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 R2 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.

中文翻译：

---

基于机器学习的树冠生长预测


在城市林业中，树木管理对于可持续的城市环境至关重要，尤其是在气候变化和城市热岛效应的背景下。本研究通过提出以下问题来探索树冠几何发展的复杂动力学：周围的物体，如附近的树木、建筑物和其他城市结构，如何影响树冠的形状？它旨在揭示在竞争激烈的城市环境中，对光线和空间的竞争如何影响树冠的生长。我们的研究使用来自 LiDAR 扫描的测量数据，对慕尼黑的六个主要物种采用机器学习模型，其中 Hist 梯度提升回归器 （HGBR） 成为各种指标中最有前途的表现者。值得注意的是，对 13 个模型的评估表明，HGBR 在所有评估的树冠尺寸中始终处于最佳或第二好的排名，树高模型的 R2 值达到 0.83，树冠半径在 8 个方向上的平均值为 0.7。采用 SHapley 加法解释 （SHAP） 值阐明了影响模型预测的因素，强调了相邻树木和建筑物的重大影响。在评估了包括慕尼黑其他树种的模型后，这些模型显示出对一些其他树种的强大预测能力。尽管这些研究存在局限性——这些模型仅对选定的物种有效，并且在预测树冠起始高度方面存在限制——但我们的研究结果为城市林业管理和规划提供了宝贵的见解。