Hyperspectral and Thermal Remote Sensing of Landscape Fire Properties

野火、森林砍伐大火和农业焚烧是地球上最大的自然干扰因素，平均每年影响的面积相当于印度的面积，改变了大片地区的土地覆盖，并向我们的大气中释放了大量烟雾。近年来，澳大利亚、加利福尼亚州、欧洲大陆甚至英国的野火登上了头条新闻，而卫星数据是在如此大的范围内强有力地跟踪和量化野火现象的唯一途径，这一点现在几乎可以实时完成。我们的能力包括通过火灾仍在燃烧时发出的电磁信号来探测火灾，除了识别火灾位置外，此类信号还包含有助于了解火灾行为的信息。特别值得注意的两个特征是火焰强度和燃烧阶段(CPH；即阴燃与燃烧)，这对烟雾的数量和化学成分有很大影响，进而控制其对大气和空气质量的影响。虽然卫星数据通常被用来识别火灾在哪里燃烧，但目前还没有成熟的方法来提取这些火灾特征的信息，即使是检测火灾也需要使用人工调整的算法来优化，这是非常耗时的。火灾的短暂和迅速变化的性质也意味着，这种来自卫星的信息得到的验证相对有限。航空遥感可以为开发和验证从卫星数据估计火灾特征的算法提供非常详细的数据，并可以从先前的实验室研究中获得信息，以帮助优化航空遥感目标所针对的现象和测量的性质。

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.