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New information technology for phytocenoses regional monitoring using remote sensing data
V.V. Sergeyev1, A.Y. Bavrina1, L.M. Kavelenova1, Y.A. Bogdanova1, Y.A. Ryazanova1

1Samara National Research University, 443086, Moskovskoe Shosse 34A, Samara, Russia

  Полный текст (PDF)

DOI: 10.18287/COJ1822

ID статьи: 1822

Аннотация:
A new information technology for plant communities monitoring using remote sensing data, oriented for application at the regional level, is proposed. The technology is based on maintaining a base of reference polygons, accumulating data on the boundaries of specific plant communities and related semantic information. This database provides a source of verified and up-to-date information for solving problems of rational nature management. To expand the database of reference polygons, two algorithms for finding new ones are presented: a reliable algorithm (analyzing several growing seasons) and an urgent algorithm (based on the current growing season). The advantage of the proposed system is the integration of data storage, processing, and analysis, which enables the automation of the creation of new and the monitoring of existing polygons, as well as the solution of a wide range of problems based on remote sensing data and artificial intelligence algorithms. Practical tasks in studying phytocenoses in the Samara Region, implemented using the proposed monitoring technology, are considered: updating forest inventory data, monitoring the status of a rare reintroduced species (Paeonia tenuifolia), searching for new reference polygons across a vast territory of several steppe protected areas, and searching for areas of presence of an invasive plant species (Elaeagnus angustifolia L).

Ключевые слова:
information technology, phytocenoses monitoring, Earth remote sensing, geoinformation system, reference polygons, classification.

Citation:
Sergeyev VV, Bavrina AY, Kavelenova LM, Bogdanova YA, Ryazanova YA. New information technology for phytocenoses regional monitoring using remote sensing data. Computer Optics 2026; 50(2): 1822. DOI: 10.18287/COJ1822.

References:

  1. Ecosystem services of Russia: Prototype National Report. Vol. 2. Biodiversity and Ecosystem Services: Accounting Principles in Russia. Moscow: BCC Press; 2020. ISBN: 978-5-93699-107-3.
  2. Fajardo T, Campins Eritja M. Biological diversity and international law. Challenges for the post 2020 scenario. Springer; 2021. ISBN: 978-3-030-72961-5.
  3. On approval of the biodiversity conservation strategy of the Samara Region for the period up to 2030 [In Russian]. Decree of the Government of the Samara Region, No. 596 dated 20.08.2021. Samara; 2021.
  4. Kavelenova L, Prokhorova N, Fedoseyev V. On the experience of field monitoring and remote sensing technologies integration in regional phytodiversity conservation. E3S Web Conf 2023; 419: 02013. DOI: 10.1051/e3sconf/202341902013.
  5. Schowengerdt RA. Remote sensing: models and methods for image processing. London: Academic Press; 2006. ISBN: 978-0-12-369407-2.
  6. Waśniewski A, Hościło A, Aune-Lundberg L. The impact of selection of reference samples and DEM on the accuracy of land cover classification based on Sentinel-2 data. Remote Sens Appl: Soc Environ 2023; 32: 101035. DOI: 10.1016/j.rsase.2023.101035.
  7. Tuia D, Pasolli E, Emery WJ. Using active learning to adapt remote sensing image classifiers. Remote Sens Environ 2011; 115: 2232-2242. DOI: 10.1016/j.rse.2011.04.022.
  8. Field methods for vegetation mapping. USGS/NPS Vegetation mapping program. The Nature Conservancy (TNC), Environmental Systems Research Institute (ESRI), USA; 1994.
  9. Nikiforenko YY, Melnik OA. Methods of environmental research: a tutorial [In Russian]. Krasnodar: "KubSAU" Publisher; 2022. ISBN: 978-5-907550-36-0.
  10. Pengra BW, Stehman SV, Horton JA, Dockter DJ, Schroeder TA, Yang Z, Cohen WB, Healey SP, Loveland TR. Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program. Remote Sens Environ 2020; 238: 111261. DOI: 10.1016/j.rse.2019.111261.
  11. Malinowski R, Lewiński S, Rybicki M, Gromny E, Jenerowicz M, Krupiński M, Nowakowski A, Wojtkowski C, Krupiński M, Krätzschmar E, Schauer P. Automated production of a land cover/use map of Europe based on Sentinel-2 imagery. Remote Sens 2020; 12(21): 3523. DOI: 10.3390/rs12213523.
  12. Bykova D, Denisova A, Fedoseev V, Korchikov E. Methods for updating forest inventory data through multi-temporal Sentinel-2 image analysis. In: Bajaj A, Abraham A, Reddy Madhavi K, Castillo O, eds. Proceedings of the 15th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2023). Volume 4: Real World Applications. Cham, Switzerland: Springer Nature Switzerland AG; 2025: 258-266. DOI: 10.1007/978-3-031-81086-2_29.
  13. Wu T, Luo J, Gao L, Sun Y, Dong W, Zhou Y, Liu W, Hu X, Xi J, Wang C, Yang Y. Geo-object-based vegetation mapping via machine learning methods with an intelligent sample collection scheme: a case study of Taibai mountain, China. Remote Sensing 2021; 13(2): 249. DOI: 10.3390/rs13020249.
  14. Soifer VA, ed. Computer image processing, Part II: Methods and algorithms. Saarbrücken: VDM Verlag Dr. Müller; 2010. ISBN: 978-3-639-17545-5.
  15. Sergeyev VV, Bavrina AY, Zaitsev ID, Lazutov MY, Shapiro DA. On estimating the local entropy of an image in a sliding window. Computer Optics 2024; 48(5): 714-725. DOI: 10.18287/2412-6179-CO-1509.
  16. Pazur R, Prishchepov A, Mjachina K, Verburg P, Levykin S, Ponkina E, Kazachkov G, Yakovlev I, Akhmetov R, Rogova N, Bürgi M. Restoring steppe landscapes: patterns, drivers and implications in Russia's steppes. Landsc Ecol 2021; 36(2): 407-425. DOI: 10.1007/s10980-020-01174-7.
  17. Komisarenko V, Voormansik K, El Shawi R, Sakr Sh. Exploiting time series of Sentinel-1 and Sentinel-2 to detect grassland mowing events using deep learning with reject region. Sci Rep 2022; 12(1): 983. DOI: 10.1038/s41598-022-04932-6.
  18. Fukunaga K, Olsen DR. An algorithm for finding intrinsic dimensionality of data. IEEE Trans Comput 1971; C-20(2): 176-183. DOI: 10.1109/T-C.1971.223208.
  19. Bavrina A, Kuzovenko O, Ryazanova Y. AI for selecting of reference polygons for remote sensing monitoring of natural plant communities in the Samara Region, Russia. In: Márquez FPG, Hameed AA, Jamil A, eds. Pattern Recognition and Artificial Intelligence: Selected papers from the 6th Mediterranean Conference on Pattern Recognition and Artificial Intelligence (MedPRAI24). Cham, Switzerland: Springer Nature Switzerland AG; 2025. ISBN: 978-3-031-90892-7.
  20. Wald A. Sequential analysis. Mineola, New York: Dover Publications Inc; 1973. ISBN: 978-048661579-0.
  21. Gorodetskaya LA, Denisova AY, Kavelenova LM, Fedoseev VA. Rare plants detection using YOLO neural network. Computer Optics 2024; 48(3): 397-405. DOI: 10.18287/2412-6179-CO-1405.
  22. Bogdanova Y, Kavelenova L, Khalikova L. To the possibilities of combining ground-based and remote monitoring of secondary ecosystems development during overgrowth of fallow lands with the participation of Elaeagnus angustifolia [In Russian]. Proc XII Int Sci Conf "Regional Problems of Remote Sensing of the Earth" (RPRSZ-2024) 2024: 294-297.
  23. Kuzovenko OA, Ryazanova YA, Prokhorova NV. Specially protected natural area "Sestrinskie okamenelosti" – a promising reference polygon for remote identification of valuable steppe ecosystems [In Russian]. Samara Journal of Science 2023; 12(1): 57-63. DOI: 10.55355/snv2023121109.

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