Operational Fuel Models Map for Atlantic Landscapes Using ALS and Sentinel-2 Images

Abstract

Background: In the new era of extreme wildfire events, new fire prevention and extinction strategies are emerging using software that simulates fire behavior. Having updated fuel models maps is critical in order to obtain reasonable simulations. Previous studies have proven that remote sensing is a key tool for obtaining these maps. However, there are many environments where remote sensing has not yet been evaluated in an operational context. One of these contexts are Atlantic environments. In this study, we describe a remote-sensing-data-based methodology for obtaining an operational fuel models map for an Atlantic-vegetation-covered area in Galicia (Northwestern Spain). We used Sentinel-2 images and ALS (Aerial Laser Scanner) data. Results: We have developed a methodology that allows to objectify the fuel models mapping for this type of environments since. For that we obtained the correspondences between the vegetation of the area and Rothermel fuel models. Additionally, since the methodology relies in remote sensing data, it allows us to obtain upgradable fuel models maps. For the study area, we obtained a map with high accuracy metrics. The accuracy of the supervised classifications involved in the mapping ranges between 70% and 100% (user’s and producer’s accuracies). Conclusions: The obtained methodology and the upgradable fuel models map will help to improve fire prevention and suppression strategies in Atlantic landscapes, aiding to shift towards more modern fire-simulation-based mitigation strategies.