Coupling remote sensing with wildfire spread modeling in Mediterranean areas

Wildfires are a threat to the ecosystems and in the future this threat could become stronger due to climate change. Spatially explicit fire spread models are effective tools to study fire behavior and wildfire risk. However, to run fire spread simulations, one of the most important inputs is represented by fuel models and this information is not always available. In the last decades, remote sensing technologies have offered valuable information for the classification and characterization of fuels. For this reason, in this work we created accurate maps of main fuel types for Mediterranean areas combining multispectral and LiDAR data. This information improves the current available information, which derives from the Land Use Map of Sardinia. We also enhanced the characterization of canopy fuel models using LiDAR data producing canopy layers ready to be used for wildfire spread modeling. Finally, we compared the variation in simulated wildfire spread and behavior determined by the use of fine-scale maps v. lower resolution maps. In these simulations, we assessed also the effect of using LiDAR-derived canopy layers as well. The results showed more accurate outputs when using our custom fuel and canopy layers produced in this work. In conclusion, this work suggests that the use of LiDAR and satellite imagery data can contribute to improve estimates of modeled wildfire behavior.