Machine learning-based prediction of tree crown development in competitive urban environments

In urban forestry, managing trees is crucial for sustainable urban environments, especially in the context of climate change and the urban heat island effect. This research explores the complex dynamics of tree crown geometry development by asking the question: how do surrounding objects, such as nearby trees, buildings, and other urban structures, affect the shape of tree crowns? It aims to uncover how competition for light and space influences tree crown development in competitive urban environments. Our study employs machine learning models on six main species in Munich, using the measured data from the LiDAR scans, with the Hist Gradient Boosting Regressor (HGBR) emerging as the most promising performer across various metrics. Notably, the evaluation of 13 models reveals the HGBR’s consistent ranking as the best or second-best across all tree crown dimensions assessed, with R 2 values reaching 0.83 for the tree height model and 0.7 on average for crown radiuses in eight directions. Employing SHapley Additive exPlanations (SHAP) values elucidate factors influencing model predictions, emphasising the significant impact of adjacent trees and buildings. After evaluating the models to include additional tree species in Munich, the models show strong predictive capabilities for some additional species. Despite the studies’ limitations - the models are only valid for selected species, and there are constraints in predicting tree crown start height - our findings contribute valuable insights for urban forestry management and planning. • Machine learning method helps the tree crown development prediction in cities. • The tree-to-tree light competition effect on crown geometry is predictable and measurable by machine learning. • Among local environmental factors, neighbouring trees have the greatest impact on the geometry of a tree’s crown shape.