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U. S. Geological Survery
USGS/EROS
Sioux Falls
SD
57192-0001
custserv@usgs.gov
US
47914 252nd Street
605-594-6151
605-594-6589
605-594-6933
0800 - 1600 CT, M -- F (-6h CST/-5h CDT GMT)
The USGS point of contact is for questions relating to the data display and download from this web site. For questions regarding data content and quality, email: mrlc@usgs.gov
U.S. Geological Survey
Customer Service Representative
U. S. Geological Survery
ArcGIS Metadata
1.0
U. S. Geological Survery
USGS/EROS
Sioux Falls
SD
57192-0001
custserv@usgs.gov
US
47914 252nd Street
605-594-6151
605-594-6589
605-594-6933
0800 - 1600 CT, M -- F (-6h CST/-5h CDT GMT)
The USGS point of contact is for questions relating to the data display and download from this web site. For questions regarding data content and quality, email: mrlc@usgs.gov
U.S. Geological Survey
Customer Service Representative
U. S. Geological Survery
U. S. Geological Survery
Raster Dataset
NLCD 2016 Tree Canopy Cover (CONUS)
2018-06-01
1.0
raster digital data
none
none
NLCD TCC 2016
U. S. Geological Survery
USGS/EROS
Sioux Falls
SD
57192-0001
custserv@usgs.gov
US
47914 252nd Street
605-594-6151
605-594-6589
605-594-6933
0800 - 1600 CT, M -- F (-6h CST/-5h CDT GMT)
The USGS point of contact is for questions relating to the data display and download from this web site. For questions regarding data content and quality, email: mrlc@usgs.gov
U.S. Geological Survey
Customer Service Representative
<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>Percent tree canopy cover is a measure of local tree density that has a variety of applications in wildlife habitat analysis, fire behavior modeling, carbon accounting, and hydrologic analysis. Using medium spatial resolution imagery (30m) from Landsat, we have produced consistent, seamless per-pixel estimates of percent tree canopy cover ranging from 0 to 100 percent. This dataset is part of the larger National Land Cover Database (NLCD) that also includes information on land cover, percent imperviousness, and percent shrub/grass. The NLCD effort is coordinated by a partnership of federal agencies called the Multi-Resolution Land Characteristics (MRLC) Consortium (</SPAN><A href="http://www.mrlc.gov"><SPAN><SPAN>www.mrlc.gov</SPAN></SPAN></A><SPAN>).</SPAN></P><P><SPAN><SPAN>The U.S. Forest Service (USFS) built multiple versions of percent tree canopy cover data, including the tree canopy cover data that are included with the NLCD. The NLCD tree canopy cover data were designed as an integrated data stack for users in the NLCD community who are in need of coordinated datasets/layers that have the characteristic of “Time 1 TCC + change in TCC = Time 2 TCC”. In addition to the NLCD tree canopy cover products, the USFS built and distributes “Analytical” and “Cartographic” versions of the tree canopy cover products. The “Analytical” version of the USFS tree canopy cover data prioritizes objectivity and statistics over visual appearance of the product. The “Cartographic” version of the USFS Tree canopy cover data includes masking of some of the TCC values output from the modeling process, and it prioritizes the visual appearance of the TCC maps for cartographic uses. The “Analytic” and “Cartographic” versions of the datasets were documented and are described in more detail and available at </SPAN></SPAN><A href="https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/"><SPAN><SPAN>https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/</SPAN></SPAN></A><SPAN><SPAN>, while the NLCD tree canopy cover products are available through the Multi-Resolution Land Characteristics Consortium (MRLC) at </SPAN></SPAN><A href="http://www.mrlc.gov"><SPAN><SPAN>www.mrlc.gov</SPAN></SPAN></A><SPAN><SPAN>.</SPAN></SPAN></P></DIV></DIV></DIV>
The goal of this project is to provide the Nation with complete, current and consistent public domain information on its tree canopy cover.
Funding for this project was provided by the U.S. Forest Service (USFS). RedCastle Resources, Inc. produced the dataset under contract to the USFS Geospatial Technology and Applications Center.
U.S. Department of Commerce, 1995, Countries, dependencies, areas of special sovereignty, and their principal administrative divisions, Federal Information Processing Standard 10-4: Washington, D.C., National Institute of Standards and Technology
Conterminous United States
United States of America
USA
US
U.S.A.
United States
U.S.
GIS
Remote Sensing
Tree Canopy Cover
Tree Density
Percent Tree Canopy
Continuous
Digital Spatial Data
ISO 19115 Category
EarthCover
BaseMaps
Environment
Imagery
NGDA Portfolio Themes
Land Use Land Cover Theme
National Geospatial Data Asset
NGDA
Land Use Land Cover Theme
GIS
Remote Sensing
Tree Canopy Cover
EarthCover
Conterminous United States
Tree Density
United States of America
National Geospatial Data Asset
USA
US
U.S.A.
Percent Tree Canopy
United States
BaseMaps
U.S.
Environment
Continuous
NGDA
Imagery
Digital Spatial Data
See access and use constraints information.
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></P><P><SPAN>U.S. Forest Service. 2019. NLCD 2016 Tree Canopy Cover (CONUS). Sioux Falls, SD. U.S. Geological Survey. </SPAN><SPAN /><SPAN /></P><P><SPAN><SPAN>Appropriate use includes regional to national assessments of tree cover, total extent of tree cover, aggregated summaries of tree cover, and construction of cartographic products. Accuracy of the models is provided in the supplemental metadata for the “FS-Analytical” version of the TCC products.</SPAN></SPAN></P></DIV></DIV></DIV>
NLCD 2016
2019-05-01T00:00:00
5.0
raster digital data
none
none
References: 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.
withheld
U. S. Geological Survery
USGS/EROS
Sioux Falls
SD
57192-0001
custserv@usgs.gov
US
47914 252nd Street
605-594-6151
605-594-6589
605-594-6933
0800 - 1600 CT, M -- F (-6h CST/-5h CDT GMT)
The USGS point of contact is for questions relating to the data display and download from this web site. For questions regarding data content and quality, email: mrlc@usgs.gov
U.S. Geological Survey
Customer Service Representative
U. S. Geological Survery
U. S. Geological Survery
U. S. Geological Survery
-130.232828
-63.672192
21.742308
52.877264
Ground condition
2013-04-14
2016-11-22
Corner Coordinates (center of pixel, meters): upper left: -2362845 (X), 3180555 (Y); lower right: 2266440 (X), 251175 (Y).
U. S. Geological Survery
USGS/EROS
Sioux Falls
SD
57192-0001
custserv@usgs.gov
US
47914 252nd Street
605-594-6151
605-594-6589
605-594-6933
0800 - 1600 CT, M -- F (-6h CST/-5h CDT GMT)
The USGS point of contact is for questions relating to the data display and download from this web site. For questions regarding data content and quality, email: mrlc@usgs.gov
U.S. Geological Survey
Customer Service Representative
U. S. Geological Survery
U. S. Geological Survery
Esri ArcGIS 10.7.1.11595
1
-107.249941
-93.321062
36.706972
25.373245
There are no restrictions to access for this metadata.
Data is for the conterminous United States only (lower 48 states and District of Columbia).
No formal, independent accuracy assessment of this product has been made at the time of publication. However, an assessment is planned. Users should check at www.mrlc.gov or send an inquiry to the metadata contact to inquire if new accuracy information is available. The random forests regression algorithm (R Core Team 2017; Cutler et al. 2007; Breiman 2001) employed in creating this product calculates the mean of squared residuals along with percent variability explained by the model for assessing prediction reliability. The random forests models consisted of 500 decision trees, which were used to determine the final response value. The response of each tree depended on a randomly chosen subset of predictor variables chosen independently (with replacement) for evaluation by that tree. The responses of the trees were averaged to obtain an estimate of the dependent variable. Because the random forests bias correction option was used, it was possible to obtain estimates less than 0 or greater than 100. These estimates were reset to either 0 or 100. The estimates were also rounded to the nearest integer. The standard error is the square root of the variance of the estimates given by all trees. A summary of the random forests models is available in the supplemental metadata for the “FS-Analytical” version of the TCC products. Breiman, L. 2001. Random forests. Machine Learning 45:15–32. 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. R Core Team. 2017. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL www.R-project.org
elevation data
Digital Elevation Model (DEM)
DEM
U.S. Geological Survey Publication_Date: unpublished material
raster digital data
National Agricultural Statistics Service, 2016 Cultivated Layer
2017-01-30
National Agricultural Statistics Service, 2016 Cultivated Layer
land cover information
NLCD 2011 Land Cover
NLCD11LC
2011-01-01
Multi-Resolution Land Characteristics Consortium (MRLC)
U.S. Geological Survey
Sioux Falls, SD
raster digital data
spectral information
Landsat 8 OLI Composite Imagery
L8OLIComp
U.S. Forest Service Geospatial Technology and Applications Center
raster digital data
mapping area extents
USGS Mapping Zones
MA
2001-01-01
U.S. Geological Survey
vector digital data
canopy cover estimate (training/validation)
Photo-interpreted Canopy Cover (FIA)
FIACC
U.S. Forest Service Forest Inventory and Analysis Program
vector digital data
spectral information
Landsat 8 Operational Land Imager
L8OLI
U.S. Geological Survey
U.S. Geological Survey
Sioux Falls, SD
raster digital data
spectral information
Landsat 8 Harmonic Regression Coefficients (2014-2016)
L8HR
Oregon State University – Department of Forest Ecosystems and Society
raster digital data
Creation of DEM derivatives. A CONUS-wide 30-m DEM was provided by USGS for this project. Slope, aspect, and the sine and cosine of aspect were calculated for each pixel following industry standards. Each of these data layers was subset to individual WRS-2 path/row boundaries for use in subsequent processes.
2013-01-01T00:00:00
DEM
Aspect
AspSin
Slope
AspCos
Creation of Landsat OLI derivatives. Spectral derivative images were calculated from the Landsat OLI composite image for each WRS-2 path/row. NDMI (normalized difference moisture index), NDVI (normalized difference vegetation index), and the 3-band tasseled cap transformation (Baig et al. 2014) were calculated following industry standards. 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
L8OLI Comp Process_Date: 20170801
NDMI
TasCap
NDVI
2017-07-01T00:00:00
Creation of Landsat OLI composite. Landsat 8 OLI scenes collected during the growing season between the years 2013 and 2016 were selected and transformed to top-of-atmosphere (Chander et al. 2009) for each WRS-2 path/row. The selection process favored scenes with minimal cloud cover and with NDVI values near the annual peak for the dominant forest cover type. Remaining clouds were removed using the Fmask 2.2 tool (Zhu and Woodcock 2012). Each set of landsat scenes for a WRS-2 path/row was combined to form a cloud-free composite image using a median value as described by Ruefenacht (2016). 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. 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. Zhu, Z.; Woodcock, C.E. 2012. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment. 118(2012): 83-94.
2017-07-01T00:00:00
L8OLI
L8OLIComp
Creation of percent tree canopy cover dataset (main process). The NLCD 2016 percent tree canopy cover (TCC 2016) CONUS dataset was created piecewise for 68 zones (Homer and Gallant, 2001). Each zone was extended to include the footprint of all WRS-2 path/rows. We refer hereafter to these extended zones as “mapping areas”. Each mapping area included between 9 and 27 WRS-2 path/rows. The final dataset is a mosaic of tree canopy cover values for all the WRS-2 path/rows. Seven major steps were employed to map tree canopy cover: collection of reference data, acquisition and/or creation of predictor layers, calibration of random forests regression models for each mapping area using reference data and predictor layers, application of those models to predict per-pixel tree canopy cover across the entire mapping area, development of thresholds for filtering pixels with high uncertainty, and creation of the CONUS-wide mosaic. A seventh step is applied to build a three-layer integrated data stack, of which this 2016 NLCD TCC dataset is a component. This 2016 NLCD dataset is one of three integrated layers that were designed to fit the criterion of “Time 1 + change = Time 2”, a need of many of NLCD users. The methodology is described further below and in Coulston et al. (2012) and Ruefenacht (2016). Step 1: Reference data for the nominal 2016 TCC products were generated via photographic interpretation of high spatial resolution images acquired by the National Agricultural Imagery Program (NAIP). The initial reference data were collected and supplied by the U.S. Forest Service Forest Inventory and Analysis (FIA) program. Of the initial 63,000 sites, about 2100 sites were identified as potentially changed between the nominal years of 2011 and 2016, through analysis of fire and NDVI data. For those sites identified as potentially changed, remote sensing analysts reviewed and then reinterpreted the tree canopy cover conditions if needed. The spatial distribution of the sample points follows the FIA quasi-systematic grid (Brand et al. 2000). Step 2: Predictor layers included Landsat 8 OLI composite imagery and spectral derivatives thereof (NDMI, NDVI, and tasseled cap); elevation data and spatial derivatives thereof (slope, aspect, sine of aspect, cosine of aspect);EWMA (exponentially weighted moving average) data, provided by and generated by Oregon State University through an implementation of a harmonic regression-based algorithm (Brooks et al. 2012) built by Virginia Polytechnic University. The processes for creating the derived layers are described separately (see related Process Steps). Step 3: Modeling was carried out using the random forests regression algorithm (R Core Team 2017; Breiman 2001) with the bias correction option as outlined in the Attribute Accuracy Report above. Step 4: The models were applied to individual WRS-2 path/rows intersecting each mapping area, producing a 2-layered image. The first layer was the random forests regressions estimate of tree canopy cover and the second layer was the standard error, which is the per-pixel square root of the variance of the random forests regression estimates from the individual trees. Step 5: Threshold values were determined for each of the mapping areas using data from 500 runs of the random forests regression algorithm on bootstrap samples. From these data, t-statistics were calculated. For each mapping area, the t-statistic at the 95th quantile was selected as the threshold value. Threshold values ranged from 0.50 to 2.80. For each pixel, the product of the t-statistic threshold value and the pixel standard error was compared to the pixel percent tree canopy value and if this product was greater than the pixel percent tree canopy, the percent tree canopy value for that pixel was set to zero; otherwise, the percent tree canopy of the pixel was left unchanged. A pixel was also set to zero if it fell within a NLCD 2011 Landcover class of 11 (open water) or 12 (perennial snow/ice) or was considered to be agriculture as defined by the cultivated layer (CL). Step 6: Since models were applied to each mapping area independently, there were multiple estimates for pixels in overlapping areas. For these pixels, the estimate with the lowest standard error was carried into the CONUS-wide mosaic. Due to the use of the bias correction option in the random forests modeling, estimates could be outside the range of 0 to 100. These estimates were reset to either 0 or 100. Estimates were also rounded to the nearest integer. Step 7: The NLCD-TCC production workflow included a step after the production of the “FS-Analytical” and “FS-Cartographic” TCC products in order to integrate three layers (2011 NLCD TCC, 2016 NLCD TCC, and a TCC change layer) into a common data stack. In this three-layer data stack, all pixels with valid TCC values (0 to 100%) in both years (2011 and 2016) meet the criterion of “Time 1 TCC + change = Time 2 TCC”. To satisfy that criterion, pixels were first identified as changed or not based on analysis of disturbance data from the USFS Forest Inventory and Analysis (FIA) program and standard error values in the “FS-Analytical” TCC product. For pixels identified as changed, and initially assuming the magnitude of change was at least 30%, the pixel value in the 2016 NLCD TCC layer was taken from the 2016 “FS-Cartographic” TCC product. For pixels in which confidence in actual change was low or non-existent, the pixel value in the 2016 NLCD TCC product was set to an average of the TCC values from the 2011 and 2016 “FS-Cartographic” TCC products. Spatial filtering was also applied to clean up noise and speckle. Through the spatial filtering process, pixels with a magnitude of change of less than 30% were occasionally re-introduced back into the NLCD TCC products and integrated data stack. Such pixels were permitted back into the NLCD TCC products in order to improve the visual appearance of the products. This processing of subtle change magnitudes (1-29%) and the spatial filtering were included in the 2016 NLCD TCC production workflow as an effort to balance visual appearance of the TCC products and the generation of the integrated data stack (“Time 1 TCC + change = Time 2 TCC”) desired by many NLCD users. While the integrated data stack was achieved, minor visual artifacts (e.g., very small islands of “No Change” surrounded by pixels identified as change) may still be present within the tree canopy cover products included in the overall 2016 NLCD Product Suite. 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. 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. Homer, C.; Gallant, A. 2001. Partitioning the conterminous United States into mapping zones for Landsat TM land cover mapping, USGS Draft White Paper. landcover.usgs.gov/pdf/homer.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.
2017-11-01T00:00:00
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