Tipping point analysis of the Scottish peatlands

Satellite data below 100 m resolution can be of great benefit for prevention of geohazards. To utilise spatial and temporal data efficiently, it is necessary to develop data science techniques that are sensitive, computationally light, and capable of revealing signatures of critical events in bulky multivariate data. This emphasis on computationally light yet physically grounded detection aligns with recent climate emulation work that motivates efficient data-driven pipelines for extracting dynamical signatures from large observational datasets [1]. We apply tipping point analysis e.g., Early Warning Indicators (EWS), adapted to multivariate data flows, to demonstrate how this methodology can help complement and augment field work in the peatlands, thus optimising resources.

EWS are based on structural changes in trajectories of dynamical systems, which are described by autocorrelations and variability of the system potential [2-4]. Conventionally, peatlands are studied using expensive and slow ground surveys, but we show that equivalent information can be derived from the satellite Interferometric Synthetic Aperture Radar (InSAR) 6-12 day surface motion data using tipping point analysis. This includes processing the order of hundreds of thousands of time series potentially over 100’s of km, in combination with GIS data provided by stakeholders.

We demonstrate a case study using InSAR surface motion data over ~400km²area in Scotland with areas of critical changes in the soil surface. In a blind test, the area of a large fire (60km²) in Scottish peatlands was identified and its detection coincided with the area of actual fire damage in comparison with ground observations and existing fire detection tools based on MODIS data. This approach is promising and may be developed further to better understand peatland behaviour before and after such extreme events.

References

[1] Sudakow, I., Pokojovy, M., & Lyakhov, D., Statistical mechanics in climate emulation: Challenges and perspectives, Environmental Data Science 1, e16 (2022).

[2] Livina et al., Potential analysis reveals changing number of climate states during the last 60 kyr, Climate of the Past 6, 77-82 (2010)

[3] Livina et al., Forecasting the underlying potential governing the time series of a dynamical system, Physica A, 392 (18), 3891-3902 (2013)

[4] Prettyman et al., A novel scaling indicator of early warning signals helps anticipate tropical cyclones, Europhysics Letters 121, 10002 (2018).