Abstract

Technological developments have allowed helicopter airborne laser scanning (HALS) to produce high-density point clouds below the forest canopy. We present a tree stem classification method that combines linear shape detection and model-based clustering, using four discrete methods to estimate stem diameter. Stem horizontal size was estimated every 25 cm below the living crown, and a cubic spline was used to estimate where there were gaps. Individual stem diameter at breast height (DBH) was estimated for 77% of field-measured trees. The root mean square error (RMSE) of DBH estimates was 7–12 cm using stem circle fitting. Adapting the approach to use an existing stem taper model reduced the RMSE of estimates (<1 cm). In contrast, estimates that were produced from a previously existing DBH estimation method (PREV) could be achieved for 100% of stems (DBH RMSE 6 cm), but only after location-specific error was corrected. The stem classification method required comparatively little development of statistical models to provide estimates, which ultimately had a similar level of accuracy (RMSE < 1 cm) to PREV. HALS datasets can measure broad-scale forest plantations and reduce field efforts and should be considered an important tool for aiding in inventory creation and decision-making within forest management. © 2025 by the authors.