Andrew Vincent Bradley

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).

Direct observation of montane and upland water resources provides valuable data in support of national scale water management and policy, but direct observation is challenging on large spatial scales. To address the need for large spatial scale hydrological data we use InSAR surface motion signals, indicative of surface swelling due to increased soil water content, at approximately 90 m resolution over a tropical Colombian mountain range covered with Páramo, a biome widespread along the Northern Andes. Considering uncertainty of vegetation and mountainous terrain on the InSAR signal, we observe a regional, spatially consistent sequence of soil surface motion, which can be related to storage and movement of water through montane catchments. Swelling on the ridges and upper slopes occurs during the wet season and is consistent with infiltration and increased saturation of ridge and upper slope soils. This is followed by a marked swelling of the valley floors towards the end of the wet season and into the dry season. The InSAR signal also captures movement of water through the basin with swelling subsiding sequentially downslope and downstream. The results indicate that a shallow hillslope flow dominates during the wet season, but this alone is insufficient to explain shallow ground water storage in superficial valley deposits lasting into the late wet season and dry season. We conclude that InSAR signals can provide a qualitative insight in the storage and release mechanisms at a basin scale, thus complementing sparse point‐scale measurements.

Agricultural peatlands are the most productive soils in the UK for the cultivation of many food crops. Historical drainage of peat for agriculture (i.e., cropland and managed grassland), without consideration of other associated environmental and climatic impacts, has resulted in a significant emission of greenhouse gases (GHGs). There is a need to reduce GHG emissions without compromising the rural economy and jeopardizing food security in the UK to a greater extent than is currently being experienced. In March 2023, in a bid to identify alternative land management systems for agricultural peatlands to support the UK’s commitment to achieving net zero GHG emissions by 2050, a group of forty investigators met at a workshop convened by the AgriFood4NetZero Network+. The workshop reviewed the state of knowledge surrounding the Fens of Eastern England and their importance for food provision, the economy, cultural identity, and climate change mitigation. A broad consensus emerged for research into how GHG emissions from agricultural peatlands could be reduced, whether alternative farming methods, such as seasonal farming or paludiculture, would offer a solution, and how a localized approach for the Fens could be defined. The development of a holistic, inclusive, and plausible land use scenario that considers all aspects of ecosystem services provided by the Fens is urgently needed.

Landscape decisions are multi‐faceted. Framing landscape decision‐making as a governance process that requires a collective approach can encourage key stakeholders to come together to co‐inform a discussion about their priorities and what constitutes good governance, leading to more holistic landscape decisions. In this paper, we recognise that a suite of complementary and multi‐dimensional approaches are in practice used to inform and evaluate land use decisions. We have called these approaches ‘lenses’ because they each provide a different perspective on the same problem. The four lenses are (i) power and market gain, (ii) ecosystem services, (iii) place‐based identity and (iv) ecocentric. Each brings a different set of evidence and viewpoints (narrative, qualitative and experiential, as well as quantitative metrics such as monetary) to the decision‐making process and can potentially reveal problems and solutions that others do not. Considering all lenses together allows dialogue to take place which can reveal the true complexities of landscape decision‐making and can facilitate more effective and more holistic decisions. Employing the lenses requires governance structures that give equal weight to all lenses, enable dialogue and coexistence between top down and bottom‐up approaches, and permit adaptation to local and granular place‐specifics rather than developing “one‐size‐fits‐all” solutions. We propose that formalising the process of balancing all the lenses requires public participation, and that a lens approach should be used to support landscape decisions alongside a checklist that facilitates transparency in the conversation, showing how all evidence has been considered and critically assessed. Read the freePlain Language Summaryfor this article on the Journal blog.

Better tools for rapid and reliable assessment of global peatland extent and condition are urgently needed to support action to prevent further decline of peatlands. Peatland surface motion is a response to changes in the water and gas content of a peat body regulated by the ecology and hydrology of a peatland system. Surface motion is therefore a sensitive measure of ecohydrological condition but has traditionally been impossible to measure at the landscape scale. Here we examine the potential of surface motion metrics derived from satellite interferometric synthetic aperture radar (InSAR) to map peatland condition in a blanket bog landscape. We show that the timing of maximum seasonal swelling of the peat is characterised by a bimodal distribution. The first maximum, usually in autumn, is typical of “stiffer” peat associated with steeper topographic gradients, peatland margins, and degraded peatland and more often associated with “shrub”-dominated vegetation communities. The second maximum, usually in winter, is typically associated with “softer” peat typically found in low topographic gradients often featuring pool systems and Sphagnum-dominated vegetation communities. Specific conditions of “soft” and “stiff” peats are also determined by the amplitude of swelling and multi-annual average motion. Peatland restoration currently follows a re-wetting strategy; however, our approach highlights that landscape setting appears to determine the optimal endpoint for restoration. Aligning the expectation for restoration outcomes with landscape setting might optimise peatland stability and carbon storage. Importantly, deployment of this approach, based on surface motion dynamics, could support peatland mapping and management on a global scale.

Better tools for rapid and reliable assessment of global peatland extent and condition are urgently needed to support action to prevent their further decline. Peatland surface motion is a response to changes in the water and gas content of a peat body regulated by the ecology and hydrology of a peatland system. Surface motion is therefore a sensitive measure of ecohydrological condition but has traditionally been impossible to measure at the landscape scale. Here we examine the potential of surface motion metrics derived from InSAR satellite radar to map peatland condition in a blanket bog landscape. We show that the timing of maximum seasonal swelling of the peat is characterized by a bimodal distribution. The first maximum is typical of steeper topographic gradients, peatland margins, degraded peatland and more often associated with ‘shrub’-dominated vegetation communities. The second maximum is typically associated with low topographic gradients often featuring pool systems, and Sphagnum dominated vegetation communities. Specific conditions associated with ‘Sphagnum’ and ‘shrub’ communities are also determined by the amplitude of swelling and average multiannual motion. Peatland restoration currently follows a re-wetting strategy, however our approach highlights that landscape setting appears to determine the optimal endpoint for restoration. Aligning expectation for restoration outcomes with landscape setting might optimise peatland stability and carbon storage. Importantly, deployment of this approach, based on surface motion dynamics, could support peatland mapping and management on a global scale.

Better tools for rapid and reliable assessment of global peatland extent and condition are urgently needed to support action to prevent their further decline. Peatland surface motion is a response to changes in the water and gas content of a peat body regulated by the ecology and hydrology of a peatland system. Surface motion is therefore a sensitive measure of ecohydrological condition but has traditionally been impossible to measure at the landscape scale. Here we examine the potential of surface motion metrics derived from InSAR satellite radar to map peatland condition in a blanket bog landscape. We show that the timing of maximum seasonal swelling of the peat is characterized by a bimodal distribution. The first maximum is typical of steeper topographic gradients, peatland margins, degraded peatland and more often associated with ‘shrub’-dominated vegetation communities. The second maximum is typically associated with low topographic gradients often featuring pool systems, and Sphagnum dominated vegetation communities. Specific conditions associated with ‘Sphagnum’ and ‘shrub’ communities are also determined by the amplitude of swelling and average multiannual motion. Peatland restoration currently follows a re-wetting strategy, however our approach highlights that landscape setting appears to determine the optimal endpoint for restoration. Aligning expectation for restoration outcomes with landscape setting might optimise peatland stability and carbon storage. Importantly, deployment of this approach, based on surface motion dynamics, could support peatland mapping and management on a global scale.

Better tools for rapid and reliable assessment of global peatland extent and condition are urgently needed to support action to prevent their further decline. Peatland surface motion is a response to changes in the water and gas content of a peat body regulated by the ecology and hydrology of a peatland system. Surface motion is therefore a sensitive measure of ecohydrological condition but has traditionally been impossible to measure at the landscape scale. Here we examine the potential of surface motion metrics derived from InSAR satellite radar to map peatland condition in a blanket bog landscape. We show that the timing of maximum seasonal swelling of the peat is characterized by a bimodal distribution. The first maximum is typical of steeper topographic gradients, peatland margins, degraded peatland and more often associated with ‘shrub’-dominated vegetation communities. The second maximum is typically associated with low topographic gradients often featuring pool systems, and Sphagnum dominated vegetation communities. Specific conditions associated with ‘Sphagnum’ and ‘shrub’ communities are also determined by the amplitude of swelling and average multiannual motion. Peatland restoration currently follows a re-wetting strategy, however our approach highlights that landscape setting appears to determine the optimal endpoint for restoration. Aligning expectation for restoration outcomes with landscape setting might optimise peatland stability and carbon storage. Importantly, deployment of this approach, based on surface motion dynamics, could support peatland mapping and management on a global scale.