Fitting maximum crown width height of Chinese fir through ensemble learning combined with fine spatial competition.

ABSTRACT: Accurate acquisition of forest spatial competition and tree 3D structural phenotype parameters is crucial for exploring tree-environment interactions. However, due to the occlusion between tree crowns, current UAV-based and ground-based LiDAR struggles to capture complete crown information in dense stands, making parameter extraction challenging such as maximum crown width height (HMCW). This study proposes a canopy spatial relationship-based method for constructing forest spatial structure units and employs five ensemble learning techniques to train 11 machine learning model combinations. By coupling spatial competition with phenotype parameters, the study identifies the optimal fitting model for HMCW of Chinese fir. The results demonstrate that the constructed spatial structure units ali

SUBMITTER: Cui Z 

PROVIDER: S-EPMC12709972 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature