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Development of Information Databases for Vegetation Fire Behavior Prediction †

Aleksandra V. Volokitina
Mikhail A. Korets
1 and
Tatiana M. Sofronova
Forest Fire Science Laboratory, V.N. Sukachev Institute of Forest SB RAS, Krasnoyarsk 660036, Russia
Department of English Philology, Krasnoyarsk State Pedagogical University named after V.P. Astafyev, Krasnoyarsk 660049, Russia
Author to whom correspondence should be addressed.
Presented at the Third International Conference on Fire Behavior and Risk, Sardinia, Italy, 3–6 May 2022.
Environ. Sci. Proc. 2022, 17(1), 17;
Published: 8 August 2022
(This article belongs to the Proceedings of The Third International Conference on Fire Behavior and Risk)


To study the vegetation affected by fires and to create databases useful for fire behavior prediction, three methodological approaches are used: (1) selective, (2) standard, and (3) individual-standard. The selective method consists of empirically studying the drying and moistening dynamics of vegetation fuels in terms of fire hazard and burning characteristics in relation to dynamic external factors. This method is used in Russia and Canada. In the standard method approach, all vegetation, forest and non-forest, is divided into pyrological types—“fuel models”. This method is used in the USA. The individual-standard method consists of compiling individual pyrological characteristics of vegetation areas from typical elements that reflect the description of the components of a complex of vegetation fuels, as well as the conditions for their moistening, drying, and burning. This method is being developed in Russia. The essence of the method consists of making an individual pyrological description for any plot of forest and non-forest area with the help of the available descriptions (for example, forest inventory) or aerial satellite images, thus creating an information database useful for the prediction of fire behavior. The method is based on long-term pyrological studies of drying and moistening rates of the primary fire carriers in different regions of Russia. Using the developed software, we will present an example of an information database for predicting the behavior of vegetation fires in the Krasnoyarsk Priangarye, the most fire-prone region of Siberia.

Author Contributions

Conceptualization, A.V.V.; methodology, A.V.V.; software, M.A.K.; validation, A.V.V. and M.A.K.; formal analysis, T.M.S.; investigation, M.A.K.; resources, M.A.K.; data curation, M.A.K.; writing—original draft preparation, A.V.V.; writing—review and editing, T.M.S.; visualization, M.A.K.; supervision, A.V.V.; project administration, A.V.V. All authors have read and agreed to the published version of the manuscript.


This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.
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Share and Cite

MDPI and ACS Style

Volokitina, A.V.; Korets, M.A.; Sofronova, T.M. Development of Information Databases for Vegetation Fire Behavior Prediction. Environ. Sci. Proc. 2022, 17, 17.

AMA Style

Volokitina AV, Korets MA, Sofronova TM. Development of Information Databases for Vegetation Fire Behavior Prediction. Environmental Sciences Proceedings. 2022; 17(1):17.

Chicago/Turabian Style

Volokitina, Aleksandra V., Mikhail A. Korets, and Tatiana M. Sofronova. 2022. "Development of Information Databases for Vegetation Fire Behavior Prediction" Environmental Sciences Proceedings 17, no. 1: 17.

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