Maps of the number, size, and species of trees in forests across the western United States are desirable for many applications such as estimating terrestrial carbon resources, predicting tree mortality following wildfires, and for forest inventory. However, detailed mapping of trees for large areas is not feasible with current technologies, but statistical methods for matching the forest plot data with biophysical characteristics of the landscape offer a practical means to populate landscapes with a limited set of forest plot inventory data. We used a modified random forests approach with Landscape Fire and Resource Management Planning Tools (LANDFIRE) vegetation and biophysical predictors to impute plot data collected by the US Forest Service’s Forest Inventory Analysis (FIA) to the landscape at 30- m grid resolution. This method imputes the plot with the best statistical match, according to a "forest" of decision trees, to each pixel of gridded landscape data. In this work, we used the LANDFIRE data set for gridded input because it is publicly available, offers seamless coverage of variables needed for fire models, and is consistent with other data sets, including burn probabilities and flame length probabilities generated for the continental United States. The main output of this project is a map of imputed plot identifiers at 30 × 30 m spatial resolution for the western United States that can be linked to the FIA databases to produce tree-level maps or to map other plot attributes. In addition, we used the imputed inventory data to generate maps of forest cover, forest height, and vegetation group at 30 × 30 m resolution for all forested pixels in the western United States, as a means of assessing the accuracy of our methodology. The results showed good correspondence between the target LANDFIRE data and the imputed plot data, with an overall within-class agreement of 79% for forest cover, 96% for forest height, and 92% for vegetation group.