Land cover describes the surface of the earth. Land cover maps are useful in urban planning, resource management, change detection, agriculture, and a variety of other applications in which information related to earth surface is required. Land cover classification is a complex exercise and is hard to capture using traditional means. Deep learning models are highly capable of learning these complex semantics and can produce superior results.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.Input8-bit, 3-band high-resolution (80 - 100 cm) imagery.OutputClassified raster with the same classes as in the Chesapeake Bay Landcover dataset (2013/2014). By default, the output raster contains 9 classes. A simpler classification with 6 classes can be performed by setting the the 'detailed_classes' model argument to false.Note: The output classified raster will not contain 'Aberdeen Proving Ground' class. Find class descriptions here.Applicable geographiesThis model is applicable in the United States and is expected to produce best results in the Chesapeake Bay Region.Model architectureThis model uses the UNet model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an overall accuracy of 86.5% for classification into 9 land cover classes and 87.86% for 6 classes. The table below summarizes the precision, recall and F1-score of the model on the validation dataset, for classification into 9 land cover classes:ClassPrecisionRecallF1 ScoreWater0.936140.930460.93329Wetlands0.816590.759050.78677Tree Canopy0.904770.931430.91791Shrubland0.516250.186430.27394Low Vegetation0.859770.866760.86325Barren0.671650.509220.57927Structures0.80510.848870.82641Impervious Surfaces0.735320.685560.70957Impervious Roads0.762810.812380.78682The table below summarizes the precision, recall and F1-score of the model on the validation dataset, for classification into 6 land cover classes: ClassPrecisionRecallF1 ScoreWater0.950.940.95Tree Canopy and Shrubs0.910.920.92Low Vegetation0.850.850.85Barren0.790.690.74Impervious Surfaces0.840.840.84Impervious Roads0.820.830.82Training dataThis model has been trained on the Chesapeake Bay high-resolution 2013/2014 NAIP Landcover dataset (produced by Chesapeake Conservancy with their partners University of Vermont Spatial Analysis Lab (UVM SAL), and Worldview Solutions, Inc. (WSI)) and other high resolution imagery. Find more information about the dataset here.Sample resultsHere are a few results from the model.

The USGS’s FORE-SCE model was used to produce unprecedented landscape projections for the Upper Missouri River Basin of the northern Great Plains of the United States. The projections are characterized by 1) high spatial resolution (30-meter cells), 2) high thematic resolution (29 land use and land cover classes), 3) broad spatial extent (covering much of the Great Plains), 4) use of real land ownership boundaries to ensure realistic representation of landscape patterns, and 5) representation of both anthropogenic land use and natural vegetation change. A variety of scenarios were modeled from 2014 to 2100, with decadal timesteps (i.e., 2014, 2020, 2030, etc.). Modeled land use and natural vegetation classes were responsive to projected future changes in environmental conditions, including changes in groundwater and water access. Eleven primary land-use scenarios were modeled, from four different scenario families. The land-use scenarios focused on socioeconomic impacts on anthropogenic land use (demographics, energy use, agricultural economics, and other socioeconomic considerations). The following provides a brief summary of the 11 major land-use scenarios. 1) Business-as-usual - Based on an extrapolation of recent land-cover trends as derived from remote-sensing data. Overall trends were provided by 2001 to 2011 change in the National Land Cover Database, while change in crop types were extrapolated from 2008 to 2014 change in the Cropland Data Layer. Overall the scenario is marked by expansion of high-value traditional crops (corn, soybeans) and higher growth in urban development than other scenarios. 2) Billion Ton Update scenario ($40 farmgate price) - This scenario is based on US Department of Energy biofuel scenarios from the Billion Ton Update (BTU). The $40 scenario represents likely agricultural conditions under an assumed farmgate price of $40 per dry ton of biomass (for the production of biofuel). This is the least aggressive BTU scenario for placing "perennial grass" (for biofuel feedstock) on the landscape. 3) Billion Ton Update scenario ($60 farmgate price) - This scenario is based on US Department of Energy biofuel scenarios from the Billion Ton Update. The $60 scenario represents likely agricultural conditions under an assumed farmgate price of $60 per dry ton of biomass (for the production of biofuel). At the higher farmgate price, the perennial grass class expands substantially compared to the $40 scenario. 4) Billion Ton Update scenario ($80 farmgate price) - This scenario is based on US Department of Energy biofuel scenarios from the Billion Ton Update. The $80 scenario represents likely agricultural conditions under an assumed farmgate price of $80 per dry ton of biomass (for the production of biofuel). With the high farmgate price, this scenario shows the highest expansion of perennial grass among the 11 modeled scenarios. 5) GCAM Reference scenario - Based on global-scale scenarios from the GCAM model, the "reference" scenario provides a likely landscape under a world without specific carbon or climate mitigation efforts. As such, it's another form of a "business-as-usual" scenario. 6) GCAM 4.5 scenario - Based on global-scale scenarios from the GCAM model, the GCAM 4.5 model represents a mid-level mitigation scenario, where carbon payments and other mitigation efforts result in a net radiative forcing of ~4.5 W/m2 by 2100. Agriculture becomes even more concentrated in the Great Plains and Midwestern US, resulting in substantial increases in cropland (including perennial grass used as feedstock for cellulosic biofuel production). Forested lands expand with carbon payments encouraging afforestation efforts. 7) GCAM 2.6 scenario - Based on global-scale scenarios from the GCAM model, the GCAM 2.6 model represents a very aggressive mitigation scenario, where carbon payments and other mitigation efforts result in a net radiative forcing of only ~2.6 W/m2 by 2100. Agriculture becomes even more concentrated in the Great Plains and Midwestern US, resulting in substantial increases in cropland (including perennial grass used as feedstock for cellulosic biofuel production). Forested lands expand with carbon payments encouraging afforestation efforts. 8) SRES A1B scenario - A scenario consistent with the Intergovernmental Panel on Climate Change (IPCC's) Special Report on Emissions Scenarios (SRES) A1B storyline. In the A1B scenario, economic activity is prioritized over environmental conservation. Agriculture expands substantially, including use of perennial grasses for biofuel production. 9) SRES A2 scenario - A scenario consistent with the IPCC's SRES A2 storyline. In the A2 scenario, global population levels reach 15 billion by 2100. Economic activity is prioritized over environmental conservation. This scenario has very high expansion of traditional cropland, given the very high demand for foodstuffs and other agricultural commodities. 10) SRES B1 scenario - A scenario consistent with the IPCC's SRES B1 storyline. In the B1 scenario, environmental conservation is valued, as is regional cooperation. Much less agricultural expansion occurs as compared to the A1B or A2 scenarios. 11) SRES B2 scenario - A scenario consistent with the IPCC's SRES B2 storyline. In the B2 scenario, environmental conservation is highly valued. Of the eleven modeled scenarios, the B2 scenarios has the smallest overall agricultural footprint (traditional cropland, hay/pasture, perennial grasses). For each of the eleven land-use scenarios, three alternative climate / vegetation scenarios were modeled, resulting in 33 unique scenario combinations. The alternative vegetation scenarios represent the potential changes in quantity and distribution of the major vegetation classes that were modeled (grassland, shrubland, deciduous forest, mixed forest, and evergreen forest), as a response to potential future climate conditions. The three alternative vegetation scenarios correspond to climate conditions consistent with 1) The Intergovernmental Panel on Climate Change (IPCC's) Representative Concentration Pathway (RCP) 8.5 scenario (a scenario of high climate change), 2) the RCP 4.5 scenario (a mid-level climate change scenario), and 3) a mid-point climate that averages RCP4.5 and RCP8.5 conditions Data are provided here for each of the 33 possible scenario combinations. Each scenario file is provided as a zip file containing 1) starting 2014 land cover for the region, and 2) decadal timesteps of modeled land-cover from 2020 through 2100. The "attributes" section of the metadata provides a key for identifying file names associated with each of the 33 scenario combinations.

U.S. Geological Survey scientists, funded by the Climate and Land Use Change Research and Development Program, developed a dataset of 2006 and 2011 land use and land cover (LULC) information for selected 100-km2 sample blocks within 29 EPA Level 3 ecoregions across the conterminous United States. The data was collected for validation of new and existing national scale LULC datasets developed from remotely sensed data sources. The data can also be used with the previously published Land Cover Trends Dataset: 1973-2000 (http:// http://pubs.usgs.gov/ds/844/), to assess land-use/land-cover change in selected ecoregions over a 37-year study period. LULC data for 2006 and 2011 was manually delineated using the same sample block classification procedures as the previous Land Cover Trends project. The methodology is based on a statistical sampling approach, manual classification of land use and land cover, and post-classification comparisons of land cover across different dates. Landsat Thematic Mapper, and Enhanced Thematic Mapper Plus imagery was interpreted using a modified Anderson Level I classification scheme. Landsat data was acquired from the National Land Cover Database (NLCD) collection of images. For the 2006 and 2011 update, ecoregion specific alterations in the sampling density were made to expedite the completion of manual block interpretations. The data collection process started with the 2000 date from the previous assessment and any needed corrections were made before interpreting the next two dates of 2006 and 2011 imagery. The 2000 land cover was copied and any changes seen in the 2006 Landsat images were digitized into a new 2006 land cover image. Similarly, the 2011 land cover image was created after completing the 2006 delineation. Results from analysis of these data include ecoregion based statistical estimates of the amount of LULC change per time period, ranking of the most common types of conversions, rates of change, and percent composition. Overall estimated amount of change per ecoregion from 2001 to 2011 ranged from a low of 370 km2 in the Northern Basin and Range Ecoregion to a high of 78,782 km2 in the Southeastern Plains Ecoregion. The Southeastern Plains Ecoregion continues to encompass the most intense forest harvesting and regrowth in the country. Forest harvesting and regrowth rates in the southeastern U.S. and Pacific Northwest continued at late 20th century levels. The land use and land cover data collected by this study is ideally suited for training, validation, and regional assessments of land use and land cover change in the U.S. because it is collected using manual interpretation techniques of Landsat data aided by high resolution photography. The 2001-2011 Land Cover Trends Dataset is provided in an Albers Conical Equal Area projection using the NAD 1983 datum. The sample blocks have a 30-meter resolution and file names follow a specific naming convention that includes the number of the ecoregion containing the block, the block number, and the Landsat image date. The data files are organized by ecoregion, and are available in the ERDAS Imagine (.img) format. U.S. Geological Survey scientists, funded by the Climate and Land Use Change Research and Development Program, developed a dataset of 2006 and 2011 land use and land cover (LULC) information for selected 100-km2 sample blocks within 29 EPA Level 3 ecoregions across the conterminous United States. The data was collected for validation of new and existing national scale LULC datasets developed from remotely sensed data sources. The data can also be used with the previously published Land Cover Trends Dataset: 1973-2000 (http:// http://pubs.usgs.gov/ds/844/), to assess land-use/land-cover change in selected ecoregions over a 37-year study period. LULC data for 2006 and 2011 was manually delineated using the same sample block classification procedures as the previous Land Cover Trends project. The methodology is based on a statistical sampling approach, manual classification of land use and land cover, and post-classification comparisons of land cover across different dates. Landsat Thematic Mapper, and Enhanced Thematic Mapper Plus imagery was interpreted using a modified Anderson Level I classification scheme. Landsat data was acquired from the National Land Cover Database (NLCD) collection of images. For the 2006 and 2011 update, ecoregion specific alterations in the sampling density were made to expedite the completion of manual block interpretations. The data collection process started with the 2000 date from the previous assessment and any needed corrections were made before interpreting the next two dates of 2006 and 2011 imagery. The 2000 land cover was copied and any changes seen in the 2006 Landsat images were digitized into a new 2006 land cover image. Similarly, the 2011 land cover image was created after completing the 2006 delineation. Results from analysis of these data include ecoregion based statistical estimates of the amount of LULC change per time period, ranking of the most common types of conversions, rates of change, and percent composition. Overall estimated amount of change per ecoregion from 2001 to 2011 ranged from a low of 370 square km in the Northern Basin and Range Ecoregion to a high of 78,782 square km in the Southeastern Plains Ecoregion. The Southeastern Plains Ecoregion continues to encompass the most intense forest harvesting and regrowth in the country. Forest harvesting and regrowth rates in the southeastern U.S. and Pacific Northwest continued at late 20th century levels. The land use and land cover data collected by this study is ideally suited for training, validation, and regional assessments of land use and land cover change in the U.S. because it’s collected using manual interpretation techniques of Landsat data aided by high resolution photography. The 2001-2011 Land Cover Trends Dataset is provided in an Albers Conical Equal Area projection using the NAD 1983 datum. The sample blocks have a 30-meter resolution and file names follow a specific naming convention that includes the number of the ecoregion containing the block, the block number, and the Landsat image date. The data files are organized by ecoregion, and are available in the ERDAS Imagine (.img) format.

6 inch resolution raster image of New York City, classified by landcover type. High resolution land cover data set for New York City. This is the 6 inch version of the high-resolution land cover dataset for New York City. Seven land cover classes were mapped: (1) tree canopy, (2) grass/shrub, (3) bare earth, (4) water, (5) buildings, (6) roads, and (7) other paved surfaces. The minimum mapping unit for the delineation of features was set at 3 square feet. The primary sources used to derive this land cover layer were the 2010 LiDAR and the 2008 4-band orthoimagery. Ancillary data sources included GIS data (city boundary, building footprints, water, parking lots, roads, railroads, railroad structures, ballfields) provided by New York City (all ancillary datasets except railroads); UVM Spatial Analysis Laboratory manually created railroad polygons from manual interpretation of 2008 4-band orthoimagery. The tree canopy class was considered current as of 2010; the remaining land-cover classes were considered current as of 2008. Object-Based Image Analysis (OBIA) techniques were employed to extract land cover information using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. More than 35,000 corrections were made to the classification. Overall accuracy was 96%. This dataset was developed as part of the Urban Tree Canopy (UTC) Assessment for New York City. As such, it represents a 'top down' mapping perspective in which tree canopy over hanging other features is assigned to the tree canopy class. At the time of its creation this dataset represents the most detailed and accurate land cover dataset for the area. This project was funded by National Urban and Community Forestry Advisory Council (NUCFAC) and the National Science Fundation (NSF), although it is not specifically endorsed by either agency. The methods used were developed by the University of Vermont Spatial Analysis Laboratory, in collaboration with the New York City Urban Field Station, with funding from the USDA Forest Service.

A 6-in resolution 8-class land cover dataset derived from the 2017 Light Detection and Ranging (LiDAR) data capture. This dataset was developed as part of an updated urban tree canopy assessment and therefore represents a ''top-down" mapping perspective in which tree canopy overhanging features is assigned to the tree canopy class. The eight land cover classes mapped were: (1) Tree Canopy, (2) Grass\Shrubs, (3) Bare Soil, (4) Water, (5) Buildings, (6) Roads, (7) Other Impervious, and (8) Railroads. The primary sources used to derive this land cover layer were 2017 LiDAR (1-ft post spacing) and 2016 4-band orthoimagery (0.5-ft resolution). Object based image analysis was used to automate land-cover features using LiDAR point clouds and derivatives, orthoimagery, and vector GIS datasets -- City Boundary (2017, NYC DoITT) Buildings (2017, NYC DoITT) Hydrography (2014, NYC DoITT) LiDAR Hydro Breaklines (2017, NYC DoITT) Transportation Structures (2014, NYC DoITT) Roadbed (2014, NYC DoITT) Road Centerlines (2014, NYC DoITT) Railroads (2014, NYC DoITT) Green Roofs (date unknown, NYC Parks) Parking Lots (2014, NYC DoITT) Parks (2016, NYC Parks) Sidewalks (2014, NYC DoITT) Synthetic Turf (2018, NYC Parks) Wetlands (2014, NYC Parks) Shoreline (2014, NYC DoITT) Plazas (2014, NYC DoITT) Utility Poles (2014, ConEdison via NYCEM) Athletic Facilities (2017, NYC Parks) For the purposes of classification, only vegetation > 8 ft were classed as Tree Canopy. Vegetation below 8 ft was classed as Grass/Shrub. To learn more about this dataset, visit the interactive "Understanding the 2017 New York City LiDAR Capture" Story Map -- https://maps.nyc.gov/lidar/2017/ Please see the following link for additional documentation on this dataset -- https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_LandCover.md

High-resolution (one-meter) land use outside of incorporated areas in Kansas. The focus is on tree cover with an emphasis on windbreaks and riparian forests that do not meet the US Forest Service definition of forest. This research is in coordination with USFS - Northern Research Station Trees outside of Forests. It is replicated in Nebraska, South Dakota, North Dakota and parts of Texas. Kansas Forest Service website: www.kansasforests.org

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

Data are intended for use in rural areas and therefore do not include land cover in cities and towns. Land cover classes (tree cover, other land cover, or water) were mapped using an object-based image analysis approach and supervised classification. These data are designed for conducting geospatial analyses and for producing cartographic products. In particular, these data are intended to depict the location of tree cover in the county. The mapping procedures were developed specifically for agricultural landscapes that are dominated by annual crops, rangeland, and pasture and where tree cover is often found in narrow configurations, such as windbreaks and riparian corridors. Because much of the tree cover in agricultural areas of the United States occurs in windbreaks and narrow riparian corridors, many geospatial datasets derived from coarser-resolution satellite data (such as Landsat), do not capture these landscape features. This dataset is intended to address this particular data gap. These data can be downloaded by county at the Forest Service Research Data Archive. Nebraska: https://www.fs.usda.gov/rds/archive/catalog/RDS-2019-0038 South Dakota: https://www.fs.usda.gov/rds/archive/catalog/RDS-2022-0068 North Dakota: https://www.fs.usda.gov/rds/archive/catalog/RDS-2022-0067 A Kansas dataset was also developed using the same methods and is located at: Kansas data download: https://www.fs.usda.gov/rds/archive/catalog/RDS-2019-0052 Kansas map service: https://data-usfs.hub.arcgis.com/documents/high-resolution-tree-cover-of-kansas-2015-map-service/explore

This dataset contains high-resolution aerial imagery from the USDA NAIP program [1], high-resolution land cover labels from the Chesapeake Conservancy, low-resolution land cover labels from the USGS NLCD 2011 dataset, low-resolution multi-spectral imagery from Landsat 8, and high-resolution building footprint masks from Microsoft Bing, formatted to accelerate machine learning research into land cover mapping. The Chesapeake Conservancy spent over 10 months and $1.3 million creating a consistent six-class land cover dataset covering the Chesapeake Bay watershed. While the purpose of the mapping effort by the Chesapeake Conservancy was to create land cover data to be used in conservation efforts, the same data can be used to train machine learning models that can be applied over even wider areas. The organization of this dataset (detailed below) will allow users to easily test questions related to this problem of geographic generalization, i.e. how to train machine learning models that can be applied over even wider areas. For example, this dataset can be used to directly estimate how well a model trained on data from Maryland can generalize over the remainder of the Chesapeake Bay.

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.

The USGS’s FORE-SCE model was used to produce unprecedented landscape projections for the Prairie Potholes region of the northern Great Plains of the United States. The projections are characterized by 1) high spatial resolution (30-meter cells), 2) high thematic resolution (29 land use and land cover classes), 3) broad spatial extent (covering much of the Great Plains), 4) use of real land ownership boundaries to ensure realistic representation of landscape patterns, and 5) representation of both anthropogenic land use and natural vegetation change. A variety of scenarios were modeled from 2014 to 2100, with decadal timesteps (i.e., 2014, 2020, 2030, etc.). Modeled land use and natural vegetation classes were responsive to projected future changes in environmental conditions, including changes in groundwater and water access. Eleven primary land-use scenarios were modeled, from four different scenario families. The land-use scenarios focused on socioeconomic impacts on anthrop ...

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe's Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe's Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Raster data in both ArcGIS Grid and ERDAS Imagine format is available for download at http://gis1.usgs.gov/csas/gap/viewer/land_cover/Map.aspx Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS. In adition to the raster datasets the data is available in Web Mapping Services (WMS) format for each of the six NVC classification levels (Class, Subclass, Formation, Division, Macrogroup, Ecological System) at the following links. http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Class_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Subclass_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Formation_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Division_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Macrogroup_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_Ecological_Systems_Landuse/MapServer

This hosted feature layer has been published in RI State Plane Feet NAD 83.A statewide, seamless, vector-formatted geospatial dataset depicting 2020 land use and land cover ground conditions. The product was developed by comparing high resolution 2020 and 2011 leaf-off aerial orthoimagery and employing both automated and manual processes to detect, delineate and photointerpret changes since 2011. The project area encompasses the State of Rhode Island and also extends 1/2 mile into the neighboring states of Connecticut and Massachusetts, or to the limits of the source orthoimagery. The minimum mapping unit for this dataset is 0.5 acre.The classification scheme is based on the same RI-modified Anderson Level III scheme used in previous classifications (1988, 1995, 2003/2004, and 2011) with the addition of two new classes (148) Ground-mounted Solar Energy Systems and (149) Wind Energy Systems. If data are used for change detection using the 2003/2004 edition be aware that marinas were coded from other transportation and developed recreation to commercial in the 2020 data to more accurately fit the classification system. The RI classification is based upon Anderson Level III coding described in the United States Geological Survey Publication: "A Land Use And Land Cover Classification System for Use With Remote Sensor Data, Geological Survey Professional Paper 964" Available Online at: https://landcover.usgs.gov/pdf/anderson.pdfPlease consider the source, spatial accuracy, attribute accuracy, and scale of these data before incorporating them into your project. These data were derived from both automated and manual photointerpretation processes and should be used for planning purposes only. The wetland areas contained in this dataset do not include all wetlands previously identified in other RIGIS land use and land cover datasets or in other separate GIS wetland datasets and interpretation of wetland areas should lean toward the side of caution. Wetland areas previously classified as forested wetlands are shown as forested areas in this dataset. Statistical comparisons with RIGIS land use and land cover data prior to 2003 should be treated with caution since some differences in the methodologies used to delineate features were employed

This land cover map is a subset of the National Land Cover Dataset (NLCD) produced by the Multi-Resolution Land Characteristics (MRLC) Consortium (USGS, EPA, NOAA, and USFS) 1/6/1999. The NLCD was produced in order to provide a consistent, land cover data layer for the conterminous U.S. utilizing early 1990s Landsat Thematic Mapper data. The raster map depicts the land cover of the Oak Ridge Reservation at a 30m spatial resolution. Yang et al. (2001) found the thematic accuracy for the MRLC land cover map for the eastern U.S. to be 59.7% at Anderson Level II thematic detail and 80.5% at Anderson Level I.

The NLCD classification scheme (based on Anderson et al. 1976) is as follows -

Water - All areas of open water or permanent ice/snow cover. 11. Open Water - all areas of open water, generally with less than 25% cover of vegetation/land cover. 12. Perennial Ice/Snow - all areas characterized by year-long surface cover of ice and/or snow.

Developed Areas characterized by a high percentage (30 percent or greater) of constructed materials (e.g. asphalt, concrete, buildings, etc). 21. Low Intensity Residential - Includes areas with a mixture of constructed materials and vegetation. Constructed materials account for 30-80 percent of the cover. Vegetation may account for 20 to 70 percent of the cover. These areas most commonly include single-family housing units. Population densities will be lower than in high intensity residential areas. 22. High Intensity Residential - Includes highly developed areas where people reside in high numbers. Examples include apartment complexes and row houses. Vegetation accounts for less than 20 percent of the cover. Constructed materials account for 80 to100 percent of the cover. 23. Commercial/Industrial/Transportation - Includes infrastructure (e.g. roads, railroads, etc.) and all highly developed areas not classified as High Intensity Residential.

Barren - Areas characterized by bare rock, gravel, sand, silt, clay, or other earthen material, with little or no green vegetation present regardless of its inherent ability to support life. Vegetation, if present, is more widely spaced and scrubby than that in the green vegetated categories; lichen cover may be extensive. 31. Bare Rock/Sand/Clay - Perennially barren areas of bedrock, desert pavement, scarps, talus, slides, volcanic material, glacial debris, beaches, and other accumulations of earthen material. 32. Quarries/Strip Mines/Gravel Pits - Areas of extractive mining activities with significant surface expression. 33. Transitional - Areas of sparse vegetative cover (less than 25 percent of cover) that are dynamically changing from one land cover to another, often because of land use activities. Examples include forest clearcuts, a transition phase between forest and agricultural land, the temporary clearing of vegetation, and changes due to natural causes (e.g. fire, flood, etc.).

Forested Upland - Areas characterized by tree cover (natural or semi-natural woody vegetation, generally greater than 6 meters tall); tree canopy accounts for 25-100 percent of the cover. 41. Deciduous Forest - Areas dominated by trees where 75 percent or more of the tree species shed foliage simultaneously in response to seasonal change. 42. Evergreen Forest - Areas dominated by trees where 75 percent or more of the tree species maintain their leaves all year. Canopy is never without green foliage. 43. Mixed Forest - Areas dominated by trees where neither deciduous nor evergreen species represent more than 75 percent of the cover present.

Shrubland - Areas characterized by natural or semi-natural woody vegetation with aerial stems, generally less than 6 meters tall, with individuals or clumps not touching to interlocking. Both evergreen and deciduous species of true shrubs, young trees, and trees or shrubs that are small or stunted because of environmental conditions are included. 51. Shrubland - Areas dominated by shrubs; shrub canopy accounts for 25-100 percent of the cover. Shrub cover is generally greater than 25 percent when tree cover is less than 25 percent. Shrub cover may be less than 25 percent in cases when the cover of other life forms (e.g. herbaceous or tree) is less than 25 percent and shrubs cover exceeds the cover of the other life forms.

Non-Natural Woody - Areas dominated by non-natural woody vegetation; non-natural woody vegetative canopy accounts for 25-100 percent of the cover. The non-natural woody classification is subject to the availability of sufficient ancillary data to differentiate non-natural woody vegetation from natural woody vegetation. 61. Orchards/Vineyards/Other - Orchards, vineyards, and other areas planted or maintained for the production of fruits, nuts, berries, or ornamentals.

He... Visit https://dataone.org/datasets/Map_ORR_Land_Cover_LandsatTM_NLCD_30m_1992.xml for complete metadata about this dataset.

The Multi-Resolution Land Characteristics (MRLC) project was established to provide multi-resolution land cover data of the conterminous United States from local to regional scales. A major component of MRLC is an objective to develop a national 30-meter land cover characteristics data base using Landsat thematic mapper (TM) data. This is a cooperative effort among six programs within four U.S. Government agencies, including the U.S. Environmental Protection Agency's (EPA) Environmental Monitoring and Assessment Program; the U.S. Geological Survey's (USGS) National Water Quality Assessment Program; the National Biological Service's Gap Analysis Program; the USGS' Earth Resources Observation Systems (EROS) Center; the National Oceanic and Atmospheric Administration's Coastal Change Analysis Program; and the EPA's North American Landscape Characterization project. Multitemporal scenes were selected for the eastern deciduous forests, agricultural regions, and selected other regions. Multitemporal pairs were selected to be in consecutive seasons (in 1992 when possible). All scenes were previewed for image quality. The participating agencies organized the joint purchase of a single national set of Landsat TM scenes. In addition, the cooperators developed a common definition for preprocessing the satellite data. The shared, consistently processed TM data are the foundation for the development of the national 30-meter land cover data base. The jointly acquired data are archived and distributed by EROS. A variety of products are available to MRLC participants, to their affiliated users, and to the general public. Multi-Resolution Land Characterization 2001 (MRLC 2001) At-Sensor Reflectance Dataset is a second-generation federal consortium to create an updated pool of nation-wide Landsat imagery, and derive a second-generation National Land Cover Database (NLCD 2001). The MRLC 2001 data cover the United States, including Alaska and Hawaii. Multi-temporal scenes may also be available, depending on the location. Most of the images are of high quality, and cloud cover is generally less than ten percent. The data will also include a 30-meter Digital Elevation Model (DEM) for all scenes that do not include the Canadian or Mexican borders.

A new version of USGS’s FORE-SCE model was used to produce unprecedented landscape projections for four ecoregions in the Great Plains (corresponding to the area represented by the Great Plains Landscape Conservation Cooperative). The projections are characterized by 1) high spatial resolution (30-meter cells), 2) high thematic resolution (29 land use and land cover classes), 3) broad spatial extent (covering much of the Great Plains), 4) use of real land ownership boundaries to ensure realistic representation of landscape patterns, and 5) representation of both anthropogenic land use and natural vegetation change. A variety of scenarios were modeled from 2014 to 2100, with decadal timesteps (i.e., 2014, 2020, 2030, etc.). Modeled land use and natural vegetation classes were responsive to projected future changes in environmental conditions, including changes in groundwater and water access. Eleven primary land-use scenarios were modeled, from four different scenario families. ...

LAND_COVER_2006_USGS_IN is a grid (30-meter cell size) showing 2006 Land Cover data in Indiana. This grid is a subset of the National Land Cover Data (NLCD 2006) data set. There are 15 categories of land use shown in this data set when the associated layer file (LAND_COVER_2006_USGS_IN.LYR) is loaded. The following is excerpted from metadata provided by the USGS for the NLCD 2006: "The National Land Cover Database products are created 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 Administration (NOAA), the U.S. Environmental Protection Agency (EPA), the U.S. Department of Agriculture (USDA), the U.S. Forest Service (USFS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (FWS), the Bureau of Land Management (BLM) and the USDA Natural Resources Conservation Service (NRCS). Previously, NLCD consisted of three major data releases based on a 10-year cycle. These include a circa 1992 conterminous U.S. land cover dataset with one thematic layer (NLCD 1992), a circa 2001 50-state/Puerto Rico updated U.S. land cover database (NLCD 2001) with three layers including thematic land cover, percent imperviousness, and percent tree canopy, and a 1992/2001 Land Cover Change Retrofit Product. With these national data layers, there is often a 5-year time lag between the image capture date and product release. In some areas, the land cover can undergo significant change during production time, resulting in products that may be perpetually out of date. To address these issues, this circa 2006 NLCD land cover product (NLCD 2006) was conceived to meet user community needs for more frequent land cover monitoring (moving to a 5-year cycle) and to reduce the production time between image capture and product release. NLCD 2006 is designed to provide the user both updated land cover data and additional information that can be used to identify the pattern, nature, and magnitude of changes occurring between 2001 and 2006 for the conterminous United States at medium spatial resolution. For NLCD 2006, there are 3 primary data products: 1) NLCD 2006 Land Cover map; 2) NLCD 2001/2006 Change Pixels labeled with the 2006 land cover class; and 3) NLCD 2006 Percent Developed Imperviousness. Four additional data products were developed to provide supporting documentation and to provide information for land cover change analysis tasks: 4) NLCD 2001/2006 Percent Developed Imperviousness Change; 5) NLCD 2001/2006 Maximum Potential Change derived from the raw spectral change analysis; 6) NLCD 2001/2006 From-To Change pixels; and 7) NLCD 2006 Path/Row Index vector file showing the footprint of Landsat scene pairs used to derive 2001/2006 spectral change with change pair acquisition dates and scene identification numbers included in the attribute table. In addition to the 2006 data products listed in the paragraph above, two of the original release NLCD 2001 data products have been revised and reissued. Generation of NLCD 2006 data products helped to identify some update issues in the NLCD 2001 land cover and percent developed imperviousness data products. These issues were evaluated and corrected, necessitating a reissue of NLCD 2001 data products (NLCD 2001 Version 2.0) as part of the NLCD 2006 release. A majority of NLCD 2001 updates occur in coastal mapping zones where NLCD 2001 was published prior to the National Oceanic and Atmospheric Administration (NOAA) Coastal Change Analysis Program (C-CAP) 2001 land cover products. NOAA C-CAP 2001 land cover has now been seamlessly integrated with NLCD 2001 land cover for all coastal zones. NLCD 2001 percent developed imperviousness was also updated as part of this process. As part of the NLCD 2011 project, NLCD 2006 data products have been revised and reissued (2011 Edition) to provide full compatibility with all other NLCD 2011 Edition products. The 2014 amended version corrects for the over-elimination of small areas of the four developed classes. Land cover maps, derivatives and all associated documents are considered "provisional" until a formal accuracy assessment can be conducted. The NLCD 2006 is created on a path/row basis and mosaicked to create a seamless national product. Questions about the NLCD 2006 land cover product can be directed to the NLCD 2006 land cover mapping team at the USGS/EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov."

This map shows high-resolution (1 meter) land cover in the EPA Region 3, covering the parts of West Virginia, Virginia, and Pennsylvania outside of the Chesapeake Bay Watershed. It contains the following classes: Water, Tree Canopy, Scrub\Shrub, Low Vegetation, Barren, Impervious Structures, Other Impervious, Impervious Roads, Tree Canopy Over Impervious Structures, Tree Canopy Over Other Impervious, and Tree Canopy Over Impervious Roads. Using object-based image analysis mapping techniques, it was mapped from a combination of remote-sensing imagery and GIS datasets, including LiDAR, multispectral imagery, and thematic layers (e.g., roads, building footprints). Draft output was then manually reviewed and edited to eliminate obvious errors of omission and commission. The classification scheme closely follows a similar mapping effort for the Chesapeake Bay Watershed; together, maps from the two projects cover the entirety of the EPA Region 3 states. One difference between the projects, however, is that tidal wetlands were mapped in the Chesapeake Bay effort, included as the class Emergent Wetlands, but not in the EPA Region 3 zones outside of the watershed. The map is considered current as of 2020 for West Virginia, 2021 for Virginia, and 2022 for Pennsylvania.

The National Land Cover Database 2001 Land Cover 2011 Edition layer is produced 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 Administration (NOAA), the U.S. Environmental Protection Agency (EPA), the U.S. Department of Agriculture - Forest Service (USDA-FS), the National Park Service (NPS), the U.S. Fish and Wildlife Service (FWS), the Bureau of Land Management (BLM) and the USDA Natural Resources Conservation Service (NRCS). One of the primary goals of the project is to generate a current, consistent, seamless, and accurate National Land Cover Database (NLCD) circa 2001 for the United States at medium spatial resolution. This land cover map and all documents pertaining to it are considered "provisional" until a formal accuracy assessment can be conducted. For a detailed definition and discussion on MRLC and the NLCD 2001 products, refer to Homer et al. (2004) and http://www.mrlc.gov/mrlc2k.asp. The NLCD 2001 is created by partitioning the U.S. into mapping zones. A total of 66 mapping zones were delineated within the conterminous U.S. based on ecoregion and geographical characteristics, edge matching features and the size requirement of Landsat mosaics. This update represents a seamless assembly of updated NLCD 2001 Land Cover (2011 Edition) for all 66 MRLC mapping zones. Questions about the NLCD the NLCD 2001 Land Cover 2011 Edition can be directed to the NLCD 2001 land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.

This 30-meter resolution raster data set of land cover for the conterminous United States ("NLCDep0306") was designed to describe conditions representative of the year 2000 and is the result of overlaying enhanced 1992 National Land Cover Data with 1990 and 2000 population data at the block group geographic level. Any area (excluding water, developed land, or wetlands) with population density of less than 1,000 people per square mile in 1990 and at least 1,000 people per square mile in 2000 was reclassified as "newly urbanized" land in the derivative product. Areas of water, developed land, or wetlands existing in the original national land-cover data set were preserved. This data set supersedes the one called "Enhanced National Land Cover Data 1992 revised with 2000 population data to indicate urban development between 1992 and 2000" ("NLCDep0905") dated September 2005. NLCDep0905 coded any area having 2000 population density of at least 1,000 people per square mile as being recently urbanized and did not consider that the area could already have been urbanized in 1990. The approach used in developing NLCDep0905 was determined to have misclassified lands that already were urban in 1990 as newly urbanized and therefore overrepresented new urban land.