20191007140201001.0FGDC CSDGM MetadataFALSEnlcd_2016_coastal_alaska_treecanopy.imgfile://\\166.2.127.149\tcc_ak\Alaska_Coastal\TCC2016_Alaska\Final_Products\Cartographic_NLCD\nlcd_2016_coastal_alaska_treecanopy.imgLocal Area Network-48915.0000001492815.000000710595.0000001319415.0000001002ProjectedGCS_WGS_1984Linear Unit: Meter (1.000000)Albers_Conical_Equal_Area<ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/10.5'><WKT>PROJCS["Albers_Conical_Equal_Area",GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Albers"],PARAMETER["false_easting",0.0],PARAMETER["false_northing",0.0],PARAMETER["central_meridian",-154.0],PARAMETER["standard_parallel_1",55.0],PARAMETER["standard_parallel_2",65.0],PARAMETER["latitude_of_origin",50.0],UNIT["Meter",1.0]]</WKT><XOrigin>-13752200</XOrigin><YOrigin>-8948200</YOrigin><XYScale>327482121.21482342</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.001</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision></ProjectedCoordinateSystem>8TRUERLE1IMAGINE ImageTRUEdiscreteunsigned integer201911151516020020191115151653001500000005000FGDCArcGIS Metadata1.0Downloadable dataRaster DatasetU.S. Geological SurveyU.S. Geological SurveyU.S. Geological SurveyNLCD 2016 Tree Canopy Cover (Coastal Alaska)2019-10-01T00:00:00raster digital datanonenoneNLCD 2016 TCC CartographicU.S. Geological SurveySioux Falls, SDU.S. Geological Survey<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><P><SPAN STYLE="font-weight:bold;">Analytical</SPAN></P><UL><LI><P><A href="https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/"><SPAN>USFS Tree Canopy Cover Datasets</SPAN></A><SPAN>(Download)</SPAN></P></LI><LI><P><A href="https://apps.fs.usda.gov/fsgisx01/rest/services/RDW_LandscapeAndWildlife"><SPAN>USFS Enterprise Data Warehouse</SPAN></A><SPAN>(Image Service)</SPAN></P></LI></UL><P><SPAN STYLE="font-weight:bold;">Cartographic</SPAN></P><UL><LI><P><A href="https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/"><SPAN>USFS Tree Canopy Cover Datasets</SPAN></A><SPAN>(Download)</SPAN></P></LI><LI><P><A href="https://apps.fs.usda.gov/arcx/rest/services/RDW_LandscapeAndWildlife"><SPAN>USFS Enterprise Data Warehouse</SPAN></A><SPAN>(Map Service)</SPAN></P></LI></UL><P><SPAN STYLE="font-weight:bold;">NLCD</SPAN></P><UL><LI><P><A href="https://www.mrlc.gov/data"><SPAN>Multi-Resolution Land Characteristics (MRLC) Consortium</SPAN></A><SPAN>(Download)</SPAN></P></LI><LI><P><A href="https://apps.fs.usda.gov/fsgisx01/rest/services/RDW_LandscapeAndWildlife"><SPAN>USFS Enterprise Data Warehouse</SPAN></A><SPAN>(Image Service)</SPAN></P></LI></UL><P><SPAN><SPAN>The </SPAN></SPAN><SPAN>Coastal Alaska TCC 2016 NLCD dataset is comprised of a single layer. The pixel values range from 0 to 91 percent. The background is represented by the value 255. Data gaps (which are explained in more detail below) are represented by the value 127.</SPAN></P><P><SPAN>The NLCD data include three components: 2011 NLCD TCC, 2016 NLCD TCC, and 2011-to-2016 change in TCC. For nearly all pixels, the values meet the criterion of “2011 TCC + change in TCC = 2016 TCC”. However, there are some pixels with no TCC values because of a lack of imagery in persistently cloudy areas. These data gaps were given a value 127. </SPAN></P><P><SPAN>In summary, if a data gap was present in the original 2011 or 2016 data, that data gap was carried through to all three components of the NLCD data. Recall that the three NLCD components (2011 NLCD TCC, 2016 NLCD TCC, and change between the two nominal years) are intended to coordinate and “line up”.</SPAN></P><P><SPAN>The USFS’s GTAC also makes available the original 2011 and 2016 TCC datasets (prior to development of any integrated data stack for NLCD) that are output as part of the production workflows. If a user would like the original datasets for the nominal years of 2011 and 2016 (prior to integrating into a common data stack for NLCD), they should visit </SPAN><A href="https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/"><SPAN>https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/</SPAN></A><SPAN>and download the “FS-Cartographic” version of the 2011 and/or 2016 datasets for their cartographic applications.</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 TechnologyUSUnited StatesAlaskaAKU.S.U.S.A.United States of AmericaUSATree DensityDigital Spatial DataTree Canopy CoverContinuousPercent Tree CanopyRemote SensingGISNGDA Portfolio ThemesNGDANational Geospatial Data AssetLand Use Land Cover ThemeISO 19115 CategoryBaseMapsEarthCoverImageryEnvironmentBaseMapsTree DensityEarthCoverImageryDigital Spatial DataTree Canopy CoverContinuousPercent Tree CanopyUSNGDARemote SensingNational Geospatial Data AssetUnited StatesAlaskaAKLand Use Land Cover ThemeU.S.U.S.A.United States of AmericaUSAEnvironmentGISSee 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;margin:7 0 7 0;"><SPAN>USDA Forest Service. 2019. NLCD 2016 Tree Canopy Cover (Coastal Alaska). Salt Lake City, UT.</SPAN></P><P><SPAN>Appropriate use includes regional to national assessments of tree cover, total extent of tree cover, and aggregated summaries of tree cover.</SPAN></P></DIV></DIV></DIV>Ground condition2013-07-07T00:00:002017-08-29T00:00:00Corner Coordinates (center of pixel, meters): upper left: -2232330 (X), 2380110 (Y); lower right: 1494720 (X), 344820 (Y). Version 6.2 (Build 9200) ; Esri ArcGIS 10.5.1.73331-154.932178-127.12024261.85192454.036068U.S. Geological SurveyU.S. Geological SurveyThere are no restrictions to access for this metadata. The user should be aware that supplemental metadata is available.Data is for Coastal Alaska only.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 https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/ 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. The standard error is the square root of the variance of the estimates given by all trees.
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.orgcanopy cover estimate (training/validation)Photointerpreted Canopy CoverPI_CC USDA Forest Service Geospatial Technology and Applications Centervector digital dataspectral informationLandsat 8 Operational Land ImagerL8OLIU.S. Geological SurveyU.S. Geological SurveySioux Falls, SDraster digital dataspectral informationLandsat 8 Harmonic Regression Coefficients (2014-2016)L8HROregon State University Department of Forest Ecosystems and Societyraster digital dataelevation dataASTER Global Digital Elevation Model (GDEM 2) V002AstDEMNASA (National Aeronautics and Space Administration)raster digital dataOnline at https://search.earthdata.nasa.gov/search?m=-26.15625!17.71875!0!1!0!0%2C2&fi=ASTERspectral informationLandsat 8 OLI Composite ImageryL8OLICompUSDA Forest Service Geospatial Technology and Applications Centerraster digital dataCreation of Landsat OLI composite. Landsat 8 OLI surface reflectance scenes were collected in Google Earth Engine (GEE) during the growing season between the years 2013 and 2017. 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 built-in FMask algorithm in GEE (Zhu and Woodcock 2012). The collection of Landsat scenes for the study area were combined to form a cloud-free composite image using a median value as described by Ruefenacht (2016).
References:
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): 83-94.2015-12-15T00:00:00L8OLIL8OLICompPhotointerpretation of Sample Plots. Response data used to train the computer models is made by photographic interpretation of sample plots. Each plot representing approximately 2,400 hectares is part of a national hexagonal grid which covers all land types and is considered an equal probability sample for the total surface area (McRoberts et al., 1999). A total of 1,397 points were used for Coastal Alaska modeling. A circle with a radius of 43.9 m (144 ft) was placed over each plot center. Each circle contained a 109-dot grid, which was oriented 15 degrees east of true north, with each dot separated by 8 m. Photo-interpreters evaluated each dot as being either tree or not tree. For each plot, percent tree canopy cover was calculated from these dot counts. The bulk of the imagery upon which the photo interpreters based their observations was provided by the Alaska Geospatial Imagery Services via two web map services (WMS) at http://gis.dnr.alaska.gov/terrapixel/cubeserv/OIM_BDL and http://gis.dnr.alaska.gov/terrapixel/cubeserv/ortho . This imagery was complemented in areas of the Chugach and Tongass National Forests with aerial photography acquired by the USDA Forest Service. High resolution imagery (spatial resolution of 1m or less) was used wherever available within the constraints of the 2016 Landsat temporal period.
References:
McRoberts, Ronald E.; Hansen, Mark H. 1999. Annual forest inventories for the north central region of the United States. Journal of Agricultural, Biological, and Environmental Statistics. 4(4): 361–371.
2017-04-14T00:00:00PI_CCCreation of Landsat OLI derivatives. Spectral derivative images were calculated from the Landsat OLI composite image. 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.
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):423-4312018-05-16T00:00:00L8OLICompTasCapNDVINDMICreation of DEM derivatives. A 30-m Global DEM for Coastal Alaska was downloaded from NASA's Earthdata Search website for this project. Slope, aspect, and the sine and cosine of aspect were calculated for each pixel following industry standards. 2018-05-17T00:00:00GDEMSlopeAspSinAspectAspCosCreation of percent tree canopy cover dataset (main process). The FS Analytical 2016 percent tree canopy cover for Coastal Alaska dataset was created as a single unit using six Landsat 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 using reference data and predictor layers, application of those models to predict per-pixel tree canopy cover across Coastal Alaska, development of a threshold for filtering pixels with high uncertainty, resetting canopy estimate to 0 to 100 if needed, and data stack preparation. 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 1,397 FIA plot locations, were generated via photographic interpretation (PI_CC) of high spatial resolution images, as described in the Photointerpretation of Sample Plots process step. 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) as outlined in the Attribute Accuracy Report above.
Step 4: The random forest model trained from the PI points was applied to the median composite image for each Landsat WRS-2 path/row included within Coastal Alaska producing a 2-layered image. The first layer was the random forest regression estimate of percent tree canopy cover and the second layer was the standard error, which is the per-pixel square root of the variance of the random forest regression estimates from the individual trees.
Step 5: Threshold values were determined using data from 100,000 runs of random forests regression algorithm on bootstrap samples where 200 random data samples were witheld from each run. Using the observed data (the withheld random data samples) and the randomForest predicted values, t-test values were calculated using the formula (predicted - observed) / SE. SE is the standard error generated from the 500 randomForest trees used for each prediction. The t-test value at the 93rd, 95th, and 97th interval were evaluated with the 95th interval selected as the threshold value. For each pixel, the product of the 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.
Step 6: 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) met 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, 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. 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.
References:
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 45:1532.
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): 715727.
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):199-211.2018-07-03T00:00:00PI_CC, L8OLI, NDMI, NDVI, TasCap, AstDEM, Aspect, AspCos , AspSin, Slope, L8HR2029430.0000005139130.000000210-48915.000000 710595.000000-48915.000000 1319415.0000001492815.000000 1319415.0000001492815.000000 710595.000000721950.000000 1015005.000000Layer_1255.00000011.0000008FalseFalseFalseFalseFalsenlcd_2016_coastal_alaska_treecanopy.img.vatTable256OIDOIDOID400Internal feature number.EsriSequential unique whole numbers that are automatically generated.ValueValueInteger000Percent tree canopy cover091PercentCountCountDouble000RedRedInteger000GreenGreenInteger000BlueBlueInteger000dataset20191115U.S. Geological SurveySioux Falls, SDU.S. Geological SurveyU.S. Geological Survey