20251009 12173100 1.0 ISO19115_3 002 USFS_EDW_LCMS_MostRecentYearSlowLoss_HI -17842716.381900 -17226696.381900 2139038.749500 2544308.749500 1 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 20 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 307200 30 30720 Thematic name,minps,maxps,lowps,highps,tag,groupname,productname,centerx,centery,zorder,st_area(shape),st_perimeter(shape) minps,maxps,lowps,highps,centerx,centery,zorder,st_area(shape),st_perimeter(shape) -1 1 1 0 0 0 0 0 0 150000000 5000 20251016 17320200 FALSE 20251016 16022200 <div style='text-align:Left;'><div><div><p><span>This product is part of the Landscape Change Monitoring System (LCMS) data suite. It is a summary of all annual Slow Loss into a single layer showing the most recent year LCMS detected Slow Loss. See additional information about Slow Loss in the Entity_and_Attribute_Information or Fields section below.</span><span /></p><p><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><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. 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> USFS_EDW_LCMS_MostRecentYearSlowLoss_HI 2025-10-24T08:24:04 2024-10 Landscape Change Monitoring System none raster digital data 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 sm.fs.lcms@usda.gov 125 S. State Street, Suite 7105 Salt Lake City Utah 84138 US 801-975-3826 801-975-3478 0800 - 1600 MT, M - F Kevin A. Megown The National Land Cover Database Hawaii study area. -160.2838483605192 -154.7812343850654 22.252781479407908 18.87550044213921 1 1985-01-01T08:27:00 2024-12-31T00:00:00 Kevin A. Megown USDA Forest Service Field Services and Innovation Center Geospatial Office (FSIC-GO) Program Lead - Resource, Mapping, Inventory and Monitoring sm.fs.lcms@usda.gov 125 S. State Street, Suite 7105 Salt Lake City Utah 84138 US 801-975-3826 801-975-3478 0800 - 1600 MT, M - F 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 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 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). The TCC data products were produced by RedCastle Resources, Inc. under contract to the USFS Field Services and Innovation Center Geospatial Office (FSIC-GO). The data are developed and maintained by the USDA Forest Service through integrated teams and support from the 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 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 Hawaii version 2024-10. Salt Lake City, Utah.</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> Esri ArcGIS 13.5.3.57366 Corner Coordinates (center of pixel, meters): upper left: -341745.0 (X), 2130345.0 (Y); lower right: 234135.0 (X), 1757205.0 (Y). U.S. USA United States of America US United States U.S.A. HA Hawaii dataset 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 20251016 Kevin A. Megown USDA Forest Service Field Services and Innovation Center Geospatial Office (FSIC-GO) Program Lead - Resource, Mapping, Inventory and Monitoring sm.fs.lcms@usda.gov 125 S. State Street, Suite 7105 Salt Lake City Utah 84138 US 801-975-3826 801-975-3478 0800 - 1600 MT, M – F Primary data sources include Landsat 4, 5, 7, 8, and 9 Tier 1 and Sentinel 2a and 2b Level-1C top of atmosphere reflectance data, and Digital Elevation Models of Puerto Rico and the USVI National Oceanic and Atmospheric Administration Digital Elevation Model 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. 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 2025-10-24T08:30:31 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 January 1st to December 31st for HAWAII, 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 2025-10-24T08:30:28 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 2025-10-24T08:30:26 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 surface reflectance data for HAWAII 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 2025-10-24T08:29:46 No assemblage rules or thresholding were applied to the summary product. The summary product band was included in annual assembled outputs. Summary product assemblage 2021-10-25T20:30:00 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 2025-10-24T08:20:54 2025-10-24T08:20:57 2025-10-24T08:20:58 Data extend across the Hawaiian islands 2025-10-24T08:21:01 2025-10-24T08:21:01 2025-10-24T08:21:03 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 22 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 2025-10-24T08:21:08 2025-10-24T08:21:10 2025-10-24T08:21:12 Cause_of_Change: Change_Level_2Overall Accuracy: 88.56 +/- 0.22Balanced Accuracy: 27.52 +/- 5.43Kappa: 0.27Users Accuracy (100%-Commission Error): Fire: Too few samples to assess accuracyVeg-Growth: 33.81Harvest: Too few samples to assess accuracyInsect-Disease-Drought: 24.81Mechanical: 0.00Other: Too few samples to assess accuracyStable: 93.71Users Error: Fire: Too few samples to assess accuracyVeg-Growth: 1.43Harvest: Too few samples to assess accuracyInsect-Disease-Drought: 1.73Mechanical: 0.00Other: Too few samples to assess accuracyStable: 0.17Producers Accuracy (100%-Omission Error): Fire: Too few samples to assess accuracyVeg-Growth: 33.15Harvest: Too few samples to assess accuracyInsect-Disease-Drought: 22.93Mechanical: 0.00Other: Too few samples to assess accuracyStable: 94.25Producers Error: Fire: Too few samples to assess accuracyVeg-Growth: 1.41Harvest: Too few samples to assess accuracyInsect-Disease-Drought: 1.62Mechanical: 0.00Other: Too few samples to assess accuracyStable: 0.17Number of Samples in each class: Fire: 19 (Too few samples to assess accuracy)Veg-Growth: 1456Harvest: 13 (Too few samples to assess accuracy)Insect-Disease-Drought: 763Mechanical: 71Other: 20 (Too few samples to assess accuracy)Stable: 19545 1 Raster Dataset 1.2 Kevin A. Megown USDA Forest Service Field Services and Innovation Center Geospatial Office (FSIC-GO) Program Lead - Resource, Mapping, Inventory and Monitoring sm.fs.lcms@usda.gov 125 S. State Street, Suite 7105 Salt Lake City Utah 84138 US 801-975-3826 801-975-3478 0800 - 1600 MT, M – F Downloadable data EPSG 6.18.3(9.3.1.2) Year of most recent Slow Loss minus 1970. This layer only shows the most recent year of Slow Loss across all years of Slow Loss modeled by LCMS. The class value of the original individual years of Slow Loss is 2, while the class values in this layer are the most recent year of Slow Loss minus 1970. A loss of vegetation cover over a period of time generally not associated with a discrete event such as a fire or harvest. Slow loss is not categorized explicitly in the training data, however, specific change process classifications are collected. The following describes the change processes, any of which could be present and are collectively considered Slow Loss. 1: STRUCTURAL DECLINE: Land where trees or other woody vegetation is physically altered by unfavorable growing conditions brought on by non-anthropogenic or non-mechanical factors. This type of loss should generally create a trend in the spectral signal(s) (e.g. NDVI decreasing, Wetness decreasing, SWIR increasing, etc.) however the trend can be subtle. Structural decline occurs in woody vegetation environments, most likely from insects, disease, drought, acid rain, etc. Structural decline can include defoliation events that do not result in mortality such as in Gypsy moth and spruce budworm infestations which may recover within 1 or 2 years. 1: SPECTRAL DECLINE: A plot where the spectral signal shows a trend in one or more of the spectral bands or indices (e.g. NDVI decreasing, Wetness decreasing, SWIR increasing, etc.). Examples include cases where: a) non-forest/non-woody vegetation shows a trend suggestive of decline (e.g. NDVI decreasing, Wetness decreasing, SWIR increasing, etc.), or b) where woody vegetation shows a decline trend which is not related to the loss of woody vegetation, such as when mature tree canopies close resulting in increased shadowing, when species composition changes from conifer to hardwood, or when a dry period (as opposed to stronger, more acute drought) causes an apparent decline in vigor, but no loss of woody material or leaf area. 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. 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. 2 13509 30.000000 20534 30.000000 Check points used to assess positional accuracy were derived from high-precision GPS surveys conducted between 2015 and 2020 across the Hawaiian Islands. A total of 150 ground control points were selected based on stable, identifiable landscape features such as road intersections, building corners, and coastal markers. These points were evenly distributed across major islands (Hawai‘i, Maui, O‘ahu, Kaua‘i) to ensure spatial coverage. Each check point was validated against orthorectified aerial imagery and LiDAR datasets with sub-meter accuracy. The comparison between mapped features and check point coordinates was used to calculate RMSE values for horizontal accuracy. No vertical accuracy assessment was performed due to the nature of the dataset. 17534706.381900 2341673.749500 17842716.381900 2139038.749500 17842716.381900 2544308.749500 17226696.381900 2544308.749500 17226696.381900 2139038.749500 Key attributes include land cover classification codes, change detection flags, vegetation indices (e.g., NDVI), elevation data, and temporal markers. Each feature is tagged with metadata such as acquisition date, source imagery, and confidence level. Attribute values follow standardized classification schemes to ensure consistency across time periods and geographic regions. 1 Good 19 54 8 Band_1 ArcGIS Metadata 1.0