Light Detection and Ranging (LiDAR) returns from the spaceborne Geoscience Laser Altimeter (GLAS) sensor may offer an alternative to solely field-based forest biomass sampling. Such an approach would rely upon model-based inference, which can account for the uncertainty associated with using modeled, instead of field-collected, measurements. Model-based methods have been thoroughly described in the statistical literature, and an increasing number of model-based forestry applications use tactically acquired airborne LiDAR. Adapting these methods to GLAS’s irregular acquisition pattern requires a strategy for identifying a subset of GLAS “shots” that can be considered a simple random sample. We have developed a flexible method of dividing the landscape into equal-area polygons from which a GLAS shot can be chosen at random as a member of the sample. This process bears similarities to the approach used by the Forest Inventory and Analysis (FIA) Program as it moved toward its current hexagonal sample grid. Although the ultimate application of this approach would be production of consistent biomass estimates across different countries, well-calibrated FIA estimates over the United States provide a convenient testing ground. Applied to California, this approach produced almost exactly the same estimate of biomass density (Mg/ha) as the FIA sample. The GLAS-based estimate had a considerably higher standard error than FIA’s estimate, but it comes at a much lower cost and is based upon globally available GLAS measurements.