eng
utf8
dataset
dataset
Kevin Megown
USDA Forest Service Geospatial Technology and Applications Center (GTAC)
Program Lead: Resource, Mapping, Inventory and Monitoring
801-975-3826
801-975-3478
125 S. State Street, Suite 7105
Salt Lake City
UT
84138
US
sm.fs.tcc@usda.gov
0800 - 1600 MT, M – F
pointOfContact
2025-12-03
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2023-12-15
publication
v2021-4
Kevin Megown
USDA Forest Service Geospatial Technology and Applications Center (GTAC)
Program Lead: Resource, Mapping, Inventory and Monitoring
801-975-3826
801-975-3478
125 S. State Street, Suite 7105
Salt Lake City
UT
84138
US
sm.fs.tcc@usda.gov
0800 - 1600 MT, M - F
originator
mapDigital
USFS Percent Tree Canopy Cover (Science Version)
none
USFS Percent Tree Canopy Cover (Science Version)
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/
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.
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 Percent Tree Canopy Hawaii (Science Version) v2023-5. Salt Lake City, UT.
completed
Kevin Megown
USDA Forest Service Geospatial Technology and Applications Center (GTAC)
Program Lead: Resource, Mapping, Inventory and Monitoring
801-975-3826
801-975-3478
125 S. State Street, Suite 7105
Salt Lake City
UT
84138
US
sm.fs.tcc@usda.gov
0800 - 1600 MT, M - F
pointOfContact
asNeeded
U.S.
USA
United States of America
US
United States
U.S.A.
HI
Hawaii
place
1985
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2014
2015
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2017
2018
2019
2020
2021
2022
2023
temporal
Tree Density
Digital Spatial Data
Tree Canopy Cover
Continuous
Percent Tree Canopy
Remote Sensing
GIS
Change
Landsat
Sentinel-2
LandTrendr
theme
NGDA
National Geospatial Data Asset
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<gco:CharacterString>NGDA Portfolio Themes</gco:CharacterString>
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Downloadable Data
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. 2023. USFS Percent Tree Canopy (Science Version) Hawaii v2021-4. Salt Lake City, UT. 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.
unclassified
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eng
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environment
imageryBaseMapsEarthCover
Esri ArcGIS 13.5.0.57366
The National Land Cover Database Hawaii study area.
true
-160.2838483605192
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2008-01-01
2021-12-31
Corner Coordinates (center of pixel, meters): upper left: -341745.0 (X), 2130345.0 (Y); lower right: 234135.0 (X), 1757205.0 (Y).
Percent tree canopy cover
image
Band_1
254
Unified Code of Units of Measure
8
Raster Dataset
Kevin Megown
USDA Forest Service Geospatial Technology and Applications Center (GTAC)
Program Lead: Resource, Mapping, Inventory and Monitoring
801-975-3826
801-975-3478
125 S. State Street, Suite 7105
Salt Lake City
UT
84138
US
sm.fs.tcc@usda.gov
0800 - 1600 MT, M – F
distributor
dataset
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 includes the eight main islands: Niihau, Kauai, Oahu, Molokai, Lanai, Kahoolawe, Maui, and Hawaii
Model performance metrics including root mean of squared error (RMSE) and percent variability explained were obtained from the random forest regression model (Breiman, 2001; R Core Team 2020) used to derive tree canopy cover estimates. The RMSE was 22.0. The percent variability explained was 71.4. An independent collection of 325 plots were used as an independent error assessment over the 2011 NLCD TCC v2021.4 map output. This analysis returned a RMSE of 20.8. 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.
2023-12-15
creation
2023-12-15
publication
2023-12-15
revision
For HI the RMSE is 21.0% TCC.
true
No annual binary agriculture data masks were produced for OCONUS to classify tree and non-tree CDL crops.
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 105-point grids placed in 90x90 squares centered on USFS FIA plot design. A total of 1,284 PI 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. Morin, S. Randall, G.C. Liknes (Comps.), Moving from status to trends: Forest Inventory and Analysis (FIA) symposium 2012 December 4-6, Baltimore MD (pp. 237-241). (General Technical Report NRS-P-105). U.S. Department of Agriculture, Forest Service, Northern Research Station. Newtown Square, PA. https://www.fs.fed.us/nrs/pubs/gtr/gtr_nrs-p-105.pdf
Photo-interpreted Canopy Cover (FIA)
2012-01-01T00:00:00
Creation of Digital Elevation Model (DEM) derivatives. A Hawaii-wide terrain dataset used a predictor layer was provided by the USGS 3D Elevation Program (U.S. Geological Survey, 2019). 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, accessed August 2022 at https://developers.google.com/earth-engine/datasets/catalog/USGS_3DEP_10m
Digital Elevation Model (DEM)
2022-09-01T00:00:00
To generate annual composites Landsat and Sentinel 2 imagery were collected from 1984-2022, from a specified date range. Date ranges used to collect imagery were Julian day 01-365 for 1984-2015, and Julian day 01-365 for 2016-2022. 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, 2017). 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 Hawaii. 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. In Remote Sensing of Environment (Vol. 221, pp. 274-285). https://doi.org/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., Hughes, M.J., Laue, B. (2017). Cloud detection algorithm comparison and validation for operational Landsat data products. In Remote Sensing of Environment (Vol. 194, pp. 379-390). http://doi.org/10.1016/j.rse.2017.03.026.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. (2017) Improving Cloud Detection With Machine Learning. Online: https://medium.com/sentinel-hub/improvingcloud-detection-with-machine-learningc09dc5d7cf13. Accessed 20 November 2022.
Annual Landsat-Sentinel2 image composites
2023-06-15T00:00:00
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, 15-32. https://doi.org/10.1023/A:1010933404324Cohen, W.B., Yang, Z., Healey, S.P., Kennedy, R.E., Gorelick, N. 2018, A LandTrendr multispectral ensemble for forest disturbance detection, Remote Sensing of Environment, 205, pp. 131-140. https://doi.org/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
2023-07-01T00:00:00
Creation of the percent tree canopy cover (TCC) Science dataset (main process). The Hawaii FS Science TCC dataset was created for years 2008 through 2021. For Hawaii, 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 forests 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, in the technical methods document (Housman et al., 2023), and in an upcoming manuscript in preparation (Heyer et al., 2023).Step 1: Reference data, consisting of estimated TCC at each of the 1,284 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 1,284 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 Hawaii, 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 forests mean predicted TCC value and the second layer was the standard error (SE), which is the per-pixel standard error of the random forests 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 TCC images were exported from GEE to local computers for post-processing. During post-processing cloud optimized GeoTIFFs (COGs) were generated, statistics were calculated, pyramid layers were built, and color ramps were applied to each Science TCC image. For each Science TCC image, the non-area processing value is 254, and the background value is 255Brand, 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: 8-13Breiman, L. (2001). Random forests. Machine Learning (Vol. 45, pp. 5-32)Brooks, E.B., Thomas, V.A., Wynne, R.H., Coulston, J.W. (2012). Fitting the multitemporal curve: a fourier series approach to the missing data problem in remote sensing analysis. IEEE Transactions on Geoscience and Remote Sensing (Vol. 50, Issue 9, pp. 3340-3353)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, (Vol. 82, Issue 3, pp. 189-197.Heyer, J., Schleeweis, K., Ruefenacht, B., Housman, I., Megown, K., and Bogle, M. 2023. A time invariant modeling approach to produce annual tree-canopy cover for the conterminous United States. Salt Lake City, UT: U.S. Department of Agriculture, Forest Service, Geospatial Technology and Applications Center. [Manuscript in Preparation]Housman, I., Heyer, J., Ruefenacht, B., Schleeweis, K., Bogle, M., and Megown, K. 2023. National Land Cover Database Tree Canopy Cover Methods v2021-4. Salt Lake City, UT: U.S. Department of Agriculture, Forest Service, Geospatial Technology and Applications Center. [Manuscript in Preparation] 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).
USFS Percent Tree Canopy Cover (Standard Error of Science Version)
2023-09-01T00:00:00
otherRestrictions
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. Tree Canopy Cover changes 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.