USFS Tree Canopy Cover Datasets

The U.S. Forest Service (USFS) Geospatial Technology and Applications Center (GTAC) builds and maintains tree canopy cover (TCC) datasets. The TCC datasets built by USFS/GTAC are Landsat-based and available at 30-m resolution for the conterminous United States, coastal Alaska in the southern portion of the state, Hawai'i, Puerto Rico, and the U.S. Virgin Islands.

The most recent TCC product suite is the 2016 TCC Product Suite, which was released in 2019 and includes several components. The 2016 TCC Product Suite includes:

  • TCC maps and data nominally for the years 2011 and 2016, in multiple versions, to serve multiple user communities. One of the versions is the TCC dataset built for the National Land Cover Database (NLCD) that is maintained by the Multi-Resolution Land Characteristics Consortium (MRLC).
  • A TCC change layer that provides estimates of where and how much TCC change has occurred between the nominal years of 2011 and 2016 (included with the 2016 NLCD-TCC product).
  • Metadata for all products
  • See Table 1 for a tabular overview of the 2016 TCC Product Suite components.

Additional documentation describing the components of the 2016 TCC Product Suite (workflows, inputs, models, QC and review processes, etc.) is available at the bottom of this page.

Please direct any questions or comments to SM.FS.TCC@usda.gov.

In addition to the 2016 TCC Product Suite, a previous version of the 2011 TCC datasets built by the USFS (i.e., the “2011 TCC Product Suite”) is also archived and available below. The 2011 TCC products are the same as those released as part of the overall 2011 NLCD. Please note that the nominal 2011 products included in the 2016 Product Suite released in 2019 are updated and different datasets from the nominal 2011 products included in the 2011 TCC Product Suite that was originally released in 2014. The 2011 TCC Product Suite is provided for user communities who still need access to the previous iteration of the 2011 TCC products. See Table 2 for a tabular overview.

sample, tree canopy cover map

Table 1:


Components of the 2016 TCC Product Suite released in 2019
FS “Analytical” TCC FS “Cartographic” TCC NLCD TCC
Description:
  • Two-layer dataset, with modeled TCC values on every pixel, along with a standard error value + metadata
  • Closest to objective numerical model outputs
  • Data for the years of 2011 and 2016 are available
Description:
  • Single-layer dataset, with TCC values only (no standard error layer included) + metadata
  • TCC layer from the upstream “FS-Analytical” version of the product + application of masks to refine dataset visual appearance
  • Masks are applied to remove modeled TCC in water bodies, non-tree croplands, and in areas where the standard error is much higher than the TCC value itself. Essentially, pixels for which confidence in the pixel being tree-covered is very low are filtered out.
  • Data for the years of 2011 and 2016 are available
Description:
  • Three-layer dataset that is an integrated data stack (i.e., 2011 TCC + change = 2016 TCC for all pixels)
  • Data for the years of 2011 and 2016 are available, as well as estimated TCC change between the nominal years of 2011 and 2016
  • Produced by FS/GTAC as a partner in the Multi-Resolution Land Characteristics Consortium (MRLC)
Primary User Community:
  • This version serves a user community that prizes statistics over visual appearance.
  • This community typically has access to advanced geospatial and statistical analysis resources.
Primary User Community:
  • This version serves a user community desiring better visual appearance of 2011 and 2016 timesteps for cartographic purposes.
Primary User Community:
  • This version serves a large part of the NLCD user community that desires:
    • an integrated data stack with 2011 TCC + change = 2016 TCC
    • an estimate of change beyond simple subtraction on all pixels, everywhere across all AOIs
    • maps with reasonable cartographic appearance, yet this user community is willing to trade minor visual artifacts for the integrated data stack, where values “line up” (i.e., 2011 TCC + change = 2016 TCC)
Downloads:
  • CONUS
    2011 – zip (10GB)
    2016 – zip (11GB)
  • Coastal Alaska
    2011 – zip (240MB)
    2016 – zip (247MB)
  • Puerto Rico/ U.S. Virgin Islands
    2011 – zip (17MB)
    2016 – zip (18MB)
  • Hawaii
    2011 – zip (30MB)
    2016 – zip (28MB)
Downloads:
  • CONUS
    2011 – zip (4GB)
    2016 – zip (3GB)
  • Coastal Alaska
    2011 – zip (76MB)
    2016 – zip (75MB)
  • Puerto Rico/ U.S. Virgin Islands
    2011 – zip (8MB)
    2016 – zip (8MB)
  • Hawaii
    2011 – zip (7MB)
    2016 – zip (7MB)
Source:

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Table 2:


Components of the 2011 TCC Product Suite (released in 2014)
FS “Analytical” TCC FS “Cartographic” TCC
Description:
  • Released in 2014 as part of the 2011 NLCD
  • Two-layer dataset, with modeled TCC values on every pixel, along with a standard error value + metadata
  • Closest to objective numerical model outputs
  • Data for the year 2011 are available
Description:
  • Released in 2014 as part of the 2011 NLCD
  • Single-layer dataset, with TCC values only (no standard error layer included) + metadata
  • TCC layer from the upstream “FS-Analytical” version of the product + application of masks to refine dataset visual appearance
  • Masks are applied to remove modeled TCC in water bodies, non-tree croplands, and in areas where the standard error is much higher than the TCC value itself. Essentially, pixels for which confidence in the pixel being tree-covered is very low, are filtered out.
  • Data for the year 2011 are available
Primary User Community:
  • This dataset serves a user community that prizes statistics over visual appearance and still needs access to the previous version of the 2011 TCC Products.
  • This community typically has access to advanced geospatial and statistical analysis resources.
Primary User Community:
  • This dataset serves a user community desiring better visual appearance of data for cartographic purposes and still needs access to the previous version of the 2011 TCC Products.
Downloads:
  • CONUSzip (10GB)
  • Coastal Alaskazip (255MB)
  • Puerto Rico/ U.S. Virgin Islandszip (18MB)
  • Hawaiizip (28MB)
Downloads:
  • CONUSzip (3GB)
  • Coastal Alaskazip (85MB)
  • Puerto Rico/ U.S. Virgin Islandszip (5MB)
  • Hawaiizip (10MB)

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Documentation and References


  • Posters:  
  • References:
    • Baig, M.H.A.; Zhang, L.; Shuai, T.; Tong, Q. 2014. Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance, Remote Sensing Letters 5(5):423-431
    • Brand, G.J.; Nelson, M.D.; Wendt, D.G.; 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 45:15–32.
    • 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 50(9):3340-3353.
    • Chander, G.; Markham, B.L.; Helder, D.L. 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment 113(2009): 893-903.
    • Chavez, P.S. 1988. An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment 24(1988): 459-479.
    • Coulston, J.W.; Moisen, G.G.; Wilson, B.T.; Finco, M.V.; Cohen, W.B.; Brewer, C.K. 2012. Modeling percent tree canopy cover: a pilot study. Photogrammetric Engineering & Remote Sensing 78(7): 715–727.
    • Crist, E.P.; Kauth, R.J. 1986. The tasseled cap de-mystified. Photogrammetric Engineering & Remote Sensing 52(1):81-86.
    • Cutler, R.D.; Edwards, T.C.; Beard, K.H.; Cutler, A.; Hess, K.T.; Gibson, J.; Lawler, J.J. 2007. Random forests for classification in ecology. Ecology 88 (11):2783-2792.
    • Homer, C.G.; Dewitz, J.A.; Yang, L.; Jin, S.; Danielson, P.; Xian, G.; Coulston, J.; Herold, N.D.; Wickham, J.; Megown, K. 2015. Completion of the National Land Cover Database for the conterminous United States–representing a decade of land cover change information. Photogrammetric Engineering & Remote Sensing 81(5):345–354.
    • Homer, C.; Gallant, A. 2001. Partitioning the conterminous United States into mapping zones for Landsat TM land cover mapping, USGS Draft White Paper. See PDF.
    • R Core Team. 2017. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL www.R-project.org.
    • Ruefenacht, B. 2016. Comparison of three Landsat TM compositing methods: a case study using modeled tree canopy cover. Photogrammetric Engineering & Remote Sensing 82(3):199-211.
    • Yang, L., et al. 2018. A new generation of the United States National Land Cover Database: Requirements, research priorities, design, and implementation strategies. ISPRS Journal of Photogrammetry and Remote Sensing 146:108-123.
    • Zhu, Z.; Woodcock, C.E. 2012. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment. 118(2012): 83-94.
     

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