USDA Forest Service Forest Invenstory and Analysis, Remote Sensing Applications Center 2008 remote-sensing image Remote Sensing of Environment 112:1658-1677 Elsevier http://svinetfc4.fs.fed.us/rastergateway/biomass/ alaska_forest_biomass_mg_per_ha.img An aboveground live forest biomass map for the conterminous U.S., Alaska and Puerto Rico is derived from modeling field biomass estimates, collected nationwide by the USDA Forest Service Forest Inventory and Analysis (FIA) program, as functions of 250-m resolution satellite image products and other digital geographic layers. These predictor layers included the following: 16-day Moderate Resolution Imaging Spectrometer (MODIS) composites, associated vegetation indices, and percent tree cover; vegetative diversity and type synthesized from the National Land Cover Dataset (NLCD); topographic variables; monthly and annual climate parameters; and other ancillary variables. We segmented the U.S. into 65 ecologically similar mapping zones, plus Alaska and Puerto Rico. Before modeling biomass, inventory data served as the basis for classifying the predictor layers into a forest mask with the nonparametric classifier, See5©. Forest biomass models within the predicted forest areas used tree-based algorithms in Cubist©. Using independent test data, the estimated proportion of correctly classified pixels for the forest mask ranged from 0.85 in the Pacific Northwest to 0.94 in Alaska, while estimates of Kappa ranged from 0.57 in Puerto Rico to 0.88 in Alaska. For biomass, the largest model correlation coefficients between observed and predicted values, of 0.66 to 0.78, occurred in the Pacific Northwest and Interior West, while model correlation coefficients were smaller, with most below 0.40 in the eastern mapping zones. Design- and map-based estimates of total forest area and total aboveground live forest biomass are compared for individual states as well as four scales of spatial aggregation. An estimate of C pools in live forest biomass of U.S. forests, derived from the nationwide biomass map, is also compared to previously published estimates. This article documents the national geospatial predictor layer database, standardizing the national FIA data, developing predictive models, producing the maps with accompanying map uncertainty, and assessing model errors. The purpose of this dataset is to portray broad distribution patterns of biomass in Alaska and provide input to national scale modeling projects. en 2004 ground condition Complete Irregular -180.000000 180.000000 71.407166 50.567766 -1791375.000000 1491875.000000 414062.000000 2378562.000000 None Forest Biomass Forest Inventory and Analysis FIA CART Modeling MODIS AK Alaska None None. It is the responsibility of the data user to use the data appropriately and consistent within the limitations of geospatial data in general and these data in particular. Using the data for other than their intended purpose may yield inaccurate or misleading results. Ken Winterberger USDA Forest Service Forest Inventory and Analysis Research Scientist 907-743-9419 907-743-9482 kwinterberger@fs.fed.us Acknowledgement of the USDA Forest Service Forest Inventory and Analysis Program and Remote Sensing Applications Center would be appreciated in products derived from these data. ESRI ArcCatalog 9.1.0.722 Raster Dataset USDA Forest Service Remote Sensing Applications Center 2002 raster digital data Created using USGS National Elevation Dataset (http://www.usgs.gov) Processing Steps 1. Imported BILmeters format into ESRI GRID format. 2. Reprojected into Albers Conical Equal Area NAD 27 with a 60m resolution 3. Mosaicked tiles into a contiguous dataset 4. Resampled to 30m resolution to maintan continuity with CONUS dataset 5. Used a 3x3 focal mean function to output a 90m DEM dataset 6. Created an Aspect Dataset from the 90m DEM 7. Reclassified the Aspect dataset into 4 categories Category 1: 0° - 90° Category 2: 90° - 180° Category 3: 180° - 270° Category 4: 270° - 360° 8. Performed a 3x3 Focal Majority output to 270m resolution 9. Reprojected/Resampled to a 250m NAD83 dataset. 30-meter CD-ROM 2002 ground condition Dominant Aspect USDA Forest Service Remote Sensing Applications Center 2002 Created using USGS National Elevation Dataset (http://www.usgs.gov) Processing Steps 1. Imported BILmeters format into ESRI GRID format. 2. Reprojected into Albers Conical Equal Area NAD 27 with a 60m resolution 3. Mosaicked tiles into a contiguous dataset 4. Resampled to 30m resolution to maintan continuity with CONUS dataset 5. Used a 3x3 focal mean function to output a 90m DEM dataset 6. Reprojected / Resampled to NAD83 with 250m cell size using Bilear Interpolation. 30-meter CD-ROM 2002 ground condition Mean Elevation USDA Forest Service Remote Sensing Applications Center 2002 Created using USGS National Elevation Dataset (http://www.usgs.gov) Processing Steps 1. Imported BILmeters format into ESRI GRID format. 2. Reprojected into Albers Conical Equal Area NAD 27 with a 60m resolution 3. Mosaicked tiles into a contiguous dataset 4. Resampled to 30m resolution to maintan continuity with CONUS dataset 5. Used a 3x3 focal mean function to output a 90m DEM dataset 6. Reprojected / Resampled to NAD83 with 250m cell size using Bilear Interpolation. 30-meter CD-ROM 2002 ground condition Percent Slope USDA Forest Service Remote Sensing Applications Center 2002 Created using USGS National Elevation Dataset (http://www.usgs.gov) Processing Steps 1. Imported BILmeters format into ESRI GRID format. 2. Reprojected into Albers Conical Equal Area NAD 27 with a 60m resolution. 3. Mosaicked tiles into a contiguous dataset. 4. Resampled to 30m resolution to maintan continuity with CONUS dataset. 5. Used a 3x3 focal mean function to output a 90m DEM dataset. 6. Created an Aspect Dataset from the 90m DEM. 7. Reclassified the Aspect dataset into 4 categories. Category 1: 0° - 90° Category 2: 90° - 180° Category 3: 180° - 270° Category 4: 270° - 360° 8. Performed 3x3 Focal Variety function output to 270m. 9. Reprojeced / Resampled to NAD83 at 250m resolution. 30-meter CD-ROM 2002 ground condition Variety Dominate Aspect USDA Forest Service 200403 vector digital data This map layer is commonly called Bailey’s ecoregions and shows ecosystems of regional extent in the United States, Puerto Rico, and the U.S. Virgin Islands. Processing Steps: 1. Downloaded file from http://www.fs.fed.us/institute/ecoregions/eco_download.html. 2. Imported ArcInterchange file into ArcCoverage format (Albers Conical Equal Area Clark1866) 3. Imported ArcCoverage file into raster format with 250m cell resolution. 4. Reprojected / Resampled to common Albers Conical Equal Area NAD83 projection. http://www.nationalatlas.gov online 200403 publication date Bailey's Ecoregions Nowacki, Gregory; Spencer, Page; Fleming, Michael; Brock, Terry; and Jorgenson, Torre 2001 vector digital data This ecoregion map combines the Bailey and Omernik approach to ecoregion mapping in Alaska. The ecoregions were developed cooperatively by the U.S. Forest Service, National Park Service, U.S. Geological Survey, The Nature Conservancy, and personnel from many other agencies and private organizations. Processing Steps: 1. Downloaded file from http://www.nps.gov/akso/gis/Alaska/alaskaBiol.htm 2. Imported ArcInterchange file into ArcCoverage format (Albers Conical Equal Area NAD27) 3. Imported ArcCoverage file into raster format with 250m cell resolution. 4. Reprojected / Resampled to common Albers Conical Equal Area NAD83 projection. http://agdc.usgs.gov/ecoreg/ecoreg.html online 2001 publication date Unified Ecoregions University of Maryland 2002 raster digital data The GLCF develops and distributes remotely sensed satellite data and products concerned with land cover from the local to global scales. http://glcf.umiacs.umd.edu/index.shtml 250-meter online 2002 ground condition MODIS U.S. Geological Survey 2002 Created using MODIS data from the Land Processes Distribution Active Archive Center (http://edcdaac.usgs.gov/main.html) LP DAAC Data Set - MODIS/Terra Vegetation Continuous Fields Yearly L3 Global 500m ISIN v003 MODIS Product - MOD44B Processing Steps: 1. Imported MODIS EOD HDF format file into ERDAS Imagine (*,img) format. 2. Reprojected into Lambert Conformal Conic NAD27 from Integerized Sinusoidal using ERDAS Imagine 8.5 with the Nearest Neighbor and Rigerous Transformation options selected. 3. Subset area of interest from entire image 4. Resampled / reprojected to a common coordinate system & resolution (250m) http://edcdaac.usgs.gov/main.html 250-meter online 2002 ground condition MODIS VCF U.S. Geological Survey (USGS) 2002 Created using MODIS data from the Land Processes Distribution Active Archive Center (http://edcdaac.usgs.gov/main.html) LP DAAC Data Set - MODIS/Terra Vegetation Indices 16-Day L3 Global 250 ISIN GRID v003 MODIS Product - MOD13Q1 Processing Steps: 1. Imported MODIS EOD HDF format file into ERDAS Imagine (*,img) format. 2. Reprojected into Albers Conical Equal Area NAD27 from Integerized Sinusoidal using ERDAS Imagine 8.5 with the Nearest Neighbor and Rigerous Transformation options selected. 3. Mosaicked Tiled data into a contiguous dataset. 4. Subset area of interest from entire image 5. Resampled / reprojected to a common coordinate system & resolution (250m) in an Albers Conical Equal Area NAD83 projection. http://edcdaac.usgs.gov/main.html 250-meter online 2002 ground condition MODIS EVI U.S. Geological Survey 2002 Created using MODIS data from the Land Processes Distribution Active Archive Center (http://edcdaac.usgs.gov/main.html) LP DAAC Data Set - MODIS/Terra Vegetation Indices 16-Day L3 Global 250 ISIN GRID v003 MODIS Product - MOD13Q1 Processing Steps: 1. Imported MODIS EOD HDF format file into ERDAS Imagine (*,img) format. 2. Reprojected into Albers Conical Equal Area NAD27 from Integerized Sinusoidal using ERDAS Imagine 8.5 with the Nearest Neighbor and Rigerous Transformation options selected. 3. Mosaicked Tiled data into a contiguous dataset. 4. Subset area of interest from entire image 5. Resampled / reprojected to a common coordinate system & resolution (250m) in an Albers Conical Equal Area NAD83 projection. http://edcdaac.usgs.gov/main.html 250-meter online 2002 ground condition MODIS NDVI The methodology used to produce the national forest type database combined ground-truth (from FIA plot data) with multi-date imagery and variety of other spatially continuous geospatial data. The predictor data themes include, - Elevation, slope, and aspect - Unified Ecoregions - MODIS Vegetation Indices such as EVI, NDVI. - MODIS Vegetation Continuous Fields - MODIS fire points for developed from the MODIS Active Fire Maps - MODIS 8-day composites Statistical models developed in Rulequest's Cubist data mining software link the FIA plot variables with the imagery and geospatial data. Cubist creates rulesets, which have the advantage of not assuming parametric properties within the predictor data and are thus are more appropriate for the multi-scale, multi-source data, which are being used. Raster Pixel 7858 13133 1 250.000000 250.000000 32 Upper Left FALSE LZ77 1 pixel codes TRUE FGDBR row and column 250.000000 250.000000 meters North American Datum of 1983 Geodetic Reference System 80 6378137.000000 298.257222 GCS_North_American_1983 Albers Conical Equal Area Layer 1 Biomass 2.2417 694.93 Mg/ha USDA Forest Service Remote Sensing Applications Center

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West Valley City Utah 84119 USA 801-975-3750 801-975-3478 Downloadable Data Although these data have been used by the USDA Forest Service, the USDA Forest Service shall not be held liable for improper or incorrect use of the data described and/or contained herein. These data are not legal documents and are not intended to be used as such. It is the responsibility of the data user to use the data appropriately and consistent within the limitations of geospatial data in general and these data in particular. Using the data for other than their intended purpose may yield inaccurate or misleading results. The USDA Forest Service gives no warranty, expressed or implied, as to the accuracy, reliability, or completeness of these data. It is strongly recommended that these data are directly acquired from the USDA Forest Service server and not indirectly through other sources which may have changed the data in some way. Although these data have been processed successfully on a computer system at the USDA Forest Service, no warranty expressed or implied is made regarding the utility of the data on another system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. This disclaimer applies both to individual use of the data and aggregate use with other data. The USDA Forest Service reserves the right to correct, update or modify this data and related materials without notification. ERDAS 0.000 0.000 20081105 20070726 USDA Forest Service Remote Sensing Applications Center Bonnie Ruefenacht mailing and physical address

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West Valley City Utah 84119 USA 801-975-3828 801-975-3478 bruefenacht@fs.fed.us FGDC Content Standards for Digital Geospatial Metadata FGDC-STD-001-1998 local time http://www.esri.com/metadata/esriprof80.html ESRI Metadata Profile en Esri ArcGIS 13.4.0.55405 Alaska_biomass_Lambert_Clip -1791375 1491875 2378562 414062 1 -180 180 71.407166 50.567766 1 1 -180.000000 180.000000 81.534370 40.329075 ISO 19115 Geographic Information - Metadata DIS_ESRI1.0 dataset 002 withheld Local Area Network 0.000 Raster Dataset File Geodatabase Raster Dataset Albers Conical Equal Area 2 1 13133 250 Meter 1 Meter = 1 Meter(s) 7858 250 Meter 1 Meter = 1 Meter(s) 20250131 18291100 20250131 18291100 Alaska_biomass_Lambert_Clip 002 -4415747.636096 -742747.636096 1177457.938546 4267457.938546 1 Projected GCS_Sphere_ARC_INFO Linear Unit: Meter (1.000000) Sphere_ARC_INFO_Lambert_Azimuthal_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/3.4.0'><WKT>PROJCS[&quot;Sphere_ARC_INFO_Lambert_Azimuthal_Equal_Area&quot;,GEOGCS[&quot;GCS_Sphere_ARC_INFO&quot;,DATUM[&quot;D_Sphere_ARC_INFO&quot;,SPHEROID[&quot;Sphere_ARC_INFO&quot;,6370997.0,0.0]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]],PROJECTION[&quot;Lambert_Azimuthal_Equal_Area&quot;],PARAMETER[&quot;false_easting&quot;,0.0],PARAMETER[&quot;false_northing&quot;,0.0],PARAMETER[&quot;central_meridian&quot;,-100.0],PARAMETER[&quot;latitude_of_origin&quot;,45.0],UNIT[&quot;Meter&quot;,1.0]]</WKT><XOrigin>-12742100</XOrigin><YOrigin>-12742100</YOrigin><XYScale>353442495.92849648</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> 20250131