20190910 20342800 1.0 TRUE USGS_NLCD_Tree_Canopy_2016 USGS NLCD Land Cover Trees Tree Canopy USGS NLCD Tree Canopy 2016. <div style='text-align:Left;font-size:12pt'><p><span>USGS NLCD Tree Canopy 2016. 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 (www.mrlc.gov).</span></p></div> USGS 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