Peat makes up approximately a quarter of Scotland's soil by area. Healthy, undisturbed, peatland habitats are critical to providing resilient biodiversity and habitat support, water management, and carbon sequestration. A high and stable water table is a prerequisite to maintain carbon sink function; any drainage turns this major terrestrial carbon store into a source that feeds back further to global climate change. Drainage and erosion features are crucial indicators of peatland condition and are key for estimating national greenhouse gas emissions. Previous work on mapping peat depth and condition in Scotland has provided maps with reasonable accuracy at 100‐m resolution, allowing land managers and policymakers to both plan and manage these soils and to work towards identifying priority peat sites for restoration. However, the spatial variability of the surface condition is much finer than this scale, limiting the ability to inventory greenhouse gas emissions or develop site‐specific restoration and management plans. This work involves an updated set of mapping using high‐resolution (25 cm) aerial imagery, which provides the ability to identify and segment individual drainage channels and erosion features. Combining this imagery with a classical deep learning‐based segmentation model enables high spatial resolution, national scale mapping to be carried out allowing for a deeper understanding of Scotland's peatland resource and which will enable various future analyses using these data.

中文翻译：

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苏格兰泥炭地退化高分辨率绘图的深度学习方法


按面积计算，泥炭约占苏格兰土壤的四分之一。健康、不受干扰的泥炭地栖息地对于提供有弹性的生物多样性和栖息地支持、水管理和碳封存至关重要。较高且稳定的地下水位是维持碳汇功能的前提；任何排水都会将这一主要的陆地碳储存转变为进一步反馈全球气候变化的来源。排水和侵蚀特征是泥炭地状况的重要指标，也是估算国家温室气体排放量的关键。先前绘制苏格兰泥炭深度和状况的工作提供了分辨率为 100 米的具有合理精度的地图，使土地管理者和政策制定者能够规划和管理这些土壤，并努力确定优先恢复的泥炭地点。然而，地表条件的空间变化比这个尺度要精细得多，限制了盘查温室气体排放或制定特定地点恢复和管理计划的能力。这项工作涉及使用高分辨率（25 厘米）航空图像进行更新的绘图，从而能够识别和分割各个排水通道和侵蚀特征。将该图像与经典的基于深度学习的分割模型相结合，可以进行高空间分辨率、国家尺度的测绘，从而可以更深入地了解苏格兰的泥炭地资源，并且可以使用这些数据进行各种未来的分析。