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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2025, V. 22, No. 3, pp. 149-160

Estimation of drying out of dark coniferous forests caused by the spread of the four-eyed fir bark beetle in Perm Krai based on satellite and field observations

L.A. Ivanchina 1 , L.A. Shilonosov 1 , A.N. Shikhov 1 
1 Perm State University, Perm, Russia
Accepted: 19.03.2025
DOI: 10.21046/2070-7401-2025-22-3-149-160
The signs of drying out of dark coniferous plantations in the central part of Perm Krai caused by the spread of the four-eyed fir bark beetle are considered using Sentinel-2 satellite images of the MSI (Multispectral Instrument) sensor. Field surveys were carried out on 51 test areas within which the proportion of living, dead, windbreak and windfall dark coniferous trees, as well as some taxation characteristics, were determined. Twenty eight vegetation indices and spectral brightness coefficients (SBC) in separate bands were calculated using Sentinel-2 satellite images obtained in June and July 2024. The correlation coefficients between the indicators of sanitary condition of the plantations and vegetation indices and SBC from the Sentinel-2 images were calculated. Statistically significant correlations with indicators of sanitary condition were revealed for a number of indices, but for both images the significance was confirmed only for the correlation between the proportion of living trees and the values of the leaf water indices (LWI) and normalized difference moisture indices (NDMI). The informative value of these indices was previously demonstrated using Landsat images as an example. The values of the indices in the studied areas are approximately equally determined by both the proportion of living or dead dark coniferous trees and the taxation indicators, primarily the proportion of dark coniferous species, the completeness of planting, the proportion of windbreak and windfall. In order to clarify the dependencies between vegetation indices and sanitary condition indicators of forest stand, the selection of sample areas should include more homogeneous stands according to taxation indicators.
Keywords: dark coniferous forests, drying out, four-eyed fir bark beetle, Sentinel-2 images, spectral brightness coefficient, vegetation indices, correlation
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References:

  1. Bun’kova N. P., Zalesov S. V., Zalesova E. S., Magasumova A. G., Osipenko R. A., Osnovy fitomonitoringa: uchebnoe posobie (Basics of phytomonitoring: Textbook), Ekaterinburg: Ural State Forest Engineering University, 2020, 90 p. (in Russian).
  2. GOST R 57973-2017 “Sanitarnaya bezopasnost’ v lesakh. Terminy i opredeleniya” (GOST R 57973-2017 “Salvage safety in forests. Terms and definitions”), Moscow: Standartinform, 2017, 8 p. (in Russian).
  3. Katkovsky L. V., Siliuk V. A., Belyaev B. I. et al., Detection of drying coniferous forests from aerospace data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, V. 19, No. 3, pp. 88–102 (in Russian), DOI: 10.21046/2070-7401-2022-19-3-88-102.
  4. Kerchev I. A., Maslov K. A., Markov N. G., Tokareva O. S., Semantic segmentation of damaged fir trees in unmanned aerial vehicle images, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2021, V. 18, No. 1, pp. 116–126 (in Russian), DOI: 10.21046/2070-7401-2021-18-1-116-126.
  5. Krivets S. A., Kerchev I. A., Bisirova E. M. et al., Overview of the current secondary range of the Four-eyed fir bark beetle (Polygraphus proximus Blandford) in the Russian Federation, Russian J. Biological Invasions, 2024, V. 15, No. 2, pp. 180–197, DOI: 10.1134/S2075111724700061.
  6. Krylov A. M., Sobolev A. A., Vladimirova N. A., Identifying the foci of bark beetle in the Moscow Oblast using Landsat images, Vestnik Moskovskogo gosudarstvennogo universiteta lesa — Lesnoi vestnik, 2011, No. 4, pp. 54–60 (in Russian).
  7. Ovesnov S. A., Mestnaya flora. Flora Permskogo kraya i ee analiz (Local flora: Flora of Perm Krai and its analysis), Perm: Perm State University, 2009, 171 p. (in Russian).
  8. Postanovlenie Pravitel’stva RF “Ob utverzhdenii Pravil sanitarnoi bezopasnosti v lesakh” (Resolution of the Government of the Russian Federation “On approval of the Rules for sanitary safety in forests”), Dec. 9, 2020, No. 2047 (in Russian).
  9. Prikaz Ministerstva prirodnykh resursov i ehkologii Rossiiskoi Federatsii “Ob utverzhdenii Perechnya lesorastitel’nykh zon Rossiiskoi Federatsii i Perechnya lesnykh raionov Rossiiskoi Federatsii” (Order of the Ministry of Natural Resources of the Russian Federation “On approval of the List of forest growth zones of the Russian Federation and the List of forest regions of the Russian Federation”), Aug. 18, 2014, No. 367 (in Russian).
  10. Kharuk V. I., Shushpanov A. S., Petrov I. A. et al., Fir (Abies sibirica Ledeb.) mortality in mountain forests of the Eastern Sayan Ridge, Siberia, Sibirskii Ehkologicheskii Zhurnal, 2019, No. 4, pp. 369–382 (in Russian), DOI: 10.15372/SEJ20190401.
  11. Curtis P. G., Slay C. M., Harris N. L. et al., Classifying drivers of global forest loss, Science, 2018, V. 361, No. 6407, pp. 1108–1111, DOI: 10.1126/science.aau3445.
  12. Forzieri G., Dakos V., McDowell N. G. et al., Emerging signals of declining forest resilience under climate change, Nature, 2022, V. 608, pp. 534–539, DOI: 10.1038/s41586-022-05071-9.
  13. Gao Y., Skutsch M., Paneque-Gálvez J., Ghilardi A., Remote sensing of forest degradation: A review, Environmental Research Letters, 2020, V. 15, No. 10, Article 103001, DOI: 10.1088/1748-9326/abaad7.
  14. Hlásny T., König L., Krokene P. et al., Bark beetle outbreaks in Europe: State of knowledge and ways forward for management, Current Forestry Reports, 2021, V. 7, pp. 138–165, DOI: 10.1007/s40725-021-00142-x.
  15. Kautz M., Meddens A. J. H., Hall R. J., Arneth A., Biotic disturbances in Northern Hemisphere forests — a synthesis of recent data, uncertainties and implications for forest monitoring and modelling, Global Ecology and Biogeography, 2017, V. 26, No. 5, pp. 533–552, DOI: 10.1111/geb.12558.
  16. Kharuk V. I., Im S. T., Petrov I. A. et al., Climate-driven conifer mortality in Siberia, Global Ecology and Biogeography, 2021, V. 30, No. 2, pp. 543–556, DOI: 10.1111/geb.13243.
  17. Krylov A., McCarty J. L., Potapov P. et al., Remote sensing estimates of stand-replacement fires in Russia, 2002–2011, Environmental Research Letters, 2014, V. 9, No. 10, Article 105007, DOI: 10.1088/1748-9326/9/10/105007.
  18. Lausch A., Heurich M., Gordalla D. et al., Forecasting potential bark beetle outbreaks based on spruce forest vitality using hyperspectral remote-sensing techniques at different scales, Forest Ecology and Management, 2013, V. 308, pp. 76–89, DOI: 10.1016/j.foreco.2013.07.043.
  19. Malysheva N., Shvidenko A., Nilsson S., Petelina S., Öskog A., An overview of remote sensing in Russian forestry: Interim Report IR-00-034, Laxenburg: Intern. Institute for Applied Systems Analysis, 2000, 89 p.
  20. Patacca M., Lindner M., Lucas-Borja M. E. et al., Significant increase in natural disturbance impacts on European forests since 1950, Global Change Biology, 2023, V. 29, No. 5, pp. 1359–1376, DOI: 10.1111/gcb.16531.
  21. Safonova A., Tabik S., Alcaraz-Segura D. et al., Detection of fir trees (Abies sibirica) damaged by the bark beetle in unmanned aerial vehicle images with deep learning, Remote Sensing, 2019, V. 11, No. 6, Article 643, 19 p., DOI: 10.3390/rs11060643.
  22. Seidl R., Thom D., Kautz M. et al., Forest disturbances under climate change, Nature Climate Change, 2017, V. 7, No. 6, pp. 395–402, DOI: 10.1038/nclimate3303.
  23. Senf C., Seidl R., Hostert P., Remote sensing of forest insect disturbances: Current state and future directions, Intern. J. Applied Earth Observation and Geoinformation, 2017, V. 60, pp. 49–60, DOI: 10.1016/j.jag.2017.04.004.
  24. Wulder M. A., White J. C., Carroll A. L., Coops N. C., Challenges for the operational detection of mountain pine beetle green attack with remote sensing, The Forestry Chronicle, 2009, V. 85, No. 1, pp. 32–38, DOI: 10.5558/tfc85032-1.
  25. Yang J., Dai G., Wang S., China’s national monitoring program on ecological functions of forests: An analysis of the protocol and initial results, Forests, 2015, V. 6, No. 3, pp. 809–826, DOI: 10.3390/f6030809.