Comparison of Single and Ensemble Regression Model Workflows for Estimating Basal Area by Tree Size Class in Pine Forests of Southeastern U.S - Joseph St. Peter; Jason Drake; Paul Medley; Eben Broadbent; Gang Chen; Victor Ibeanusi (Remote Sensing)
Quantifying basal area in terms of diameter classes is important for informing forest management decisions. It is commonly derived from stand diameter distributions using field measurements, LiDAR, and a distribution function. This study compares alternative methods for directly estimating basal area in three tree diameter classes that are relevant to timber operations and wildlife habitat planning in southern United States pine forests. Specifically, linear modeling, ensemble linear modeling (ELM) and ensemble general additive modeling (EGAM) were compared. The results showed that the EGAM method provided the highest r-squared values and the lowest RMSE, and the ELM method provided good interpretability and 30 times faster processing than the EGAM method. Both ensemble methods produced a spatially explicit standard error estimate output without additional steps, unlike the single linear model. In general, the estimation results of this study were comparable or improved over prior studies’ estimates of basal area by tree diameter class.