Monitoring the distribution and size structure of long-living shrubs, such as Juniperus communis, can be used to estimate the long-term effects of climate change on high-mountain and high latitude ecosystems. Historical aerial very-high resolution imagery offers a retrospective tool to monitor shrub growth and distribution at high precision. Currently, deep learning models provide impressive results for detecting and delineating the contour of objects with defined shapes. However, adapting these models to detect natural objects that express complex growth patterns, such as junipers, is still a challenging task. This research presents a novel approach that leverages remotely sensed RGB imagery in conjunction with Mask R-CNN-based instance segmentation models to individually delineate Juniperus shrubs above the treeline in Sierra Nevada (Spain). In this study, we propose a new data construction design that consists in using photo interpreted (PI) and field work (FW) data to respectively develop and externally validate the model. We also propose a new shrub-tailored evaluation algorithm based on a new metric called Multiple Intersections over Ground Truth Area (MIoGTA) to assess and optimize the model shrub delineation performance. Finally, we deploy the developed model for the first time to generate a wall-to-wall map of Juniperus individuals. The experimental results demonstrate the efficiency of our dual data construction approach in overcoming the limitations associated with traditional field survey methods. They also highlight the robustness of MIoGTA metric in evaluating instance segmentation models on species with complex growth patterns showing more resilience against data annotation uncertainty. Furthermore, they show the effectiveness of employing Mask R-CNN with ResNet101-C4 backbone in delineating PI and FW shrubs, achieving an F1-score of 87,87% and 76.86%, respectively.

翻译：监测长寿灌木（如刺柏）的分布和大小结构，可用于评估气候变化对高山及高纬度生态系统的长期影响。历史高分辨率航空影像为高精度监测灌木生长与分布提供了回顾性工具。目前，深度学习模型在检测和描绘具有明确形状的物体轮廓方面表现优异，但将这些模型应用于检测表达复杂生长模式的自然物体（如刺柏）仍具挑战。本研究提出一种创新方法，利用遥感RGB影像结合基于Mask R-CNN的实例分割模型，对西班牙内华达山脉树线以上刺柏个体进行轮廓勾勒。研究中，我们提出新的数据构建方案：分别使用照片判读（PI）数据和野外工作（FW）数据开发模型并进行外部验证。同时，提出基于新指标——多交并比基准真值面积（MIoGTA）的灌木定制化评估算法，用于评估和优化模型对灌木轮廓的分割性能。最终，我们首次部署该模型生成刺柏个体的全覆盖地图。实验结果表明，双重数据构建方法有效克服了传统野外调查方法的局限性，MIoGTA指标在评估具有复杂生长模式的物种实例分割模型时，展现了对数据标注不确定性更强的鲁棒性。此外，采用ResNet101-C4主干的Mask R-CNN在PI和FW灌木轮廓分割中分别实现了87.87%和76.86%的F1分数，验证了其有效性。