PhD Studentship: Using Earth Observation to Identify Drivers and Barriers to Post-fire Recovery of Forests. NERC GW4+ DTP PhD Studentship for September 2025 Entry Ref: 5430

About the Partnership

This project is one of a number that are in competition for funding from the NERC Great Western Four+ Doctoral Training Partnership (GW4+ DTP).  The GW4+ DTP consists of the Great Western Four alliance of the University of Bath, University of Bristol, Cardiff University and the University of Exeter plus five Research Organisation partners:  British Antarctic Survey, British Geological Survey, Centre for Ecology and Hydrology,  the Natural History Museum and Plymouth Marine Laboratory. The partnership aims to provide a broad training in earth and environmental sciences, designed to train tomorrow’s leaders in earth and environmental science. For further details about the programme please see nercgw4plus.ac.uk.

Project details

For information relating to the research project please contact the lead Supervisor via M.Miltiadou@exeter.ac.uk

Project Aims and Methods

This large-scale ecological project investigates the barriers and drivers of post-fire forest recovery. With climate change and the spread of forest fires to new areas, it is important to investigate the conditions that support forest recovery after a fire. The study areas can be defined using global forest fire maps from 2000–2023, provided by Hansen et al. The relaunch of GEDI provides repeated global LiDAR measurements related to biomass. Sentinel missions offer increased temporal resolution in both multispectral (for vegetation indices) and SAR data (for moisture and structure). Regarding climate-related data, Landsat and Sentinel-3 provide land surface temperature. BOKU offers daily precipitation measurements across Europe. Land-related parameters are available through JULES. The DR would co-develop the research objectives and select the methods to be implemented with supervisory support.  

Some ideas to discuss include integrating repeat GEDI LiDAR surveys with time-series multispectral and/or SAR data to improve biomass recovery estimations, measuring biases between GEDI and EO time-series estimations, developing machine learning algorithms to understand the effects of varying climatic conditions on post-fire recovery, and evaluating spatial recovery patterns across different elevations, slopes, and forest types. Together, we will co-develop a personal development plan for both technical and interpersonal skills. 

Training

The DTP offers funding to undertake specialist training relating to the student’s specialist area of research.

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