Modelling of live fuel moisture content in different vegetation scenarios during dry periods using meteorological data and spectral indices

Wildfires are of increasing concern to society, the scientific community and managers. Each year the forest fire season is becoming longer or deseasonalised. One of the main factors in fire behaviour is the live fuel moisture content. Climatological variables such as air humidity, temperature and precipitation directly affect the fuel that is likely to burn. Soil moisture also contributes to fire spread and severity. For these reasons, an analysis of fuel moisture is carried out at 3 sampling sites in the interior of Galicia (Spain), through multiple linear regression models. The 3 plots represent different characteristics for shrub growth: one plot is composed of pine trees, another plot contains a high density of undergrowth and the last plot was burned in 2020 and is regenerating undergrowth. A specific model was generated for each plot from field samples of live fuel and soil field samples collected from September to May, meteorological variables obtained from the stations and Sentinel-2 satellite spectral indices for the field sampling days.The best resulting spectral index for plots without trees was the NDI45, but with a low correlation (R2 < 0.2), while in the plot with pine trees it was the SAVI index (R-2 = 0.85). On the other hand, specific model equations were estimated combining these spectral indices with the soil moisture samples and the average temperature of the last 7 days. In all cases the result was considerably improved (R-2 between 0.72 and 0.91). These models can be helpful in estimating the probability of fire danger, outside the summer season, with long periods of drought, enabling decision making by managers in each region.