利用卫星图像和深度学习对高山大型多态灌木进行个体制图
Individual mapping of large polymorphic shrubs in high mountains using satellite images and deep learning
AI总结：
该研究利用免费高分辨率卫星图像和实例分割模型，结合照片解译与野外调查双重数据构建方法及软交并比指标，实现了对西班牙内华达山脉整个生物圈保护区高山多态杜松的个体制图，F1分数最高达87.87%，并揭示了灌木尺寸随海拔变化的分布规律。
摘要：
监测长寿大型灌木（如杜松）的分布和大小，对于评估全球变化对高山生态系统的长期影响至关重要。虽然深度学习模型在目标分割方面已展现出显著成功，但将这些模型用于检测具有多态性质的灌木物种仍然具有挑战性。在本研究中，我们发布了基于免费可用卫星图像的大规模个体灌木勾绘数据集，并使用实例分割模型对整个生物圈保护区（西班牙内华达山脉）林线以上的所有杜松进行了制图。为优化性能，我们引入了一种新颖的双重数据构建方法：使用照片解译（PI）数据进行模型开发，并使用野外调查（FW）数据进行验证。为了在模型评估中考虑杜松的多态性质，我们开发了交并比指标的软版本。最后，我们评估了所得地图在冠层覆盖度和各尺寸等级灌木密度方面的不确定性。我们的模型在灌木勾绘上的F1分数在PI数据上达到87.87%，在FW数据上达到76.86%。观测值与预测值关系的R²和RMSE分别为：冠层覆盖度为0.63和6.67%，灌木密度为0.90和20.62。模型输出中观察到的低海拔较大灌木密度更高、高海拔较小灌木密度更高的现象也出现在PI和FW数据中，表明该物种最佳表现存在海拔抬升。本研究证明，将深度学习应用于免费可用的高分辨率卫星图像，有助于在区域尺度上检测具有高生态价值的中型至大型灌木，该方法可推广至世界其他高山以及历史和未来图像。
英文摘要：
Monitoring the distribution and size of long-living large shrubs, such as junipers, is crucial for assessing the long-term impacts of global change on high-mountain ecosystems. While deep learning models have shown remarkable success in object segmentation, adapting these models to detect shrub species with polymorphic nature remains challenging. In this research, we release a large dataset of individual shrub delineations on freely available satellite imagery and use an instance segmentation model to map all junipers over the treeline for an entire biosphere reserve (Sierra Nevada, Spain). To optimize performance, we introduced a novel dual data construction approach: using photo-interpreted (PI) data for model development and fieldwork (FW) data for validation. To account for the polymorphic nature of junipers during model evaluation, we developed a soft version of the Intersection over Union metric. Finally, we assessed the uncertainty of the resulting map in terms of canopy cover and density of shrubs per size class. Our model achieved an F1-score in shrub delineation of 87.87% on the PI data and 76.86% on the FW data. The R2 and RMSE of the observed versus predicted relationship were 0.63 and 6.67% for canopy cover, and 0.90 and 20.62 for shrub density. The greater density of larger shrubs in lower altitudes and smaller shrubs in higher altitudes observed in the model outputs was also present in the PI and FW data, suggesting an altitudinal uplift in the optimal performance of the species. This study demonstrates that deep learning applied on freely available high-resolution satellite imagery is useful to detect medium to large shrubs of high ecological value at the regional scale, which could be expanded to other high-mountains worldwide and to historical and forthcoming imagery.
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