U.S. Geological Survey 20250625 raster digital data None None Sioux Falls, SD U.S. Geological Survey https://doi.org/10.5066/P94UXNTS https://www.mrlc.gov/data U.S. Geological Survey 2025 U.S. Geological Survey Fact Sheet 2025–3001, 4 p. publication U.S. Geological Survey, 2025, Annual NLCD (National Land Cover Database)—The next generation of land cover mapping: U.S. Geological Survey Fact Sheet 2025–3001, 4 p., https://doi.org/10.3133/fs20253001. https://doi.org/10.3133/fs20253001 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 second release, Annual NLCD, Collection 1.1, the six products are available for the Conterminous U.S. for 1985–2024. 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 goal of this project is to provide the Nation with complete, current, and consistent public domain information on its land use and land cover. 1985 2024 ground condition Complete Annually -129.27731989810934 -63.118429521429235 52.921719733858104 21.805095225544456 ISO 19115 Topic Category imageryBaseMapsEarthCover environment USGS Thesaurus Time Series Landsat Analysis Ready Data (ARD) Land Cover Change Detection Earth Observations Image Processing Geographic Information Science (GIS) U.S. Geological Survey (USGS) Digital Spatial Data Common Geographic Areas United States USA CONUS None. Please see 'Distribution Info' for details. None. Users are advised to read the dataset's metadata thoroughly to understand appropriate use and data limitations. U.S. Geological Survey Customer Service Representative mailing and physical

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Sioux Falls SD 57198-0001 USA 605-594-6151 605-594-6589 custserv@usgs.gov These products were created by the Annual NLCD team at USGS EROS, Sioux Falls, SD. Refer to the contact information throughout the metadata to contact the team. Custom cloud based processing environment Jesslyn F. Brown Heather J. Tollerud Christopher P. Barber Qiang Zhou John L. Dwyer James E. Vogelmann Thomas R. Loveland Curtis E. Woodcock Stephen V. Stehman Zhe Zhu Bruce W. Pengra Kelcy Smith Josephine A. Horton George Xian Roger F. Auch Terry L. Sohl Kristi L. Sayler Alisa L. Gallant Daniel Zelenak Ryan R. Reker Jennifer Rover 20200301 publication Remote Sensing of Environment vol. 238, issue 111356 https://www.sciencedirect.com/science/article/pii/S003442571930375X https://doi.org/10.1016/j.rse.2019.111356 Bruce W. Pengra Jordan Long Devendra Dahal Stephen V. Stehman Thomas R. Loveland 201508 publication Remote Sensing of Environment vol. 165, pages 234-248 https://www.sciencedirect.com/science/article/pii/S003442571500036X https://doi.org/10.1016/j.rse.2015.01.018 Bruce W. Pengra Stephen V. Stehman Josephine A. Horton Daryn J. Dockter Todd A. Schroeder Zhiqiang Yang Warren B. Cohen Sean P. Healey Thomas R. Loveland 20200301 publication Remote Sensing of Environment vol. 238, issue 111261 https://www.sciencedirect.com/science/article/pii/S0034425719302809 https://doi.org/10.1016/j.rse.2019.111261 George Xian Hua Shi Qiang Zhou Roger Auch Kevin Gallo Zhuoting Wu Michael Kolian 202202 publication Remote Sensing of Environment vol. 269, issue 112803 https://www.sciencedirect.com/science/article/pii/S003442572100523X https://doi.org/10.1016/j.rse.2021.112803 Heather J. Tollerud Zhe Zhu Kelcy Smith Danika F. Wellington Reza A. Hussain Donna Viola 20230201 publication Remote Sensing of Environment vol. 285, issue 113372 https://www.sciencedirect.com/science/article/pii/S0034425722004783 https://doi.org/10.1016/j.rse.2022.113372 Suming Jin Jon Dewitz Congcong Li Daniel Sorenson Zhe Zhu Md Rakibul Islam Shogib Patrick Danielson Brian Granneman Catherine Costello Adam Case Leila Gass 20230228 publication Journal of Remote Sensing vol. 3, article id 0021 https://spj.science.org/doi/10.34133/remotesensing.0021 https://doi.org/10.34133/remotesensing.0021 Suming Jin Jon Dewitz Patrick Danielson Brian Granneman Catherine Costello Kelcy Smith Zhe Zhu 20230221 publication Journal of Remote Sensing vol. 3, article id 0022 https://spj.science.org/doi/10.34133/remotesensing.0022 https://doi.org/10.34133/remotesensing.0022 A formal accuracy assessment is in progress for the Annual NLCD product suite. An internal review process was conducted to check for erroneous data artifacts, duplications, and omissions to ensure the integrity of the geospatial data products. Dataset is considered complete for the information presented, as described in the abstract. Users are advised to read the rest of the metadata record carefully for additional details. No formal positional accuracy tests were conducted. No formal positional accuracy tests were conducted. U.S. Geological Survey 2025 publication https://www.mrlc.gov/documentation Digital and/or Hardcopy 2025 ground condition Annual NLCD SPUG The Annual National Land Cover Database (NLCD) Collection 1 Science Product User Guide describes in detail the source inputs used, data product suite details, methodology, validation, and other important details of the Annual NLCD Product Suite. Reference Document Please reference the Annual National Land Cover Database (NLCD) Collection 1 Science Product User Guide for more detailed methodology and information. The user guide can be found at https://www.mrlc.gov/documentation. 2025 Time Series The Landsat STAC-server is queried for all observations in a specific Landsat Collection 2 ARD tile. The surface reflectance, brightness temperature, and pixel quality layers are then accessed from Landsat cloud storage and placed in a time series optimized Zarr array to efficiently support downstream operations. 2025 Continuous Change Detection Surface reflectance, brightness temperature, and pixel quality information is read in from a time series optimized Zarr array for a given tile. This data is then processed through the Band First Probability (BFP) Continuous Change Detection (CCD) algorithm, with the results stored as parquet files within cloud data stores. 2025 Leaf-On Annual Composites Surface reflectance and pixel quality information is read from the Landsat Collection 2 ARD cloud archive stores for a given year and tile. The data is then passed to the compositing algorithm, with additional data for neighboring years gathered as needed. Results are stored as 6 band geotiffs within cloud data stores. 2025 Leaf-Off Annual Synthetics BFP CCD parquet results are read in for a given tile and surface reflectance predictions are calculated based on the harmonic coefficients for corresponding years. Results are stored as 6 band geotiffs within cloud data stores. 2025 Land Cover Classification Training Landsat Collection 1 ARD based leaf-on composites, leaf-off synthetics, digital elevation model (DEM), DEM slope, DEM positional index, DEM aspect, wetlands potential index (WPI), NLCD 2021, and LCMAP 1.3 CCD results are processed through the LCAMS algorithm using high performance/throughput computing architectures. The resulting models are then transitioned to cloud storage. 2025 Land Cover Classification Prediction Leaf-on composites, leaf-off synthetics, and BFP CCD results are access from cloud storage and passed to the LCAMS algorithm. Spatial and refined soft max values are stored as Zarr arrays in cloud storage. 2025 Impervious Classification Training Landsat Collection 1 ARD based leaf-on composites, leaf-off synthetics, NLCD 2021 science product, NLCD 2021 impervious descriptor, and the DEM are processed through the LCAMS algorithm using HPC/HTC computing architectures. The resulting models are transitioned to cloud storage. 2025 Impervious Classification Prediction Leaf-on composites, leaf-off synthetics, and the trained impervious model are read in and passed to the LCAMS algorithm with results being stored as Zarr arrays in cloud storage. 2025 Post Classification Spatial, refined, and impervious probabilities are joined with BFP CCD results to pass through the different post classification methodologies. These steps include the STIPP method and various expert informed heuristics to identify and mitigate potential issues with any singular set of prediction probabilities. Product values are stored as intermediate tiff files. 2025 Product Generation Intermediate product tiff files are read from cloud storage with the final data formatting steps are applied to generate cloud optimized geotiffs (COG) and associated metadata. 2025 Intra-Collection Consistency The land cover product from the previous version is used as a base, and the newly generated land cover results are compared to determine areas of change to be updated across the products, except for spectral change DoY. The spectral change DoY product is updated for the latest 5 years to account for missed spectral disturbances due to timeseries dynamics. 2025 Raster Grid Cell 105000 160000 1 AEA_WGS84 29.5 45.5 -96.0 23.0 0.0 0.0 row and column 30.0 30.0 meters WGS_1984 WGS 84 6378137.0 298.257223563 Annual NLCD C1V1 Land Cover class counts and descriptions for the Annual NLCD Land Cover Database U.S. Geological Survey OID Internal feature number. Annual NLCD Sequential unique whole numbers that are automatically generated. Count A nominal integer value that designates the number of pixels that have each value in the file; histogram column in raster attributes table. Annual NLCD Integer Value Land Cover Class Code Value Annual NLCD 250 NoData Annual NLCD 11 Open Water - All areas of open water, generally with less than 25% cover or vegetation or soil. Annual NLCD 12 Perennial Ice/Snow - All areas characterized by a perennial cover of ice and/or snow, generally greater than 25% of total cover. Annual NLCD 21 Developed, Open Space - Includes areas with a mixture of some constructed materials, but mostly vegetation in the form of lawn grasses. Impervious surfaces account for less than 20 percent 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. Annual NLCD 22 Developed, Low Intensity -Includes areas with a mixture of constructed materials and vegetation. Impervious surfaces account for 20-49 percent of total cover. These areas most commonly include single-family housing units. Annual NLCD 23 Developed, Medium Intensity - Includes areas with a mixture of constructed materials and vegetation. Impervious surfaces account for 50-79 percent of the total cover. These areas most commonly include single-family housing units. Annual NLCD 24 Developed, High Intensity - Includes 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 percent of the total cover. Annual NLCD 31 Barren Land (Rock/Sand/Clay) - Barren 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. Annual NLCD 41 Deciduous Forest - Areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. More than 75 percent of the tree species shed foliage simultaneously in response to seasonal change. Annual NLCD 42 Evergreen Forest - Areas dominated by trees generally greater than 5 meters tall, and greater than 20% of total vegetation cover. More than 75 percent of the tree species maintain their leaves all year. Canopy is never without green foliage. Annual NLCD 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 percent of total tree cover. Annual NLCD 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. Annual NLCD 71 Grassland/Herbaceous - Areas dominated by graminoid 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. Annual NLCD 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 percent of total vegetation. Annual NLCD 82 Cultivated Crops - Areas used to produce 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 percent of total vegetation. This class also includes all land being actively tilled. Annual NLCD 90 Woody Wetlands - Areas where forest or shrub land vegetation accounts for greater than 20 percent of vegetative cover and the soil or substrate is periodically saturated with or covered with water. Annual NLCD 95 Emergent Herbaceous Wetlands - Areas where perennial herbaceous vegetation accounts for greater than 80 percent of vegetative cover and the soil or substrate is periodically saturated with or covered with water. Annual NLCD Land Cover Class RGB Color Value Table. The specific RGB values for the Land Cover classes that were used for Annual NLCD Collection 1. Attributes defined by USGS Value Red, Green, Blue 250 0, 0, 0 11 70, 107, 159 12 209, 222, 248 21 222, 197, 197 22 217, 146, 130 23 235, 0, 0 24 171, 0, 0 31 179, 172, 159 41 104, 171, 95 42 28, 95, 44 43 181, 197, 143 52 204, 184, 121 71 223, 223, 194 81 220, 217, 57 82 171, 108, 40 90 184, 217, 235 95 108, 159, 184 U.S. Geological Survey (USGS), 2024, Annual NLCD Collection 1 Science Products: U.S. Geological Survey data release (ver. 1.1, June 2025), https://doi.org/10.5066/P94UXNTS. U.S. Geological Survey Earth Resources Observation and Science (EROS) Center mailing address

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Sioux Falls SD 57198-0001 United States 800-252-4547 custserv@usgs.gov Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty. Digital Data https://doi.org/10.5066/P94UXNTS None 20250428 U.S. Geological Survey Customer Service Representative mailing and physical

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Sioux Falls SD 57198-0001 USA 605-594-6151 605-594-6589 custserv@usgs.gov FGDC Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998 local time