20250617 11122500 1.0 FALSE usfs_vegetation.usfs.USFS_EDW_LCMS_AnnualQABits_HI 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 -17842716.381900 -17226696.381900 2139038.749500 2544308.749500 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> 16 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 19200 30 1920 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 45910.8874657523 45910.8890594907 CalculateStatistics D:\DBConnectionFiles\usfs_Vegetation.sde\usfs_vegetation.usfs.USFS_EDW_LCMS_AnnualQABits_HI 5 5 0 OVERWRITE "Feature Set" 20250910 15272200 20250930 15334300 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 20250930 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_AnnualQABits_HI 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 QA bit product is part of the Landscape Change Monitoring System (LCMS) data suite. It provides information about each pixel of the annual composites that are used as inputs to LandTrendr data used in the model. This information includes whether the data value is an observation or is interpolated, the Landsat sensor that observed that data value, and the Julian day of that observation. See additional information about QA 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 /><span>Outputs fall into three categories: Change, Land Cover, and Land Use. Change relates specifically to vegetation cover and includes slow loss (not included for PRUSVI), Fast Loss (which also includes hydrologic changes such as Inundation or Desiccation), and Gain. These values 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. HA Hawaii 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 13.5.3.57366 Hawaii study area. 1 -160.2838483605192 -154.7812343850654 18.87550044213921 22.252781479407908 1985-01-01T00:00:00 2024-12-31T00:00:00 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 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.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 Forests 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 No assemblage rules or thresholding were applied to the QA bits band. The QA bits band was included in annual assembled outputs. QA bit band assemblage 20250301 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). No Landsat 4 or 5 imagery were acquired for Hawaii from 1985-1987, resulting in wall-to-wall non-processing area for those years. 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 Southeastern coastal Alaska No accuracy assement was performed for the QA bit. See the LCMS products metadata - Change, Land Use, Land Cover, and Summary Products - for information on product accuracy for a given year. References: 20250401 20250401 20250401 None 1 13509 30.000000 20534 30.000000 2 1 0 -17842716.381900 2139038.749500 -17842716.381900 2544308.749500 -17226696.381900 2544308.749500 -17226696.381900 2139038.749500 -17534706.381900 2341673.749500 EPSG 6.18.3(9.3.1.2) Band_1 0.000000 0.000000 16 False False False False False The QA bit decimal number for each year The Quality Assessment Bit band indicates whether there was a valid cloud and cloud shadow-free value available that year or if it was entirely interpolated by LandTrendr, as well as the sensor and the Julian day of the observation used. Some invalid data (no observations) were caused by CCDC. For valid data, the sensor and the Julian day of the observation are specific to LandTrendr. These attributes are presented in 16 bit space decimal format. See the Entity_Type_Definition_Source below for the bit space representation. Bitwise operations can be leveraged to unpack the QA decimal numbers to vaild pixel values for the non-interpolated data, sensor, and Julian day. The following are the operations and bitwise operations needed to unpack the decimal values: non-interpolated data are valid if the modulo of the QA decimal value divided by 2 does not equal 0, Julian day bitwise RIGHT-SHIFT 6, and sensor bitwise LEFT-SHIFT 10, and then bitwise RIGHT-SHIFT 11. For example, for decimal number 16367, the modulo of 16367 divided by 2 does not equal 0 so the data is valid. Next, for the julian day the bitwise RIGHT-SHIFT 6 = julian day 239. Finally, for the sensor a bitwise LEFT-SHIFT 10 then a bitwise RIGHT-SHIFT 11 = sensor 22. The following are the valid pixel values: non-interpolated data 0-1, sensor 4-23, and Julian day 1-365. For whether there was a valid cloud and cloud shadow-free value available or it was interpolated, the following are the valid pixel values: data = 1 and interpolated = 0. The following are the valid pixel values for the sensor: Landsat 4 TM = 4, Landsat 5 TM = 5, Landsat 7 ETM+ = 7, Landsat 8 OLI = 8, Landsat 9 OLI-2 = 9, Sentinel-2A = 21, Sentinel-2B = 22, and Sentinel-2C = 23. Julian day is the day of year the pixel for that year was taken. Bitwise operations are available in GEE, Python/NumPy, etc. that can be leveraged to unpack the decimal number to valid pixel values. 1: NON-INTERPOLATED DATA: Bit 1 2: SENSOR: Bits 2-6 3: JULIAN DAY: Bits 7-15 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