Comparison of UAV-LiDAR-driven biomass estimation approaches in planted forests with different management

Planted forests play a crucial role in forest restoration and carbon sequestration. However, different management practices in planted forests can alter forest structure and composition, potentially affecting UAV-LiDAR-based biomass estimation. This study evaluated tree-centric, object-based, and area-based approaches in both managed planted forests (MPF) and unmanaged planted forests (UPF). Furthermore, an object biomass index (OBI) was developed for the object-based approach to better capture the structural complexity of forest objects. We then compared the selected metrics and estimation accuracy of the three approaches across forests with different management types. The results showed that in MPF, metrics characterizing canopy height and horizontal structure were more important. Whereas in the UPF, metrics capturing fine-scale structural details contributed to a more accurate biomass estimation. Across all approaches, higher performance was observed in MPF subjected to intensive management. Among the three approaches, the proposed object-based approach proved to be the most robust, demonstrating its potential to mitigate the effects of individual tree segmentation errors. This study underscores the impact of forest management on UAV-LiDAR-driven biomass estimation approaches in planted forests, and emphasizes the importance of accounting for management regimes when estimating forest biomass at larger scales.