An application of the Perpendicular Moisture Index for the prediction of fire hazard

Various factors contribute to forest fire hazard, and among them vegetation moisture is the one that dictates susceptibility to fire ignition and propagation. The scientific community has developed a number of spectral indices based on remote sensing measurements in the optical domain for the assessment of vegetation equivalent water thickness (EWT), which is defined as the mass of liquid water per unit of leaf surface. However, fire models rely on the live fuel moisture content (LFMC) as a measure of vegetation moisture. LFMC is defined as the ratio of the mass of the liquid water in a fresh leaf over the mass of oven dry leaf, and spectral indices proposed so far fail in capturing LFMC variability. Recently, the perpendicular moisture index (PMI), based on MODIS, was pro-posed to overcome this limitation and provide a direct measure of LFMC. The aim of this research was to understand the potential and limitations of the PMI in predicting fire hazard, towards its ap-plication in a practical context. To this purpose, a data set of more than 7,700 fires recorded in Campania (13,595 km2), Italy, between 2000 and 2008 was compared with PMI derived from MODIS images. Results show that there is no relationship between PMI and fire size, whereas a linear correlation was found between the spectral index and fire rate of spread.

诸多因素均可影响森林火灾风险，其中植被湿度是决定火灾引燃与蔓延易感性的关键因素。学界基于光学域遥感测量手段开发了多款光谱指数，用于评估植被等效水厚度（EWT）——该指标被定义为单位叶面积内的液态水总质量。但火灾模型多以活燃料含水量（LFMC）作为植被湿度的衡量指标。LFMC被定义为新鲜叶片内液态水质量与烘干叶片质量的比值，而迄今提出的各类光谱指数均无法有效捕捉LFMC的变化特征。近期，基于MODIS开发的垂直水分指数（PMI）被提出，旨在克服上述局限，实现LFMC的直接量化。本研究旨在探究垂直水分指数（PMI）在火灾风险预测中的应用潜力与局限，以推动其落地至实际应用场景。为此，研究将2000年至2008年间意大利坎帕尼亚大区（面积13595平方千米）内记录的7700余起火灾数据集，与MODIS影像反演得到的PMI数据进行了对比分析。结果表明，垂直水分指数与火灾规模并无显著关联，但该光谱指数与火灾蔓延速率存在线性相关关系。