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Project Overview
The USDA Forest Service Forest Inventory and Analysis program (FIA) has been conducting state-by-state and ultimately nationwide forest inventories for decades. Yet these field plot based inventories have not been able to produce precise county and local estimates and useful operational maps. Additionally, traditional satellite-based forest classifications have been unable to match detailed forest type identification with ground based survey definitions to provide for interpolation and extrapolation of FIA data. Precise classification has been limited to general or aggregate classes of little use for improving inventory precision and providing truly useful operational forest maps. |
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| The k-nearest neighbors approach (kNN), adapted from the Finnish Forest Research Institute, offers a means of applying satellite and GIS data so as to impute forest cover type, timber volume, and other FIA data from field plots surrounding large or small areas on the basis of the spectral characterization of neighboring pixels. To the extent that such post-stratification can be successfully applied, the method offers agencies and industry a) greater precision at survey unit to local levels of estimation, and b) detailed inventory attributes within type polygons over large areas. The method produces estimates and maps according to the actual inventory classifications and definitions rather than an abstract set that must later be reinterpreted. |
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