20150708 13054500 1.0 TRUE Map of the U.S. Forest Service Forest Inventory and Analysis (FIA) sample plots. The sample plots are classified by their elevations. The raster layer is generated by interpolating the sample plots on the EXPVOL field, which is the volume expansion factor for forest land and timberland. FIA Forest Inventory Web GIS Tutorial Map of the U.S. Forest Service Forest Inventory and Analysis (FIA) sample plots and EXPVOL U.S. Forest Service Forest Inventory and Analysis (FIA) sample data. 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