This project investigated use of the Gradient Nearest Neighbor (GNN) method for mapping vegetation and fuels in three contrasting ecoregions in the Western US. The GNN method uses multivariate direct gradient analysis to link field plot data, satellite imagery, and maps of environmental variables in a raster GIS database. Individual pixels are associated with forest inventory plots that have the most similar spectral and environmental characteristics. A suite of detailed plot variables is then imputed to each pixel, allowing simultaneous and consistent predicting of a wide range of vegetation attributes. Because the plot-level variables are attached to the GIS database, a wide array of summary variables and classifications can be portrayed to meet different objectives.