20250509 13061400 1.0 FALSE usfs_vegetation.usfs.USFS_EDW_LCMS_AnnualChange_AK Server=iipp-arcgis-d-3.cmwoth4tqfhc.us-east-1.rds.amazonaws.com; Service=sde:postgresql:iipp-arcgis-d-3.cmwoth4tqfhc.us-east-1.rds.amazonaws.com; Database=usfs_vegetation; User=usfs; Version=sde.DEFAULT ArcSDE Connection -20037507.067200 -13063317.067200 6529759.379700 11545849.379700 1 002 Projected GCS_WGS_1984 Linear Unit: Meter (1.000000) WGS_1984_Web_Mercator_Auxiliary_Sphere <ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/3.5.0'><WKT>PROJCS[&quot;WGS_1984_Web_Mercator_Auxiliary_Sphere&quot;,GEOGCS[&quot;GCS_WGS_1984&quot;,DATUM[&quot;D_WGS_1984&quot;,SPHEROID[&quot;WGS_1984&quot;,6378137.0,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Mercator_Auxiliary_Sphere&quot;],PARAMETER[&quot;False_Easting&quot;,0.0],PARAMETER[&quot;False_Northing&quot;,0.0],PARAMETER[&quot;Central_Meridian&quot;,0.0],PARAMETER[&quot;Standard_Parallel_1&quot;,0.0],PARAMETER[&quot;Auxiliary_Sphere_Type&quot;,0.0],UNIT[&quot;Meter&quot;,1.0],AUTHORITY[&quot;EPSG&quot;,3857]]</WKT><XOrigin>-20037700</XOrigin><YOrigin>-30241100</YOrigin><XYScale>148923141.92838538</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.001</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>102100</WKID><LatestWKID>3857</LatestWKID></ProjectedCoordinateSystem> 8 FALSE None 1 AMD TRUE continuous unsigned integer 100000 100000 1000 20 2048 80 75 0.01 10 600 300 Basic 0 NorthWest,Center,LockRaster,ByAttribute,Nadir,Viewpoint,Seamline,None None,JPEG,LZ77,LERC Basic None,Basic,Base-Top Height,Top-Top Shadow Height,Base-Top Shadow Height,3D -1 -1 0.8 1 NorthWest,Center,LockRaster,ByAttribute,Nadir,Viewpoint,Seamline,None None,JPEG,LZ77,LERC None,Basic,Full NONE <_docversion_ Sync="TRUE">10 name,minps,maxps,lowps,highps,tag,groupname,productname,centerx,centery,zorder,st_area(shape),st_perimeter(shape),year,start_time,end_time 307200 30 30720 name,minps,maxps,lowps,highps,tag,groupname,productname,centerx,centery,zorder,year,start_time,end_time,st_area(shape),st_perimeter(shape) minps,maxps,lowps,highps,centerx,centery,zorder,st_area(shape),st_perimeter(shape),year -1 1 1 0 0 0 0 -1 start_time end_time YYYYMMDD 0 Years 45819.6885417708 45819.7439943634 20250807 16220700 20250919 14001200 ISO19115_3 150000000 5000 dataset Kevin A. Megown USDA Forest Service Field Services and Innovation Center Geospatial Office (FSIC-GO) Program Lead - Resource, Mapping, Inventory and Monitoring 801-975-3826 801-975-3478 125 S. State Street, Suite 7105 Salt Lake City Utah US sm.fs.lcms@usda.gov 84138 0800 - 1600 MT, M – F 20250919 ArcGIS Metadata 1.0 Kevin A. Megown USDA Forest Service Field Services and Innovation Center Geospatial Office (FSIC-GO) Program Lead - Resource, Mapping, Inventory and Monitoring 801-975-3826 801-975-3478 125 S. State Street, Suite 7105 Salt Lake City Utah US sm.fs.lcms@usda.gov 84138 0800 - 1600 MT, M – F Downloadable data https://data.fs.usda.gov/geodata/rastergateway/LCMS/ https://apps.fs.usda.gov/lcms-viewer Raster Dataset USFS_EDW_LCMS_AnnualChange_AK 20250401 2024-10 raster digital data Landscape Change Monitoring System none Landscape Change Monitoring System Kevin A. Megown USDA Forest Service Field Services and Innovation Center Geospatial Office (FSIC-GO) Program Lead - Resource, Mapping, Inventory and Monitoring 801-975-3826 801-975-3478 125 S. State Street, Suite 7105 Salt Lake City Utah US sm.fs.lcms@usda.gov 84138 0800 - 1600 MT, M - F Kevin A. Megown <div style='text-align:Left;'><div><div><p><span>This product is part of the Landscape Change Monitoring System (LCMS) data suite. It supplies LCMS Change classes for each year that are a refinement of the modeled LCMS Change classes (Slow Loss, Fast Loss, and Gain) and provide information on the cause of landscape change. See additional information about Change in the Entity_and_Attribute_Information or Fields section below.</span></p><p><span /><span>LCMS is a remote sensing-based system for mapping and monitoring landscape change across the United States. Its objective is to develop a consistent approach using the latest technology and advancements in change detection to produce a "best available" map of landscape change. Because no algorithm performs best in all situations, LCMS uses an ensemble of models as predictors, which improves map accuracy across a range of ecosystems and change processes (Healey et al., 2018). The resulting suite of LCMS Change, Land Cover, and Land Use maps offer a holistic depiction of landscape change across the United States over the past four decades.</span></p><p><span>Predictor layers for the LCMS model include outputs from the LandTrendr and CCDC change detection algorithms and terrain information. These components are all accessed and processed using Google Earth Engine (Gorelick et al., 2017). To produce annual composites, the cFmask (Zhu and Woodcock, 2012), cloudScore, Cloud Score + (Pasquarella et al., 2023), and TDOM (Chastain et al., 2019) cloud and cloud shadow masking methods are applied to Landsat Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data. The annual medoid is then computed to summarize each year into a single composite. The composite time series is temporally segmented using LandTrendr (Kennedy et al., 2010; Kennedy et al., 2018; Cohen et al., 2018). All cloud and cloud shadow free values are also temporally segmented using the CCDC algorithm (Zhu and Woodcock, 2014). LandTrendr, CCDC and terrain predictors can be used as independent predictor variables in a Random Forest (Breiman, 2001) model. LandTrendr predictor variables include fitted values, pair-wise differences, segment duration, change magnitude, and slope. CCDC predictor variables include CCDC sine and cosine coefficients (first 3 harmonics), fitted values, and pairwise differences from the Julian Day of each pixel used in the annual composites and LandTrendr. Terrain predictor variables include elevation, slope, sine of aspect, cosine of aspect, and topographic position indices (Weiss, 2001) from the USGS 3D Elevation Program (3DEP) (U.S. Geological Survey, 2019). Reference data are collected using TimeSync, a web-based tool that helps analysts visualize and interpret the Landsat data record from 1984-present (Cohen et al., 2010).</span></p><p><span>Outputs fall into three categories: Change, Land Cover, and Land Use. At its foundation, Change maps areas of Disturbance, Vegetation Successional Growth, and Stable landscape. More detailed levels of Change products are available and are intended to address needs centered around monitoring causes and types of variations in vegetation cover, water extent, or snow/ice extent that may or may not result in a transition of land cover and/or land use. Change, Land Cover, and Land Use are predicted for each year of the time series and serve as the foundational products for LCMS. </span></p></div></div></div> To provide annual maps of vegetation cover Change, Land Cover, and Land Use type for mapping and monitoring landscape change. Funding for this project was provided by the U.S. Forest Service (USFS). RedCastle Resources, Inc. produced the dataset under contract to the USFS Field Services and Innovation Center Geospatial Office (FSIC-GO). References: Breiman, L. (2001). Random Forests. In Machine Learning (Vol. 45, pp. 5-32). https://doi.org/10.1023/A:1010933404324 Chastain, R., Housman, I., Goldstein, J., Finco, M., and Tenneson, K. (2019). Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM top of atmosphere spectral characteristics over the conterminous United States. In Remote Sensing of Environment (Vol. 221, pp. 274-285). https://doi.org/10.1016/j.rse.2018.11.012 Cohen, W. B., Yang, Z., and Kennedy, R. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync - Tools for calibration and validation. In Remote Sensing of Environment (Vol. 114, Issue 12, pp. 2911-2924). https://doi.org/10.1016/j.rse.2010.07.010 Cohen, W. B., Yang, Z., Healey, S. P., Kennedy, R. E., and Gorelick, N. (2018). A LandTrendr multispectral ensemble for forest disturbance detection. In Remote Sensing of Environment (Vol. 205, pp. 131-140). https://doi.org/10.1016/j.rse.2017.11.015 Foga, S., Scaramuzza, P.L., Guo, S., Zhu, Z., Dilley, R.D., Beckmann, T., Schmidt, G.L., Dwyer, J.L., Hughes, M.J., Laue, B. (2017). Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sensing of Environment, 194, 379-390. http://doi.org/10.1016/j.rse.2017.03.026 Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. In Remote Sensing of Environment (Vol. 202, pp. 18-27). https://doi.org/10.1016/j.rse.2017.06.031 Healey, S. P., Cohen, W. B., Yang, Z., Kenneth Brewer, C., Brooks, E. B., Gorelick, N., Hernandez, A. J., Huang, C., Joseph Hughes, M., Kennedy, R. E., Loveland, T. R., Moisen, G. G., Schroeder, T. A., Stehman, S. V., Vogelmann, J. E., Woodcock, C. E., Yang, L., and Zhu, Z. (2018). Mapping forest change using stacked generalization: An ensemble approach. In Remote Sensing of Environment (Vol. 204, pp. 717-728). https://doi.org/10.1016/j.rse.2017.09.029 Helmer, E. H., Ramos, O., del MLopez, T., Quinonez, M., and Diaz, W. (2002). Mapping the forest type and Land Cover of Puerto Rico, a component of the Caribbean biodiversity hotspot. Caribbean Journal of Science, (Vol. 38, Issue 3/4, pp. 165-183) Kennedy, R. E., Yang, Z., and Cohen, W. B. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr - Temporal segmentation algorithms. In Remote Sensing of Environment (Vol. 114, Issue 12, pp. 2897-2910). https://doi.org/10.1016/j.rse.2010.07.008 Kennedy, R., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W., and Healey, S. (2018). Implementation of the LandTrendr Algorithm on Google Earth Engine. In Remote Sensing (Vol. 10, Issue 5, p. 691). https://doi.org/10.3390/rs10050691 Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., and Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. In Remote Sensing of Environment (Vol. 148, pp. 42-57). https://doi.org/10.1016/j.rse.2014.02.015 Pasquarella, V. J., Brown, C. F., Czerwinski, W., and Rucklidge, W. J. (2023). Comprehensive Quality Assessment of Optical Satellite Imagery Using Weakly Supervised Video Learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2124-2134) Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. and Duchesnay, E. (2011). Scikit-learn: Machine Learning in Python. In Journal of Machine Learning Research (Vol. 12, pp. 2825-2830). Pengra, B. W., Stehman, S. V., Horton, J. A., Dockter, D. J., Schroeder, T. A., Yang, Z., Cohen, W. B., Healey, S. P., and Loveland, T. R. (2020). Quality control and assessment of interpreter consistency of annual Land Cover reference data in an operational national monitoring program. In Remote Sensing of Environment (Vol. 238, p. 111261). https://doi.org/10.1016/j.rse.2019.111261 Pesaresi, M. and Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) PID: http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA Stehman, S.V. (2014). Estimating area and map accuracy for stratified random sampling when the strata are different from the map classes. In International Journal of Remote Sensing (Vol. 35, pp. 4923-4939). https://doi.org/10.1080/01431161.2014.930207 USDA National Agricultural Statistics Service Cropland Data Layer (2023). Published crop-specific data layer [Online]. Available at https://nassgeodata.gmu.edu/CropScape/ (accessed 2024). USDA-NASS, Washington, DC. U.S. Geological Survey (2019). USGS 3D Elevation Program Digital Elevation Model, accessed August 2022 at https://developers.google.com/earth-engine/datasets/catalog/USGS_3DEP_10m U.S. Geological Survey (2023). Landsat Collection 2 Known Issues, accessed March 2023 at https://www.usgs.gov/landsat-missions/landsat-collection-2-known-issues Weiss, A.D. (2001). Topographic position and landforms analysis Poster Presentation, ESRI Users Conference, San Diego, CA Yang, L., Jin, S., Danielson, P., Homer, C., Gass, L., Case, A., Costello, C., Dewitz, J., Fry, J., Funk, M., Grannemann, B., Rigge, M., and Xian, G. (2018). A New Generation of the United States National Land Cover Database: Requirements, Research Priorities, Design, and Implementation Strategies (https://www.sciencedirect.com/science/article/abs/pii/S092427161830251X), (pp. 108-123) Zhu, Z., and Woodcock, C. E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery. In Remote Sensing of Environment (Vol. 118, pp. 83-94). https://doi.org/10.1016/j.rse.2011.10.028 Zhu, Z., and Woodcock, C. E. (2014). Continuous change detection and classification of Land Cover using all available Landsat data. In Remote Sensing of Environment (Vol. 144, pp. 152-171). https://doi.org/10.1016/j.rse.2014.01.011 Kevin A. Megown USDA Forest Service Field Services and Innovation Center Geospatial Office (FSIC-GO) Program Lead - Resource, Mapping, Inventory and Monitoring 801-975-3826 801-975-3478 125 S. State Street, Suite 7105 Salt Lake City Utah US sm.fs.lcms@usda.gov 84138 0800 - 1600 MT, M - F U.S. USA United States of America US United States U.S.A. Alaska AK Change Detection Cause of Change Vegetation Cover Change Vegetation Cover Monitoring Land Cover Change Land Cover Monitoring Land Use Change Land Use Monitoring Disturbance Mapping Digital Spatial Data Remote Sensing GIS NGDA Portfolio Themes NGDA National Geospatial Data Asset Land Use Land Cover Theme ISO 19115 Category BaseMaps EarthCover Imagery Environment BaseMaps EarthCover Imagery Digital Spatial Data Continuous Land Cover Land Use Land Cover Change Land Use Change Change Detection NGDA Remote Sensing National Geospatial Data Asset Land Use Land Cover Theme Environment GIS These data were collected using funding from the U.S. Government and can be used without additional permissions or fees. If you use these data in a publication, presentation, or other research product please use the following citation: USDA Forest Service. 2025. USFS Landscape Change Monitoring System Alaska version 2024-10. Salt Lake City, Utah. Appropriate use includes regional to national assessments of vegetation cover, land cover, or land use change trends, total extent of vegetation cover, land cover, or land use change, and aggregated summaries of vegetation cover, land cover, or land use change. This product is the initial output from the modeling process. No post-processing (such as applying a minimum mapping unit or manually burning in known features such as roads) has been performed.The USDA Forest Service makes no warranty, expressed or implied, including the warranties of merchantability and fitness for a particular purpose, nor assumes any legal liability or responsibility for the accuracy, reliability, completeness or utility of these geospatial data, or for the improper or incorrect use of these geospatial data. These geospatial data and related maps or graphics are not legal documents and are not intended to be used as such. The data and maps may not be used to determine title, ownership, legal descriptions or boundaries, legal jurisdiction, or restrictions that may be in place on either public or private land. Natural hazards may or may not be depicted on the data and maps, and land users should exercise due caution. The data are dynamic and may change over time. The user is responsible to verify the limitations of the geospatial data and to use the data accordingly.Additionally, The U.S. Forest Service waives copyright and related rights in the work worldwide through the CC0 (which can be found at https://creativecommons.org/public-domain/cc0/).In accordance with Federal civil rights law and U.S. Department of Agriculture (USDA) civil rights regulations and policies, the USDA, its Agencies, offices, and employees, and institutions participating in or administering USDA programs are prohibited from discriminating based on race, color, national origin, religion, sex, disability, age, marital status, family/parental status, income derived from a public assistance program, political beliefs, or reprisal or retaliation for prior civil rights activity, in any program or activity conducted or funded by USDA (not all bases apply to all programs). Remedies and complaint filing deadlines vary by program or incident. Persons with disabilities who require alternative means of communication for program information (e.g., Braille, large print, audiotape, American Sign Language, etc.) should contact the State or local Agency that administers the program or contact USDA through the Telecommunications Relay Service at 711 (voice and TTY). Additionally, program information may be made available in languages other than English. To file a program discrimination complaint, complete the USDA Program Discrimination Complaint Form, AD-3027, found online at How to File a Program Discrimination Complaint and at any USDA office or write a letter addressed to USDA and provide in the letter all of the information requested in the form. To request a copy of the complaint form, call (866) 632-9992. Submit your completed form or letter to USDA by: (1) mail: U.S. Department of Agriculture, Office of the Assistant Secretary for Civil Rights, 1400 Independence Avenue, SW, Mail Stop 9410, Washington, D.C. 20250-9410; (2) fax: (202) 690-7442; or (3) email: program.intake@usda.gov. USDA is an equal opportunity provider, employer, and lender. none n/a <div style='text-align:Left;'><div><div><p><span>These data were collected using funding from the U.S. Government and can be used without additional permissions or fees. If you use these data in a publication, presentation, or other research product please use the following citation: </span><span>USDA Forest Service. 2025. USFS Landscape Change Monitoring System Alaska version 2024-10. Salt Lake City, Utah. Appropriate use includes regional to national assessments of vegetation cover, Land Cover, or Land Use change trends, total extent of vegetation cover, Land Cover, or Land Use change, and aggregated summaries of vegetation cover, Land Cover, or Land Use change. This product is the initial output from the modeling process with post-processing applied in the map assemblage rulesets as described in the process steps below..</span></p><p><span>The USDA Forest Service makes no warranty, expressed or implied, including the warranties of merchantability and fitness for a particular purpose, nor assumes any legal liability or responsibility for the accuracy, reliability, completeness or utility of these geospatial data, or for the improper or incorrect use of these geospatial data. These geospatial data and related maps or graphics are not legal documents and are not intended to be used as such. The data and maps may not be used to determine title, ownership, legal descriptions or boundaries, legal jurisdiction, or restrictions that may be in place on either public or private land. Natural hazards may or may not be depicted on the data and maps, and land users should exercise due caution. The data are dynamic and may change over time. The user is responsible to verify the limitations of the geospatial data and to use the data accordingly.</span></p><p><span>Additionally, The U.S. Forest Service waives copyright and related rights in the work worldwide through the CC0 (which can be found at https://creativecommons.org/public-domain/cc0/).</span></p><p><span>In accordance with Federal civil rights law and U.S. Department of Agriculture (USDA) civil rights regulations and policies, the USDA, its Agencies, offices, and employees, and institutions participating in or administering USDA programs are prohibited from discriminating based on race, color, national origin, religion, sex, disability, age, marital status, family/parental status, income derived from a public assistance program, political beliefs, or reprisal or retaliation for prior civil rights activity, in any program or activity conducted or funded by USDA (not all bases apply to all programs). Remedies and complaint filing deadlines vary by program or incident. </span></p><p><span>Persons with disabilities who require alternative means of communication for program information (e.g., Braille, large print, audiotape, American Sign Language, etc.) should contact the State or local Agency that administers the program or contact USDA through the Telecommunications Relay Service at 711 (voice and TTY). Additionally, program information may be made available in languages other than English. </span></p><p><span>To file a program discrimination complaint, complete the USDA Program Discrimination Complaint Form, AD-3027, found online at How to File a Program Discrimination Complaint and at any USDA office or write a letter addressed to USDA and provide in the letter all of the information requested in the form. To request a copy of the complaint form, call (866) 632-9992. Submit your completed form or letter to USDA by: (1) mail: U.S. Department of Agriculture, Office of the Assistant Secretary for Civil Rights, 1400 Independence Avenue, SW, Mail Stop 9410, Washington, D.C. 20250-9410; (2) fax: (202) 690-7442; or (3) email: program.intake@usda.gov. </span></p><p><span>USDA is an equal opportunity provider, employer, and lender. </span></p></div></div></div> Corner Coordinates (center of pixel, meters): upper left: -2171805.0 (X), 2379465.0 (Y); lower right: 1492755.0 (X), 412875.0 (Y). Microsoft Windows Server 2016 Technical Preview Version 10.0 (Build 17763) ; Esri ArcGIS 13.5.2.57366 Coastal and Interior Alaska study area. 1 157.4892484217836 -132.23776882479035 51.50392499638192 63.90611489450259 19850615 20240915 1985-2024 The USDA Forest Service makes no warranty, expressed or implied, including the warranties of merchantability and fitness for a particular purpose, nor assumes any legal liability or responsibility for the accuracy, reliability, completeness or utility of these geospatial data, or for the improper or incorrect use of these geospatial data. These geospatial data and related maps or graphics are not legal documents and are not intended to be used as such. The data and maps may not be used to determine title, ownership, legal descriptions or boundaries, legal jurisdiction, or restrictions that may be in place on either public or private land. Natural hazards may or may not be depicted on the data and maps, and land users should exercise due caution. The data are dynamic and may change over time. The user is responsible to verify the limitations of the geospatial data and to use the data accordingly. Collect reference data using the TimeSync image analysis tool (Cohen et al., 2010). To set up the tool, acquire all Landsat 4 and 5 TM, 7 ETM+, and 8 OLI Tier 1 data and mask out any values with cFmask cloud and cloud shadow qa bits. Use the tool to attribute annual Change process, Land Cover, and Land Use classes for each 30 m x 30 m pixel in the reference data sample.Cohen, W. B., Yang, Z., and Kennedy, R. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync - Tools for calibration and validation. In Remote Sensing of Environment (Vol. 114, Issue 12, pp. 2911-2924). https://doi.org/10.1016/j.rse.2010.07.010 TimeSync reference data 20240601 Acquire all Landsat 4 and 5 TM, 7 ETM+, 8 OLI Tier 1, 9 OLI-2 Tier 1, and Sentinel 2a and 2b Level-1C top of atmosphere data from June 15th to September 15th for AK, and mask out clouds and cloud shadows using the cFmask (Zhu and Woodcock, 2012) and Cloud Score + (Pasquarella et al., 2023) and cloudScore cloud and TDOM cloud shadow masking algorithms (Chastain et al., 2019). For each year, compute the geometric medoid of the remaining values, resulting in an annual composite.Chastain, R., Housman, I., Goldstein, J., Finco, M., and Tenneson, K. (2019). Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM top of atmosphere spectral characteristics over the conterminous United States. In Remote Sensing of Environment (Vol. 221, pp. 274-285). https://doi.org/10.1016/j.rse.2018.11.012Zhu, Z., and Woodcock, C. E. (2012). Object-based cloud and cloud shadow detection in Landsat imagery. In Remote Sensing of Environment (Vol. 118, pp. 83-94). https://doi.org/10.1016/j.rse.2011.10.028 Annual Landsat-Sentinel2 image composites 20231121 Apply the LandTrendr algorithm (Kennedy et al., 2010; Kennedy et al., 2018) to the medoid pixels composite time series.Kennedy, R. E., Yang, Z., and Cohen, W. B. (2010). Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr - Temporal segmentation algorithms. In Remote Sensing of Environment (Vol. 114, Issue 12, pp. 2897-2910). https://doi.org/10.1016/j.rse.2010.07.008Kennedy, R., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W., and Healey, S. (2018). Implementation of the LandTrendr Algorithm on Google Earth Engine. In Remote Sensing (Vol. 10, Issue 5, p. 691). https://doi.org/10.3390/rs10050691 Annual LandTrendr Fitted Images 20241001 Apply the Continuous Change Detection and Classification (CCDC) algorithm (Zhu and Woodcock, 2014) to the cloud and cloud shadow-free values of Landsat 4 and 5 TM, 7 ETM+, 8 OLI, and 9 OLI-2 Tier 1 top of atmosphere data for AK from January 1 to December 31. For CONUS and AK, rather than completely rerunning CCDC for the entire time series, we feathered together two partial time series CCDC runs. The first run was the 1984-2022 CCDC run used in v2022.8, while the second run was 2014-2024. Between 2015 and 2022, the v2022.8 CCDC run was feathered together with the new CCDC run of 2014-2024 using a linearly weighted feathering method.Zhu, Z., and Woodcock, C. E. (2014). Continuous change detection and classification of Land Cover using all available Landsat data. In Remote Sensing of Environment (Vol. 144, pp. 152-171). https://doi.org/10.1016/j.rse.2014.01.011 Annual CCDC Predicted Images 20241001 Acquire USGS 3D Elevation Program (3DEP) data (U.S. Geological Survey, 2019) and compute slope, sine (aspect), and cosine (aspect).U.S. Geological Survey. (2019). USGS 3D Elevation Program Digital Elevation Model, accessed August 2022 at https://developers.google.com/earth-engine/datasets/catalog/USGS_3DEP_10m Digital Elevation Model (DEM) 20230301 Train Random Forest models (Breiman, 2001) using reference data from TimeSync and predictor data from LandTrendr, CCDC, and terrain indices to predict annual Change, Land Cover, and Land Use classes.Breiman, L. (2001). Random Forests. In Machine Learning (Vol. 45, pp. 5-32). https://doi.org/10.1023/A:1010933404324 Random Forest models 20250101 Annual modeled Change classifications are assembled from the individual class binary models. The modeled Change classes are Slow Loss, Fast Loss, Gain, and Stable. For each year on a pixel-wise basis, the class with the highest confidence is the class chosen for the modeled LCMS Change class. A Change assemblage rule was applied to all study areas that prevents Change in non-vegetated Land Cover classes. Following this assemblage rule, a pixel is finally classified according to the highest probability class. For the Change model, the class with the highest confidence must also have a value above the threshold of that class. This is done because the "Stable" class is not modeled explicitly. Modeled Change assemblage 20250201 The final Change product is a reclassification of the modeled LCMS Change classes (Slow Loss, Fast Loss, and Gain) that provides information on the cause of landscape change (e.g., Tree Removal, Wildfire, Wind damage). The LCMS science team tested several machine learning approaches to derive the cause of LCMS Change. However, these tests proved less successful than a ruleset approach. The ruleset approach relies on ancillary datasets such as the Monitoring Trends in Burn Severity (MTBS) Burn Severity Images (USDA FS/USGS) to attribute and reclassify LCMS Slow Loss and Fast Loss into more detailed classes. Final Change classes includes 15 classes: Wind; Hurricane; Snow or Ice Transition; Desiccation; Inundation; Prescribed Fire; Wildfire; Mechanical Land Transformation; Tree Removal; Defoliation; Southern Pine Beetle; Insect, Disease, or Drought Stress; Other Loss; Vegetation Successional Growth; Stable; and Non-Processing Area Mask.The modeled LCMS Change data were used to identify Slow Loss, Fast Loss, and Gain events. LCMS Land Cover data were used to differentiate between vegetated and non-vegetated land.1. Insect, Disease, or Drought Stress is the reclassification where Slow Loss of vegetation occurred.2. Wildfire is the reclassification where fire data (MTBS, RAVG, PIAD, IAFP) identified Wildfire and Fast Loss occurred (National Interagency Fire Center Open Data, 2023; USDA FS/USGS; Miller et al., 2015).3. Prescribed Burn is the reclassification where fire data (MTBS or IAFP) identified Prescribed Burn and Fast Loss occurred (USDA FS/USGS).4. Tree Removal is the reclassification where Fast Loss occurred, the LCMS Land Cover class mode of the previous three years is a Tree class, the area of connected pixels is 1.5 hectare or greater, Tree Canopy Cover loss is 30 percent or greater (USDA FS, 2025b), pixels are not located in protected wilderness (USGS GAP, 2018), and there was not a Wildfire or Prescribed Burn.5. Hurricane (CONUS and PRUSVI only) is the reclassification where Fast Loss occurred, a Hurricane occurred (Landsea et al., 2013), tree damage was caused by a storm event (Gardiner et al., 2000; Gardiner et al., 2008), inundation occurred (pixels below 3 meters elevation) (USGS, 2019), and there was not a Wildfire or Prescribed Burn event.6. Wind is the reclassification where Fast Loss occurred, insect and disease surveys (IDS) data identified wind damage (USDA FS, 2023), and there was not a Wildfire, Prescribed Burn, or Hurricane.7. Desiccation is the reclassification where LCMS Land Cover data identified Water in the previous year, followed by a class other than Water in the current year (USDA FS, 2025a).8. Inundation is the reclassification where LCMS Land Cover data identified a class other than Water in the previous year, followed by Water in the current year (USDA FS, 2025a).9. Southern Pine Beetle (CONUS only) is the reclassification where Fast Loss occurred and the area of connected pixels totals less than 1.5 hectare, the LCMS Land Cover class mode of the previous three years was a Tree class, digitized polygons or insect and disease surveys (IDS) data identified southern pine beetle (USDA FS, 2023), and there was not a Wildfire, Prescribed Burn, Hurricane, or Wind event.10. Defoliation is the reclassification where Fast Loss occurred, the LCMS Land Cover class mode of the previous three years was a Tree class, digitized polygons or insect and disease surveys (IDS) data identified defoliation (USDA FS, 2023), Tree Canopy Cover loss is 50 percent or greater, and there was not a Wildfire, Prescribed Burn, Hurricane, or Wind event.11. Mechanical Land Transformation is the reclassification if one of three scenarios occurred: Scenario 1: Fast Loss occurred outside protected wilderness, the LCMS Land Use class mode of the following three years was Developed, and there was not a Wildfire, Prescribed Burn, Hurricane, Wind, Desiccation, or Inundation event. Scenario 2: Fast Loss occurred outside protected wilderness, the LCMS Land Use class mode of the previous three years was Agriculture, LCMS Land Cover data indicates there was a Land Cover change, and there was not a Wildfire, Prescribed Burn, Hurricane, Wind, Desiccation, or Inundation event. Scenario 3: Fast Loss occurred outside protected wilderness, mining occurred (Tang and Werner, 2023), and there was not a Wildfire, Prescribed Burn, Hurricane, Wind, Desiccation, or Inundation event.12. Snow or Ice Transition (CONUS and AK only) is the reclassification where LCMS Land Cover data identified a change from or to Snow or Ice between the previous year and current year.13. Other Loss is the reclassification where Fast Loss occurred and none of the other previous change events occurred.14. Vegetation Successional Growth is the reclassification where modeled vegetation Gain occurred.15. Stable is the reclassification where no Loss or Gain event occurred.Miller, J. D., and Quayle, B. (2015). Calibration and validation of immediate post-fire satellite-derived data to three severity metrics. Fire Ecology, (Vol. 11, Issue 2, pp. 12-30.National Interagency Fire Center Open Data. (2023, March 13). Environmental Systems Research Institute, Inc. ArcGIS Open Data. https://data-nifc.opendata.arcgis.com/datasets/e02b85c0ea784ce7bd8add7ae3d293d0/exploreTang, L. and Werner, T.T. (2023). Global mining footprint mapped from high-resolution satellite imagery. Communications earth and environment, 4(134). https://doi.org/10.5281/zenodo.7894216USDA Forest Service (2023). Insect and Disease Survey Maps and Data. USDA Forest Service. https://data.nal.usda.gov/dataset/insect-disease-survey-maps-and-data. Accessed 2024-07-25USDA Forest Service/US Geological Survey. Monitoring Trends in Burn Severity Thematic Burn Severity. Salt Lake City, Utah/Sioux Falls, South DakotaUSDA Forest Service. (2025). USFS NLCD Percent Tree Canopy CONUS v2023-5. Salt Lake City, UT.U.S. Geological Survey (USGS) Gap Analysis Project (GAP). (2018). Protected Areas Database of the United States (PAD-US): U.S. Geological Survey data release, https://doi.org/10.5066/P955KPLE Cause of Change Ruleset 20250301 Following map assemblage a map accuracy assessment was run. The accuracy assessment for Land Use and Land Cover was performed using a grouped, stratified, 10-fold cross validation, a method that accounts for the stratified random sample design following Stehman (2014), with the annual TimeSync training data collected from each plot location. For Change, the assembled, rule-based predictions based on modeled Change and ancillary datasets were compared with the annual training data collected from each plot location to assess accuracy.Stehman, S.V. (2014). Estimating area and map accuracy for stratified random sampling when the strata are different from the map classes. In International Journal of Remote Sensing (Vol. 35, pp. 4923-4939). https://doi.org/10.1080/01431161.2014.930207 Accuracy Assessment 20250401 Primary data sources include USGS Landsat Collection 2 Landsat 4, 5, 7, 8 and 9 Tier 1 top of atmosphere reflectance data, Sentinel 2a and 2b Level-1C top of atmosphere reflectance data, and the USGS 3D Elevation Program (3DEP) terrain data. These serve as the foundational raster data sources for all modeling. These data originate from the US Geological Survey Earth Resource Observation and Science (EROS) Center (Landsat and NED data) and the European Space Agency (ESA; Sentinel 2 data). All data access and processing is performed using the Google Earth Engine API (Gorelick, 2017).All other data, including reference data, predictor variables, and final map products, are derived within the LCMS framework. All years are modeled separately. As such, measurements from one year to another are not inherently dependent on other years See the data quality report for methods Data extend across all of Alaska The accuracy assessment for Land Use and Land Cover was performed using a grouped, stratified, 10-fold cross validation, a method that accounts for the stratified random sample design following Stehman (2014), with the annual TimeSync training data collected from each plot location. For Change, the final, assembled, rule-based predictions were based on the combination of model-predicted Slow Loss, Fast Loss, Gain, or Stable (using the same grouped, stratified, 10-fold cross validation method) with ancillary datasets, which were then compared with the annual training data collected from each plot location to assess accuracy. Accuracy is measured using the proportion of correctly predicted vs incorrectly predicted outputs. Accuracy metrics for each Change, Land Cover, and Land Use model are assessed separately using a grouped, stratified 10-fold cross validation. This is a variant of the k-fold cross validation method whereby each sample point is classified as belonging to a specific, non-overlapping group, in this case each TimeSync plot. Since each TimeSync plot provides 39 years of training data, this method ensures that each of the folds is appropriately balanced and that each plot is only included in one of the cross-validation folds. Following guidance from Healey et al (2018), Change predictions are given a one-year buffer to be counted as correct. For a correct Change prediction, the reference Change call must have an equivalent predicted Change call within one year before or after. Additionally, an optimum model confidence threshold is determined for each model by assessing the precision and recall at every possible threshold (from 0-100) and selecting the threshold that maximizes both precision and recall. The accuracy metrics of the grouped 10-fold cross validation are then evaluated using a method that accounts for the stratified random sample design, following Stehman (2014). References: Cohen, W. B., Healey, S. P., Yang, Z., Stehman, S. V., Brewer, C. K., Brooks, E. B., Gorelick, N., Huang, C., Hughes, M. J., Kennedy, R. E. and Loveland, T. R. (2017). How similar are forest disturbance maps derived from different Landsat time series algorithms?. Forests, (Vol. 8, Issue 4, p. 98)Healey, S. P., Cohen, W. B., Yang, Z., Kenneth Brewer, C., Brooks, E. B., Gorelick, N., Hernandez, A. J., Huang, C., Joseph Hughes, M., Kennedy, R. E., Loveland, T. R., Moisen, G. G., Schroeder, T. A., Stehman, S. V., Vogelmann, J. E., Woodcock, C. E., Yang, L., and Zhu, Z. (2018). Mapping forest change using stacked generalization: An ensemble approach. In Remote Sensing of Environment (Vol. 204, pp. 717-728). https://doi.org/10.1016/j.rse.2017.09.029Stehman, S.V. (2014). Estimating area and map accuracy for stratified random sampling when the strata are different from the map classes. In International Journal of Remote Sensing (Vol. 35, pp. 4923-4939). https://doi.org/10.1080/01431161.2014.930207 Results from Grouped, Stratified 10-fold Cross Validation 20250401 20250401 20250401 Cause_of_Change: Change_Level_2Overall Accuracy: 96.80 +/- 0.07Balanced Accuracy: 23.97 +/- 6.33Kappa: 0.41Users Accuracy (100%-Commission Error): Desiccation: Too few samples to assess accuracyFire: 82.76Veg-Growth: 51.33Harvest: 64.81Insect-Disease-Drought: 6.28Inundation: Too few samples to assess accuracyMechanical: Too few samples to assess accuracyOther: 0.51Stable: 98.48Wind: Too few samples to assess accuracyUsers Error: Desiccation: Too few samples to assess accuracyFire: 4.42Veg-Growth: 1.47Harvest: 15.30Insect-Disease-Drought: 4.74Inundation: Too few samples to assess accuracyMechanical: Too few samples to assess accuracyOther: 0.41Stable: 0.05Wind: Too few samples to assess accuracyProducers Accuracy (100%-Omission Error): Desiccation: Too few samples to assess accuracyFire: 53.08Veg-Growth: 55.37Harvest: 16.90Insect-Disease-Drought: 5.24Inundation: Too few samples to assess accuracyMechanical: Too few samples to assess accuracyOther: 0.55Stable: 98.29Wind: Too few samples to assess accuracyProducers Error: Desiccation: Too few samples to assess accuracyFire: 4.68Veg-Growth: 1.52Harvest: 6.13Insect-Disease-Drought: 3.98Inundation: Too few samples to assess accuracyMechanical: Too few samples to assess accuracyOther: 0.44Stable: 0.05Wind: Too few samples to assess accuracyNumber of Samples in each class: Desiccation: 2 (Too few samples to assess accuracy)Fire: 147Veg-Growth: 1477Harvest: 101Insect-Disease-Drought: 85Inundation: 9 (Too few samples to assess accuracy)Mechanical: 23 (Too few samples to assess accuracy)Other: 185Stable: 55984Wind: 0 1 167203 30.000000 232473 30.000000 2 1 0 -20037507.067200 6529759.379700 -20037507.067200 11545849.379700 -13063317.067200 11545849.379700 -13063317.067200 6529759.379700 -16550412.067200 9037804.379700 EPSG 6.18.3(9.3.1.2) Band_1 16.0 7.0 8 False False False False False Change class code for each year. A total of three Change classes (Slow Loss, Fast Loss, and Gain) are modeled for each year (no slow loss for PRUSVI). Slow Loss is a loss of vegetation cover over a period of time generally not associated with a discrete event such as a fire or harvest. Fast Loss is a loss of vegetation cover over a short period of time generally associated with a discrete event such as a fire or harvest. Gain is when vegetative indices indicate a positive trend over time. For a given year, each Change class is predicted using a binary Random Forest model, which outputs a probability (proportion of the trees within the Random Forest model) that the pixel belongs to each class. A modeled Change class (Slow Loss, Fast Loss, Gain, or Stable) is assigned to the class with the highest probability that is also above a specified threshold. Any pixel that does not have any value above each class' respective threshold is assigned to the Stable class. We then reclassify the modeled Change values using a ruleset based on ancillary data to more detailed classes that provide information on the cause of change. The result is a Change product with values that range from 1 to 15: 1: WIND: Fast Loss occurred, the Storm Prediction Center severe report database or digitized Wind polygons identified a tornado or Wind event, and there was not a Wildfire, Prescribed Burn, or Hurricane. 2: HURRICANE: Fast Loss occurred, Hurdat data identified a hurricane, tree damage was caused by a storm event, inundation occurred (this rule only applied to areas less than or equal to 3 meters), and there was not a Wildfire or Prescribed Burn. 3: SNOW OR ICE TRANSITION: LCMS Land Cover data identified a change from or to snow and ice between the previous year and current year. 4: DESICCATION: LCMS Land Cover data identified water in the previous year, followed by no water in the current year. 5: INUNDATION: LCMS Land Cover data identified no water in the previous year, followed by water in the current year. 6: PRESCRIBED FIRE: Fast Loss occurred and fire data (MTBS or IAFP) identified prescribed burn. 7: WILDFIRE: Fast Loss occurred and fire data (MTBS, RAVG, PIAD, IAFP) identified wildfire. 8: MECHANICAL LAND TRANSFORMATION: One of three scenarios occurred. Scenario 1: Fast Loss occurred in non-protected wilderness, the LCMS Land Use class mode of the following three years was developed, and there was not a Wildfire, Prescribed Burn, Hurricane, Wind, Desiccation, or Inundation event. Scenario 2: Fast Loss occurred in non-protected wilderness, the LCMS Land Use class mode of the previous three years was Agriculture, LCMS Land Cover data indicates there was a Land Cover change, and there was not a Wildfire, Prescribed Burn, Hurricane, Wind, Desiccation, or Inundation event. Scenario 3: Fast Loss occurred in non-protected wilderness, mining occurred (Tang and Werner, 2023), and there was not a Wildfire, Prescribed Burn, Hurricane, Wind, Desiccation, or Inundation event 9: TREE REMOVAL: Fast Loss occurred, the LCMS Land Cover class mode of the previous three years was a Tree class, the Tree Removal was 1.5 hectare or greater, Tree Canopy Cover loss was 30 percent or greater, Tree Removal occurred in non-protected wilderness, and there was not a Wildfire or Prescribed Burn. 10: DEFOLIATION: Fast Loss occurred, the LCMS Land Cover class mode of the previous three years was a Tree class, digitized polygons identified defoliation, insect and disease surveys (IDS) data identified defoliation, Tree Canopy Cover loss was 50 percent or greater, and there was not a Wildfire, Prescribed Burn, Hurricane, or Wind event. 11: SOUTHERN PINE BEETLE: Fast Loss occurred and was less than 1.5 hectare, the LCMS Land Cover class mode of the previous three years was a Tree class, digitized polygons identified southern pine beetle, insect and disease surveys (IDS) data identified southern pine beetle, and there was not a Wildfire, Prescribed Burn, Hurricane, or Wind event. 12: INSECT DISEASE OR DROUGHT STRESS: Slow Loss to vegetation occurred. 13: OTHER LOSS: Land (regardless of use) where the spectral trend or other supporting evidence suggests a disturbance or change event has occurred but the definitive cause cannot be determined or the type of change fails to meet any of the change process categories defined above. 14: VEGETATION GROWTH: Land exhibiting an increase in vegetation cover due to growth and succession over one or more years. Applicable to any areas that may express spectral change associated with vegetation regrowth. In developed areas, growth can result from maturing vegetation and/or newly installed lawns and landscaping. In forests, growth includes vegetation growth from bare ground, as well as the over topping of intermediate and co-dominate trees and/or lower-lying grasses and shrubs. Growth/Recovery segments recorded following forest harvest will likely transition through different land cover classes as the forest regenerates. For these changes to be considered growth/recovery, spectral values should closely adhere to an increasing trend line (e.g. a positive slope that would, if extended to ~20 years, be on the order of 0.10 units of NDVI) which persists for several years. 15: STABLE: Where no significant change is evident in the spectral response and the trend is essentially flat. 16: NON-PROCESSING AREA MASK: Where no cloud or cloud shadow-free data are available to produce an output. 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