Artificial Intelligence and Geospatial Information in Forest Fire Prevention
A Proactive Decision Support Model
Abstract
The article examines strategic modernisation of domestic forest fire protection, focusing on shifting from reactive damage elimination to proactive risk management. The paper aims to present an integrated protection system that supports decision-making through geographic information systems (GIS) and artificial intelligence (AI), minimising intervention time and environmental damage. The first part of the study discusses dynamic risk assessment. The GIS-based model synthesises static environmental factors (forest stand, relief) and real-time meteorological variables using the “Overlay” technology. The paper pays special attention to the Great Plain pine forests and urban-forest interface zones (WUI), which are most exposed to climate change and where human and natural risks cumulatively increase. The prevention chapter analyses new directions in biological and technical risk reduction. The concept of “Green Firebreaks” is presented in detail, preventing the formation and spread of crown fires by dividing flammable pine forests into strips of deciduous trees. Among the technical solutions, the study covers modern monitoring systems and compares various optical and infrared detection technologies, thereby shortening the alarm chain.
References
Radeloff, V. C. et al. (2005). The Wildland-Urban Interface in the United States. Ecological Applications, 15 (3), pp. 799–805.
Burrough, P. A., & McDonnell, R. A. (1998). Principles of Geographical Information Systems. Oxford University Press.
Chuvieco, E. et al. (2010). Development of a framework for fire risk assessment using remote sensing and GIS. Ecological Modelling, 221 (1), pp 46-58.
Rothermel, R. C. A mathematical model for predicting fire spread in wildland fuels. USDA Forest Service Research Paper INT-115. 1972.
Agee, J. K., & Skinner, C. N. (2005). Basic principles of forest fuel reduction treatments. Forest Ecology and Management, 211, pp. 83–96.
Ganteaume, A. et al. (2013). A review of the main driving factors of forest fire ignition. Journal of Environmental Management, 117, pp. 360–378.
A 2009. évi XXXVII. törvény az erdőről, az erdő védelméről és az erdőgazdálkodásról. [Online]. Elérhetőség: http://net.jogtar.hu (2026.02.16.)
A 4/2008. (VIII. 1.) ÖM rendelet az erdők tűz elleni védelméről. [Online]. Elérhetőség: http://net.jogtar.hu (2026.02.16.)
Az 54/2014. (XII. 5.) BM rendelet az Országos Tűzvédelmi Szabályzatról. [Online]. Elérhetőség: http://net.jogtar.hu (2026.02.16.)
National Weather Service (2024). Red Flag Warning and Fire Weather Watches. NOAA. [Online]. Elérhetőség: https://www.weather.gov/lot/firewx_definition (2026.02.11)
Country Fire Authority (2024). Total Fire Bans and Fire Danger Ratings. Victoria State Government. (Country Fire Authority (CFA): Total Fire Bans and Fire Danger Ratings. Victoria State Government; cfa.vic.gov.au,
Météo-France (2024). La Météo des forêts: un nouvel outil pour la prévention des incendies
Fernandes, P. M. (2013). Fire-smart management of forest landscapes in the Mediterranean basin under global change. Landscape and Urban Planning, 110 (1), pp. 175–182.
Dimitrakopoulos, A. P., & Papaioannou, K. K. (2001). Flammability Assessment of Mediterranean Forest Fuels. Fire Technology, 37 (2), pp. 143–152.
Curran, T. J. et al. (2019). Green Firebreaks as a Management Tool for Wildfires. Journal of Environmental Management, 233, pp 329-336.
De Frenne, P. et al. (2019). Global buffering of temperatures under forest canopies. Nature Ecology & Evolution, 3 (5), pp. 744–749.
Hotspots Fire Project (2024). Understanding Fuel: Fuel Management Zones Fact Sheet. NSW Rural Fire Service. [Online]. Elérhetőség: https://www.hotspotsfireproject.org.au/news/2024-12-20/understanding-fuel-fuel-management-zones-fact-sheet (2026.02.07.)
Agee, J. K. et al. (2000). The use of shaded fuelbreaks in landscape fire management. Forest Ecology and Management, 127, pp. 55–66.
FAO Fire Management: Voluntary Guidelines. Fire Management Working Paper 17. Rome 2006.
Rossi, J. L. et al. Fuelbreaks: a part of wildfire prevention. Contributing Paper to Global Assessment Report on Disaster Risk Reduction. United Nations Office for Disaster Risk Reduction (UNDRR). 2019.)
Insight Robotics (2026). Saving the world, bit by bit. [Online]. Elérhetőség: https://www.insightrobotics.com/en/ (2026.02.24.)
IQ FireWatch (2026). Korai erdőtüzek észlelése [Online]. Elérhetőség: http://iq.firewatch.com (2026.02.17)
Alkhatib, A. A. A. (2014). A review on forest fire detection techniques. International Journal of Distributed Sensor Networks 10 (3), 597368 [Online]. Elérhetőség: https://journals.sagepub.com/doi/10.1155/2014/597368 (2026.02.22.)
Arrue, B. C. et al. (2000). An intelligent system for false alarm reduction in infrared forest-fire detection. IEEE Intelligent Systems and their Applications, 15 (3), pp. 64–73. 2000.)
IQ FireWatch (2026). Korai erdőtüzek észlelése [Online]. Elérhetőség: http://iq.firewatch.com (2026.02.17)
Paratronic ADELIE – Alarme Détection Localisation des Incendies [Online]. Elérhetőség: https://www.paratronic.com/secteur-activite/detection-des-incendies/ (2026.02.01)
National Weather Service (2024). Red Flag Warning and Fire Weather Watches. NOAA. [Online]. Elérhetőség: https://www.weather.gov/lot/firewx_definition (2026.02.11)
Insight Robotics (2026). Saving the world, bit by bit. [Online]. Elérhetőség: https://www.insightrobotics.com/en/ (2026.02.24.)
Bosch Magyarország IoT Blog (2023). Mire vagy kíváncsi? [Online]. Elérhetőség: https://iot.boschblog.hu/ (2026.02.12.)
Copyright (c) 2026 Defence Science
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
