Farrer Owsley-Brown

Environmental Hazards and Pollution

KCL

KCL

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. The project will also test various ways of pre-processing the airborne remote sensing data, exploring machine learning as a potential alternative to traditional algorithms.

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