ARSF project WM06/04: Estimating forest fuel moisture content using hyperspectral imaging spectrometer and imaging LiDAR data. PI: M.Danson. Site: Cabaneros National Park, Spain

Fuel moisture content (FMC) is a key variable in forest fire risk modelling because it is related to the probability of ignition, and to the rate of spread of a fire. FMC is influenced by the interaction of plant physiology with soil moisture conditions, and is therefore spatially and temporally highly variable (Chuvieco et al., 2002). This variability presents a significant challenge to forest fire managers and remotely sensed data represent the only effective means of mapping the spatial and temporal dynamics of vegetation FMC. Recent empirical studies have shown that vegetation indices derived from satellite imagery may be related to FMC, but also that the relationships are often site or even species-specific. In related model-based studies (Danson and Bowyer, 2004) we demonstrated that the incorporation of structural information on vegetation properties may allow more accurate FMC estimation when using a canopy reflectance model inversion approach. This proposal for the acquisition of ARSF data aimed to combine the spectral measurements from the hyperspectral Specimen instruments and structural data from the LiDAR as inputs to a geometric-optical canopy reflectance model which were inverted to estimate spatial variation in FMC at test sites in southern Spain.