We welcome applications for an 8-week summer placement project on the topic of Wildfires versus Forest Fragmentation, with Imperial’s Undergraduate Research Opportunities Programme, on the topic of wildfires.
This UROPs is offered on a remote or hybrid basis. On-campus attendance will be possible, but it will not be essential.
*Applications are especially welcomed by Black students, as well as from individuals who are members of current and historically underrepresented groups*
Please check the Imperial College London UROP pages for general information and eligibility
A bursary is available, of £319/week to the successful applicant.
“It has been fantastic to learn so much about climate physics and modelling through this placement, and the people at the centre have gone out of their way to make this experience enjoyable and useful for me. I thank them especially for enabling me to participate in the journal clubs and the risk and resilience workshop, both of which I thoroughly enjoyed, not least because they exposed me to areas of research I had never been able to hear about before.” Clara Bayley – previous UROP student with Leverhulme Wildfires in the Dept. of Physics. Read about Clara’s research here.
Project title: Wildfires versus forest fragmentation
Most biomes are adapted to regular fire or repeated burning especially grassland, savanna (open and woody savannas) and dry forest in comparison to tropical rainforest. This is because wildfire is a natural process which is as ancient as plant kingdom. In some biomes of the tropics, wildfire would be more frequent without human activities; while others would be unchanged, or less frequent. For example, in the Amazon basin a positive relationship between fragmentation and burnt area in fire-free evergreen broadleaf forests while decreased fire in fire-adapted savannas has been observed (below figure) (Harrison et al. 2021).
Recent research has numerically demonstrated that efficient nowcasting of fire burnt area can be obtained by using local geological and climate features (e.g., vegetation, slope, wind direction) as model inputs (below figure) (Cheng et al. 2022).
In this research, the spatio-temporal relationship between burnt area/ fire frequency and forest fragmentation in the selected tropical biome across the Amazonia, Africa and southeast Asia would be explored. These sites have different fire regimes based on different vegetation types (evergreen forest, savanna forest, mixed peat swamp forest) with different fire frequency in different years. This will involve long-term multispectral Landsat and Sentinel-2 and pan-tropical forest fragmentation datasets developed by Hansen et al. (2020). Furthermore, forest fragmentation will also use as inputs in machine learning algorithms to enhance the current burnt area/ fire frequency predictions. As a first step, image-based machine learning algorithms will be employed to inspect the impact of forest fragmentation on fire propagation/fire duration.
The participant will be co-supervised by Dr Ramesh Ningthoujam, Dr Sibo Cheng, Dr Rossella Arcucci and Prof Colin Prentice. The output will be useful in understanding how wildfires affect fragmentation process and vice-versa at landscape level, developing fire regime definition for the tropics in the LCWES and present results in upcoming EGU2023 Conference in Vienna.
Skills and experience required:
Ecology, Analysis skills, Python programming, Initiative as part of a team.
To apply, please download and complete the UROP Application form 2022 – Ningthoujam and Cheng, and email it along with your CV to Dr Ramesh Ningthoujam and Dr Sibo Cheng email@example.com, firstname.lastname@example.org by Monday 11th July, 5pm BST.
Cheng, S., et al. (2022). Data-driven surrogate model with latent data assimilation: Application to wildfire forecasting. Journal of Computational Physics, page 111302.
Hansen, M. C., et al. (2020). The fate of tropical forest fragments Sci. Adv. 6 eaax8574.
Harrison, S.P., et al. (2021). Wildfire regimes: an ecological perspective, Environ. Res. Lett. 16 125008.