 This study compared several methods for estimating stem volume, stem number, and basal area from airborne laser scanning ALS data. The results showed that the best method was a regression model based on height and density metrics derived from 0.5M raster cells, which had a root mean square error, RMSE, of 37.3%. The worst method was a model based on height and density metrics derived from the entire field plot, which had an RMSE of 41.9%. Additionally, the authors found that area-based regression models using height and density metrics from the ALS data were more accurate than single tree-based information derived from local maxima in a normalized digital surface model, NDSM, which had RMSEs of 52.7% for stem number and 21.5% for basal area. Furthermore, the accuracy of these estimates depended on the filter size and the conditions of the applied filter. This article was authored by Eva Lindbergh and Marcus Hallows. We are article.tv, links in the description below.