Object oriented data analysis of surface motion time series in peatland landscapes

TL;DR

This paper introduces an object-oriented data analysis method for peatland surface motion time series, enabling efficient assessment of peatland condition and degradation levels using InSAR data, which supports targeted restoration efforts.

Contribution

It presents a novel application of object-oriented data analysis and Gibbs sampling to classify peatland types based on surface motion time series, improving computational efficiency and assessment accuracy.

Findings

Successfully identified peatland types correlating with known conditions

Enabled assessment of peatland degradation scale

Provided a scalable method for large-area peatland monitoring

Abstract

Peatlands account for 10% of UK land area, 80% of which are degraded to some degree, emitting carbon at a similar magnitude to oil refineries or landfill sites. A lack of tools for rapid and reliable assessment of peatland condition has limited monitoring of vast areas of peatland and prevented targeting areas urgently needing action to halt further degradation. Measured using interferometric synthetic aperture radar (InSAR), peatland surface motion is highly indicative of peatland condition, largely driven by the eco-hydrological change in the peatland causing swelling and shrinking of the peat substrate. The computational intensity of recent methods using InSAR time series to capture the annual functional structure of peatland surface motion becomes increasingly challenging as the sample size increases. Instead, we utilize the behavior of the entire peatland surface motion time series using object oriented data analysis to assess peatland condition. In a Gibbs sampling scheme, our cluster analysis based on the functional behavior of the surface motion time series finds features representative of soft/wet peatlands, drier/shrubby peatlands and thin/modified peatlands align with the clusters. The posterior distribution of the assigned peatland types enables the scale of peatland degradation to be assessed, which will guide future cost-effective decisions for peatland restoration.