20250509 13084400 1.0 FALSE usfs_vegetation.usfs.USFS_EDW_LCMS_AnnualLandcover_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 20037507.842788 6298429.379700 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 0 0 -1 1 -1 Generic 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 0 0 start_time end_time YYYYMMDD 0 Years 45824.7002569329 45824.7037413079 20250707 19222700 20250919 14242900 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_AnnualLandcover_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 shows LCMS modeled Land Cover classes for each year. See additional information about Land Cover 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 /><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 /><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 Land Cover maps were assembled from the model confidence class scores. For each year on a pixel-wise basis, the class with the highest confidence is the class chosen for the LCMS Land Cover product.A ruleset was implemented to limit Tree and Snow/Ice commission in AK intertidal zones. At elevations less than 2m, the probability of the Tree class is set to zero. At elevations less than 2m and where Snow or Ice probability is less than 40, the probability of the Snow or class is set to zero.Another rule was implemented to limit Water commission in urban areas. A developed mask produced from the Joint Research Center Global Human Settlement Layer (GHSL) (Pesaresi and Politis, 2023) was used in the ruleset. The Barren-Impervious class was set to the maximum probability for pixels that intersected the GHSL developed mask, had Water probability less than 35, and had Barren-Impervious probability greater than 5.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 Land cover assemblage 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 Land_CoverOverall Accuracy: 64.45 +/- 0.20Balanced Accuracy: 35.76 +/- 0.35Kappa: 0.57Users Accuracy (100%-Commission Error): Trees 71.86Tall Shrubs and Trees Mix 0.00Shrubs and Trees Mix 0.88Grass/forb/herb and Trees Mix 1.81Barren and Trees Mix nanTall Shrubs 57.69Shrubs 25.66Grass/forb/herb and Shrubs Mix nanBarren and Shrubs Mix nanGrass/forb/herb 42.29Barren and Grass/forb/herb Mix 2.56Barren or Impervious 67.68Snow or Ice 90.59Water 96.83Users Error: Trees 0.31Tall Shrubs and Trees Mix 0.00Shrubs and Trees Mix 1.34Grass/forb/herb and Trees Mix 0.66Barren and Trees Mix nanTall Shrubs 0.64Shrubs 0.80Grass/forb/herb and Shrubs Mix nanBarren and Shrubs Mix nanGrass/forb/herb 0.45Barren and Grass/forb/herb Mix 2.41Barren or Impervious 0.62Snow or Ice 0.36Water 0.31Producers Accuracy (100%-Omission Error): Trees 91.85Tall Shrubs and Trees Mix 0.00Shrubs and Trees Mix 0.03Grass/forb/herb and Trees Mix 0.74Barren and Trees Mix 0.00Tall Shrubs 63.39Shrubs 12.88Grass/forb/herb and Shrubs Mix 0.00Barren and Shrubs Mix 0.00Grass/forb/herb 63.47Barren and Grass/forb/herb Mix 0.20Barren or Impervious 81.79Snow or Ice 96.68Water 89.60Producers Error: Trees 0.21Tall Shrubs and Trees Mix 0.00Shrubs and Trees Mix 0.04Grass/forb/herb and Trees Mix 0.27Barren and Trees Mix 0.00Tall Shrubs 0.65Shrubs 0.43Grass/forb/herb and Shrubs Mix 0.00Barren and Shrubs Mix 0.00Grass/forb/herb 0.54Barren and Grass/forb/herb Mix 0.19Barren or Impervious 0.56Snow or Ice 0.23Water 0.52Number of Samples in each class: Trees 21599Tall Shrubs and Trees Mix 472Shrubs and Trees Mix 1787Grass/forb/herb and Trees Mix 1121Barren and Trees Mix 204Tall Shrubs 5407Shrubs 5641Grass/forb/herb and Shrubs Mix 3541Barren and Shrubs Mix 271Grass/forb/herb 7843Barren and Grass/forb/herb Mix 617Barren or Impervious 4587Snow or Ice 2828Water 2095 1 174914 30.000000 1335834 29.999996 2 1 0 -20037507.067200 6298429.379700 -20037507.067200 11545849.379700 20037507.842788 11545849.379700 20037507.842788 6298429.379700 0.387794 8922139.379700 EPSG 6.18.3(9.3.1.2) Band_1 15.0 1.0 8 False False False False False Land cover class code for each year. A total of 14 Land Cover classes are mapped on an annual basis using TimeSync reference data and spectral information derived from Landsat imagery. All classes are predicted with a single Random Forest model. 1: TREES: The majority of the pixel is comprised of live or standing dead trees. 2: TALL SHRUBS AND TREES MIX: (AK Only) The majority of the pixel is comprised of shrubs greater than 1m in height and is also comprised of at least 10 percent live or standing dead trees. 3: SHRUBS AND TREES MIX: The majority of the pixel is comprised of shrubs and is also comprised of at least 10 percent live or standing dead trees. 4: GRASS/FORB/HERB AND TREES MIX: The majority of the pixel is comprised of perennial grasses, forbs, or other forms of herbaceous vegetation and is also comprised of at least 10 percent live or standing dead trees. 5: BARREN AND TREES MIX: The majority of the pixel is comprised of bare soil exposed by disturbance (e.g., soil uncovered by mechanical clearing or forest harvest), as well as perennially barren areas such as deserts, playas, rock outcroppings (including minerals and other geologic materials exposed by surface mining activities), sand dunes, salt flats, and beaches. Roads made of dirt and gravel are also considered barren and is also comprised of at least 10 percent live or standing dead trees. 6: TALL SHRUBS: (AK Only) The majority of the pixel is comprised of shrubs greater than 1m in height. 7: SHRUBS: The majority of the pixel is comprised of shrubs. 8: GRASS/FORB/HERB AND SHRUBS MIX: The majority of the pixel is comprised of perennial grasses, forbs, or other forms of herbaceous vegetation and is also comprised of at least 10 percent shrubs. 9: BARREN AND SHRUBS MIX: The majority of the pixel is comprised of bare soil exposed by disturbance (e.g., soil uncovered by mechanical clearing or forest harvest), as well as perennially barren areas such as deserts, playas, rock outcroppings (including minerals and other geologic materials exposed by surface mining activities), sand dunes, salt flats, and beaches. Roads made of dirt and gravel are also considered barren and is also comprised of at least 10 percent shrubs. 10: GRASS/FORB/HERB: The majority of the pixel is comprised of perennial grasses, forbs, or other forms of herbaceous vegetation. 11: BARREN AND GRASS/FORB/HERB MIX: The majority of the pixel is comprised of bare soil exposed by disturbance (e.g., soil uncovered by mechanical clearing or forest harvest), as well as perennially barren areas such as deserts, playas, rock outcroppings (including minerals and other geologic materials exposed by surface mining activities), sand dunes, salt flats, and beaches. Roads made of dirt and gravel are also considered barren and is also comprised of at least 10 percent perennial grasses, forbs, or other forms of herbaceous vegetation. 12: BARREN OR IMPERVIOUS: The majority of the pixel is comprised of 1) bare soil exposed by disturbance (e.g., soil uncovered by mechanical clearing or forest harvest), as well as perennially barren areas such as deserts, playas, rock outcroppings (including minerals and other geologic materials exposed by surface mining activities), sand dunes, salt flats, and beaches. Roads made of dirt and gravel are also considered barren or 2) man-made materials that water cannot penetrate, such as paved roads, rooftops, and parking lots. 13: SNOW OR ICE: The majority of the pixel is comprised of snow or ice. 14: WATER: The majority of the pixel is comprised of water. 15: NON-PROCESSING AREA MASK: Where no cloud or cloud shadow-free data are available to produce an output. 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