England Peat Map: The challenges of using Earth observation data and machine learning approaches at the national scale

The world’s peatlands are our largest terrestrial carbon store whilst also providing a sustainable source of drinking water, a haven for wildlife and storing a record of our past. The England Peat Map aims to provide baseline maps for the extent, depth, and condition of peaty soils in England by 2024. This will enable targeting of future restoration, support nature recovery, improve greenhouse emissions reporting and natural capital accounting.

The maps will be created using a combination of multi-scale Earth observation imagery (satellite and airborne), existing and new ecological field survey data and machine/deep learning. Extent and depth mapping is implemented with random forest models and uses Sentinel satellite imagery and airborne LiDAR in combination with other ancillary datasets (e.g., geology and climate) for prediction. Assessment of peatland condition requires looking at these landscapes in different ways. Land cover mapping is used as a proxy for condition by targeting reflective classes for condition (e.g., Sphagnum, heather, and bare peat). Random forest and convolutional neural network (CNN) models are used in combination with Sentinel satellite imagery, aerial photography, and airborne LiDAR to produce national outputs. Mapping erosion/drainage features (grips, gullies and haggs) across the landscape is essential in understanding the underlying hydrological condition of the peatland and promising results have been achieved using CNNs with LiDAR and aerial photography. The final aspect of assessed condition is the movement of peat, also termed bog breathing, and is measured using Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR). This opportunity is a result of novel in-situ peat movement cameras being installed across pilot sites to provide ground truth data.

The final maps will be released free of charge under an open UK government license, allowing wider application and new opportunities for use compared with currently available datasets. For example, these baseline maps have the potential to contribute towards national peatland monitoring to address further decline of peatland habitats and target restoration interventions to achieve cost effective results. Several challenges have occurred during the initial phase of the project such as the difficulty in licensing suitable training data and in defining what we are mapping when features lack a globally agreed definition (e.g., surface features). The talk will discuss these challenges as well as the future direction of the project and how these challenges can be overcome.