U.S. Geological Survey 20241024 raster digital data None None Sioux Falls, SD U.S. Geological Survey https://doi.org/10.5066/P94UXNTS https://www.mrlc.gov/data 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. 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 2023 ground condition Complete Annually -130.2328 -63.6722 52.8510 21.7423 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 USGS Metadata Identifier USGS:66f5b27dd34e791ae5dfd2fa 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

47914 252nd Street

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 2024 publication https://www.mrlc.gov/documentation Digital and/or Hardcopy 2024 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. 2024 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. 2024 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. 2024 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. 2024 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. 2024 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. 2024 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. 2024 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. 2024 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. 2024 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. 2024 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. 2024 Raster Grid Cell 5000 5000 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 C1V0 Land Cover Change Index 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 Change Index Class Code Value Annual NLCD 250 NoData Annual NLCD 1 No change Annual NLCD 2 Water change Annual NLCD 3 Urban change Annual NLCD 4 Wetland within class change Annual NLCD 5 Herbaceous wetland change Annual NLCD 6 Agriculture within class change Annual NLCD 7 Cultivated crop change Annual NLCD 8 Hay/pasture change Annual NLCD 9 Rangeland herbaceous and shrub change Annual NLCD 10 Barren change Annual NLCD 12 Woody wetland change Annual NLCD 14 Forest type change Annual NLCD 21 Urban within class change Annual NLCD 22 Forest transition mixed rangeland change Annual NLCD 23 Forest transition mixed rangeland (shrub/scrub) change Annual NLCD Land Cover Change Index Class RGB Color Value Table. The specific RGB values for the Land Cover Change Index classes that were used for Annual NLCD Collection 1.0 Attributes defined by USGS Value Red, Green, Blue 250 = 0, 0, 0 (no data) 1 = 0, 96, 0 (no change) 2 = 0, 0, 255 (water change) 3 = 237, 131, 237 (urban change) 4 = 122, 255, 211 (wetland within class change) 5 = 0, 159, 223 (herbaceous wetland change) 6 = 255, 160, 0 (agriculture within class change) 7 = 160, 40, 40 (cultivated crop change) 8 = 255, 255, 0 (hay/pasture change) 9 = 210, 182, 134 (rangeland herbaceous and shrub change) 10 = 193, 193, 193 (barren change) 12 = 254, 0, 0 (woody wetland change) 14 = 255, 255, 255 (forest type change) 21 = 160, 32, 236 (urban within change) 22 = 0, 255, 0 (forest transition change 1) 23 = 0, 255, 0 (forest transition change 2 (early date or not-stand replace disturbance)) (RGB values for 22 and 23 are the same and can be combined if desired) U.S. Geological Survey Earth Resources Observation and Science (EROS) Center mailing address

47914 252nd Street

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 20260318 U.S. Geological Survey Customer Service Representative mailing and physical

47914 252nd Street

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