Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 25, issue 1
Articles | Volume 25, issue 1
https://doi.org/10.5194/nhess-25-383-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-25-383-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 25, issue 1
Research article
 | Highlight paper
 | 
27 Jan 2025
Research article | Highlight paper |  | 27 Jan 2025

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

Katharina H. Horn, Stenka Vulova, Hanyu Li, and Birgit Kleinschmit

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Cited articles

Abdollahi, A. and Pradhan, B.: Explainable artificial intelligence (XAI) for interpreting the contributing factors feed into the wildfire susceptibility prediction model, Sci. Total Environ., 879, 163004, https://doi.org/10.1016/j.scitotenv.2023.163004, 2023. a, b, c, d, e
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Achour, H., Toujani, A., Trabelsi, H., and Jaouadi, W.: Evaluation and comparison of Sentinel-2 MSI, Landsat 8 OLI, and EFFIS data for forest fires mapping. Illustrations from the summer 2017 fires in Tunisia, Geocarto Int., 37, 7021–7040, https://doi.org/10.1080/10106049.2021.1980118, 2022. a
Afreen, S., Sharma, N., Chaturvedi, R. K., Gopalakrishnan, R., and Ravindranath, N. H.: Forest policies and programs affecting vulnerability and adaptation to climate change, Mitig. Adapt. Strat. Gl., 16, 177–197, https://doi.org/10.1007/s11027-010-9259-5, 2011. a, b, c
Ambadan, J. T., Oja, M., Gedalof, Z., and Berg, A. A.: Satellite-Observed Soil Moisture as an Indicator of Wildfire Risk, Remote Sens.-Basel, 12, 1543, https://doi.org/10.3390/rs12101543, 2020. a, b
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Forest fires have become a major problem in many regions of the world, including parts of Central Europe. The modelling study addresses the different factors for Forest Fire Susceptibility (FFS), making use of high spatial resolution of input data for the state of Brandenburg, Germany. An increasing susceptibility is found under rising greenhouse gas forcing scenarios when other changes are not taken into account. Extreme weather periods are of particular relevance in this respect. However, the importance of anthropogenic and vegetation parameters for modelling FFS on a regional level can outweigh the pure climatic effects. The paper also suggests some recommendations for forest management and environmental planning for a reduction of fire risk.
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Short summary
In this study we applied a random forest machine learning algorithm to model current and future forest fire susceptibility (FFS) in north-eastern Germany using anthropogenic, climatic, topographic, soil, and vegetation variables. Model accuracy ranged between 69 % and 71 %, showing moderately high model reliability for predicting FFS. The model results underline the importance of anthropogenic and vegetation parameters. This study will support regional forest fire prevention and management.
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