Project highlights

  • Development and application of novel remote sensing approaches for measuring and monitoring UK lowland peatlands
  • Identification of how management can improve sustainability of UK lowland peatlands
  • Opportunities for training and gaining experience in a combination of novel field, analytical and remote sensing techniques.

Overview

The Cambridgeshire Fens are a highly fragmented and extensively drained lowland peatland landscape. They cover 4,000 km2 and include large areas used for crop production and grazing, ditches and rivers, unproductive highly degraded peat, small reserves of deeper peat, and areas under active restoration. The Fens, like all UK lowland peatlands, have an important role in climate regulation, acting as both sources and sinks for GHGs. Intact peatlands are an important carbon store, but degraded peatlands now account for 3.5% of total UK GHG emissions. Conventional agricultural production in the Cambridgeshire Fens is worth £1.23 billion per year but is driving continued peat degradation. The highly fragmented nature of these peatlands requires decision-making to reduce risks and enhance climate resilience at a landscape scale.

Potential strategies to enhance resilience range from the adoption of more sustainable grazing, crop, soil and water management practices which will reduce the rates of peat erosion, to wetland crop production, and full restoration. While the benefits of peatland restoration are broadly understood, the merits of widespread adoption of more sustainable, regenerative agricultural practices, and the production of wetland crops on peat are less clear. Sustainable agricultural practices include reduced grazing, conservation (zero) tillage which reduces soil mechanical disturbance, winter cover-cropping (for example rye), and raising water tables, but there has been no systematic assessment of their costs/benefits. Paludiculture trials are ongoing in the region, and may ultimately have the potential to replace conventional agricultural production in some areas, although the economics of scaling operations remain to be clarified. Areas of deeper peat are generally considered to be priorities for restoration/conservation, although ongoing degradation and a lack of up-to-date peatland maps means the location of these areas is often anecdotal, and there are financial implications for farmers.

Key to successful outcomes is the development of cost-effective tools for monitoring ongoing peat loss, and adopting a systematic approach to landscape management that can optimise continued agricultural productivity in suitable areas, while identifying areas appropriate for alternative management trajectories. Ultimately such tools are important in the context of schemes like the IUCN peatland code, which is currently being expanded to lowland peatlands in England and Wales, and due to the potential for trading peatland carbon credits in the near future.

 

Host

Cranfield University

Theme

  • Climate and Environmental Sustainability

Supervisors

Project investigator

  • PI: Dr Abdou Khouakhi

Co-investigators

  • Dr Nick Girkin

How to apply

Methodology

The aim of this project is to develop an evidence-based strategy for managing fenland peat to allow sustainable, climate resilient, net zero farming, and to identify alternative management trajectories for the region. The student will 1) assess the suitability of different remote sensing techniques such as Interferometric synthetic aperture radar, Lidar alongside with multisource and multitemporal data fusion techniques to measure peat wastage rates in the Cambridgeshire Fens. This will be validated using existing datasets (e.g., the Lowland Peat Survey) and fieldwork. Using outputs from 1, the student will 2) develop a landscape scale opportunity model, incorporating information on wastage, land use, and future climate impacts such as flood risk and use it to identify optimal management trajectories that enhance reduce wastage for different peatland areas. 3) assessment of trade-offs in terms of economic impacts and effects on wider ecosystem services from changes in peatland management, and incorporate into a tool for visualising impacts.

Training and skills

The student will have access to relevant MSc modules taught at Cranfield (e.g. Applied Remote Sensing, Geographical Information Systems, Environmental Resource Survey, Modelling Environmental Processes). Training will be provided in fieldwork and laboratory techniques (soil characterisations), Python and R for data science, statistical analysis and modelling by the supervisory team. There will be opportunities to attend various other trainings, short courses and seminars through Cranfield’s Doctoral Training Centre, encouraging effective and vibrant research.

Partners and collaboration

The Bedfordshire, Cambridgeshire and Northamptonshire Wildlife Trust will support the project through access to potential field sites, and sharing of available data and expertise on lowland peat restoration and management

Further details

Dr Abdou Khouakhi

Lecturer in Remote Sensing Cranfield University,

School of Water, Energy and Environment

Centre for Environmental and Agricultural Informatics

Email : [email protected]

https://www.cranfield.ac.uk/people/dr-abdou-khouakhi-26326359

Dr Nick Girkin

Lecturer in Plant Soil Systems

School of Water, Energy and Environment

Cranfield Soil and Agrifood Institute

Email: [email protected]

https://www.cranfield.ac.uk/people/dr-nick-girkin-26264651

To apply please visit: https://www.cranfield.ac.uk/research/phd/identifying-and-monitoring

Possible timeline

Year 1

Synthesise the scientific literature on management practices relevant to UK lowland peatlands, assessing impacts on production and wider-ecosystem services.

Year 2

Assess and validate the suitability of different remote sensing techniques (e.g. Lidar, inSAR with multisource and multitemporal data fusion) to measure peat wastage rates in the Cambridgeshire Fens. Carry out preliminary fieldwork. Prepare journal article, and attend relevant conferences such as the EGU.

Year 3

Will include 1) developing a landscape scale opportunity model, incorporating information on wastage, land use, and future climate impacts (e.g. flood risk) and use it to identify optimal management trajectories that enhance reduce wastage for different peatland areas and 2) assessing trade-offs in terms of economic impacts and effects on wider ecosystem services from changes in peatland management, and incorporate into a tool for visualising impacts.

Further reading

Journal:

Chaussard, Estelle, et al. (2014) Land subsidence in central Mexico detected by ALOS InSAR time-series. Remote sensing of environment 140: 94-106.

Strozzi, Tazio, et al. (2018) Sentinel-1 SAR interferometry for surface deformation monitoring in low-land permafrost areas.  Remote Sensing 10.9: 1360.

Ranjgar, Babak, et al. (2021)  Land subsidence susceptibility mapping using persistent scatterer SAR interferometry technique and optimized hybrid machine learning algorithms. Remote Sensing 13.7: 1326.

Web page with an author:

Ian D.R (2019) Peat bogs: restoring them could slow climate change – and revive a forgotten world https://theconversation.com/peat-bogs-restoring-them-could-slow-climate-change-and-revive-a-forgotten-world-139182

Angela G. and  Julie L. (2020) How human activity threatens the world’s carbon-rich peatlands https://www.carbonbrief.org/guest-post-how-human-activity-threatens-the-worlds-carbon-rich-peatlands

Web page—author as an organisation:

Climate change: UK peat emissions could cancel forest benefits https://www.bbc.co.uk/news/science-environment-53684047

COVID-19

The project is largely a desk-based study. Low risk of COVID-19 is anticipated on the delivery of the project. However, COVID-19 might limit the physical interaction of the student with other student and researchers. COVID might also affect physical attendance to conferences and meetings. Planned fieldwork will be undertaken with a minimum of one other person to ensure safety, but is conducted outside, allowing suitable distancing. Existing datasets may also be used as an alternative.