Leverhulme Wildfires Centre Leverhulme Wildfires Centre

Peatlands are the world’s largest store of terrestrial carbon, an equivalent of around 2/3 of the carbon in the atmosphere is stored in boreal peatlands alone. Peatlands also support critical biodiversity and help protect from floods and drought. Wildfires pose an existential risk to peatlands and, since carbon is never fully re-sequestered, the climate. Estimates of emissions in literature don’t account for various critical determining factors, such as moisture content, carbon mineralisation, and metal content, as well as the dynamics of the water-table and peat surface.

The project explores how different remote sensing datasets can quantify emissions from peatland wildfires. Lab-based pyrolysis and chemical experiments are used to evaluate the relation between fuel characteristics and fire smoke content. This is supported by field campaigns to Canada which have measured, from the ground and from fixed-wing aircraft, emission factors from boreal soils. A lot of the research work involved is developing and refining infrared hyperspectral remote sensing algorithms to measure emissions, both in the field and in the lab.

By improving our understanding of peatland emissions, particularly in relation to wildfires, we can strengthen the argument for peatland restoration and careful management, as well as providing improved estimates for emissions that can be fed into climate models. In addition, this research has potential to elucidate potential health concerns present from wildfires mobilising heavy metal content from the earth to the air.

Duration: 2022 – 2026

Images: by Luke Richarson-Foulger

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Twitter is increasingly being used as a real-time human-sensor network during natural disasters, detecting, tracking and documenting these events. Social media data represents a large amount of publicly available, unprocessed social data which is both opinionated, informative, and emotional. During disaster scenarios, users post a geographically and emotionally subjective account of events unfolding locally. There is scope for this information to be collated and analysed in real-time, and incorporated into wildfire models to improve their accuracy. 

This PhD project aims to help make disaster management teams make more informed, data driven decisions by including social media analysis as a data source in real-time wildfire models. In doing this, we aim to create more socially conscious wildfire models, which consider the impacts of wildfire spread.

Project duration: 2020-2024

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In recent years wildfires have made headlines in Australia, California, continental Europe and even the UK, and satellite data are the only way to robustly track and quantify the phenomena across such large scales, something that can now be done close to real-time. Two traits of particular interest are fire intensity and combustion phase (i.e. smouldering vs. flaming), which strongly influence the amount and chemical composition of smoke and in turn controls its impact on the atmosphere and on air quality. Whilst satellite data are commonly used to identify where fires are burning, there are no proven means currently of extracting these fire characteristics, and even detecting the fires requires use of manually tuned algorithms that are time-consuming to optimise.

This project will explore the use of multi and hyper-spectral laboratory and airborne remote sensing in characterising landscape fires, and ultimately the use of such metrics to help improve and validate new information extractable from satellite observations of active fires.

This project is also co-supervised by Rob Francis, KCL.

 

KCL combustion chamber at Rothamsted Research. Photo: Martin Wooster.

 

British Antarctic Survey aeroplane, fitted with KCL remote sensing equipment. Photo: Adriana Ford, Leverhulme Wildfires 2021

 

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The northern extratropics has experienced increases in fire activity in recent decades, which have had important consequences for ecosystems, carbon cycling and human societies. There is currently wide uncertainty in predictions of how fire will respond to climate changes in this region in the coming decades. Studying fire responses over palaeo timescales provides a window into how fire may respond to large environmental changes, which are anticipated to play out over the course of this century. This project will leverage a newly-created global palaeo charcoal database, the Reading Palaeofire Database, to reconstruct changes in biomass burning across the circum-northern extratropics over the Holocene. It will attempt to explain patterns in fire activity over millennia in this region by quantitatively linking sub-continental scale fire responses to climate, vegetation and human-induced landscape shifts. This will provide novel insights into the importance of various environmental reorganisations in shaping fire regimes, which can directly contribute to attempts at better constraining predictions of future changes in wildfire patterns.

Project Duration: 2019-2023

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Wildfires and other forms of landscape burning are complex, dynamic and in some ways difficult to predict and certainly potentially dangerous phenomena. Fires up to even extreme mega-fire events can be studied using the techniques of remote sensing and modelling, but these studies and those of smaller burns often need to be informed by and sometimes combined with data from in situ investigations, for example on the spectral properties of the fires if using remote sensing and on the different composition of their smoke and what controls that if estimating emissions. This in situ data can be collected in the field on planned burns or even on wildfires were possible, and can also be supplemented – where appropriate – by data collected in laboratory fires under more controlled conditions. The purpose of this technical postdoctoral project is to deliver the capability to make and analyse these measurements to support specific aspects of the Centre’s work on fire spectral signatures and smoke emissions, as well as wider investigations.

Project duration: 2019- ongoing

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