Assessment of the possibilities of using Sentinel-2 satellite imagery materials to determine the predominant tree species in forest taxation

The study is devoted to the identification of the early stages of damage to Norway spruce tree stands by a bark beetle. The scale, intensity and dynamics of bark beetle damage are difficult to predict, and the negative consequences are comparable to the consequences of forest fires. From a practical point of view, it is of crucial importance to identify the initial stage of bark beetle damage, in which colonized trees do not yet show distinct symptoms of damage, and the needles retain their green color. The objective of the study was to study by methods of mathematical statistics changes in the vegetation indices NDVI and SWVI for Norway spruce stands, which in 3 years have gone from healthy to severely damaged, using as an example the sites of mass reproduction of bark beetle and other stem pests identified during a ground survey. The objects of the study were 12 sites of mass reproduction of bark beetles and other stem pests formed in 2021 on the territory of Vyborg and Gatchina districts of the Leningrad region in mature Norway spruce tree stands. During the ground work, the survey and mapping of damaged sites were carried out. With the help of the EO Browser mapping service, Sentinel-2B satellite survey materials for June-September 2020 (no damage), 2021 (early stages of damage) and 2022 (late stages of damage) were received on the territory of the damaged sites. Vegetation indices NDVI and SWVI were calculated on the basis of the received remote sensing materials. With the help of variance analysis, a reliable and significant decrease in the values of both indices was established by the years of damage, as well as by years and months. It is noted that when bark beetles damage plantings, the seasonal dynamics of index values changes. Reliable linear trends of decreasing of both index values by year and month are revealed. Analysis of the sensitivity coefficients and synchronicity of changes in vegetation indices by year and month showed that the decrease in index values occurs independently of external (random) factors, as well as well as not synchronously relative to each other. In practical terms, the revealed patterns of changes in the vegetation indices NDVI and SWVI can serve as a signal about the initial stage of damage to plantings and the basis for conducting ground surveys with the aim of early detection of damage and timely prevention of their spread.

1. Abdullah H.J. Remote sensing of European spruce (Ips typographus, L.) bark beetle green attack: PhD Thesis. University of Twente, 2019. 151 p. DOI: 10.3990/1.9789036547956

2. Abdullah H., Darvishzadeh R., Skidmore A.K., Heurich M. Sensitivity of Landsat- 8 OLI and TIRS Data to Foliar Properties of Early Stage Bark Beetle (Ips typographus L.) Infestation. Remote Sensing, 2019a, vol. 11, iss. 4, art. no. 398. DOI: 10.3390/rs11040398

3. Abdullah H., Skidmore A.K., Darvishzadeh R., Heurich M. Sentinel-2 accurately maps green-attack stage of European spruce bark beetle (Ips typographus, L.) compared with Landsat-8. Remote Sensing in Ecology and Conservation, 2019b, vol. 5, iss. 1, pp. 87–106. DOI: 10.1002/rse2.93

4. Alekseev A., Chernikhovskii D. Assessment of the health status of tree stands based on Sentinel – 2B remote sensing materials and the short-wave vegetation index SWVI. IOP Conf. Series: Earth and Environmental Science, 2021, vol. 876, art. no. 012003. DOI: 10.1088/1755-1315/876/1/012003

5. Alekseev A.S., Chernikhovskii D.M. Assessment of the State of Forests Based on Joint Statistical Processing of Sentinel-2B Remote Sensing Data and the Data from Network of Ground-Based ICP-Forests Sample Plots. Open Journal of Ecology, 2022, vol. 12, pp. 513–528. DOI:10.4236/oje.2022.128028

6. Ali M.S., Vorobev O.N., Kurbanov E.A. Decision Tree Algorithm for Forest Classification of Syrian Arab Republic with the Use of Sentinel-2 Image. Vestnik of Volga State University of Technology. Ser.: Forest. Ecology. Nature Management, 2020, no. 1 (45), pp. 5–30. DOI: 10.25686/2306-2827.2020.1.5 (In Russ.)

7. Arkhipov V.I., Baskov V.I., Belov V.A., Berezin V.I., Chernikhovskii D.M. Manual on forest taxation by decryption method. St. Petersburg: LESPROEKT LLC, 2021. 99 p. (In Russ.)

8. Arkhipov V.I., Chernikhovskii D.M., Berezin V.I. An Experience of Forest Inventory by Photo Interpretation Method Based on Advanced Firmware and Digital Aerial Photographs of New Generation. Sibirskij Lesnoj Zurnal (Siberian Journal of Forest Science), 2014a, no. 5, pp. 29–37. (In Russ.)

9. Arkhipov V.I., Chernikhovskii D.M., Berezin V.I., Belov V.A. Modern technology of forest mensuration by interpretation method «From survey – to the project». Izvestia Sankt-Peterburgskoj Lesotehniceskoj Akademii, 2014b, iss. 208, pp. 22–42. (In Russ.)

10. Berezin V.I., Chernikhovskii D.M. Modern development of the methodology of forest stereoscopic decryption by means of photogrammetric software (theory and practice). Problems of forest inventory and forest management: Proceedings of 5th International Scientific and Practical Conference. M: Roslesinforg, 2019, pp. 112–117. (In Russ.)

11. Cherepanov A.S. Vegetation indices. Geomatics, 2011, no. 2, pp. 98–102. (In Russ.)

12. Cherepanov A.S., Druzhinina E.G. Spectral properties of vegetation and vegetation indices. Geomatics, 2009, no. 3, pp. 28–32. (In Russ.)

13. Chernikhovskii D.M. Theory and methods of forest inventory based on data from remote sensing of the Earth, digital terrain modeling and GIS technologies: Dis. … Doctor of Agricultural Sciences. St. Petersburg: St. Peterburg State Forest Technical University, 2020. 386 p. (In Russ.)

14. Danyulis E.P., Osipenko G.S., Reznik M.H., Berezin V.I. Probabilistic analysis of decryption signs. Economic and mathematical modeling of forestry measures: Collection of scientific papers. L.: LenNIILH, 1980, pp. 153–160. (In Russ.)

15. Dmitriev E.V., Kondranin T.V., Melnik P.G., Donskoi S.A. Determining mixed forest species composition based on joint processing of public satellite maps and multi-temporal Sentinel-2 images. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa (Current problems in remote sensing of the Earth from space), 2024, vol. 21, no. 1, pp. 31–50. DOI: 10.21046/2070-7401-2024-21-1-31-50 (In Russ.)

16. Georgiev G., Georgieva M., Belilov S., Mirchev P., Deliyanchev S., Mladenov V., Kropov K., Haydarova S. Early Detection of Ips Typographus Infestations by Using Sentinel-2 Satellite Images In Windthrow Affected Norway Spruce Forests In Smolyan Region, Bulgaria. Silva Balcanica, 2022, vol. 23, iss. 2, pp. 27–34. DOI: 10.3897/silvabalcanica.22.e98314

17. Gusev A.P. Long-term changes in vegetation indices as an indicator of the dynamics of the state of natural and anthropogenic geosystems. Geopolitics and ecogeodynamics of regions, 2021, vol. 7 (17), iss. 2, pp. 202–209. (In Russ.)

18. Kopytkov V.V., Gusev A.P. Investigations of the state of forest crops and biological stability of plants in the zone of priority resettlement using multi-zone space image. Bulletin of the I.P. Shamyakin Mozyr State Pedagogical University, 2023, no. 1(61), pp. 22–28. (In Russ.)

19. Krylov A.M., Sobolev A.A., Vladimirova N.А. Revealing of Centers Ips typographus in Moscow Region with Use of Pictures Landsat. Lesnoy vestnik (Forestry Bulletin), 2011, no. 4, pp. 54–60. (In Russ.).

20. Kurbanov E.A., Vorobiev O.N., Menshikov S.A., Smirnova L.N. Identification of forest stands and dominant tree species in Penza Region using Sentinel-2 imagery. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa (Current problems in remote sensing of the Earth from space), 2018, vol. 15, no. 5, pp. 154– 166. DOI: 10.21046/2070-7401-2018-15-5-154-166. (In Russ.)

21. Lillesand T.M., Kiefer R.W., Chipman J.W. Remote Sensing and Image Interpretation. SeventhEdition. NewYork, Wiley, 2015. 736 p.

22. Martynova M.V., Sultanova R.R., Gabdelkhakov A.K., Rakhmatullin Z.Z., Odintsov G.E. Assessment of reforestation on abandoned agricultural lands using Landsat data on the example of a site in the Bakalinsky Region of the Republic Bashkortostan. Forest ecosystems in the context of climate change: biological productivity and remote monitoring, 2020, no. 6, pp. 128–134. (In Russ.)

23. Pakhuchiy V.V., Pakhuchaya L.M. Experience of vegetation indices use for complex researches on the objects of hydro and forest amelioration. Vestnik of Volga State University of Technology Series «Forest. Ecology. Nature Management», 2014, no. 1(21), pp. 33–41. (In Russ.)

24. Pakhuchiy V.V., Shevelev D.A. Use of forest management materials and satellite technologies at the forest drainage researches. Advances in current natural sciences, 2016, no. 5, pp. 69–73. (In Russ.)

25. Pushkin A.A., Sidelnik N.Yа., Kovalevskiy S.V. Estimation of possibilities of using the EVI spectral vegetation index for deciphering forest stands by satellite imagery. Trudy BGTU [Proceedings of BSTU], 2020, no. 1 (228): Forestry, environmental management and processing of renewable resources, pp. 5–11. (In Russ.)

26. Rakhmatullina I.R., Rakhmatullin Z.Z., Tagirova O.V., Khamidullina G.G., Kulagin A.Yu. Using vegetation indices to assess the characteristics of pine plantations in Ufa and its suburbs. Vestnik of Orenburg State Pedagogical University. Electronic Scientific Journal, 2021, no. 3(39), pp. 35–48. DOI: 10.32516/2303-9922.2021.39.3 (In Russ.)

27. Silkina O.V., Vinokurova R.I. Seasonal dynamics of the content of chlorophylls and trace elements in the emerging conifers Abies sibirica and Picea abies. Plant Physiology, 2009, vol. 56, no. 6, pp. 864–870. (In Russ.)

28. Stytsenko F.V., Bartalev S.A., Bukas A.V., Ershov D.V., Saigin I.A. The possibilities of a prolonged assessment of the post-fire state of coniferous evergreen forests according to multispectral satellite measurements. Modern problems of remote sensing of the Earth from space, 2019, vol. 16, no. 5, pp. 217–227. DOI: 10.21046/2070-7401-2019-16-5-217-227 (In Russ.)

29. Sultson S.M., Ponomarev E.I., Shvetsov E.G., Tretyakov P.D., Goroshko A.A., Kulakova N.N., Mikhailov P.V. The use of remote sensing for forecasting violations of dark coniferous forests after the outbreak of the Siberian silkworm population. Biosphere, 2023, vol. 15, no. 1, pp. 21–32. DOI: 10.24855/biosfera.v15i1.790 (In Russ.)

30. Tarasova L.V. Remote monitoring and spectral separability of classes of forest cover of water protection zones of rivers (on the example of the Mari forest Volga region): Dis. … Candidate of Agricultural Sciences. Yoshkar-Ola: Volga State University of Technology, 2023. 179 p. (In Russ.)

31. The use of vegetation indices for vegetation analysis [Electronic resource]. URL: https://gisproxima.ru/ispolzovanie_vegetatsionnyh_indeksov?ysclid=lxavkdetdj933222750 (assessed: 12.06.2024) (In Russ.)

32. Vorobyev O.N., Kurbanov E.A., Gubayev A.V., Demisheva E.N. Method of stepwise classification of satellite images for the thematic mapping of forest cover. Vestnik of Volga State University of Technology. Ser.: Forest. Ecology. Nature Management, 2015, no. 4 (28), pp. 57–72. (In Russ.)

33. Zhirin V.M., Knyazeva S.V., Eidlina S.P. Dynamics of spectral brightness of the breed-age structure of groups of forest types on Landsat satellite images. Forest science, 2014, no. 5, pp. 3–12. (In Russ.)

34. Zhirin V.M., Knyazeva S.V., Eidlina S.P. Long-term dynamics of vegetation indices of dark coniferous forests after damage by Siberian silkworm. Forest science, 2016, no. 1, pp. 3–14. (In Russ.)