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ItemDescription
U. S. Geological Survery
Customer Service Representative
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
ArcGIS Metadata
1.0
U. S. Geological Survery
Customer Service Representative
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 Survery
Raster Dataset
NLCD 2011 Tree Canopy Cover (CONUS)
2019-07-12T00:00:00
2.0
U. S. Geological Survery
USGS/EROS
custserv@usgs.gov
Sioux Falls
SD
57192-0001
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
Customer Service Representative
raster digital data
none
none
NLCD TCC 2011
<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>www.mrlc.gov</SPAN></A><SPAN>).</SPAN></P><P><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. For the overall 2016 NLCD Product Suite, the nominal 2011 tree canopy cover product was reprocessed with updated inputs and a modified production workflow. Users should be aware that the 2011 NLCD tree canopy cover product that is part of the overall 2016 NLCD Product Suite is an updated version that is different from the 2011 NLCD tree canopy cover product that was released with the overall 2011 NLCD. This nominal 2011 tree canopy cover product that is part of the 2016 NLCD may be considered as “2011 NLCD TCC, Epoch 2”. In addition, the tree canopy cover data that are included in the 2016 NLCD Product Suite 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”.</SPAN></P><P><SPAN>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><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
U.S.
USA
United States of America
Conterminous United States
US
United States
U.S.A.
NGDA Portfolio Themes
NGDA
National Geospatial Data Asset
Land Use Land Cover Theme
ISO 19115 Category
BaseMaps
EarthCover
Imagery
Environment
GIS
Tree Density
Remote Sensing
Tree Canopy Cover
Digital Spatial Data
Continuous
Percent Tree Canopy
U.S.
GIS
USA
United States of America
Conterminous United States
NGDA
National Geospatial Data Asset
Tree Density
Land Use Land Cover Theme
BaseMaps
US
EarthCover
Remote Sensing
United States
Tree Canopy Cover
U.S.A.
Imagery
Digital Spatial Data
Continuous
Percent Tree Canopy
Environment
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 STYLE="margin:1 1 1 20;"><SPAN>U.S. Forest Service. 2019. NLCD 2011 Tree Canopy Cover (CONUS). Sioux Falls, SD. U.S. Geological Survey. </SPAN></P><P STYLE="margin:1 1 1 0;"><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
Customer Service Representative
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 Survery
U. S. Geological Survery
-130.232828
-63.672192
21.742308
52.877264
Ground condition
2007-01-01
2011-11-10
Corner Coordinates (center of pixel, meters): upper left: -2362845 (X), 3180555 (Y); lower right: 2266440 (X), 251175 (Y).
U. S. Geological Survery
USGS/EROS
custserv@usgs.gov
Sioux Falls
SD
57192-0001
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
Customer Service Representative
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
raster digital data
spectral information
Landsat 5 Multispectral Thematic Mapper Imagery
L5TM
U.S. Geological Survey
Sioux Falls, SD
U.S. Geological Survey
raster digital data
land cover information
NLCD 2011 Land Cover
NLCD11LC
2011-01-01
U.S. Geological Survey
Sioux Falls, SD
Multi-Resolution Land Characteristics Consortium (MRLC)
raster digital data
spectral information
Landsat 5 Harmonic Regression Coefficients (2009-2011)
L5HR
Oregon State University – Department of Forest Ecosystems and Society
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
agriculture information
National Agricultural Statistics Service, 2011 Cultivated Layer
CL
2017-01-30T00:00:00
National Agricultural Statistics Service, 2011 Cultivated Layer
NASS
NASS Marketing and Information Services Office, Washington, D.C.
raster digital data
spectral information
Landsat 5 TM Composite Imagery
L5TMComp
U.S. Forest Service Geospatial Technology and Applications Center
raster digital data
Creation of Landsat TM composite. Fifteen Landsat 5 TM scenes were selected and processed for each WRS-2 path/row. Selected scenes were acquired between 2007 and 2011, with the majority evenly distributed between 2009, 2010 and 2011. The selection process favored scenes with minimal cloud cover and 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). Six spectral bands (TM bands 1-5 and 7) within each scene were atmospherically corrected with dark object subtraction and transformed to surface reflectance (Chander et al. 2009; Chavez 1988). Each set of 15 6-band scenes was then combined to form a cloud-free composite image for the given path/row. 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. Zhu, Z.; Woodcock, C.E. 2012. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment. 118(2012): 83-94.
2013-01-01T00:00:00
L5TM
L5TMComp
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
Slope
Aspect
AspSin
AspCos
Creation of Landsat 5 TM derivatives. Spectral derivative images were calculated from the Landsat 5 TM 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 (Crist and Kauth 1986) were calculated following industry standards. Crist, E.P.; Kauth, R.J. 1986. The tasseled cap de-mystified. Photogrammetric Engineering & Remote Sensing 52(1):81-86.
L5TM Comp Process_Date: 20130101
NDMI
TasCap
NDVI
2013-09-30T00:00:00
Creation of percent tree canopy cover dataset (main process). The NLCD 2011 percent tree canopy cover (TCC 2011) 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 2011 NLCD TCC dataset is a component. This 2011 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, consisting of estimated tree canopy cover at each of 63,008 FIA plot locations, were generated via photographic interpretation of high spatial resolution images acquired by the National Agricultural Imagery Program (NAIP). The reference data were 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). Step 2: Predictor layers included Landsat 5 TM 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 regression 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 interval was selected as the threshold value. Threshold values ranged from 0.48 to 2.57. 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 2011 NLCD TCC layer was taken from the 2011 “FS-Cartographic” TCC product. For pixels in which confidence in actual change was low or non-existent, the pixel value in the 2011 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
FIACC, L5TM, NDMI, NDVI, TasCap, DEM, Aspect, AspCos , AspSin, Slope, L5HR
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