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Lesnoy Zhurnal

Mathematical Estimation of Information Reliability Regarding Forest Fires. P. 21–34

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R.V. Kotelnikov, A.A. Martynyuk

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UDС

630*614.849

DOI:

10.37482/0536-1036-2023-3-21-34

Abstract

The rapid development of digital technologies, especially methods for processing a large amount of information, offers vast opportunities for obtaining new algorithms for supporting management decisions, including the prevention of forest fires. Therefore, the requirements for data accuracy on fire hazards in forests and forest fires considerably increase. Even though the remote sensing of the Earth from space is a potential method for acquiring information independent of the human factor, it still has several technical limitations that hinder total automation. Therefore, it is important to provide а comprehensive control over the information coming from the forest fire departments. Besides, the long-term fire risk prognoses must consider retrospective statistics and cyclical weather conditions. This requires the creation of methods for evaluating the reliability of the initial data. An analysis of the records on the number of forest fires that happened in the Russian Federation from 1969 to 2020 revealed that the distribution of the values in a large sampling set is close to lognormal, which is the author’s fundamental principle. The few deviations on the right side of the distribution indirectly support the hypothesis that, in the provided information, the large forest fires in each case were presented as smaller, fragmented events. This is also consistent with the fact that such information usually occurs when the forest fire situation is complex and has many burning locations. An analysis of the records on the forest fires extinguished within one day identified a characteristic deviation, which indirectly supports the assumption that the data was probably distorted to improve recording. In such a situation, the deviation from the pattern corresponds to low combustibility and completely loses its meaning in the conditions of a severe forest fire situation with many burning areas. The authors have formed a ranking of the regions according to the validity of the archival records on the forest fires using the correspondence of the statistical data to the lognormal distribution. The proposed method can become one of the elements of a risk-oriented approach for planning control and supervisory measures in forestry policy.

Authors

Roman V. Kotelnikov*, Candidate of Engineering; ResearcherID: B-2453-2018, ORCID: https://orcid.org/0000-0002-9984-5913
Аlexsander A. Martynyuk, Doctor of Agriculture, Prof.; ResearcherID: AAB-7622-2020, ORCID: https://orcid.org/0000-0001-7592-2614

Affiliation

All-Russian Research Institute for Silviculture and Mechanization of Forestry, ul.Institutskaya, 15, Pushkino, Moscow region, 141202, Russian Federation; kotelnikovrv@firescience.ru*, vniilm_martinuk@mail.ru

Keywords

forest fires, forest fire conditions, burning of wooded area, forest fire suppression efficiency, statistical analysis, lognormal distribution, information technologies, risk-oriented approach

For citation

Kotelnikov R.V., Martynyuk A.A. Mathematical Estimation of Information Reliability Regarding Forest Fires. Lesnoy Zhurnal = Russian Forestry Journal, 2023, no. 3, pp. 21–34. (In Russ.). https://doi.org/10.37482/0536-1036-2023-3-21-34

References

  1. Bartalev S.A., Egorov V.A., Efremov V.Yu., Lupyan E.A., Stytsenko F.V., Flitman E.V. Integrated Burnt Area Assessment Based on Combine Use of Multi-Resolution Modis and Landsat-TM/ETM+ Satellite Data. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa = Current Problems in Remote Sensing of the Earth from Space, 2012, no. 2, pp. 9–26. (In Russ.).

  2. Kovalev N.A., Lupyan E.A., Balashov I.V., Bartalev S.A., Burtsev M.A., Ershov D.V., Krivosheev N.P., Mazurov A.A. ISDM-Rosleskhoz: 15 Years of Operation and Evolution. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa = Current Problems in Remote Sensing of the Earth from Space, 2020, no. 7, pp. 283–291. (In Russ.). https://doi.org/10.21046/2070-7401-2020-17-7-283-291

  3. Komorovskiy V.S. Models of Organization and Management in Fighting Forest Fires: Monograph. Moscow, Infra-M Publ., 2012. 120 p. (In Russ.).

  4. Kotelnikov R.V., Korshunov N.A., Giryaev N.A. Objectives of Decision Making in Protecting Forests from Fires. Main Priorities on Development of Informational Support. Sibirskiy lesnoy zhurnal = Siberian Journal Forest Science, 2017, no. 5, pp. 18–24. (In Russ.). https://doi.org/10.15372/SJFS20170502

  5. Kotelnikov R.V., Martynyuk A.A. Application of the Benford Law in Assessment of Wildfire Data Accuracy.Lesotekhnicheskiy zhurnal= Forestry Engineering Journal, 2018, vol. 8, no. 1 (29), pp. 28–34. (In Russ.). https://doi.org/10.12737/article_5ab0dfbb946859.24647128

  6. Lemeshko B.Yu., Lemeshko S.B., Postovalov S.N., Chimitova E.V. Statistical Data Analysis, Simulation and Study of Probability Regularities. Computer Approach: Monograph. Novosibirsk, NSTU Publ., 2011. 888 p. (In Russ.).

  7. Loupian E.A., Bartalev S.A., Balashov I.V., Egorov V.A., Ershov D.V., Kobets D.A., Senko K.S., Stytsenko F.V., Sychugov I.G. Satellite Monitoring of Forest Fires in the 21st Century on the Territory of the Russian Federation (Facts and Figures Based on Active Fire Detection). Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa = Current Problems in Remote Sensing of the Earth from Space, 2017, vol. 14, no. 6, pp. 158–175. (In Russ.). https://doi.org/10.21046/2070-7401-2017-14-6-158-175

  8. Ponomarev E.I., Shvetsov E.G. Satellite Detection of Forest Fires and Geoinformation Methods for Calibrating Results. Issledovaniye Zemli iz kosmosa = Earth Observation and Remote Sensing, 2015, no. 1, pp. 84–91. (In Russ.). https://doi.org/10.7868/S0205961415010054

  9. Giglio L., Randerson J.T., Werf G.R. Analysis of Daily, Monthly, and Annual Burned Area Using the Fourth-Generation Global Fire Emissions Database (GFED4): Analysis of Burned Area. Journal of Geophysical Research, Biogeosciences series, 2013, vol. 118, no. 1, pp. 317–328. https://doi.org/10.1002/jgrg.20042

  10. Jolly W.M., Cochrane M.A., Freeborn P.H., Holden Z.A., Brown T.J., Williamson G.J., Bowman D.M.J.S. Climate-Induced Variations in Global Wildfire Danger from 1979 to 2013. Nature Communications, 2015, vol. 6, no. 7537, рр. 1–11. https://doi.org/10.1038/ncomms8537

  11. Katz R.W., Brush G.S., Parlange M.B. Statistics of Extremes: Modeling Ecological Disturbances. Ecology, 2005, vol. 86, no. 5, pp. 1124–1134. https://doi.org/10.1890/04-0606

  12. Kotelnikov R.V., Lupyan E.A., Bartalev S.A., Ershov D.V. Space Monitoring of Forest Fires: History of the Creation and Development of ISDM-Rosleskhoz. Contemporary Problems of Ecology, 2020, vol. 13, no. 7, pp. 795–802. https://doi.org/10.1134/S1995425520070045

  13. Langmann B., Duncan B., Textor C., Trentmann J., Werf G. Vegetation Fire Emissions and Their Impact on Air Pollution and Climate. Atmospheric Environment, 2009, vol. 43, no. 1, pp. 107–116. https://doi.org/10.1016/j.atmosenv.2008.09.047

  14. Limpert E., Stahel W.A., Abbt M. Log-Normal Distributions Across the Sciences: Keys and Clues. Bioscience, 2001, vol. 51, no. 5, p. 341. https://doi.org/10.1641/0006-3568(2001)051[0341:lndats]2.0.co;2

  15. Loboda T.V., Csiszar I.A. Estimating Burned Area from AVHRR and MODIS: Validation Results and Sources of Error. Contemporary Earth Remote Sensing from Space, 2005, vol. 2, pp. 415–421.

  16. Pereira Jr. A.C., Oliveira S.L.J., Pereira J.M.C., Turkman M.A.A. Modelling Fire Frequency in a Cerrado Savanna Protected Area. PloS One, 2014, vol. 9, no. 7, art. no. e102380. https://doi.org/10.1371/journal.pone.0102380

  17. Royston J.P. An Extension of Shapiro and Wilk’s W. Test for Normality to Large Samples. Journal of the Royal Statistical Society, Series C: Applied Statistics, 1982, vol. 31, no. 2, pp. 115–124. https://doi.org/10.2307/2347973

  18. Taylor S.W., Woolford D.G., Dean C.B., Martell D.L. Wildfire Prediction to Inform Fire Management: Statistical Science Challenges. Journal of the Institute of Mathematical Statistics, 2013, vol. 28, no. 4, pp. 586–615. https://doi.org/10.1214/13-sts451

  19. Torres-Rojo J.M. Index for the Estimation of the Occurrence of Forest Fires in Large Areas. Revista Chapingo, Serie: Ciencias Forestales Y Del Ambiente, 2020, vol. 26, no. 3, pp. 433–449. https://doi.org/10.5154/r.rchscfa.2019.11.082

  20. Werf G.R., Dempewolf J., Trigg S.N., Randerson J.T., Kasibhatla P.S., Giglio L., Murdiyarso D., Peters W., Morton D.C., Collatz G.J., Dolman A.J., DeFries R.S. Climate Regulation of Fire Emissions and Deforestation in Equatorial Asia. PNAS, 2008, vol. 105, no. 51, pp. 20350–20355. https://doi.org/10.1073/pnas.0803375105



 

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