Modelling Current and Future Forest Fire Susceptibility in north-east Germany

Horn, Katharina Heike; Vulova, Stenka; Li, Hanyu; Kleinschmit, Birgit

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. It is crucial to identify the conditions that cause the emergence and spread of forest fires to improve prevention and management. We applied Random Forest (RF) machine learning (ML) algorithm to model current and future forest fire susceptibility (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 metres for current (2014–2022) and future scenarios (2081–2100) considering different shared socioeconomic pathways (SSP3.70 and SSP5.85). Model accuracy ranged between 69 % (RF test) and 71 % (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 better identify areas, which are most susceptible to forest fires, enhancing warning systems and prevention measures.

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Horn, Katharina Heike / Vulova, Stenka / Li, Hanyu / et al: Modelling Current and Future Forest Fire Susceptibility in north-east Germany. 2024. Copernicus Publications.

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Rechteinhaber: Katharina Heike Horn et al.

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