20250613 10434700 1.0 FALSE USFS_EDW_NLCD_TCC_PRUSVI -7564179.603700 -7168749.603700 1922424.717600 2192634.717600 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["WGS_1984_Web_Mercator_Auxiliary_Sphere",GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Mercator_Auxiliary_Sphere"],PARAMETER["False_Easting",0.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",0.0],PARAMETER["Standard_Parallel_1",0.0],PARAMETER["Auxiliary_Sphere_Type",0.0],UNIT["Meter",1.0],AUTHORITY["EPSG",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 100 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,beginyear,endyear,dataset_name,public,resolution,metadata 307200 30 30720 Years name,minps,maxps,lowps,highps,tag,groupname,productname,centerx,centery,zorder,beginyear,endyear,dataset_name,public,resolution,metadata,st_area(shape),st_perimeter(shape) minps,maxps,lowps,highps,centerx,centery,zorder,st_area(shape),st_perimeter(shape),beginyear,endyear,resolution -1 1 1 0 0 0 0 -1 beginyear endyear YYYY 0 45953.8600257986 45953.8626735764 20251023 15420200 20251023 16124900 ISO19139 150000000 5000 dataset U.S. Geological Survey Customer Services Representative 605-594-6151 605-594-6589 125 S. State Street, Suite 7105 Salt Lake City UT US sm.fs.tcc@usda.gov 84138 0800 - 1600 MT, M – F 20251023 ArcGIS Metadata 1.0 U.S. Geological Survey Customer Services Representative 605-594-6151 605-594-6589 125 S. State Street, Suite 7105 Salt Lake City UT US sm.fs.tcc@usda.gov 84138 0800 - 1600 MT, M – F Downloadable data ONLINE_LINKAGES Raster Dataset USFS_EDW_NLCD_TCC_PRUSVI 20250801 v2023-5 raster digital data NLCD Tree Canopy Cover none NLCD Tree Canopy Cover U.S. Geological Survey Customer Services Representative 605-594-6151 605-594-6589 125 S. State Street, Suite 7105 Salt Lake City UT US sm.fs.tcc@usda.gov 84138 0800 - 1600 CT, M - F (-6h VST/-5h CDT GMT) <div style='text-align:Left;'><div><div>The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2023-5 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 1985 through 2023. The NCLD data are processed to mask TCC from non-treed features such as water and non-tree crops, and to reduce interannual noise and smooth the NLCD time series. TCC pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here. Data Download and Methods Documents: - https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/ </div></div></div> The goal of this project is to provide CONUS and OCONUS with complete, current and consistent public domain tree canopy cover information. Funding for this project was provided by the U.S. Forest Service (USFS). The TCC data products are developed and maintained by the USDA Forest Service through integrated teams and support from the Geospatial Office and Research and Development - Forest Inventory and Analysis. U.S. Geological Survey Customer Services Representative 605-594-6151 605-594-6589 125 S. State Street, Suite 7105 Salt Lake City UT US sm.fs.tcc@usda.gov 84138 0800 - 1600 CT, M - F (-6h VST/-5h CDT GMT) U.S. USA United States of America US United States U.S.A. PR-USVI Puerto Rico - US Virgin Islands Tree Density Digital Spatial Data Tree Canopy Cover Continuous Percent Tree Canopy Remote Sensing GIS Landsat Sentinel-2 LandTrendr NGDA Portfolio Themes NGDA National Geospatial Data Asset ISO 19115 Category BaseMaps EarthCover Imagery Environment BaseMaps EarthCover Imagery Digital Spatial Data Continuous NGDA Remote Sensing National Geospatial Data Asset Percent Tree Canopy Environment GIS TCC Tree Canopy Cover NLCD Puerto Rico US Virgin Islands PR USVI PRUSVI 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 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>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 NLCD Percent Tree Canopy Puerto Rico - US Virgin Islands v2023-5. Salt Lake City, UT.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/). Non-Discrimination Statement 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. </div></div></div> Corner Coordinates (center of pixel, meters): upper left: 3042585.0 (X), 61395.0 (Y); lower right: 3407865.0 (X), -78285.0 (Y). Esri ArcGIS 13.5.3.57366 The National Land Cover Database Puerto Rico - US Virgin Islands study area. 1 -67.57623438032374 -64.80792035992204 17.013923037381524 19.31989419592775 20000601 20010531 Puerto Rico, PR, USVI, US Virgin Islands, PRUSVI Appropriate use includes regional to national assessments of tree cover, total extent of tree cover, and aggregated summaries of tree cover. The Science product is the initial output from the modeling process. The NLCD 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. The USFS Forest Inventory and Analysis (FIA) program photo-interpreted percent tree canopy cover (TCC) response data. Photointerpretation (PI) measured TCC using a custom ArcGIS plug-in tool (Goeking et al., 2012) from 109-dot grids placed in circles with 43.9 m radii centered on FIA plots. The dots were rotated 15 degrees east of true north and the dots were separated by 8 m. A total of 980 PI FIA plots were used in TCC modeling.Goeking, S. A., Liknes, G. C., Lindblom, E., Chase, J., Jacobs, D. M., and Benton, R. (2012). A GIS-based tool for estimating tree canopy cover on fixed-radius plots using high-resolution aerial imagery. In R. S. Morin and G. C. Liknes (Eds.), Moving from status to trends: Forest Inventory and Analysis (FIA) symposium 2012. Gen. Tech. Rep. NRS-P-105 (pp. 237-241). Newtown Square, PA: Department of Agriculture, Forest Service, Northern Research Station. Retrieved from https://research.fs.usda.gov/treesearch/42752 Photo-interpreted Canopy Cover (FIA) 20120101 Creation of Digital Elevation Model (DEM) derivatives. A PRUSVI-wide terrain dataset used as a predictor layer was provided by the USGS 3D Elevation Program (U.S. Geological Survey, 2019). Slope, the components of slope, aspect, and the sine and cosine of aspect were calculated for each pixel following industry standards.U.S. Geological Survey. (2019). USGS 3D Elevation Program Digital Elevation Model [Data set]. https://developers.google.com/earth-engine/datasets/catalog/USGS_3DEP_10m. Retrieved from https://developers.google.com/earth-engine/datasets/catalog/USGS_3DEP_10m Digital Elevation Model (DEM) 20220901 To generate annual composites Landsat and Sentinel 2 imagery were collected from 1984-2024, from a specified date range. Date ranges used to collect imagery were Julian day 152-151 for 1984-2015, and Julian day 152-151 for 2016-2024. For Landsat image collections the CFmask cloud masking algorithm, an implementation of Fmask 2.0 (Zhu and Woodcock 2012), was applied (Foga et al., 2017), and the cloudScore algorithm (Chastain et al., 2019). For Sentinel-2 data, we used the s2Cloudless algorithm to mask clouds (Zupanc, 2020). We use the Temporal Dark Outlier Mask (TDOM) method to mask cloud shadows in both Landsat and Sentinel-2 (Chastain et al., 2019). For each year, the annual geometric medoid was computed to summarize the data into a single annual composite for PRUSVI. The blue and green bands were not used because of stripping artifacts caused by the Landsat 7 scan line corrector failure. Stripping artifacts were observed in preliminary model tests when blue and green bands were included.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. Remote Sensing of Environment, 221, 274-285. doi: 10.1016/j.rse.2018.11.012Foga, S., Scaramuzza, P. L., Guo, S., Zhu, Z., Dilley, R. D., Beckmann, T., Schmidt, G. L., Dwyer, J. L., Joseph Hughes, M., and Laue, B. (2017). Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sensing of Environment, 194, 379-390. doi: 10.1016/j.rse.2017.03.026Zhu, Z., Woodcock, C.E. 2012, Object-based cloud and cloud shadow detection in Landsat imagery, Remote Sensing of Environment, 118, pp. 83-94.Zupanc, A. (2020, July 3). Improving Cloud Detection with Machine Learning. Retrieved May 28, 2025, from Planet Stories website: https://medium.com/sentinel-hub/improving-cloud-detection-with-machine-learning-c09dc5d7cf13 Annual Landsat-Sentinel2 image composites 20250615 The Landsat-based detection of Trends in Disturbance and Recovery (LandTrendr) temporal segmentation algorithm was applied to the composite time series in Google Earth Engine (GEE) (Kennedy et al., 2018; Cohen et al., 2018). The resulting LandTrendr time-series fitted values were used as independent predictor variables in random forest models (Breiman 2001). Stripping artifacts were observed in preliminary modeling of TCC when LandTrendr blue and green bands were included as predictor layers. To avoid stripping artifacts the blue and green band LandTrendr fitted values were not used in modeling.Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32. doi: 10.1023/A:1010933404324Cohen, W. B., Yang, Z., Healey, S. P., Kennedy, R. E., and Gorelick, N. (2018). A LandTrendr multispectral ensemble for forest disturbance detection. Remote Sensing of Environment, 205, 131-140. doi: 10.1016/j.rse.2017.11.015Kennedy, 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 20250701 Creation of the National Land Cover Database (NLCD) TCC dataset (main process). The NLCD dataset is generated from the FS Science product for years 1985 through 2023. For PRUSVI, model calibration data were gathered and a random forest model was created and applied to generate the Science product.Six major steps were employed to map TCC and produce the NLCD product: 1) collection of reference data, 2) acquisition and/or creation of predictor layers, 3) calibration of random forest regression models for each mapping area using response data and predictor layers, 4) application of those models to predict per-pixel TCC across the entire mapping area, 5) a series of data quality filtering steps to generate the NCLD TCC product, and 6) exporting NLCD images from Google Earth Engine (GEE) to local computers for further post-processing. The methodology is described further below, in the technical methods document (Housman et al., 2025), and in an upcoming manuscript in preparation (Heyer et al., in preparation). For the NLCD product, additional post-processing steps were performed.Step 1: Reference data supplied by the U.S. Forest Service Forest Inventory and Analysis (FIA) program consisted of photo-interpreted TCC for 1,965 FIA plot locations. The spatial distribution of the sample points follows the FIA systematic grid (Brand et al. 2000). Low quality FIA PI observations were removed for a total of 980 FIA plots used in modelingStep 2: Predictor layers include LandTrendr fitted images spectral derivatives. The LandTrendr fitted images excludes Landsat 7 blue and green bands to avoid stripping artifacts. Other predictor layers include elevation data, and terrain derivatives (slope, aspect, sine of aspect, cosine of aspect). The processes for creating the derived layers are described separately (see related Process Steps).Step 3: For PRUSVI, a random forest model was built from 2011 response and predictor data. Models were generated locally using the random forest regression algorithm "sklearn.ensemble.RandomForestRegressor" from the Scikit-Learn package in python (Pedregosa et al. 2011).Step 4: In GEE, the random forest model was applied to produce a 2-layered Science image. The first layer was the random forest mean predicted TCC value and the second layer was the standard error (SE), which is the per-pixel standard error of the random forest regression predictions from the individual regression trees.Step 5: From the Science TCC product the NLCD TCC product was generated following a series of post-processing steps, including various masking of non-treed pixels, and a process to reduce interannual noise. For masking, a three-year moving window tree mask was produced from the Landscape Change and Monitoring System (LCMS) Land Cover product tree classes (USDA FS, 2025). A three-year moving window ensured TCC predictions in forested pixels were used. Next, the annual LMCS Land Cover water class (USDA FS, 2025) was used to mask water from the three-year moving window LCMS tree masks. In order to avoid masking highly fragmented tree cover common over urban areas, a separate urban tree mask was produced. The urban TCC mask includes the TIGER U.S. Census Block 2024 data (U.S. Census Bureau, 2024), LCMS land use developed data, and statistic that normalized the expected error, which we refer to as tau was calculated (Coulston et al., 2016). The TIGER and LCMS developed data (USDA FS, 2025) were used to separate urban TCC from non-urban TCC. The tau statistic at the 91 percent confidence level (or quantile) was used to threshold the TCC values in urban areas. In addition, the tau statistic at the 97 percent confidence level was used for areas where there was TCC Science data and LCMS tree mask non-processing area. If a TCC value subtracted from the tau multiplied by the standard error value was less than 0, the TCC value was changed to 0. The final urban TCC mask was the combination of the TIGER, LCMS land use developed data and tau thresholded mask. The LCMS tree mask and urban TCC masks were applied to annual TCC images to produce the NLCD TCC v2023-5 product.Step 6: Following model application, the NLCD TCC images were exported from GEE to local computers for post-processing. During post-processing, mosaics were created, cloud optimized GeoTIFFs (COGs) were generated, statistics were calculated, pyramid layers were built, and color ramps were applied to each NLCD TCC image. For each NLCD TCC image, the non-area processing value is 254, and the background value is 255.Brand, G. J., Nelson, M. D., Wendt, D. G., and Nimerfro, K. K. (2000). The hexagon/panel system for selecting FIA plots under an annual inventory. In R. E. McRoberts, G. A. Reams, and P. C. Van Deusen (Eds.), Proceedings of the First Annual Forest Inventory and Analysis Symposium; Gen. Tech. Rep. NC-213 (pp. 8-13). St. Paul, MN: U.S. Department of Agriculture, Forest Service, North Central Research Station. Retrieved from https://research.fs.usda.gov/treesearch/14368Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32. doi: 10.1023/A:1010933404324Coulston, J. W., Blinn, C. E., Thomas, V. A., and Wynne, R. H. (2016). Approximating Prediction Uncertainty for Random Forest Regression Models. Photogrammetric Engineering and Remote Sensing, 82(3), 189-197. doi: 10.14358/PERS.82.3.189Dewitz, J., and U.S. Geological Survey. (2021). National Land Cover Database (NLCD) 2019 Products (ver. 3.0, February 2024): U.S. Geological Survey data release (Version 3.0) [Data set]. U.S. Geological Survey. doi: 10.5066/P9KZCM54Heyer, J., Schleeweis, K., Ruefenacht, B., Housman, I., Zhiqiang, Y., Ryerson, D., Reischmann, J., Megown, K., and Bogle, M. S. (2025). Annual National Tree Canopy Cover Mapping: A Novel Workflow with Temporal Transferability and Improved Uncertainty Quantification. Science of Remote Sensing, [Manuscript in Preparation]Housman, I. W., Heyer, J. P., Ruefenacht, B., Schleeweis, K., Megown, K., Bogle, S., Reischmann, J., and Ryerson, D. (2025). National Land Cover Database Tree Canopy Cover Methods v2023.5. FSIC-GO-10268-RPT2 (pp. 1-29). Salt Lake City, UT: U.S. Department of Agriculture, Forest Service, Field Services and Innovation Center- Geospatial Office. Retrieved from U.S. Department of Agriculture, Forest Service, Field Services and Innovation Center- Geospatial Office website: https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/index.phpPedregosa, 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. Journal of Machine Learning Research, 12(85), 2825-2830. doi: https://doi.org/10.48550/arXiv.1201.0490USDA Forest Service. 2025. USFS Landscape Change Monitoring System Conterminous United States version 2024-10 (Version 2024-10) [Data set]. Retrieved from https://data.fs.usda.gov/geodata/rastergateway/LCMS/index.phpU.S. Census Bureau, Geography Division. (2024). TIGER/Line Shapefiles, 2024: Urban Areas [Shapefile]. Retrieved from https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html Annual NLCD Tree Canopy Cover Images 20250901 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 Puerto Rico and the US Virgin Islands Model performance metrics including root mean of squared error (RMSE) and percent variance explained (PVE) were obtained from the random forest regression model (Breiman, 2001; R Core Team 2024) used to derive tree canopy cover estimates. The RMSE was 20.6. The PVE was 69.7. An independent collection of 324 plots was used as an independent error assessment over the 2011 NLCD TCC v2021.4 map output. This analysis returned a RMSE of 21.0 and a mean adjusted error (MAE) of 15.6. Additionally, 500 random forest trees were used to derive a tree canopy cover prediction for each pixel. Standard errors were calculated for each pixel using the estimates from the 500 trees. The standard errors provide information on the certainty of TCC predictions 20250801 20250801 20250801 For PRUSVI, the RMSE is 21.0% TCC and the MAE is 15.6% TCC. 1 9007 30.000000 13181 30.000000 2 1 0 -7564179.603700 1922424.717600 -7564179.603700 2192634.717600 -7168749.603700 2192634.717600 -7168749.603700 1922424.717600 -7366464.603700 2057529.717600 EPSG 6.18.3(9.3.1.2) Band_1 254.000000 0.000000 8 Percent tree canopy cover False False False False False usfs_edw_nlcd_tcc_prusvi.tif Percent tree canopy cover OID ObjectID Field Short integer 1 byte 3 0 Value Percent tree canopy cover Short integer 1 byte 3 0 Blue Total number of pixels per classification Long integer 8 bytes 10 0 Green Color ramp red value Short integer 1 byte 3 0 Red Color ramp green value Short integer 1 byte 3 0 Count Color ramp blue value Short integer 1 byte 3 0 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