Forest biomass estimation from airborne LiDAR data using machine learning approaches

Abstract During the past decade, procedures for forest biomass quantification from light detection and ranging (LiDAR) data have been improved at a rapid pace. The scope of these methods ranges from simple regression between LiDAR-derived height metrics and biomass to methods including automated tree crown delineation, stochastic simulation, and machine learning approaches. This study compared the effectiveness of four modeling techniques—linear mixed-effects (LME) regression, random forest (RF), support vector regression (SVR), and Cubist—for estimating biomass in moderately dense forest (40–60% canopy closure) at both tree and plot levels. Tree crowns were delineated to provide model estimates of individual tree biomass and investigate the effects of delineation accuracy on biomass modeling. We used our previously developed method (COTH) to delineate tree crowns. Results indicate that biomass estimation accuracy improves when modeled at the plot level and that SVR produced the most accurate biomass model (671 kg RMSE per 380 m2 plot when forest plots were modeled as a collection of trees). All models provided similar results when estimating biomass at the individual tree level (505, 506, 457, and 502 kg RMSE per tree). We assessed the effect of crown delineation accuracy on biomass estimation by repeating the modeling procedures with manually delineated crowns as inputs. Results indicated that manually delineated crowns did not always produce superior biomass models and that the relationship between crown delineation accuracy and biomass estimation accuracy is complex and needs to be further investigated.