Scholarlink

<p>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 <i>Platanus x hispanica</i> and <i>Tilia cordata</i> 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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A 3D target-driven optimisation tool for tree planting location using temporal tree crown geometry development

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月12日

查看全文
  • Hadi Yazdi,
  • Xi Chen,
  • Thomas Rötzer,
  • Leila Parhizgar,
  • Nayanesh Pattnaik,
  • Ferdinand Ludwig

摘要

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.