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. 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.

The landscape of the conterminous United States has changed dramatically over the last 200 years, with agricultural land use, urban expansion, forestry, and other anthropogenic activities altering land cover across vast swaths of the country. While land use and land cover (LULC) models have been developed to model potential future LULC change, few efforts have focused on recreating historical landscapes. Researchers at the US Geological Survey have used a wide range of historical data sources and a spatially explicit modeling framework to model spatially explicit historical LULC change in the conterminous United States from 1992 back to 1938. Annual LULC maps were produced at 250-m resolution, with 14 LULC classes. Assessment of model results showed good agreement with trends and spatial patterns in historical data sources such as the Census of Agriculture and historical housing density data, although comparison with historical data is complicated by definitional and methodological differences. The completion of this dataset allows researchers to assess historical LULC impacts on a range of ecological processes.

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 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). 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 landcover 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. The Commonwealth of Puerto Rico encompasses the territory of Puerto Rico. Questions about the NLCD landcover layer for the Commonwealth of Puerto Rico can be directed to the NLCD 2001 land cover mapping team at the USGS/EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.

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 Chesapeake Bay Land Use and Land Cover Database (LULC) facilitates characterization of the landscape and land change for and between discrete time periods. The database was developed by the University of Vermont’s Spatial Analysis Laboratory in cooperation with Chesapeake Conservancy (CC) and U.S. Geological Survey (USGS) as part of a 6-year Cooperative Agreement between Chesapeake Conservancy and the U.S. Environmental Protection Agency (EPA) and a separate Interagency Agreement between the USGS and EPA to provide geospatial support to the Chesapeake Bay Program Office. The database contains one-meter 13-class Land Cover (LC) and 54-class Land Use/Land Cover (LULC) for all counties within or adjacent to the Chesapeake Bay watershed for 2013/14 and 2017/18, depending on availability of National Agricultural Imagery Program (NAIP) imagery for each state. Additionally, 54 LULC classes are generalized into 18 LULC classes for ease of visualization and communication of LULC trends ...

The National Land Cover Database 2001 land cover layer for The Hawaiian mapping zone was 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 (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). 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 landcover 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. The Hawaiian Mapping zone encompasses the state of Hawaii. Questions about the NLCD Hawaiian mapping zone can be directed to the NLCD 2001 land cover mapping team at the USGS/EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.

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

The USGS Land Cover program has combined the tried-and-true methodologies from premier land cover projects, National Land Cover Database (NLCD) and Land Change Monitoring, Assessment, and Projection (LCMAP), together with modern innovations in geospatial deep learning technologies to create the next generation of land cover and land change information. The product suite is called, “Annual NLCD” and includes six annual products that represent land cover and surface change characteristics of the U.S.: 1) Land Cover, 2) Land Cover Change, 3) Land Cover Confidence, 4) Fractional Impervious Surface, 5) Impervious Descriptor, and 6) Spectral Change Day of Year. These land cover science product algorithms harness the remotely sensed Landsat data record to provide state-of-the-art land surface change information needed by scientists, resource managers, and decision-makers. Annual NLCD uses a modernized, integrated approach to map, monitor, synthesize, and understand the complexities of land use, cover, and condition change. With this first release, Annual NLCD, Collection 1.0, the six products are available for the Conterminous U.S. for 1985 – 2023. Questions about the Annual NLCD product suite can be directed to the Annual NLCD mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or custserv@usgs.gov. See included spatial metadata for more details. The Land Cover Change Count product shows the number of times Land Cover changes (i.e. Annual Land Cover Change) over the entire period.

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.

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.

Introduction and Rationale: Due to our increasing understanding of the role the surrounding landscape plays in ecological processes, a detailed characterization of land cover, including both agricultural and natural habitats, is ever more important for both researchers and conservation practitioners. Unfortunately, in the United States, different types of land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this data gap and reduce duplicative efforts in geospatial processing, we merged two major datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce an integrated land cover map. Our workflow leveraged strengths of the NVC and the CDL to produce detailed rasters comprising both agricultural and natural land-cover classes. We generated these maps for each year from 2012-2021 for the conterminous United States, quantified agreement between input layers and accuracy of our merged product, and published the complete workflow necessary to update these data. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved a majority of these conflicts based on surrounding agricultural land, leaving only 0.6% of agricultural pixels unresolved in our merged product. Contents: Spatial data Attribute table for merged rasters Technical validation data Number and proportion of mismatched pixels Number and proportion of unresolved pixels Producer's and User's accuracy values and coverage of reference data Resources in this dataset:Resource Title: Attribute table for merged rasters. File Name: CombinedRasterAttributeTable_CDLNVC.csvResource Description: Raster attribute table for merged raster product. Class names and recommended color map were taken from USDA-NASS Cropland Data Layer and LANDFIRE National Vegetation Classification. Class values are also identical to source data, except classes from the CDL are now negative values to avoid overlapping NVC values. Resource Title: Number and proportion of mismatched pixels. File Name: pixel_mismatch_byyear_bycounty.csvResource Description: Number and proportion of pixels that were mismatched between the Cropland Data Layer and National Vegetation Classification, per year from 2012-2021, per county in the conterminous United States.Resource Title: Number and proportion of unresolved pixels. File Name: unresolved_conflict_byyear_bycounty.csvResource Description: Number and proportion of unresolved pixels in the final merged rasters, per year from 2012-2021, per county in the conterminous United States. Unresolved pixels are a result of mismatched pixels that we could not resolve based on surrounding agricultural land (no agriculture with 90m radius).Resource Title: Producer's and User's accuracy values and coverage of reference data. File Name: accuracy_datacoverage_byyear_bycounty.csvResource Description: Producer's and User's accuracy values and coverage of reference data, per year from 2012-2021, per county in the conterminous United States. We defined coverage of reference data as the proportional area of land cover classes that were included in the reference data published by USDA-NASS and LANDFIRE for the Cropland Data Layer and National Vegetation Classification, respectively. CDL and NVC classes with reference data also had published accuracy statistics. Resource Title: Data Dictionary. File Name: Data_Dictionary_RasterMerge.csv

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

[ Derived from parent entry - See data hierarchy tab ]

The LUCAS LUC future dataset consists of annual land use and land cover maps from 2016 to 2100 for North America. It is based on land cover data from the LANDMATE PFT dataset for the year 2015. The LANDMATE PFT consists of 16 plant functional types and non-vegetated classes that were converted from the ESA-CCI LC land cover data according to the method of Reinhart et al. (2022). For version 1.1 of the LUCAS LUC dataset, the improved LANDMATE PFT map version 1.1 was employed. The land use change information from the Land-Use Harmonization Data Set version 2 (LUH2 v2.1f, Hurtt et al. 2020) were imposed using the land use translator developed by Hoffmann et al. (2023). The projected land use change information was derived for different Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) combinations used in the framework of the 6th phase of Coupled Modelling Intercomparison Project (CMIP6). For each year, a map is provided that contains 16 fields. Each field holds the fraction the respective plant functional types and non-vegetated classes in the total grid cell (0-1). The LUCAS LUC dataset was constructed within the HICSS project LANDMATE and the WCRP flagship pilot study LUCAS to meet the requirements of downscaling experiments within CORDEX. Plant functional types and non-vegetative classes: 1 - Tropical broadleaf evergreen trees 2 - Tropical deciduous trees 3 - Temperate broadleaf evergreen trees 4 - Temperate deciduous trees 5 - Evergreen coniferous trees 6 - Deciduous coniferous trees 7 - Coniferous shrubs 8 - Deciduous shrubs 9 - C3 grass 10 - C4 grass 11 - Tundra 12 - Swamp 13 - Non-irrigated crops 14 - Irrigated crops 15 - Urban 16 - Bare

Historic land uses on lots that were vacant, privately owned, and zoned for manufacturing in 2009. Information came from a review of several years of historical Sanborn maps over the past 100 years. When the SPEED 1.0 mapping application was created in 2009, OER had its vendor examine historic land use maps on vacant, privately-owned, industrially-zoned tax lots. Up to seven years of maps for each lot were examined, and information was recorded that indicated industrial uses or potential environmental contamination such as historic fill. Data for an additional 139 lots requested by community-based organizations was added in 2014. Each record represents the information from a map from a particular year on a particular tax lot at that time. Limitations of funding determined the number of lots included and entailed that not all years were examined for each lot.

This dataset provides annual raster maps of historical and projected future land use and land cover (LULC) for California, USA. Changes in LULC over time were simulated using the Land Use and Carbon Scenario Simulator (LUCAS) model. The model was run at 1-km resolution on an annual timestep for historical (1985-2020) and projected future time periods (2021-2100). Simulations for the projected future time period were run under all combinations of four climate scenarios, two urbanization scenarios, and two vegetation management scenarios with 40 Monte Carlo realizations for each simulation.

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 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

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.

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.

Human use of the land has a large effect on the structure of terrestrial ecosystems and the dynamics of biogeochemical cycles. For this reason, terrestrial ecosystem and biogeochemistry models require moderate resolution information on land use in order to make realistic predictions. Few such datasets currently exist.

This collection consists of output from models that estimate the spatial pattern of land use in four land-use categories by relating a high-resolution land-cover dataset to state-level census data on land use. The models have been parameterized using a goodness-of-fit measure.

The land cover product used was from the IGBP DISCover global product, derived from 1 km AVHRR imagery, with 16 land cover classes (Belward et al., 1999). Land-use data at state-level resolution came from the USDA's Major Land Uses database (USDA, 1996), aggregated into the four general land-use categories described below.

The model was used to generate maps of land use in 1992 for the conterminous U.S. at 0.5 degree spatial resolution. Two different parameterization schemes were used to spatially interpolate land use from land cover, based on the state-level land use census data: 1) a National Parameterization, and 2) a Regional Parameterization.

For the National Parameterization, a single parameterization relating aggregate land cover and state-level land use. For the Regional Parameterization, a separate parameterization was used for each of seven different regions. The seven regions include: Northeast, Southeast, East North-central, West North-central, Southern Plains, Mountain, and Pacific. These regions are substantially different in terms of land use and land cover. In both cases, the results are a nationally gridded map at 0.5 degrees of land use categories for cropland, pasture/range, forest, and other land use; the other land use category is also further spilt into three additional subcategories (forested, non-forested, non-vegetated).

This project is currently being extended to other regions of the globe, and for other time periods, where both land use census data and image-derived land cover data are available.

Available Datasets:

1) US Land Use - 1992 National Parameterization 2) US Land Use - 1992 Regional Parameterization

Each dataset has 4 major land use categories and 3 subcategories of the Other major land use category.