Using Deep Learning and High-Resolution Imagery to Map the Condition of Scotland’s Peatland Resource.

Peat makes up roughly 28% of Scotland’s soil and is critical in many areas, including biodiversity and habitat support, water management, and carbon sequestration. The latter is only possible in healthy, undisturbed peatland habitats where the water table is sufficiently high, otherwise this potential carbon sink becomes a carbon source, that if left untreated will disappear forever. 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 metre 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 this data.