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S. Geological SurveryUSGS/EROSSioux FallsSD57192-0001custserv@usgs.govUS47914 252nd Street605-594-6151605-594-6589605-594-69330800 - 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.govU.S. Geological SurveyCustomer Service RepresentativeU. S. Geological SurveryArcGIS Metadata1.0U. S. Geological SurveryUSGS/EROSSioux FallsSD57192-0001custserv@usgs.govUS47914 252nd Street605-594-6151605-594-6589605-594-69330800 - 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.govU.S. Geological SurveyCustomer Service RepresentativeU. S. Geological SurveryU. S. Geological SurveryRaster DatasetNLCD 2011 to 2016 Percent Tree Canopy Change (CONUS)2019-10-01T00:00:001.0raster digital datanonenoneNLCD TCC 2011 to 2016 ChangeU. S. Geological SurveryUSGS/EROSSioux FallsSD57192-0001custserv@usgs.govUS47914 252nd Street605-594-6151605-594-6589605-594-69330800 - 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.govU.S. Geological SurveyCustomer Service Representative<DIV STYLE="text-align:Left;"><DIV><DIV><P STYLE="margin:0 0 0 0;"><SPAN><SPAN>The USDA Forest Service (USFS) builds multiple versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass CONUS, Coastal Alaska, Hawaii, U.S. Virgin Islands and</SPAN></SPAN><SPAN /><SPAN /><SPAN><SPAN>Puerto Rico. There are three versions of data within the 2016 TCC Product Suite, which include:</SPAN></SPAN></P><UL><LI><P><SPAN><SPAN>The initial model outputs referred to as the Analytical data;</SPAN></SPAN></P></LI></UL><UL><LI><P><SPAN><SPAN>A masked version of the initial output referred to as Cartographic data;</SPAN></SPAN></P></LI></UL><UL><LI><P><SPAN><SPAN>And a modified version built for the National Land Cover Database and referred to as NLCD data, which includes a canopy cover change dataset derived from subtraction of datasets for the nominal years of 2011 and 2016.</SPAN></SPAN></P></LI></UL><P STYLE="margin:0 0 0 0;"><SPAN><SPAN>The Analytical data are the initial model outputs generated in the production workflow. 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 nominal years of 2011 and 2016 are available. </SPAN></SPAN></P><P><SPAN><SPAN>The Cartographic products mask the initial model outputs to improve the visual appearance of the datasets. These data are best suited for users who prioritize visual appearance of the data for cartographic and illustrative purposes. Datasets for the nominal years of 2011 and 2016 are available. </SPAN></SPAN></P><P STYLE="margin:0 0 0 0;"><SPAN><SPAN>The NLCD data are the result of further processing of the masked data. The goal was to generate three coordinated components. The components are (1) a dataset for the nominal year of 2011, (2) a dataset for the nominal year of 2016, and (3) a dataset that captures the change in canopy cover between the two nominal years of 2011 and 2016. For the NLCD data, the three components meet the criterion of “2011 TCC + change in TCC = 2016 TCC”. These NLCD data are best suited for users who require a coordinated three-component data stack where each pixel’s values meet the criterion of “2011 TCC + change in TCC = 2016 TCC”. Datasets for the nominal years of 2011 and 2016 are available, as well as a dataset that captures the change (loss or gain) in canopy cover between those two nominal years of 2011 and 2016, in areas where change was identified.</SPAN></SPAN></P><P STYLE="margin:0 0 0 0;"><SPAN /><SPAN /></P><P STYLE="margin:0 0 0 0;"><SPAN><SPAN>These tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms, as listed below:</SPAN></SPAN></P><UL><LI><P><SPAN STYLE="font-weight:bold;">Analytical</SPAN></P></LI></UL><UL><LI><P><A href="https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/"><SPAN><SPAN>USFS Tree Canopy Cover Datasets</SPAN></SPAN></A></P></LI><LI><P><A href="https://data.fs.usda.gov/geodata/edw/index.php"><SPAN><SPAN>USFS Enterprise Data Warehouse</SPAN></SPAN></A><SPAN /><SPAN /></P></LI></UL><UL><LI><P><SPAN STYLE="font-weight:bold;">Cartographic</SPAN></P></LI></UL><UL><LI><P><A href="https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/"><SPAN><SPAN>USFS Tree Canopy Cover Datasets</SPAN></SPAN></A></P></LI></UL><UL><LI><P><SPAN STYLE="font-weight:bold;">NLCD</SPAN></P></LI></UL><UL><LI><P><A href="https://www.mrlc.gov/data"><SPAN><SPAN>Multi-Resolution Land Characteristics (MRLC) Consortium</SPAN></SPAN></A></P></LI><LI><P><A href="https://data.fs.usda.gov/geodata/edw/index.php"><SPAN><SPAN>USFS Enterprise Data Warehouse</SPAN></SPAN></A></P></LI></UL><P><SPAN>The CONUS TCC NLCD change dataset is comprised of a single layer. Percent canopy gains are represented as values 0 to 100. Percent canopy losses are represented as values 101 to 200 such that a percent canopy loss of 25 would be represented by the value 125. The background is represented by the value 255.</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 TechnologyConterminous United StatesUnited States of AmericaUSAUSU.S.A.United StatesU.S.GISRemote SensingTree Canopy CoverTree DensityPercent Tree CanopyContinuousDigital Spatial DataChangeISO 19115 CategoryEarthCoverBaseMapsEnvironmentImageryNGDA Portfolio ThemesLand Use Land Cover ThemeNational Geospatial Data AssetNGDALand Use Land Cover ThemeGISRemote SensingTree Canopy CoverEarthCoverConterminous United StatesTree DensityUnited States of AmericaNational Geospatial Data AssetUSAUSU.S.A.Percent Tree CanopyUnited StatesBaseMapsU.S.EnvironmentContinuousNGDAImageryDigital Spatial DataChangeSee access and use constraints information.nonen/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="text-indent:20;"><SPAN /><SPAN>USDA Forest Service. 2019. National Land Cover Database (NLCD) 2011 to 2016 Tree Canopy Cover Change (CONUS). Salt Lake City, UT.</SPAN></P><P><SPAN>Appropriate use includes regional to national assessments of tree cover, total extent of tree cover, aggregated summaries of tree cover, changes in tree cover between the nominal years of 2011 and 2016, and construction of cartographic products.</SPAN></P></DIV></DIV></DIV>NLCD 20162019-05-01T00:00:005.0raster digital datanonenoneReferences:
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.www.mrlc.govU. S. Geological SurveryUSGS/EROSSioux FallsSD57192-0001custserv@usgs.govUS47914 252nd Street605-594-6151605-594-6589605-594-69330800 - 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.govU.S. Geological SurveyCustomer Service RepresentativeU. S. Geological SurveryU. S. Geological SurveryU. S. Geological Survery-130.232828-63.67219221.74230852.877264Ground condition2007-07-01T00:00:002016-11-22T00:00:00Corner Coordinates (center of pixel, meters): upper left: -2362845 (X), 3180555 (Y); lower right: 2266440 (X), 251175 (Y).U. S. Geological SurveryUSGS/EROSSioux FallsSD57192-0001custserv@usgs.govUS47914 252nd Street605-594-6151605-594-6589605-594-69330800 - 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.govU.S. Geological SurveyCustomer Service RepresentativeU. S. Geological SurveryU. S. Geological Survery Version 6.2 (Build 9200) ; Esri ArcGIS 10.5.1.73331-130.232828-63.67219252.87726421.742308There 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.
References:
Breiman, L. 2001. Random forests. Machine Learning 45:1532.
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.orgmodeled tree canopy cover and associated error estimates2011 and 2016 USFS analytical productsUSFSAnalytical2018-12-14T00:00:00USFS Geospatial Technology and Applications Center (GTAC)125 S. State StreetSalt Lake CityUT84138masked tree canopy cover2011 and 2016 USFS cartographic productsUSFSCartographic2018-12-14T00:00:00USFS Geospatial Technology and Applications Center (GTAC)125 S. State StreetSalt Lake CityUT84138USFS Geospatial Technology and Applications Center (GTAC)estimates of landscape changeEVALIDator Web ToolFIAEVUSDA Forest Service Forest Inventory and Analysis ProgramCreation of percent tree canopy cover change dataset for the 2016 NLCD (main process). The percent tree canopy cover change CONUS dataset was built as the last component in the TCC production workflow utilized by the USFS for the 2016 NLCD. The dataset represents an estimate of tree canopy cover change between the nominal years of 2011 and 2016. The percent tree canopy cover change dataset for the 2016 NLCD was built with components from upstream steps in the 2016 TCC production workflow, specifically, the standard error layer from the “FS-Analytical” TCC product and TCC values from the “FS-Cartographic” TCC product. (See metadata for the “FS-Analytical” and “FS-Cartographic” products for descriptions of how those products were constructed.) For this percent tree canopy cover change dataset for the 2016 NLCD, eight steps were employed:
Step 1: Disturbance data from the USFS Forest Inventory and Analysis (FIA) program were analyzed to estimate the “percent of area disturbed” between the nominal years of 2011 and 2016, for CONUS.
Step 2: Pixels were sampled from the 2011 and 2016 “FS-Analytical” TCC products for CONUS, at 900,000 locations. TCC and standard error values were collected for each sampled pixel.
Step 3: The compiled TCC and standard error values from each sampled pixel were input to a numerical optimization tool. The target of the optimization was the “percent of area disturbed”, as derived from the FIA data described in Step 1 above. The optimization tool was used to determine the condition such that (A) the percentage of the CONUS sampled pixels identified as loss between the nominal years of 2011 and 2016 matched (B) the “percent of area disturbed” between the nominal years of 2011 and 2016, as derived from FIA data. The conditions estimated using the optimization tool, sampled data from the 2011 and 2016 datasets, and the target were two multiplier values that were subsequently used to determine whether canopy cover had changed or not, between the nominal years of 2011 and 2016 for individual pixels.
Step 4: The CONUS-wide multiplier values, output from the optimization tool, were applied with an ERDAS model across CONUS. Specifically, upper and lower bounds of a range of TCC were computed for each pixel, in each year, by (A) using the pixel’s TCC value as the midpoint of the range, (B) setting the lower bound of the range to be the pixel’s TCC value minus the product of the multiplier value and the pixel’s standard error value, and (C) setting the upper bound of the range to be the TCC value plus the product of the multiplier value and the pixel’s standard error value. If the range computed for a pixel in 2011 did not overlap with the range computed for a pixel in 2016, the pixel was identified as changed. If the ranges overlapped, then the pixel was identified as “No change”.
Step 5: For pixels identified as changed, the pixel value in the percent tree canopy cover change dataset was set to the simple difference between the 2016 and 2011 “FS-Cartographic” TCC values for the pixel.
Step 6: For pixels in which confidence in actual change was low or non-existent (i.e., the ranges computed in Step 4 above overlapped and the pixel was identified as “No change”), the pixel value in the percent tree canopy cover change dataset for the 2016 NLCD was set to zero.
Step 7: Results were reviewed by geospatial analysts, especially in areas of severe change (e.g., severe fires, harvest and regrowth in timber areas, etc.), and compared to high-resolution aerial photography and imagery.
Step 8: Spatial filtering was also applied to clean up noise and speckle. 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.
2017-11-01T00:00:00USFSAnalytical, USFSCartographic, FIAEV2019-07-12T00:00:0010442430.00000016119030.000000210-2493045.000000 177285.000000-2493045.000000 3310005.0000002342655.000000 3310005.0000002342655.000000 177285.000000-75195.000000 1743645.000000Layer_1255.0000000.0000008FalseFalseFalseFalseFalsenlcd_2011_to_2016_treecanopy_change_2019_08_31_u8.img.vatTable256OIDInternal feature number.ESRIOIDOID400Sequential unique whole numbers that are automatically generated.ValueValueInteger000 Percent tree canopy cover-100100PercentCountCountDouble000RedRedInteger000GreenGreenInteger000BlueBlueInteger000dataset20191007