Abstract
Wildland fires burn large areas of the earth's land surface annually, causing significant environmental damage and danger to human health. In order to mitigate the effects, and to better manage the incidence of such fires, fire behaviour models are used to predict, among other things, the likelihood of ignition, the rate of spread, and the intensity and duration of burning. A key input parameter to these models is the amount of water in the vegetation, described as the fuel moisture content (FMC). A number of studies have shown that vegetation indices (VI) calculated from red and NIR reflectances may be used to map spatial and temporal variation in FMC in a range of fire-prone environments, with varying degrees of success. Strong empirical relationships may be established between VI and FMC over grasslands, yet over shrublands and forests, the relationships are weaker. If FMC is to be estimated with greater accuracy and consistency than is currently achieved, it is necessary to fully understand the relative contribution that spatial and temporal variation in the various biophysical and geometrical variables make to reflectance variability at the leaf and canopy level. This paper uses a modelling approach to investigate the sensitivity of reflectance data at leaf and canopy level to variation in the biophysical variables that are used to compute FMC. At the leaf level, the results show that the sensitivity of reflectance to variation in leaf water and dry matter content, used to compute FMC, is greatest in the SWIR and NIR, respectively. Variation in FMC has no effect in the visible wavelengths. At the canopy level, the results show that the sensitivity of reflectance to variation in leaf water and dry matter content is heavily dependent upon the type of model used and the range of variation over which the variables are tested. In the longer wavelengths of the SWIR, the competing influence of variable leaf area index, fractional vegetation cover, and solar zenith angle is shown to be greater than that at the shorter wavelengths of the SWIR and NIR. Empirical relationships between the normalised difference water index (NDWI) and FMC are shown to be weaker than that with canopy water content. However, when the range of the variables under study is more limited, useful empirical relationships between FMC and remotely sensed VI may be established.
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