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

Mitchell, Emily; Ian, Dryden; Christopher, Fallaize; Roxanne, Andersen; Bradley, Andrew; David, Large; Andrew, Sowter

Authors

Emily Mitchell

IAN DRYDEN IAN.DRYDEN@NOTTINGHAM.AC.UK
Professor of Statistics

Andersen Roxanne

DAVID LARGE David.Large@nottingham.ac.uk
Abbott Professor of Geoscience

Sowter Andrew



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 behaviour of the entire peatland surface motion time series using object oriented data analysis to assess peatland condition. Bayesian cluster analysis based on the functional structure of the surface motion time series finds areas indicative of soft/wet peatlands, drier/shrubby peatlands and thin/modified peatlands. 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

Citation

Mitchell, E., Ian, D., Christopher, F., Roxanne, A., Bradley, A., David, L., & Andrew, S. (in press). Object oriented data analysis of surface motion time series in peatland landscapes. Journal of the Royal Statistical Society: Series C,

Journal Article Type Article
Acceptance Date Sep 28, 2024
Deposit Date Oct 2, 2024
Journal Journal of the Royal Statistical Society: Series C
Print ISSN 0035-9254
Electronic ISSN 1467-9876
Publisher Wiley
Peer Reviewed Peer Reviewed
Keywords InSAR, peatland condition mapping, satellite, spatial, square root velocity function, time series, warping
Public URL https://nottingham-repository.worktribe.com/output/40283675
Publisher URL https://academic.oup.com/jrsssc