|
Forest fires are one of the major environmental hazards in Mediterranean Europe. Biomass burning reduces
carbon fixation in terrestrial vegetation, while soil erosion increases in burned areas. For these reasons, more
sophisticated prevention tools are needed by local authorities to forecast fire danger, allowing a sound allocation
of intervention resources. Various factors contribute to the quantification of fire hazard, and among them
vegetation moisture is the one that dictates vegetation susceptibility to fire ignition and propagation. Many
authors have demonstrated the role of remote sensing in the assessment of vegetation equivalent water thickness
(EWT), which is defined as the weight of liquid water per unit of leaf surface. However, fire models rely on the
fuel moisture content (FMC) as a measure of vegetation moisture. FMC is defined as the ratio of the weight
of the liquid water in a leaf over the weight of dry matter, and its retrieval from remote sensing measurements
might be problematic, since it is calculated from two biophysical properties that independently affect vegetation
reflectance spectrum.
The aim of this research is to evaluate the potential of the Moderate Resolution Imaging Spectrometer
(MODIS) in retrieving both EWT and FMC from top of the canopy reflectance. The PROSPECT radiative
transfer code was used to simulate leaf reflectance and transmittance as a function of leaf properties, and the
SAILH model was adopted to simulate the top of the canopy reflectance. A number of moisture spectral indexes
have been calculated, based on MODIS bands, and their performance in predicting EWT and FMC has been
evaluated. Results showed that traditional moisture spectral indexes can accurately predict EWT but not FMC.
However, it has been found that it is possible to take advantage of the multiple MODIS short-wave infrared
(SWIR) channels to improve the retrieval accuracy of FMC (r2 = 0.73). The effects of canopy structural
properties on MODIS estimates of FMC have been evaluated, and it has been found that the limiting factor is
leaf area index (LAI). The best results are recorded for LAI>2 (r2 = 0.83), while acceptable results (r2 = 0.58)
can still be achieved for lower vegetation cover density.
|
