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Who are the actors and what are the factors that are used in models to map forest fire susceptibility? A systematic review

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  • Santos Daniel Chicas

    (Humboldt-Universität Zu Berlin)

  • Jonas Østergaard Nielsen

    (Humboldt-Universität Zu Berlin)

Abstract

In the last decades, natural fire regimes have experienced significant alterations in terms of intensity, frequency and severity in fire prone regions of the world. Modelling forest fire susceptibility has been essential in identifying areas of high risk to minimize threats to natural resources, biodiversity and life. There have been significant improvements in forest fire susceptibility modelling over the past two decades 2001–2021. In this study, we conducted a systematic literature review of literature covering forest fire susceptibility modelling published during this period. The review provides insights on the main themes of forest fire susceptibility modelling research, the main base input factors used in models to map forest fire susceptibility, the main researchers, the areas where this type of research were implemented, technology and models used. It also highlights collaboration opportunities, and regions, such as Central America and Africa, where mapping of forest fire susceptibility is needed. We argue that such knowledge is crucial in order to identify critical factors and opportunities which can aid in improving factor selection and forest fire management.

Suggested Citation

  • Santos Daniel Chicas & Jonas Østergaard Nielsen, 2022. "Who are the actors and what are the factors that are used in models to map forest fire susceptibility? A systematic review," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(3), pages 2417-2434, December.
    • Handle: RePEc:spr:nathaz:v:114:y:2022:i:3:d:10.1007_s11069-022-05495-5
    DOI: 10.1007/s11069-022-05495-5
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    References listed on IDEAS

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    1. Christos Vasilakos & Kostas Kalabokidis & John Hatzopoulos & Ioannis Matsinos, 2009. "Identifying wildland fire ignition factors through sensitivity analysis of a neural network," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 50(1), pages 125-143, July.
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    5. Saeedeh Eskandari & Mahdis Amiri & Nitheshnirmal Sãdhasivam & Hamid Reza Pourghasemi, 2020. "Comparison of new individual and hybrid machine learning algorithms for modeling and mapping fire hazard: a supplementary analysis of fire hazard in different counties of Golestan Province in Iran," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(1), pages 305-327, October.
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    2. Pandey, Dharen Kumar & Hunjra, Ahmed Imran & Bhaskar, Ratikant & Al-Faryan, Mamdouh Abdulaziz Saleh, 2023. "Artificial intelligence, machine learning and big data in natural resources management: A comprehensive bibliometric review of literature spanning 1975–2022," Resources Policy, Elsevier, vol. 86(PA).

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