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

The 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 Index product summarizes Annual NLCD Land Cover change into 15 change classes. These classes are intended to communicate thematic change impact, and were based on the following hierarchy: water, urban, wetland, herbaceous wetland, agriculture, cultivated crop, hay pasture, rangeland grass and shrub, barren, woody wetland, forest type, urban within, forest transition mixed rangeland and forest change, and forest transition mixed rangeland and shrub/scrub change.

This 30-meter resolution raster data set of land cover for the conterminous United States ("NLCDep0905") was designed to describe conditions representative of the year 2000 and is the result of overlaying enhanced 1992 National Land Cover Data with 2000 population data at the block group geographic level. Any area (excluding water, developed land, or wetlands) with population density of at least 1,000 people per square mile 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 has been superseded by the one called "Enhanced National Land Cover Data 1992 revised with 1990 and 2000 population data to indicate urban development between 1992 and 2000" ("NLCDep0306") dated March 2006. The approach used in developing NLCDep0905 was determined to have misclassified lands that already were urban in 1990 as newly urbanized and therefore greatly overrepresented new urban land. Although the NLCDep0905 data set has been superseded, some water-quality assessment projects utilized this data set to characterize basins before the NLCDep0306 data set was developed. Therefore, the NLCDep0905 is being published to document the land cover data set used in these analyses.

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

Release of NLCD 2006 provides the first land-cover change database for the Conterminous United States (CONUS) from Landsat Thematic Mapper data. Accuracy assessment of NLCD 2006 focused on four primary products: 2001 land cover, 2006 land cover, land-cover change between 2001 and 2006, and impervious surface change between 2001 and 2006. The accuracy assessment was conducted by selecting a stratified random sample of pixels with the reference classification interpreted from multi-temporal high resolution digital imagery. The NLCD Level II (16 classes) overall accuracies for the 2001 and 2006 land cover were 79% and 78%, respectively, with Level II user's accuracies exceeding 80% for water, high density urban, all upland forest classes, shrubland, and cropland for both dates. Level I (8 classes) accuracies were 85% for NLCD 2001 and 84% for NLCD 2006. The high overall and user's accuracies for the individual dates translated into high user's accuracies for the 2001–2006 change reporting themes for water gain and loss, forest loss, urban gain, and the no-change reporting themes for water, urban, forest, and agriculture.

This dataset contains the urban growth simulation results of future land use in 2040 of the Wasatch Range Metropolitan Area (WRMA) .In this study, we defined the WRMA as a broad, ten-county region that surrounds the Wasatch Mountain Range east of the Great Salt Lake and Salt Lake City in Utah. This region encompasses four Wasatch Front counties west of the mountain range (Weber County, Davis County, Salt Lake County, and Utah County), three Wasatch Back counties east of the mountain range (Morgan County, Summit County, and Wasatch County), and three counties neighboring the Wasatch Front (Cache County, Box Elder County, and Tooele County).

SLEUTH-3r urban growth simulation model is used to generate this dataset. Detailed SLEUTH model protocol can be found at: http://www.ncgia.ucsb.edu/projects/gig/index.html. The data used to run the SLEUTH-3r model include National Land Cover Database 2001, 2006, and 2011, US Census TIGER/Line shapefile for 2000 and 2011, United States Geological Survey 7.5 min elevation model, and Utah Landownership map from Utah Automated Geographic Reference Center.

Three alternative scenarios were developed to explore how conserving Utah’s agriculturale land and maintaining healthy watersheds would affect the patterns and trajectories of urban development: 1) The first scenario is a “Business as Usual” scenario. In this scenario, federal, state, and local parks, conservation easement areas, and surface water bodies, were completely excluded (value = 100) from development, and all the remaining lands are were naively assumed as developable (value = 0). This is the same excluded layer that was also used during model calibration. Under this scenario, we hypothesized that future urban grow will occur following the historical growth behaviors and trajectories and no changes in land designation or policies to restrict future growth will be implemented. 2) The second scenario is an “Agricultural Conservation” scenario. Within the developable areas that we identified earlier, we then identified places that are classified by the United States Department of Agriculture (USDA) as prime farmland, unique farmland, farmland of statewide importance, farmland of local importance, prime farmland if irrigated, and prime farmland if irrigated and drained. Each of these classes were assigned with an exclusion value from urban development of 100, 80, 70, 60, 50, and 40 respectively. These exclusion values reflect the relative importance of each farmland classification and preservation priorities. By doing so, the model discourages but does not totally eliminate growth from occurring on agricultural lands, which reflects a general policy position to conserve agricultural landscapes while respecting landowners’ rights to sell private property. 3) A “Healthy Watershed” scenario aims to direct urban growth away from areas prone to flooding and areas critical for maintaining healthy watersheds. First, we made a 200-meter buffer around existing surface water bodies and wetlands and assigned these areas an exclusion value of 100 to keep growth from occurring there. In addition, we assigned areas that have frequent, occasional, rare and no-recorded flooding events with exclusion values of 100, 70, 40 and 0 accordingly. We also incorporated the critical watershed restoration areas identified by the Watershed Restoration Initiative of Utah Division of Wildlife Resources (https://wri.utah.gov/wri/) into this scenario. These watershed restoration areas are priority places for improving water quality and yield, reducing catastrophic wildfires, restoring the structure and function of watersheds following wildfire, and increasing habitat for wildlife populations and forage for sustainable agriculture. However, there are not yet legal provisions for protecting them from urbanization, so we assigned these areas a value of 70 to explore the potential urban expansion outcomes if growth were encouraged elsewhere.

Future land use projections of 2040 are in GIF format, which can be reprojected and georeferenced in ArcGIS or QGIS, or be read directly as a picture.

To better map the urban class and understand how urban lands change over time, we removed rural roads and small patches of rural development from the NLCD developed class and created four wall-to-wall maps (1992, 2001, 2006, and 2011) of urban land. Removing rural roads from the NLCD developed class involved a multi-step filtering process, data fusion using geospatial road and developed land data, and manual editing. Reference data classified as urban or not urban from a stratified random sample was used to assess the accuracy of the 2001 and 2006 urban and NLCD maps. The newly created urban maps had higher overall accuracy (98.7%) than the NLCD maps (96.2%). More importantly, the urban maps resulted in lower commission error of the urban class (23% versus 57% for the NLCD in 2006) with the trade-off of slightly inflated omission error (20% for the urban map, 16% for NLCD in 2006). The removal of approximately 230,000 km2 of rural roads from the NLCD developed class resulted in maps that better characterize the urban footprint. These urban maps are more suited to modeling applications and policy decisions that rely on quantitative and spatially explicit information regarding urban lands. Digital maps of urban land in the United States for 1992, 2001, 2006, and 2011 are available (at a 30-m pixel resolution) as four compressed 2-bit IMG files. The map year is reflected in the file name.

The National Land Cover Database 2001 urban imperviousness layer was produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium, 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 http://www.mrlc.gov/. 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. Zone 13 (the northeastern U.S.) consists of mapping zones 60, 61, 63, 64, 65, and 66, which collectively encompasses the entirety of Maine, New Hampshire, Vermont, Massachusetts, Rhode Island, Connecticut, New York, New Jersey, Delaware, and Maryland, as well as portions of Pennsylvania, West Virginia, Virginia, and North Carolina.

Accuracy assessment is a standard protocol of National Land Cover Database (NLCD) mapping. Here we report agreement statistics between map and reference labels for NLCD 2011, which includes land cover for 2001, 2006, and 2011. The two main objectives were assessment of agreement between map and reference labels for the three, single-date NLCD land cover products at Level II and Level I of the classification hierarchy, and agreement for 17 land cover change themes based on Level I classes (e.g., forest loss; forest gain; forest, no change) for three change periods (2001–2006, 2006–2011, and 2001–2011). The single-date overall accuracies were 82%, 83%, and 83% at Level II and 88%, 89%, and 89% at Level I for 2011, 2006, and 2001, respectively. Overall accuracies for 2006 and 2001 land cover components of NLCD 2011 were approximately 4% higher (at Level II and Level I) than the overall accuracies for the same components of NLCD 2006. User's accuracies were high for the no change reporting themes, commonly exceeding 85%, but were typically much lower for the reporting themes that represented change. Only forest loss, forest gain, and urban gain had user's accuracies that exceeded 70%. NLCD 2011 user's accuracies for forest loss, forest gain, and urban gain compare favorably with results from other land cover change accuracy assessments.

This collection contains measures of land cover (e.g., low-, medium-, or high-density development, forest, wetland, open water) derived from the National Land Cover Database (NLCD) and aggregated by United States census tract and ZIP code tabulation area (ZCTA). For each land type, land cover is measured both in total square meters and as a proportion of all land of that type within the tract or the ZCTA.

(Link to Metadata) Circa 2001 land use / land cover (LULC) for the Lake Champlain Basin. The goal in creating this layer was to generate an "improved" version of NLCD 2001 using ancillayr GIS data and Landsat satellite imagery. The Lake Champlain Basin Program (LCBP) is a joint federal-state initiative that helps to monitor and protect Lake Champlain and its contributing watersheds. One area of particular concern is nutrient loading to the Lake, particularly phosphorus (P), from terrestrial and non-lake sources. In order to quantify how much P is entering the lake, it is crucial to have an accurate representation of land use for the Basin. This layer represents an updated digital land use - land cover (LULC) map for the entire Lake Champlain Basin, termed LCB 2001. This updated LULC layer was generated by using an expert system that integrated the 2001 National Land Cover Database (NLCD) with ancillary GIS datasets and circa 2001 Landsat satellite imagery. A primary focus of this expert system was to improve the mapping accuracy of the agriculture LULC class by reducing the confusion with urban open space. An accuracy assessment was carried out by comparing the classification to temporally comparable high resolution imagery. The overall accuracy of LCB 2001 was 88%. The user's accuracy for the urban and agricultural classes, those considered to be the greatest sources of phosphorous, was 84% and 89% respectively. LCB 2001 was produced largely by improving NLCD 2001 using ancillary data. The process of generating LCB 2001 was comprised of three phases: 1) overlay of roads, 2) expert system classification, and 3) assessment and manual correction. Phase 1 was carried out using the aggregate 8-class version of NLCD 2001. The corrected road vector lines were converted to a raster layer with a cell size and alignment matching that of NLCD 2001. The road pixels were incorporated into the NLCD 2001 layer using standard raster overlay procedures in which any pixel in NLCD 2001 that corresponded with a road pixel was reassigned to the urban category. The expert system was employed largely to deal with the accuracy issues surrounding agriculture and urban open land. Edge effects and registration differences between NLCD 2001 and the improved CLU layer made simply overlaying the two an unacceptable solution. To overcome this limitation the expert system was developed and deployed using Definiens Professional software (Definiens AG, Munich, Germany). The expert system took advantage of Definiens Professional's ability to "segment" object polygons from image and thematic raster layers. Image object polygons are groups of pixels with similar spectral and spatial characteristics. Image object polygons allow for the inclusion of rules based on complex topological relationships. Image object polygons for this project were derived from both the spring and fall circa 2001 Landsat satellite scenes, but were constrained to the boundaries of the Improved CLU layer and NLCD 2001. Thus, each object polygon consisted of groups of pixels that were spectrally and spatially similar and share the same attributes with respect to the Improved CLU layer and the NLCD 2001 layer. The expert system first evaluated whether or not the object fell into the confirmed agriculture or urban-open categories based on the Improved CLU layer. If either of these tests proved true then the object was assigned to the corresponding class. If the test failed then the alternate scenarios were evaluated. For objects originally classified as agriculture in NLCD 2001 the object was assigned to the output agriculture class only if the object bordered an object already classified as agriculture (to deal with edge effects and layer alignment issues) or if the object was also in the improved CLU layer's possible agriculture category. This rule ran in an iterative loop to compensate for the fact that once objects were classified as agriculture they would influence other border objects. The rule only stopped executing once all objects were finished changing their class assignment. If the object was not assigned to the output agriculture class at this stage (those classified as agriculture in NLCD 2001, but not in LCB 2001) it was evaluated using a series of spectral and spatial rules to assign it to the output brush or urban-open classes. This set of spectral and spatial rules applied a fuzzy class assignment. The object was considered to be more likely to be brush the darker it was and the further it was from urban areas. The object was considered more likely to be urban if it was near urban areas and brighter. For all other classes the objects adopted the NLCD 2001 class. Following the running of the expert system the output classification was manually compared to the Landsat imagery and any objects that appeared to be misclassified were reassigned. As the goal of the project was to maintain as much consistency with NLCD 2001 as possible the layer was maintained in its original coordinate system - USA Albers Equal Area Conic, USGS Version, NAD83 datum (meters).

We merged developed low, medium, and high intensity into the impervious class and changed developed open-space to managed clearing, and combined the percent impervious proportion of the developed types into the impervious class and merged the remaining proportion with herbaceous land cover to create managed clearings. Missing values indicate no comparison.

DRB2070 Version 1.0 represents a baseline forecast of urban land cover in the Delaware River Basin (DRB) out to the year 2070. It was developed by the Delaware River Basin Land Use Dynamics Project at Shippensburg University. The forecasts were developed using a SLEUTH urban growth model for the 43 county region of the DRB over the 2001-2006 time period. The model was validated for the 2006-2011 time period. The modeling team used the National Land Cover Database (NLCD) urban classes to represent urban land cover as developed or not developed. The DRB was subdivided into eight modeling subregions in order to improve the quality of the modeling. Additional information is available at: https://drbproject.org/drb2070-version-1/ https://drbproject.org/wp-content/uploads/sites/101/2017/03/DRB2070_v1.pdf - March 2017 https://drbproject.org/wp-content/uploads/2017/07/DRB2070_v2.pdf - July 2017

The present dataset is part of the published scientific paper Zhao C, Weng Q, Hersperger A M. Characterizing the 3-D urban morphology transformation to understand urban-form dynamics: a case study of Austin, Texas, USA. Landscape and urban planning, 2020, 203:103881. The overall objective of this paper is to understand urban form dynamics in the Austin metropolitan area for the periods 2006–2011 and 2011–2016. The study also aims to understand to what extent the changes in the built environment (in terms of ‘efficient growth’ versus ‘inefficient growth’) from the 1990s to 2016 in the Austin metropolitan area corresponded with ‘compact and efficient growth’ planning policy documents. The UMT distribution can be found in the paper. The area of transitioning UMT was provided in Table 2 and Table 3 can be found in the Appendix of the paper. A protocol was developed to perform the content analysis of the strategic plans and gather the data. The detailed list of protocol items can be found in Appendix B of the paper. This study demonstrates the advantage of applying Lidar data to characterize 3-D urban morphology type (UMT) transition and understand its dynamics, which helps develop a comprehensive understanding of the urbanization process and provides a tool for planning intentions and policies evaluation on urban development over time. The UMT maps can be found in Appendix A of the paper. The Lidar point datasets and the 30 × 30 m National Land Cover Database (NLCD) are the two main data sources of UMT mapping. Lidar datasets were gathered from different projects that had been conducted and collected by state agencies and other organizations between 2007 and 2017. Table A1 in the appendix in the paper shows the accuracies and acquisition parameters of the Lidar projects from 2007 to 2017. Land use/cover dynamics in Austin metropolitan area dataset provides Land use/cover patterns in the years 1992, 2001, 2004, 2006, 2008, 2011, 2013, 2016 with a spatial resolution of 30 meters. Since NLCD 1992 used a different classification system for the urban land classes, we first reclassified the NLCD 1992 using a customized Arcpy package.

Land use and land cover estimates across the urban-rural gradients of Atlanta, Charlotte, and Raleigh based on visual interpretation of 2012 National Agriculture Imagery Program imagery.

Impervious surfaces are surfaces that do not allow water to pass through. Examples of these surfaces include highways, parking lots, rooftops, and airport runways. Instead of allowing rain to pass into the soil, impervious surfaces cause water to collect at the surface, then run off. An increase in impervious surface area causes an increase of water volume which needs to be managed by stormwater systems. With the flow come pollutants, which collect on impervious surfaces then discharge with the runoff into streams and the ocean. Runoff water does not enter the water table, and that can cause other management issues, such as interruptions in baseline stream flow.The NLCD imperviousness layer represents urban impervious surfaces as a percentage of developed surface over every 30-meter pixel in the United States. The layer is organized into a time series with years 2001, 2004, 2006, 2008, 2011, 2013, 2016, and 2019 for the lower 48 conterminous US states. This information may be used in conjunction with the USA NLCD Land Cover layer. Time SeriesBy default, this service will appear in your client with a time slider which allows you to play the series as an animation. The animation will advance year by year, but the layer only changes appearance every few years, in 2001, 2004, 2006, 2008, 2011, 2013, 2016, and 2019. To select just one year in the series, first turn the time series off on the time slider, then create a definition query on the layer which selects only the desired year.Time Series DescriptorMRLC issued a set of companion rasters with this impervious surface layer showing the reason why each pixel is impervious. This companion layer, called the Developed Imperviousness Descriptor, is not currently available in this map service. The descriptor layer identifies types of roads, core urban areas, and energy production sites for each impervious pixel to allow deeper analysis of developed features. The descriptor layer may be downloaded directly from MRLC and added to ArcGIS Pro.Alaska, Hawaii, and Puerto RicoAt this time Alaska, Hawaii, and Puerto Rico are not included in this time series. Only three years for a portion of Alaska around Anchorage are available from MRLC at this time. Furthermore, these rasters are produced with a different methodology, and are not set up to be directly compared the way the CONUS time series is. To analyze change between the latest two data years for this portion of Alaska, be sure to use the NLCD 2011 to 2016 Developed Impervious Change raster. For Hawaii and Puerto Rico, only the year 2001 is available for download at the MRLC.North America Albers ProjectionAll NLCD layers in the Living Atlas are projected into the North America Albers Projection before serving in the Living Atlas. This allows the coterminous USA, Puerto Rico, Hawaii, and Alaska to be served from a common projection and analyzed together. In tests performed by esri, the NLCD land cover classes after projection to North America Albers had the exact same number of pixels in input as output, but pixels had been slightly rearranged after projection.Processing TemplatesThis layer comes with two color schemes, cool and warm. The default is a cool gray color scheme, designed to look good on light and dark gray web maps. To choose a warm color scheme which was the default until 2021, change the processing template to the Impervious Surface Warm Renderer in your map client.Dataset SummaryPhenomenon Mapped: The proportion of the landscape that is impervious to waterUnits: PercentCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerData Coordinate System: North America Albers Equal Area ConicExtent: Contiguous United StatesNoData Value: 127Source: Multi-Resolution Land Characteristics ConsortiumPublication Date: June 3, 2021ArcGIS Server URL: https://landscape10.arcgis.com/arcgis/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.What can you do with this layer?This layer can be used to create maps and to visualize the underlying data. This layer can be used as an analytic input in ArcGIS Desktop.This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

Imperviousness for the Baltimore MSA from the NLCD 2001 impervious dataset. Impervious surfaces refers to impenetrable surfaces such as rooftops, roads or parking lots. Quantification of imperviousness can offer a relatively objective measure of urban density and provide a forum for its classification. For NLCD 2001, imperviousness was chosen as the surrogate for the urban intensity classification in an effort to improve the precision of urban characterization used in the original NLCD 1992. Modeling empirical relationships between imperviousness and Landsat data is accomplished using regression tree techniques. Several one-meter digital orthophoto quadrangles are used for each Landsat scene to derive reference impervious data needed for calibrating the relationships between percent imperviousness and Landsat spectral data, which are then modeled using a commercial regression tree algorithm called Cubist. The models are then applied to all pixels in a mapping zone to produce a per-pixel estimate of imperviousness in urban areas (Yang et al., 2002). This procedure quantifies the spatial distribution of impervious surfaces as a continuous variable for urban areas from 1 to 100%, and offers a consistent and repeatable method to characterize urban areas across the Nation. This data layer is then masked to ensure only urban pixels are included and thresholded (Table 1) into NLCD 2001 urban classes and inserted into the land cover. Imperviousness information will be available as an independent product of NLCD 2001. The National Land Cover Database 2001 for mapping zone 60 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) 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. For a detailed definition and discussion on MRLC and the NLCD 2001 products, refer to Homer et al. (2003) and http://www.mrlc.gov/mrlc2k.asp. The NLCD 2001 was 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, edgematching features and the size requirement of Landsat mosaics. Mapping zone 60 encompasses whole or portions of several states in the mid-Atlantic region, including the states of New Jersey, Delaware, Maryland, Pennsylvania, Virginia, and the District of Columbia. Questions about the NLCD mapping zone 60 can be directed to the NLCD 2001 land cover mapping team at the USGS EROS Data Center (EDC), Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

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.InputRaster, mosaic dataset, or image service. (Preferred cell size is 30 meters.)OutputClassified raster with the same classes as in the National Land Cover Database (NLCD) 2016.Note: The classified raster contains 20 classes based on a modified Anderson Level II classification system as used by the National Land Cover Database.Applicable geographiesThis model is expected to work well in the United States.Model architectureThis model uses the UNet model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an overall accuracy of 77 percent. The table below summarizes the precision, recall and F1-score of the model on the validation dataset.ClassCollection 2 Level 2 ImageryCollection 1 Level 1 ImageryPrecisionRecallF1 ScorePrecisionRecallF1 ScoreOpen Water0.960.970.960.950.970.96Perennial Snow/Ice0.860.690.770.490.940.64Developed, Open Space0.510.380.440.430.380.4Developed, Low Intensity0.520.460.490.470.480.47Developed, Medium Intensity0.540.50.520.490.540.51Developed, High Intensity0.670.540.60.550.680.61Barren Land0.760.590.660.60.770.68Deciduous Forest0.740.810.780.780.760.77Evergreen Forest0.770.820.790.80.820.81Mixed Forest0.560.470.510.50.530.51Shrub/Scrub0.820.820.820.840.810.83Herbaceous0.780.740.760.790.770.78Hay/Pasture0.70.740.720.670.750.71Cultivated Crops0.870.910.890.910.90.9Woody Wetlands0.70.680.690.670.680.68Emergent Herbaceous Wetlands0.720.540.620.540.610.57Training dataThis model has been trained on the National Land Cover Database (NLCD) 2016 with the same Landsat 8 scenes that were used to produce the database. Scene IDs for the imagery were available in the metadata of the dataset.Sample resultsHere are a few results from the model.

Collaborative landscape conservation planning is largely limited by the quality of spatial data which can be applied to decision support tools to inform conservation decisions. Conservation entities across the Western Gulf Coastal Plain are taking a collaborative, strategic, landscape scale approach to conservation planning. This effort encourages communication and implementation of restoration and habitat enhancement actions within water sheds. Land cover datasets available within this geography hinder the efficiency of such efforts due to low resolution quality and limited details associated with land use categories. In collaboration with the Texas Parks and Wildlife Department and the Gulf Coast Prairie Landscape Conservation Cooperative, U.S. Geological Survey (USGS) is developing three map products to 1) improve the resolution of current reference for land use land cover, 2) identify high priority areas for pollinator conservation, and 3) define pre-settlement vegetation types to serve as a reference for restoration efforts. The Land Use Land Cover (LULC) product will attempt to utilize data training methods such as e-cognition software using the 2015 National Aerial Imagery Program (NAIP) dataset (1m) as the basemap. If this is not successful, several data sources will be referenced to produce a refined version of land cover data across the Western Gulf Coastal Plain which includes additional land use categories at a higher resolution (10m). Other data sources will include ~12,000 aerial points assigned to image objects, National Wetlands Inventory (NWI) 2008, Bureau of Ocean Energy Management (BOEM) marsh classes, National Land Cover Database (NLCD) urban areas, and Cropland Data Layer (CDL) data. The aerial points will also contribute to the development of the conservation priority map. Potential pollinator habitat will be derived by ranking land use classifications resulting from the new LULC product, and grassland quality will be based on ground truthing and remotely sensed features indicative of remnant prairie. All known prairie remnants, prairie plantings, and clusters of mima mounds will be detected from high resolution LiDar data (3m). Mima mounds are indicative of areas in which the topsoil has not been significantly disturbed, and therefore have a higher potential to contain native prairie vegetation. The third map product, pre-settlement vegetation types, were compiled using Soil Survey Geographic Database (SSURGO) data, to predict appropriate vegetation associations for plantings across southwest Louisiana based on expert elicitation, and historic references. Natural vegetation associations were examined and documented for each soil series individually using multiple references, including U.S. Department of Agriculture (USDA) Soil Series descriptions, expert elicitation, and historical spatial references. This work provides high resolution references for conservation efforts in the Chenier Eco-Region in Southwest Louisiana.