Using satellite, airborne laser scanning and socio-economic data in a machine learning framework for improved fire danger modelling in the Alps

The frequency and severity of wildfires in the Alpine region will likely increase due to climate change. Most fire danger forecasts currently adopted in this region are based on meteorological data, such as the Canadian Fire Weather Index (FWI). They are typically only available at relatively coarse spatial resolutions (up to ca. 1 km) and, therefore, are of limited use in mountain regions with complex topography. Other factors, such as vegetation type and structure and the role of humans causing ignitions, are typically not considered.  

We address this gap by presenting a novel, high-resolution, satellite-supported integrated forest fire danger system (IFDS) for Austria. For this purpose, we use radar and optical satellite data from the Copernicus Sentinel-1 and Sentinel-2 missions, airborne laser scanning (ALS), socio-economic data, and topographic properties next to meteorological data. Two independent methods were investigated: (i) an expert-based approach that allows combining various data layers with different weightings assigned by experts and (ii) a machine-learning approach. Here, we focus on the results of the machine learning approach for a study area covering the federal state of Styria in Austria (ca. 16 400 km²). We use several data layers computed within our study as predictors in random forest models. Moisture indicators and tree species maps were derived from satellite data from the Copernicus Earth observation programme. Vegetation structure parameters, solar potential and a digital surface model (DSM) were derived from ALS data. In addition to the remote sensing data, we used meteorological variables, fire weather indices (FWI) and socio-economic data. We trained the model using forest fire events from the Austrian fire database.  

The cross-validation showed that the best-performing model predicts high fire danger for most fire events (87%). By integrating all the information layers compared to a baseline model using only FWI, the overall accuracy improved from 68% to 87%. The feature importance showed that the vegetation structure parameters, tree species, socio-economic parameters and DSM are essential for the model in addition to the meteorological predictors. Using this data-driven approach allowed us to learn from past fire occurrences and improved the spatial representation of fire ignition drivers, their importance and interactions. Also, this method permitted the identification of areas with higher danger risk, typically located in the vicinity of densely populated settlements. 

This study has been performed within the CONFIRM project with funding from the Austrian Research Promotion Agency (FFG).