The global increase in observed forest dieback, characterised by the death of tree foliage, heralds widespread decline in forest ecosystems. This degradation causes significant changes to ecosystem services and functions, including habitat provision and carbon sequestration, which can be difficult to detect using traditional monitoring techniques, highlighting the need for large-scale and high-frequency monitoring. Contemporary developments in the instruments and methods to gather and process data at large-scales mean this monitoring is now possible. In particular, the advancement of low-cost drone technology and deep learning on consumer-level hardware provide new opportunities. Here, we use an approach based on deep learning and vegetation indices to assess crown dieback from RGB aerial data without the need for expensive instrumentation such as LiDAR. We use an iterative approach to match crown footprints predicted by deep learning with field-based inventory data from a Mediterranean ecosystem exhibiting drought-induced dieback, and compare expert field-based crown dieback estimation with vegetation index-based estimates. We obtain high overall segmentation accuracy (mAP: 0.519) without the need for additional technical development of the underlying Mask R-CNN model, underscoring the potential of these approaches for non-expert use and proving their applicability to real-world conservation. We also find colour-coordinate based estimates of dieback correlate well with expert field-based estimation. Substituting ground truth for Mask R-CNN model predictions showed negligible impact on dieback estimates, indicating robustness. Our findings demonstrate the potential of automated data collection and processing, including the application of deep learning, to improve the coverage, speed and cost of forest dieback monitoring.

翻译：全球范围内观测到的森林枯梢现象日益增多，其特征表现为树木叶片的死亡，预示着森林生态系统将出现大范围衰退。这种退化会导致生态系统服务与功能发生显著变化，包括栖息地提供和碳封存能力下降，而传统监测技术往往难以有效检测此类变化，凸显了进行大规模高频次监测的必要性。当前数据采集与处理仪器方法在大尺度应用方面的发展，使得此类监测成为可能。特别是低成本无人机技术的进步以及消费级硬件上深度学习能力的提升，为此提供了新的机遇。本研究采用基于深度学习与植被指数的方法，仅利用RGB航空数据即可评估树冠枯梢状况，无需依赖激光雷达等昂贵仪器。我们通过迭代方法将深度学习预测的树冠轮廓与地中海生态系统（该区域正遭受干旱引发的枯梢现象）的实地调查数据进行匹配，并将专家实地评估的树冠枯梢结果与基于植被指数的估算结果进行比较。在未对底层Mask R-CNN模型进行额外技术开发的情况下，我们获得了较高的整体分割精度（mAP：0.519），这凸显了此类方法在非专业用户中的应用潜力，并证明了其在现实世界保护实践中的适用性。我们还发现基于颜色坐标的枯梢估算结果与专家实地评估具有良好相关性。使用Mask R-CNN模型预测结果替代真实标注数据对枯梢估算的影响可忽略不计，表明该方法具有稳健性。我们的研究结果证明了自动化数据采集与处理（包括深度学习的应用）在提升森林枯梢监测的覆盖范围、速度及成本效益方面的巨大潜力。