Developing a statistical methodology for the assessment and management of peatlands

Duration: 16 mins 24 secs

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Description:	Large, D Tuesday 8th September 2020 - 14:10 to 14:30


Created:	2020-09-08 21:18
Collection:	Mathematical and statistical challenges in landscape decision making
Publisher:	Isaac Newton Institute
Copyright:	Large, D
Language:	eng (English)


Abstract:	In good condition, peatlands are the most efficient carbon store of all soils. The UK has 2 Mha of peatlands (10% land area). 80% of these peatlands are damaged to some degree and estimated to emit 10 Mt C a-1, a similar magnitude to oil refineries or landfill sites. Restoring degraded peatlands to halt carbon losses is an essential part of a global strategy to fight climate change. In the UK, £100s millions of public money have been pledged to restore peatland, yet we do not have a reliable and cost-effective way to direct and evaluate investment in restoration over large and often remote areas. In a previous research project, we showed that peatland condition can be found from satellite data that measures surface motion of the peat. However, our satellite-based approach produces too much complex data that cannot be reliably and consistently analysed by eye. To address this, we 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, help reduce unnecessary spending and evaluate investment.

Abstract:

In good condition, peatlands are the most efficient carbon store of all soils. The UK has 2 Mha of peatlands (10% land area). 80% of these peatlands are damaged to some degree and estimated to emit 10 Mt C a-1, a similar magnitude to oil refineries or landfill sites. Restoring degraded peatlands to halt carbon losses is an essential part of a global strategy to fight climate change. In the UK, £100s millions of public money have been pledged to restore peatland, yet we do not have a reliable and cost-effective way to direct and evaluate investment in restoration over large and often remote areas. In a previous research project, we showed that peatland condition can be found from satellite data that measures surface motion of the peat. However, our satellite-based approach produces too much complex data that cannot be reliably and consistently analysed by eye. To address this, we 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, help reduce unnecessary spending and evaluate investment.

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