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

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 align closely with existing research while addressing issues of incorrectly selected or omitted neighboring trees. Among the 10,191 trained HMCW models, the Bagging model integrating XGBoost, Random Forest (RF), Support Vector Regression (SVR), Gradient Boosting (GB), and Ridge exhibited the best performance. Compared to the best single model (RF), the Bagging model achieved improved accuracy (R² ​= ​0.8346, representing a 1.6 ​% improvement; RMSE ​= ​1.4042, reduced by 6.66 ​%; EVS ​= ​0.8389; MAE ​= ​0.9129; MAPE ​= ​0.0508; and MedAE ​= ​0.5076, with corresponding improvements of 1.63 ​%, 1.49 ​%, 0.1 ​%, and 7.06 ​%, respectively). This study provides a viable solution for modeling HMCW in all species with similar structural characteristics and offers a method for extracting other hard-to-measure parameters. The refined spatial structure units better link 3D structural phenotypes with environmental factors. This approach aids in canopy morphology simulation and forest management research.