This dataset provides maps of aboveground forest biomass (AGB, living trees and standing dead trees, Mg/ha) across portions of Northwestern United States, including Washington, Oregon, Idaho, and Montana at a spatial resolution of 30 m. Forest inventory data were compiled from 29 stakeholders (Federal, State, University, and private organizations) that also had overlapping lidar imagery. The collection totaled 3,805 field plots with lidar imagery for 176 collections acquired between 2002 and 2016. Field plot-level AGB estimates were calculated from field plot tree measurements using the default allometric equations found in the Fire Fuels Extension (FFE) of the Forest Vegetation Simulator (FVS). A Random Forests machine-learning algorithm was used to model AGB using lidar height and density metrics generated from the lidar returns within fixed-radius field plot footprints, gridded climate metrics obtained from the Climate-FVS Ready Data Server, and topographic estimates extracted from Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global elevation rasters. AGB was then mapped from the same lidar metrics gridded across the extent of the lidar collections at 30-m resolution. The standard deviation of estimated AGB of the terminal nodes from the Random Forest predictions was also mapped to show pixel-level model uncertainty. Note that the AGB estimates are, for the most part, a single snapshot in time and the samples of forest conditions are not necessarily representative in space and time of the larger study area.