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More info is available here - https://usfs.maps.arcgis.com/home/item.html?id=bd3c2c1ba3844ebabd8df6d1c4932387 . This image service was developed using data from over 213,000 national forest inventory plots measured during the period 2014-2018 from the USFS Forest Inventory and Analysis (FIA) program, in conjunction with other auxiliary information. Roughly 4,900 Landsat 8 OLI scenes, collected during the same time period, were processed to extract information about vegetation phenology. This information, along with climatic and topographic raster data, were used in an ecological ordination model of tree species. The model produced a feature space of ecological gradients that was then used to impute FIA plots to pixels. The plots imputed to each pixel were then used to assign values (tons per pixel) for total forest carbon. Carbon Pools can be found - https://usfs.maps.arcgis.com/home/item.html?id=4a604935bdce4a6eb77a967fab47ddff For more information about the methods used to produce this dataset please see the following references: \u2022 Wilson, Barry T.; Knight, Joseph F.; McRoberts, Ronald E. 2018. Harmonic regression of Landsat time series for modeling attributes from national forest inventory data. ISPRS Journal of Photogrammetry and Remote Sensing. 137: 29-46. \u2022 Wilson, Barry Tyler; Woodall, Christopher W.; Griffith, Douglas M. 2013. Imputing forest carbon stock estimates from inventory plots to a nationally continuous coverage. Carbon Balance and Management. 8:1. doi:10.1186/1750-0680-8-1 \u2022 Wilson, B. Tyler; Lister, Andrew J.; Riemann, Rachel I. 2012. A nearest-neighbor imputation approach to mapping tree species over large areas using forest inventory plots and moderate resolution raster data. Forest Ecology and Management. 271: 182-198. \u2022 Ohmann, Janet L.; Gregory, Matthew J. 2002. Predictive mapping of forest composition and structure with direct gradient analysis and nearest neighbor imputation in coastal Oregon, U.S.A. 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