Modelling current and future forest fire susceptibility in north-eastern Germany

Abstract

Preventing and fighting forest fires has been a challenge worldwide in recent decades. Forest fires alter forest structure and composition; threaten people's livelihoods; and lead to economic losses, as well as soil erosion and desertification. Climate change and related drought events, paired with anthropogenic activities, have magnified the intensity and frequency of forest fires. Consequently, we analysed forest fire susceptibility (FFS), which can be understood as the likelihood of fire occurrence in a certain area. We applied a random forest (RF) machine learning (ML) algorithm to model current and future FFS in the federal state of Brandenburg (Germany) using topographic, climatic, anthropogenic, soil, and vegetation predictors. FFS was modelled at a spatial resolution of 50 m for current (2014–2022) and future scenarios (2081–2100). Model accuracy ranged between 69 % (RFtest) and 71 % (leave one year out, LOYO), showing a moderately high model reliability for predicting FFS. The model results underscore the importance of anthropogenic parameters and vegetation parameters in modelling FFS on a regional level. This study will allow forest managers and environmental planners to identify areas which are most susceptible to forest fires, enhancing warning systems and prevention measures.