Estimation of fuel moisture content towards Fire Risk Assessment: A review

Recent studies have demonstrated that surface wetness can be inferred when thermal infrared measurements are coupled with a spectral vegetation index (VI) that is linearly related to percentage vegetation cover. Even though it has later been shown that a proper interpretation of this relationship requires additional information of soil evaporation and the fractional vegetation cover, the ‘temperature-vegetation’ index has been proven useful for assessment of surface environmental conditions such as, for example, water content and drought/ fire risk. One of the methodologies shows that the slope of the relationship between surface temperature and the Normalized Difference Vegetation Index (NDVI) is strongly correlated to surface moisture status. Methodologies applied on low-resolution sensors offer a higher temporal resolution compared to high spatial resolution sensors and due to this are more likely to be used operationally for fire risk assessment. The goal of this paper is to present an assessment of the as yet developed or proposed fire risk assessment methodologies dependent upon remote sensing and climatologic data. Methodologies are cross-referenced and their applicability under a range of environmental conditions (land cover, climatology, topography, etc.) is analyzed, resulting in an indication on what the immediate future will hold with respect to fire prediction modelling. It will moreover be shown that an optimized integration of fire weather forecast data, lightning prediction information, and fire risk indices can result in a robust fire prediction model.