Hyperspectral and Thermal Remote Sensing of Landscape Fire Properties

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Wildfires, deforestation fires and agricultural burning are Earth's greatest natural disturbance agent, affecting on average an area equivalent to that of India every year, changing landcover over huge areas and releasing massive amounts of smoke into our atmosphere. 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. Our ability includes detecting fires through the electromagnetic signals they emit whilst they are still burning, and in addition to identifying where fires are, such signals also contain information useful for understanding fire behaviour. Two traits of particular interest are fire intensity and combustion phase (CPh; 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 information these fire characteristics, and even detecting the fires requires use of manually tuned algorithms that are time-consuming to optimise. The ephemeral and rapidly changing nature of fires also means that such satellite-derived information has been subject to relatively limited validation. Airborne remote sensing can provide highly detailed data for the development and validation of algorithms to estimate fire characteristics from satellite data, and can be informed by prior laboratory studies helping to optimise the nature of the phenomena and measurements targeted by the airborne sensing.