Monitoring forest dynamics at an individual tree scale is essential for accurately assessing ecosystem responses to climate change, yet traditional methods relying on field-based forest inventories are labor-intensive and limited in spatial coverage. Advances in remote sensing using drone-acquired RGB imagery combined with deep learning models have promised precise individual tree crown (ITC) segmentation; however, existing methods are frequently validated against human-annotated images, lacking rigorous independent ground truth. In this study, we generate high-fidelity validation labels from co-located Terrestrial Laser Scanning (TLS) data for drone imagery of mixed unmanaged boreal and Mediterranean forests. We evaluate the performance of two widely used deep learning ITC segmentation models - DeepForest (RetinaNet) and Detectree2 (Mask R-CNN) - on these data, and compare to performance on further Mediterranean forest data labelled manually. When validated against TLS-derived ground truth from Mediterranean forests, model performance decreased significantly compared to assessment based on hand-labelled from an ecologically similar site (AP50: 0.094 vs. 0.670). Restricting evaluation to only canopy trees shrank this gap considerably (Canopy AP50: 0.365), although performance was still far lower than on similar hand-labelled data. Models also performed poorly on boreal forest data (AP50: 0.142), although again increasing when evaluated on canopy trees only (Canopy AP50: 0.308). Both models showed very poor localisation accuracy at stricter IoU thresholds, even when restricted to canopy trees (Max AP75: 0.051). Similar results have been observed in studies using aerial LiDAR data, suggesting fundamental limitations in aerial-based segmentation approaches in closed canopy forests.

翻译：在个体树木尺度上监测森林动态对于准确评估生态系统对气候变化的响应至关重要，然而依赖实地森林清查的传统方法劳动密集且空间覆盖有限。基于无人机获取的RGB影像结合深度学习模型的遥感技术进展，为实现精确的个体树冠分割带来了希望；但现有方法常以人工标注影像进行验证，缺乏严格的独立地面真值。本研究利用共置的地面激光扫描数据，为未经管理的北方与地中海混交林的无人机影像生成高保真验证标签。我们评估了两种广泛使用的深度学习树冠分割模型——DeepForest（基于RetinaNet）与Detectree2（基于Mask R-CNN）在这些数据上的表现，并与人工标注的地中海森林数据上的性能进行对比。当以地中海森林的TLS衍生地面真值验证时，模型性能较基于生态相似站点手工标注数据的评估显著下降（AP50：0.094对比0.670）。将评估范围限制于冠层树木后，这一差距大幅缩小（冠层AP50：0.365），但性能仍远低于相似手工标注数据。模型在北方森林数据上也表现不佳（AP50：0.142），尽管仅评估冠层树木时有所提升（冠层AP50：0.308）。即使在仅考虑冠层树木的情况下，两种模型在更严格的IoU阈值下均表现出极差的定位精度（最大AP75：0.051）。使用航空LiDAR数据的研究也观察到类似结果，这表明航空影像分割方法在密闭冠层森林中存在根本性局限。