20250710 13113800 1.0 FALSE USFS_EDW_TCC_Science_SE_SEAK -17819730.289600 -13875330.289600 6379822.840700 10320322.840700 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' 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dataset Kevin 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 UT US sm.fs.tcc@usda.gov 84138 0800 - 1600 MT, M – F 20260929 ArcGIS Metadata 1.0 Kevin 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 UT US sm.fs.tcc@usda.gov 84138 0800 - 1600 MT, M – F Downloadable data ONLINE_LINKAGES Raster Dataset USFS_EDW_TCC_Science_SE_SEAK 20260401 v2025-6 raster digital data USFS Percent Tree Canopy Cover (Standard Error of Science Version) none USFS Percent Tree Canopy Cover (Standard Error of Science Version) Kevin 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 UT US sm.fs.tcc@usda.gov 84138 0800 - 1600 MT, M - F Kevin Megown 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 v2025-6 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 Science data are the initial annual model outputs that consist of two images: percent tree canopy cover (TCC) and standard error. These data are best suited for users who will carry out their own detailed statistical and uncertainty analyses on the dataset, and place lower priority on the visual appearance of the dataset for cartographic purposes. Datasets for the years 1985 through 2025 are available. The Science data were produced using a random forest regression algorithm. For standard error data, the initial standard error estimates that ranged from 0 to approximately 45 were multiplied by 100 to maintain data precision (e.g., 45 = 4500). Therefore, standard error estimates pixel values range from 0 to approximately 4500. The value 65534 represents the non-processing area mask where no cloud or cloud shadow-free data are available to produce an output, and 65535 represents the background value. The Science data are accessible for multiple user communities, through multiple channels and platforms. For information on the NLCD TCC data and processing steps see the NLCD metadata. Information on the Science data and processing steps are included here., br, br, Data Download and Methods Documents:, br, br, - https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/ 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. Kevin 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 UT US sm.fs.tcc@usda.gov 84138 0800 - 1600 MT, M - F U.S. USA United States of America US United States U.S.A. Alaska AK 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 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 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:, br, br, USDA Forest Service. 2026. USFS Percent Tree Canopy (Standard Error of Science Version) Southeastern Alaska v2025-6. Salt Lake City, UT. Corner Coordinates (center of pixel, meters): upper left: -17251807.357764892 (X), 8824109.246425968 (Y); lower right: -14153458.357764892 (X), 7173446.246425968 (Y). Esri ArcGIS 13.6.0.59527 The National Land Cover Database Southeastern Alaska study area. 1 -154.9756222816582 -127.14267965929383 54.017367866493665 61.85189308182382 19850615 19850915 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 1397 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 SEAK-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 1985-2025, from a specified date range. Date ranges used to collect imagery were Julian day 153-273 for 1985-2015, and Julian day 182-244 for 2016-2025. 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 and Cloud Score + (Pasquarella et al., 2023) 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 SEAK. 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.2018.11.012



Foga, 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.2017.03.026



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)



Zhu, 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 20260615 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:1010933404324



Cohen, 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.2017.11.015



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 Annual LandTrendr Fitted Images 20260701 Creation of the percent tree canopy cover (TCC) Science dataset (main process). The SEAK FS Science TCC dataset was created for years 1985 through 2025. For SEAK, model calibration data were gathered and a random forest model was created and applied to generate the Science product.



Five major steps were employed to map TCC and produce the Science 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, and 5) exporting Science images from Google Earth Engine (GEE) to local computers for further post-processing. The methodology is described further below, and in Heyer et al., (2025).



Step 1: Reference data, consisting of estimated TCC at each of the 2035 FIA plot locations, were generated via photographic interpretation of high spatial resolution images collected and supplied by the U.S. Forest Service Forest Inventory and Analysis (FIA) program. The spatial distribution of the sample points follows the FIA systematic grid (Brand et al. 2000). Unsuitable FIA PI observations were removed for a total of 1397 FIA plots used in modeling



Step 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 SEAK, 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. In addition to the model output, a statistic that normalized the expected error, which we refer to as tau (Coulston et al., 2016), was calculated for each processing area. The tau statistics can be used to mask erroneous pixels as described in Coulston et al. (2016). The tau statistics are provided in the supplemental metadata.



Step 5: Following model application, the Science SE 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 Science SE image. For each Science SE image, the non-area processing value is 65534, and the background value is 65535



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: McRoberts, R.E.; Reams, G.A.; Van Deusen, P.C., eds. Proceedings of the First Annual Forest Inventory and Analysis Symposium; Gen. Tech. Rep. NC-213. St. Paul, MN: U.S. Department of Agriculture, Forest Service, North Central Research Station: 8-13



Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32. doi: 10.1023/A:1010933404324



Coulston, 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.82.3.189



Heyer, 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, 100301. doi: https://doi.org/10.1016/j.srs.2025.100301



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.php



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. Journal of Machine Learning Research, 12(85), 2825-2830. doi: https://doi.org/10.48550/arXiv.1201.0490 USFS Percent Tree Canopy Cover (Standard Error of Science Version) 20260901 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 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 18.8. The PVE was 70.8. An independent collection of 279 plots was used as an independent error assessment over the 2011 NLCD TCC v2021.4 map output. This analysis returned a RMSE of 21.1 and a mean adjusted error (MAE) of 10.8. 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 20260401 20260401 20260401 For SEAK, the RMSE is 21.1% TCC and the MAE is 13.3% TCC. 1 131350 30.000000 131480 30.000000 2 1 0 -17819730.289600 6379822.840700 -17819730.289600 10320322.840700 -13875330.289600 10320322.840700 -13875330.289600 6379822.840700 -15847530.289600 8350072.840700 EPSG 6.18.3(9.3.1.2) Band_1 3557.000000 0.000000 16 Percent tree canopy cover standard error False False False False False science_se_seak_mercator_v2025-6_19850101_19851231.tif ENTITY_ATTRIBUTE_DESCRIPTION OID ObjectID Field Short integer 4 bytes 5 0 Value Percent tree canopy cover standard error Short integer 4 bytes 5 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