Enhancing Forest Structural Parameter Extraction in the Greater Hinggan Mountains: Utilizing Airborne LiDAR and Species-Specific Tree Height–Diameter at Breast Height Models

Abstract

Forests, being the largest and most intricate terrestrial ecosystems, play an indispensable role in sustaining ecological balance. To effectively monitor forest productivity, it is imperative to accurately extract structural parameters such as the tree height and diameter at breast height (DBH). Airborne LiDAR technology, which possesses the capability to penetrate canopies, has demonstrated remarkable efficacy in extracting these forest structural parameters. However, current research rarely models different tree species separately, particularly lacking comparative evaluations of tree height-DBH models for diverse tree species. In this study, we chose sample plots within the Bila River basin, nestled in the Greater Hinggan Mountains of the Inner Mongolia Autonomous Region, as the research area. Utilizing both airborne LiDAR and field survey data, individual tree positions and heights were extracted based on the canopy height model (CHM) and normalized point cloud (NPC). Six tree height-DBH models were selected for fitting and validation, tailored to the dominant tree species within the sample plots. The results revealed that the CHM-based method achieved a lower RMSE of 1.97 m, compared to 2.27 m with the NPC-based method. Both methods exhibited a commendable performance in plots with lower average tree heights. However, the NPC-based method showed a more pronounced deficiency in capturing individual tree information. The precision of grid interpolation and the point cloud density emerged as pivotal factors influencing the accuracy of both methods. Among the six tree height-DBH models, a multiexponential model demonstrated a superior performance for both oak and ”birch–poplar” trees, with R2 values of 0.479 and 0.341, respectively. This study furnishes a scientific foundation for extracting forest structural parameters in boreal forest ecosystems.