Urban trees provide valuable ecosystem services but are at the same time under continuous pressure due to unfavorable site conditions. In order to better protect and manage our natural capital, urban green managers require frequent and detailed information on tree health at the city wide scale. In this paper we developed a workflow to monitor tree defoliation and discoloration of broadleaved trees in Brussels, Belgium, through the combined use of airborne hyperspectral and LiDAR data. Individual trees were delineated using an object-based tree detection and segmentation algorithm primarily based on LiDAR data with an average accuracy of 91%. We constructed Partial Least Squares Regression (PLSR) models to derive tree chlorophyll content (RMSE = 2.8 μg/cm²; R² = 0.77) and Leaf Area Index (LAI; RMSE = 0.5; R² = 0.66) from the average canopy spectrum. Existing spectral indices were found to perform significantly worse (RMSE > 7 μg/cm² and >1.5 respectively), mainly due to contamination of tree spectra by neighboring background materials. In the absence of local calibration data, the applicability of PLSR to other areas, sensors and tree species might be limited. Therefore, we identified the best performing/least sensitive spectral indices and proposed a simple pixel selection procedure to reduce disturbing background effects. For LAI, laser penetration metrics derived from LiDAR data attained comparable accuracies as PLSR and were suggested instead. Detection of healthy and unhealthy trees based on remotely sensed tree properties matched reasonably well with a more traditional visual tree assessment (93% and 71% respectively). If combined with early tree stress detection methods, the proposed methodology would constitute a solid basis for future urban tree health monitoring programs.

城市树木提供了宝贵的生态系统服务，但同时由于不利的场地条件而承受着持续的压力。为了更好地保护和管理我们的自然资本，城市绿化经理要求在城市范围内提供频繁且详细的树木健康信息。在本文中，我们通过结合使用机载高光谱和LiDAR数据，开发了一种工作流来监控比利时布鲁塞尔阔叶树的树木脱叶和变色。使用基于对象的树木检测和分割算法（主要基于LiDAR数据）来描绘单个树木，平均准确度为91％。我们构建了偏最小二乘回归（PLSR）模型，以从平均冠层光谱中推导出树木的叶绿素含量（RMSE = 2.8μg/cm²;R²= 0.77）和叶面积指数（LAI; RMSE = 0.5;R²= 0.66）。发现现有光谱指数的表现明显较差（RMSE> 7μg/cm²和> 1.5分别），这主要是由于树木光谱受到相邻背景材料的污染所致。在没有本地校准数据的情况下，PLSR在其他地区，传感器和树木的适用性可能会受到限制。因此，我们确定了性能最佳/最低灵敏度的光谱指数，并提出了一种简单的像素选择程序来减少令人讨厌的背景影响。对于LAI，从LiDAR数据得出的激光穿透率指标可达到与PLSR相当的精度，因此建议使用。根据遥感树木的特性检测健康和不健康的树木与较传统的视觉树木评估相匹配（分别为93％和71％）。如果结合早期的树木压力检测方法，