Developing a statistical methodology for the assessment and management of peatland (StAMP)

In good condition, peatlands are the most efficient carbon store of all soils. They regulate freshwater supply (peatlands are 95% water) and quality, mitigate climate change by storing greenhouse gases, and maintain biodiversity. Land use management interventions (e.g. use of peat for agriculture, drainage, forestry, burning for game management and recreation) can compromise the delivery of all these services by destabilising the vast carbon store that peat has locked away over thousands of years.  The UK has 2 Mha of peatlands (10% land area), however, up to 80% of these peatlands are damaged to some degree. It is estimated that degraded UK peatlands emit 10 Mt C a-1, a similar magnitude to oil refineries or landfill sites, placing the UK among the top 20 countries for emissions of carbon from degrading peat.  

Restoring degraded peatlands to halt carbon losses is an essential part of a global strategy to fight climate change. However, to date, we do not have a tool to help us assess how land use affects peatland condition in a cost effective manner over large and often remote areas, making it difficult to identify which areas should be prioritised for management intervention. In the UK, several millions of pounds of public money have already been invested in large-scale peatland restoration projects, yet we do not have a reliable and robust way to evaluate the effectiveness of restoration. These are important gaps in our knowledge that prevent us from being able to make cost-effective choices when it comes to peatland management.

With this project, we will develop new statistical methods to detect change in the condition of peatland landscapes from data collected by satellites. In a previous research project, we showed that peatland condition can be found from satellite data that measures surface motion of the peat. A wet peat in good condition displays very different characteristics to dry peat in poor condition. However, our satellite-based approach produces too much complex data that cannot be reliably and consistently analysed by eye. 

To address this this project will develop a new statistical method that can robustly and consistently quantify the changes in the peatland landscape from the satellite data. This requires methods capable of handling extremely large and complex structured datasets. In statistics, a new framework, known as Object-Oriented Data Analysis (OODA), is ideally suited to achieve this purpose by building models based on suitable choices of data objects. OODA can be used for developing parsimonious models for detecting change, and for quantifying uncertainty in predictions. OODA of the satellite data as functions of space and time will enable the modelling of trends and variability in the different regions, and the detection of change in the peatland.

The result will be a series of maps that illustrate the change in peatland landscape over time that are designed to be used by land managers and policy makers to guide decision making. This will help reduce unnecessary spending and prioritise the most urgent and strategic areas for peat restoration. Our novel approach combining state-of-the-art statistical methods with satellite data will provide a reliable tool to evaluate investments in peat restoration and report to funding bodies. 

PI David Large https://www.nottingham.ac.uk/engineering/departments/chemenv/people/david.large 
Co-I Roxane Andersen https://eri.ac.uk/members/roxane-andersen/
Link to the project page on the UKRI Gateway to Research https://gtr.ukri.org/projects?ref=NE%2FT010118%2F1