-2021-science-research-products. The latest release of NLCD land cover spans the timeframe from 2001 to 2021 in 2 to 3-year intervals. These new products use a streamlined compositing process for assembling and preprocessing Landsat imagery and geospatial ancillary datasets; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a theme-based post-classification protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and a scripted operational system. Unmasked Impervious - To produce the unmasked impervious layer a multilayered perceptron neural network (MLP) was deployed across CONUS. The MLP was trained to perform the regression task of predicting the 1-100 impervious fractional cover. To sample data to train the network, we broke CONUS into a grid comprised of 256x256 pixel regions of interest (ROIs) and sampled from that grid all ROIs with at least 40% impervious cover according to NLCD 2019 impervious fractional cover, which gave us samples from large impervious areas. From those ROIs, we then sampled 66 million training and 16 million validation data points with an even distribution across each impervious intensity (1-100). Those training points were then randomly split into 4 subsets, each corresponding to one of the following respective years: 2011, 2013, 2016, 2019. We used those points to query surface reflectance values from leaf-on composite and leaf-off synthetic imagery (see metadata for NLCD 2021 land cover), elevation data, and spatial urban intensity probabilities. The spatial urban intensity probabilities were generated by an ensemble of U-net models that were trained to predict the 4 urban intensity classes as defined by the NLCD product legend (open space, low intensity, medium intensity, high intensity). Two U-net models were trained using all ROIs in the CONUS 256x256 pixel grid. Inputs to these models included leaf-on composite and leaf-off synthetic imagery, and elevation data. To create the final training and validation datasets we randomly split the CONUS grid into to 2 equal sets: A and B. Using the ROIs from set A we queried the input features from the years 2011 and 2016 and from the ROIs in set B we queried input features from the years 2013 and 2019. These U-net models do not act as the final impervious predictors but instead as spatial feature generators. The spatial features learned by these convolutional neural networks were then fed into the pixel-based MLP, as spatial probabilities of urban intensity, to boost its predicting power. The U-nets were trained using categorical focal Jaccard loss and monitored with the Jaccard Index metric (IOU). The impervious fractional cover regression model (MLP) was trained using mean squared error as a loss function and monitored with mean absolute error as the metric. Initial impervious footprint - To generate an initial impervious footprint, three U-net models were trained on the multiclass-classification task of predicting “urban” and “roads”. The model was trained with 120,000 training and 40,000 validation 256X256 pixel Landsat image chips covering the entire extent of CONUS. The model inputs are consistent with what was used to generate the urban intensity U-net models; the only difference was the target mask the models were trained to predict. These models mapped all NLCD impervious footprint pixels to two classes (“urban” and “roads”); this was used to generate the impervious extent. Impervious Change Pixels - The initial 2021 impervious change pixels were created by comparing the 2021 urban footprint with the 2019 published urban descriptor and extracting the difference. These change pixels were manually edited for omission and commission errors. Ancillary data were then added to the change pixels to create the final 2021 impervious change pixels. These ancillary data consisted of solar installations, wind turbines, and roads. The solar installations dataset is an edited version of the Solar Photovoltaic Generating Units dataset produced by Kruitwagen et al (2021) (https://doi.org/10.5281/zenodo.5005867). The U.S. Wind Turbine Database from Hoen et al (2021) (https://doi.org/10.5066/F7TX3DN0) was used without edits. NavStreets road datasets were used in previous versions of NLCD but an updated version was not available to the USGS. New subdivision roads from the 2021 urban footprint and a small number of manually drawn roads were added to the 2021 impervious change pixels. 2021 impervious extent - The final impervious change pixels were added to that 2019 impervious descriptor file to create the new 2021 impervious descriptor file. This file maps the extent of all impervious for the 2021 NLCD. 2021 impervious product - The percent imperviousness values (1-100%) for the impervious change pixels were extracted from the unmasked impervious layer. Values for previously published urban remained the same except for areas that were 40% or more greater in value, in the unmasked impervious layer. 2021 impervious descriptor - The final impervious change pixels were mapped to the class legend for the NLCD 2019 published impervious descriptor. These pixels were then added to the NLCD 2019 impervious descriptor file to create the new 2021 impervious descriptor file.

The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2021-4 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 2011 through 2021. The NCLD data are processed to remove small interannual changes from the annual TCC timeseries, and to mask TCC pixels that are known to be 0 percent TCC, non-tree agriculture, and water. A small interannual change is defined as a TCC change less than an increase or decrease of 10 percent compared to a TCC baseline value established in a prior year. The initial TCC baseline value is the mean of 2008-2010 TCC data. For each year following 2011, on a pixel-wise basis TCC values are updated to a new baseline value if an increase or decrease of 10 percent TCC occurs relative to the 2008-2010 TCC baseline value. If no increase or decrease greater than 10 percent TCC occurs relative to the 2008-2010 baseline, then the 2008-2010 TCC baseline value is caried through to the next year in the timeseries. Pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

Land cover describes the surface of the earth. This time-enabled service of the National Land Cover Database groups land cover into 20 classes based on a modified Anderson Level II classification system. Classes include vegetation type, development density, and agricultural use. Areas of water, ice and snow and barren lands are also identified.The National Land Cover Database products are created through a cooperative project conducted by the Multi-Resolution Land Characteristics Consortium (MRLC). The MRLC Consortium is a partnership of federal agencies, consisting of the U.S. Geological Survey, the National Oceanic and Atmospheric Administration, the U.S. Environmental Protection Agency, the U.S. Department of Agriculture, the U.S. Forest Service, the National Park Service, the U.S. Fish and Wildlife Service, the Bureau of Land Management and the USDA Natural Resources Conservation Service.Time Extent: 2001, 2004, 2006, 2008, 2011, 2013, 2016, 2019, and 2021 for the conterminous United States. The layer displays land cover for Alaska for the years 2001, 2011, and 2016. For Puerto Rico there is only data for 2001. For Hawaii, Esri reclassed land cover data from NOAA Office for Coastal Management, C-CAP into NLCD codes. These reclassed C-CAP data were available for Hawaii for the years 2001, 2005, and 2011. Hawaii C-CAP land cover in its original form can be used in your maps by adding the Hawaii CCAP Land Cover layer directly from the Living Atlas.Units: (Thematic dataset)Cell Size: 30m Source Type: Thematic Pixel Type: Unsigned 8 bitData Projection: North America Albers Equal Area Conic (102008)Mosaic Projection: North America Albers Equal Area Conic (102008)Extent: 50 US States, District of Columbia, Puerto RicoSource: National Land Cover DatabasePublication date: June 30, 2023Time SeriesThis layer is served as a time series. To display a particular year of land cover data, select the year of interest with the time slider in your map client. You may also use the time slider to play the service as an animation. We recommend a one year time interval when displaying the series. If you would like a particular year of data to use in analysis, be sure to use the analysis renderer along with the time slider to choose a valid year.North America Albers ProjectionThis layer is served in North America Albers projection. Albers is an equal area projection, and this allows users of this service to accurately calculate acreage without additional data preparation steps. This also means it takes a tiny bit longer to project on the fly into Web Mercator projection, if that is the destination projection of the service.Processing TemplatesCartographic Renderer - The default. Land cover drawn with Esri symbols. Each year's land cover data is displayed in the time series until there is a newer year of data available.Cartographic Renderer (saturated) - This renderer has the same symbols as the cartographic renderer, but the colors are extra saturated so a transparency may be applied to the layer. This renderer is useful for land cover over a basemap or relief. MRLC Cartographic Renderer - Cartographic renderer using the land cover symbols as issued by NLCD (the same symbols as is on the dataset when you download them from MRLC).Analytic Renderer - Use this in analysis. The time series is restricted by the analytic template to display a raster in only the year the land cover raster is valid. In a cartographic renderer, land cover data is displayed until a new year of data is available so that it plays well in a time series. In the analytic renderer, data is displayed for only the year it is valid. The analytic renderer won't look good in a time series animation, but in analysis this renderer will make sure you only use data for its appropriate year.Simplified Renderer - NLCD reclassified into 10 broad classes. These broad classes may be easier to use in some applications or maps.Forest Renderer - Cartographic renderer which only displays the three forest classes, deciduous, coniferous, and mixed forest.Developed Renderer - Cartographic renderer which only displays the four developed classes, developed open space plus low, medium, and high intensity development classes.Hawaii data has a different sourceMRLC redirects users interested in land cover data for Hawaii to a NOAA product called C-CAP or Coastal Change Analysis Program Regional Land Cover. This C-CAP land cover data was available for Hawaii for the years 2001, 2005, and 2011 at the time of the latest update of this layer. The USA NLCD Land Cover layer reclasses C-CAP land cover codes into NLCD land cover codes for display and analysis, although it may be beneficial for analytical purposes to use the original C-CAP data, which has finer resolution and untranslated land cover codes. The C-CAP land cover data for Hawaii is served as its own 2.4m resolution land cover layer in the Living Atlas.Because it's a different original data source than the rest of NLCD, different years for Hawaii may not be able to be compared in the same way different years for the other states can. But the same method was used to produce each year of this C-CAP derived land cover to make this layer. Note: Because there was no C-CAP data for Kaho'olawe Island in 2011, 2005 data were used for that island.The land cover is projected into the same projection and cellsize as the rest of the layer, using nearest neighbor method, then it is reclassed to approximate the NLCD codes. The following is the reclass table used to make Hawaii C-CAP data closely match the NLCD classification scheme:C-CAP code,NLCD code0,01,02,243,234,225,216,827,818,719,4110,4211,4312,5213,9014,9015,9516,9017,9018,9519,3120,3121,1122,1123,1124,025,12USA NLCD Land Cover service classes with corresponding index number (raster value):11. Open Water - areas of open water, generally with less than 25% cover of vegetation or soil.12. Perennial Ice/Snow - areas characterized by a perennial cover of ice and/or snow, generally greater than 25% of total cover.21. Developed, Open Space - areas with a mixture of some constructed materials, but mostly vegetation in the form of lawn grasses. Impervious surfaces account for less than 20% of total cover. These areas most commonly include large-lot single-family housing units, parks, golf courses, and vegetation planted in developed settings for recreation, erosion control, or aesthetic purposes.22. Developed, Low Intensity - areas with a mixture of constructed materials and vegetation. Impervious surfaces account for 20% to 49% percent of total cover. These areas most commonly include single-family housing units.23. Developed, Medium Intensity - areas with a mixture of constructed materials and vegetation. Impervious surfaces account for 50% to 79% of the total cover. These areas most commonly include single-family housing units.24. Developed High Intensity - highly developed areas where people reside or work in high numbers. Examples include apartment complexes, row houses and commercial/industrial. Impervious surfaces account for 80% to 100% of the total cover.31. Barren Land (Rock/Sand/Clay) - areas of bedrock, desert pavement, scarps, talus, slides, volcanic material, glacial debris, sand dunes, strip mines, gravel pits and other accumulations of earthen material. Generally, vegetation accounts for less than 15% of total cover.41. Deciduous Forest - areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. More than 75% of the tree species shed foliage simultaneously in response to seasonal change.42. Evergreen Forest - areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. More than 75% of the tree species maintain their leaves all year. Canopy is never without green foliage.43. Mixed Forest - areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. Neither deciduous nor evergreen species are greater than 75% of total tree cover. 51. Dwarf Scrub - Alaska only areas dominated by shrubs less than 20 centimeters tall with shrub canopy typically greater than 20% of total vegetation. This type is often co-associated with grasses, sedges, herbs, and non-vascular vegetation.52. Shrub/Scrub - areas dominated by shrubs; less than 5 meters tall with shrub canopy typically greater than 20% of total vegetation. This class includes true shrubs, young trees in an early successional stage or trees stunted from environmental conditions.71. Grassland/Herbaceous - areas dominated by gramanoid or herbaceous vegetation, generally greater than 80% of total vegetation. These areas are not subject to intensive management such as tilling, but can be utilized for grazing.72. Sedge/Herbaceous - Alaska only areas dominated by sedges and forbs, generally greater than 80% of total vegetation. This type can occur with significant other grasses or other grass like plants, and includes sedge tundra, and sedge tussock tundra.73. Lichens - Alaska only areas dominated by fruticose or foliose lichens generally greater than 80% of total vegetation.74. Moss - Alaska only areas dominated by mosses, generally greater than 80% of total vegetation.Planted/Cultivated 81. Pasture/Hay - areas of grasses, legumes, or grass-legume mixtures planted for livestock grazing or the production of seed or hay crops, typically on a perennial cycle. Pasture/hay vegetation accounts for greater than 20% of total vegetation.82. Cultivated Crops - areas used for the production of annual crops, such as corn, soybeans, vegetables, tobacco, and cotton, and also perennial woody crops such as orchards and vineyards. Crop vegetation accounts for greater than 20% of total vegetation. This class also includes all land being actively tilled.90. Woody Wetlands - areas where forest or shrubland vegetation accounts for greater than 20% of vegetative cover and the soil or

The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.

The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.

The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released five National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, 2011, and 2016. The 2016 release saw landcover created for additional years of 2003, 2008, and 2013. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2019. The NLCD 2019 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2019 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2019: continued integration between impervious surface and all landcover products with impervious surface being directly mapped as developed classes in the landcover, a streamlined compositing process for assembling and preprocessing based on Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2019 production. The performance of the developed strategies and methods were tested in twenty composite referenced areas throughout the conterminous U.S. An overall accuracy assessment from the 2016 publication give a 91% overall landcover accuracy, with the developed classes also showing a 91% accuracy in overall developed. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2019 operational mapping. Questions about the NLCD 2019 land cover product can be directed to the NLCD 2019 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.

The National Land Cover Database (NLCD) provides nationwide data on land cover and land cover change at a 30m resolution with a 16-class legend based on a modified Anderson Level II classification system. The database is designed to provide cyclical updates of United States land cover and associated changes. Systematically aligned over time, the database provides the ability to understand both current and historical land cover and land cover change, and enables monitoring and trend assessments. The latest evolution of NLCD products are designed for widespread application in biology, climate, education, land management, hydrology, environmental planning, risk and disease analysis, telecommunications and visualization. Data customized (clipped) for the Orange County boundary extent.For additional information regarding creation of NLCD Land Cover products:Dewitz, J., and U.S. Geological Survey, 2021, National Land Cover Database (NLCD) 2019 Products (ver. 2.0, June 2021): U.S. Geological Survey data release, https://doi.org/10.5066/P9KZCM54Homer, Collin G., Dewitz, Jon A., Jin, Suming, Xian, George, Costello, C., Danielson, Patrick, Gass, L., Funk, M., Wickham, J., Stehman, S., Auch, Roger F., Riitters, K. H., Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database: ISPRS Journal of Photogrammetry and Remote Sensing, v. 162, p. 184–199, at https://doi.org/10.1016/j.isprsjprs.2020.02.019Jin, Suming, Homer, Collin, Yang, Limin, Danielson, Patrick, Dewitz, Jon, Li, Congcong, Zhu, Z., Xian, George, Howard, Danny, Overall methodology design for the United States National Land Cover Database 2016 products: Remote Sensing, v. 11, no. 24, at https://doi.org/10.3390/rs11242971Yang, L., Jin, S., Danielson, P., Homer, C., Gass, L., Case, A., Costello, C., Dewitz, J., Fry, J., Funk, M., Grannemann, B., Rigge, M. and G. Xian. 2018. A New Generation of the United States National Land Cover Database: Requirements, Research Priorities, Design, and Implementation Strategies, ISPRS Journal of Photogrammetry and Remote Sensing, 146, pp.108-123.

This is a dataset download, not a document. The Open button will start the download.This data layer is an element of the Oregon GIS Framework and has been clipped to the Oregon boundary and reprojected to Oregon Lambert (2992). The U.S. Geological Survey (USGS), in partnership with several federal agencies, has developed and released four National Land Cover Database (NLCD) products over the past two decades: NLCD 1992, 2001, 2006, and 2011. These products provide spatially explicit and reliable information on the Nation’s land cover and land cover change. To continue the legacy of NLCD and further establish a long-term monitoring capability for the Nation’s land resources, the USGS has designed a new generation of NLCD products named NLCD 2016. The NLCD 2016 design aims to provide innovative, consistent, and robust methodologies for production of a multi-temporal land cover and land cover change database from 2001 to 2016 at 2–3-year intervals. Comprehensive research was conducted and resulted in developed strategies for NLCD 2016: a streamlined process for assembling and preprocessing Landsat imagery and geospatial ancillary datasets; a multi-source integrated training data development and decision-tree based land cover classifications; a temporally, spectrally, and spatially integrated land cover change analysis strategy; a hierarchical theme-based post-classification and integration protocol for generating land cover and change products; a continuous fields biophysical parameters modeling method; and an automated scripted operational system for the NLCD 2016 production. The performance of the developed strategies and methods were tested in twenty World Reference System-2 path/row throughout the conterminous U.S. An overall agreement ranging from 71% to 97% between land cover classification and reference data was achieved for all tested area and all years. Results from this study confirm the robustness of this comprehensive and highly automated procedure for NLCD 2016 operational mapping. Questions about the NLCD 2016 land cover product can be directed to the NLCD 2016 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. See included spatial metadata for more details.

The Multi-Resolution Land Characteristics (MRLC) consortium is a group of federal agencies who coordinate and generate consistent and relevant land cover information at the national scale for a wide variety of environmental, land management, and modeling applications. The creation of this consortium has resulted in the mapping of the lower 48 United States, Hawaii, Alaska and Puerto Rico into a comprehensive land cover product termed, the National Land Cover Database (NLCD), from decadal Landsat satellite imagery and other supplementary datasets.The primary objective of the MRLC NLCD is to provide the Nation with nationally complete, current, consistent, and public domain information on the Nation's land cover. Land cover information is critical for local, state, and federal managers and officials to assist them with issues such as assessing ecosystem status and health, modeling nutrient and pesticide runoff, understanding spatial patterns of biodiversity, land use planning, deriving landscape pattern metrics, and developing land management policies.All MRLC NLCD data products are available at no charge to the user.This version of the 2021 NLCD CONUS gives improved functionality within ArcGIS online to allow for 1) raster inquire results, and 2) the ability to display only selected thematic classifications

The tree canopy layer displays the proportion of the land surface covered by trees for the years 2011 to 2021 from the National Land Cover Database. Source: https://www.mrlc.govPhenomenon Mapped: Proportion of the landscape covered by trees.Time Extent: 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021Units: Percent (of each pixel that is covered by tree canopy)Cell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerData Coordinate Systems: North America Albers Equal Area ConicMosaic Projection: WGS 1984 Web Mercator Auxiliary SphereExtent: CONUS, Southeastern Alaska, Hawaii, Puerto Rico and the US Virgin IslandsSource: Multi-Resolution Land Characteristics ConsortiumPublication Date: April 1, 2023ArcGIS Server URL: https://landscape10.arcgis.com/arcgis/Time SeriesBy default, this layer will appear in your client with a time slider which allows you to play the series as an animation. The animation will advance year by year changing appearance every year in the lower 48 states from 2011 to 2021. (In Alaska, Hawaii, Puerto Rico and the US Virgin Islands, the animation will only show a change between 2011 and 2016.) To select just one year in the series, first turn the time series off on the time slider, then create a definition query on the layer which selects only the desired year.Alaska, Hawaii, Puerto Rico, and the US Virgin IslandsAt this time Alaska, Hawaii, Puerto Rico, and the US Virgin Islands do not have tree canopy cover for every year in the series like MRLC produced for the Lower 48 states. Furthermore, only a portion of coastal Southeastern Alaska from Kodiak to the Panhandle is available, but not the entire state. Alaska, Hawaii, Puerto Rico, and the US Virgin Islands have data in the series only from 2011 and 2016. Dataset SummaryThe National Land Cover Database products are created through a cooperative project conducted by the Multi-Resolution Land Characteristics Consortium (MRLC). The MRLC Consortium is a partnership of federal agencies, consisting of the U.S. Geological Survey, the National Oceanic and Atmospheric Administration, the U.S. Environmental Protection Agency, the U.S. Department of Agriculture, the U.S. Forest Service, the National Park Service, the U.S. Fish and Wildlife Service, the Bureau of Land Management and the USDA Natural Resources Conservation Service.What can you do with this layer?This layer can be used to create maps and to visualize the underlying data. This layer can be used as an analytic input in ArcGIS Desktop.This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.

The MRLC, a consortium of federal agencies who coordinate and generate consistent and relevant land cover information at the national scale for a wide variety of environmental, land management, and modeling applications, have been providing the scientific community with detailed land cover products for more than 30 years. Over that time, NLCD has been one of the most widely used geospatial datasets in the U.S., serving as a basis for understanding the Nation’s landscapes in thousands of studies and applications, trusted by scientists, land managers, students, city planners, and many more as a definitive source of U.S. land cover. With the latest release, NLCD now includes map products characterizing land cover and land cover change across nine epochs from 2001 to 2021 (2001, 2004, 2006, 2008, 2011, 2013, 2016, 2019, and 2021). The 2021 suite of NLCD products follow the same protocols and procedures of the previously released NLCD epochs (2001-2019), are directly comparable to the 2019 release across the full time series, and are suitable for multi-temporal analysis. Science products and the change index, however, will need to be reacquired for the additional 2021 change information.

The base Land Cover data layer for the Commonwealth of Puerto Rico was produced by the International Institute of Tropical Forestry(IITF) and crosswalked to NLCD classes, with additional crop type modeling conducted by the National Oceanic and Atmospheric Administration (NOAA). This original base data layer is available at http://fsgeodata.fs.fed.us/rastergateway/ An additional link for the publication associated with this work is http://tropicalforestry.net/Members/ehelmer/caribbean-vegetation-and-land-cover The full reference for this work is Kennaway, T., and E. H. Helmer. 2007. The forest types and ages cleared for land development in Puerto Rico. GIScience and Remote Sensing 44:356-382. NLCD data layers are made through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC Consortium is a partnership of federal agencies (www.mrlc.gov), consisting of the U.S. Geological Survey (USGS), the National Oceanic and Atmospheric Adm ...

This product is part of the Tree Canopy Cover (TCC) data suite. It includes modeled TCC, standard error (SE), and National Land Cover Database's (NLCD) TCC data for each year. TCC data produced by the the United States Department of Agriculture, Forest Service (USFS) are included in the Multi-Resolution Land …

Impervious surfaces are surfaces that do not allow water to pass through. Examples of these surfaces include highways, parking lots, rooftops, and airport runways. Instead of allowing rain to pass into the soil, impervious surfaces cause water to collect at the surface, then run off. An increase in impervious surface area causes an increase of water volume which needs to be managed by stormwater systems. With the flow come pollutants, which collect on impervious surfaces then discharge with the runoff into streams and the ocean. Runoff water does not enter the water table, and that can cause other management issues, such as interruptions in baseline stream flow.The NLCD imperviousness layer represents urban impervious surfaces as a percentage of developed surface over every 30-meter pixel in the United States. Phenomenon Mapped: The proportion of the landscape that is impervious to water.Time Extent: 2001, 2004, 2006, 2008, 2011, 2013, 2016, 2019, and 2021 for the lower 48 conterminous US states. A small portion of Alaska around Anchorage displays a time series of 2001, 2011, and 2016. Hawaii, Puerto Rico, and the US Virgin Islands unfortunately only have data for 2001 so there is only one image in the series. This information may be used in conjunction with the USA NLCD Land Cover layer.Units: PercentCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerData Coordinate System: North America Albers Equal Area Conic (102008)Mosaic Projection: North America Albers Equal Area Conic (102008)Extent: CONUS, Hawaii, A portion of Alaska around Anchorage, District of Columbia, Puerto RicoNoData Value: 127Source: Multi-Resolution Land Characteristics ConsortiumPublication Date: June 30, 2023ArcGIS Server URL: https://landscape10.arcgis.com/arcgis/Time SeriesBy default, this layer will appear in your client with a time slider which allows you to play the series as an animation. The animation will advance year by year, but the layer only changes appearance every few years in the lower 48 states, in 2001, 2004, 2006, 2008, 2011, 2013, 2016, 2019, and 2021. To select just one year in the series, first turn the time series off on the time slider, then create a definition query on the layer which selects only the desired year.Time Series DescriptorMRLC issued a set of companion rasters with this impervious surface layer showing the reason why each pixel is impervious. This companion layer, called the Developed Imperviousness Descriptor, is not currently available in this map service. The descriptor layer identifies types of roads, core urban areas, and energy production sites for each impervious pixel to allow deeper analysis of developed features. The descriptor layer may be downloaded directly from MRLC and added to ArcGIS Pro.Alaska, Hawaii, and Puerto RicoAt this time Alaska, Hawaii, and Puerto Rico are produced with a different methodology, and are not set up to be directly compared the way the CONUS time series is. To analyze change between the latest two data years for this portion of Alaska, be sure to use the NLCD 2011 to 2016 Developed Impervious Change raster. For Hawaii and Puerto Rico, only the year 2001 is available for download at the MRLC.North America Albers ProjectionAll NLCD layers in the Living Atlas are projected into the North America Albers Projection before serving in the Living Atlas. This allows the coterminous USA, Puerto Rico, Hawaii, and Alaska to be served from a common projection and analyzed together. In tests performed by esri, the NLCD land cover classes after projection to North America Albers had the exact same number of pixels in input as output, but pixels had been slightly rearranged after projection. Processing TemplatesThis layer comes with two color schemes, cool and warm. The default is a cool gray color scheme, designed to look good on light and dark gray web maps. To choose a warm color scheme which was the default until 2021, change the processing template to the Impervious Surface Warm Renderer in your map client.Dataset SummaryThe National Land Cover Database products are created through a cooperative project conducted by the Multi-Resolution Land Characteristics Consortium (MRLC). The MRLC Consortium is a partnership of federal agencies, consisting of the U.S. Geological Survey, the National Oceanic and Atmospheric Administration, the U.S. Environmental Protection Agency, the U.S. Department of Agriculture, the U.S. Forest Service, the National Park Service, the U.S. Fish and Wildlife Service, the Bureau of Land Management and the USDA Natural Resources Conservation Service.What can you do with this layer?This layer can be used to create maps and to visualize the underlying data. This layer can be used as an analytic input in ArcGIS Desktop.This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.

This tabular data set represents the percent of land cover classes from the new generation of the 2016 National Land Cover Datasets for the years 2001, 2004, 2006, 2008, 2011, 2013, and 2016 compiled for two spatial components of the NHDPlus version 2.1 data suite (NHDPlusv2) for the conterminous United States; 1) individual reach catchments and 2) reach catchments accumulated upstream through the river network. This dataset can be linked to the NHDPlus version 2 data suite by the unique identifier COMID. The source data is the "NLCD 2016 Land Cover Conterminous United States" datasets for the years 2001, 2004, 2006, 2008, 2011, 2013, and 2016 produced by the United States Geological Survey (Yang and others, 2018). Units are percent. The "NLCD 2016 Land Cover Conterminous United States" datasets (NLCD 2016) are a 16-class (additional four classes in Alaska only) land cover classification scheme that has been applied consistently across all 50 United States and Puerto Rico at a spatial resolution of 30 meters. Reach catchment information characterizes data at the local scale. Reach catchments accumulated upstream through the river network characterizes cumulative upstream conditions. Network-accumulated values are computed using two methods, 1) divergence-routed and 2) total cumulative drainage area. Both approaches use a modified routing database to navigate the NHDPlus reach network to aggregate (accumulate) the metrics derived from the reach catchment scale. (Schwarz and Wieczorek, 2018). First posted: January 20, 2020. Revised: January 25, 2021.

Coastwide vegetation surveys have been conducted multiple times over the past 50 years (e.g., Chabreck and Linscombe 1968, 1978, 1988, 1997, 2001, and 2013) by the Louisiana Department of Wildlife and Fisheries (LDWF) in support of coastal management activities. The last survey was conducted in 2013 and was funded by the Louisiana Coastal Protection and Restoration Authority (CPRA) and the U.S. Geological Survey (USGS) as a part of the Coastal Wetlands Planning, Protection, and Restoration Act (CWPPRA) monitoring program. These surveys provide important data that have been utilized by federal, state, and local resource managers. The surveys provide information on the condition of Louisiana’s coastal marshes by mapping plant species composition and vegetation change through time. During the summer of 2021, the U.S. Geological Survey, Louisiana State University, and the Louisiana Department of Wildlife and Fisheries jointly completed a helicopter survey to collect data on 2021 vegetation types using the same field methodology at previously sampled data points. Plant species were identified and their abundance classified at each point. Based on species composition and abundance, each marsh sampling station was assigned a marsh type: fresh, intermediate, brackish, or saline marsh. The field point data were interpolated to classify marsh vegetation into polygons and map the distribution of vegetation types.
We then used the 2021 polygons with additional remote sensing data to create the final raster dataset. We used the polygon marsh type zones (available in this data release), as well as National Land Cover Database (NLCD; https://www.usgs.gov/centers/eros/science/national-land-cover-database) and NOAA Coastal Change Analysis Program (CCAP; https://coast.noaa.gov/digitalcoast/data/ccapregional.html) datasets to create a composite raster dataset. The composite raster was created to provide more detail, particularly with regard to “Other”, “Swamp”, and “Water” categories, than is available in the polygon dataset. The overall boundary of the raster product was extended beyond past surveys to better inform swamp, water, and other boundaries across the coast. A majority of NLCD and CCAP classification during a 2010-2019 period was used, rather than creating a raster classification specific to 2021, as there was a desire to use published datasets. Users are cautioned that the raster dataset is generalized but more specific than the polygon dataset.
This data release includes 3 datasets: the point field data collected by the helicopter survey team, the polygon data developed from the point data, and the raster data developed from the polygon data plus additional remote sensing data as described above.

The USDA Forest Service (USFS) builds two versions of percent tree canopy cover (TCC) data to serve needs of multiple user communities. These datasets encompass the conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2021-4 TCC product suite include:- The raw model outputs referred to as the annual Science data; and- A modified version built for the National Land Cover Database referred to as NLCD data. They are available at the following locations:Science:https://data.fs.usda.gov/geodata/rastergateway/treecanopycover, https://apps.fs.usda.gov/fsgisx01/rest/services/RDW_LandscapeAndWildlife NLCD:https://www.mrlc.gov/datahttps://apps.fs.usda.gov/fsgisx01/rest/services/RDW_LandscapeAndWildlife, The Science data - the focus of this metadata - are the initial annual model outputs that consist of two images: percent tree canopy cover (TCC) and standard error. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset, and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the nominal years of 2008 through 2021 are available. The Science data were produced using a random forests regression algorithm. TCC pixel values range from 0 to 100 percent. The value 254 represents the non-processing area mask where no cloud or cloud shadow-free data are available to produce an output, and 255 represents the background value. The Science data are accessible for multiple user communities, through multiple channels and platforms. For information on the NLCD TCC data and processing steps see the NLCD metadata. Information on the Science data and processing steps are included here.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.