Observations of intra-peatland variability using multiple spatially coincident remotely sensed data sources and machine learning

Peatlands are important sites of ecosystem services, particularly as soil carbon stores, and are recognised in many international climate strategies. However, drained peatlands, which have been modified for industrial extraction or agriculture, are responsible for carbon emission. Peatland restoration aims to return these degraded sites to a natural state. Multiple means of remotely monitoring the success of peat restoration are available, ranging from space-based satellite measurements (optical and radar) to airborne geophysical measurements (electro-magnetic and radiometric). This paper integrates multi-band, spatially coincident, remotely sensed data into a single framework, resulting in a comprehensive interpretation of intra-peatland variation of key restoration indicators. It uses a semi-automatic, data driven approach with unsupervised neural network machine learning clustering. A Multi-Cluster Average Standard Deviation metric is introduced which can determine the appropriate number of clusters for any dataset. The method was applied to a site in Ireland, representative of degraded peatlands, where optical satellite and airborne radiometric geophysical measurements were combined. The method was successful at determining the appropriate number of clusters for single and combined datasets, and the resulting cluster signatures provided visually compelling representations of the intra-peatland variation. This resulted in a comprehensive interpretation of intra-peatland variation of several key peatland restoration indicators, namely surface vegetation levels and soil moisture to ∼ 60 cm of the peat surface. The study provides a framework for high spatial and temporal resolution monitoring of peatland restoration using future drone-based platforms.