Abstract The effectiveness of urban green infrastructure depends on selecting optimal tree planting locations, as local conditions influence tree growth and their ability to provide key ecosystem services such as cooling and shading. This study presents a novel 3D target-driven optimization tool for tree placement in urban environments, focusing on optimizing tree locations based on 3D target canopy shapes and planting areas. This optimization model uniquely incorporates complex 3D tree crown geometries and their interactions with surrounding structures. By simulating tree canopy development over time, the model enhances long-term urban forest planning, providing a more accurate method for achieving environmental objectives like canopy coverage and microclimate regulation. Results from sample case studies using Platanus x hispanica and Tilia cordata trees showed varying optimization outcomes depending on the tree species and target conditions. The tool achieved various scores and optimal planting locations with different settings for the number of trees and target age. This experiment shows the applicability and feasibility of the tool in tree planting location optimisation. However, limitations include high computational demands, the exclusion of below-ground factors, and the validation of the crown growth model is restricted to Munich. Despite these constraints, the model’s integration of spatial and temporal dynamics represents a significant advancement in urban tree placement optimization. Future research can improve computational efficiency and expand the model to diverse urban environments and species.
Yazdi et al. (Thu,) studied this question.
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