LAUSR

ABSTRACT Fuel moisture content (FMC) is a critical parameter in fire behavior prediction. Although remote sensing is an efficient way to estimate the spatial and temporal variations of FMC, most of the existing spectral indices are oriented to live fuels. Estimation of dead fuels is commonly done using weather indices instead. In this study, dehydration experiments were designed for both live and dead fallen litter leaves in order to determine the best hyperspectral indices for different fuel types by tracking the time-varying water contents of both fuel materials. The identified best index for FMC including both fuel types was a derivative spectra-based normalized index (dND) of dND(1900, 2095) with an R2 of 0.85 and an RMSE of 32%. Estimation of FMC in both fuel types were well separated by normalizing dND(1900, 2095) combined with NDVI ((dND-NDVI)/(dND+NDVI)). In addition, new indices were also identified for practical large scale applications when atmospheric water vapor absorption must be taken into account. All the recommend indices should be validated with more plant species in the future.