Methodology for discovery of aquatic ecosystems primary biosynthesis intensity within-year dynamics on the base of the Earth remote sensing

DOI: 10.35595/2414-9179-2025-2-31-245-258

View or download the article (Rus)

About the Authors

Victor Yu. Tretyakov

Saint Petersburg State University,
7/9, Universitetskaya emb., St. Petersburg, 199034, Russia,
E-mail: v.tretiyakov@spbu.ru

Vasiliy V. Dmitriev

Saint Petersburg State University,
7/9, Universitetskaya emb., St. Petersburg, 199034, Russia,
E-mail: v.dmitriev@spbu.ru

Stepan M. Klubov

State Budgetary Institution of Additional Education, Palace of Child Youth Art “At the Voznesensky Bridge” of the Admiralteyskiy District,
26, Grazhdanskaya str., St. Petersburg, 190031, Russia,
E-mail: klubov_stepan@mail.ru

Abstract

Aquatic ecosystems are characterized by the spotty spatial distribution of phytoplankton and the quick variance of its specific biomass and the primary production intensity throughout the year, and especially for the vegetation period. Therefore, it is easy during natural research to miss zones of the phytoplankton’s increased specific biomass and the periods of its intensive development, so-called “blooming”, because the periods can last only a few days. Because of this reason, it can oftentimes be difficult to evaluate the productivity of an aquatic ecosystem, its trophic state, degree of steadiness to alteration of the natural regime parameters, and anthropogenic impacts. There is considered the proprietary methodology for research of within-year dynamics of an aquatic ecosystem primary biosynthesis intensity by analysis of temporal variability of the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Turbidity Index (NDTI). The indices are evaluated by satellite data from Landsat Program. The raster layers of the satellite data can in full occupy researched water areas. The data frequency is equal to a few times per a month. Results of the spatial and temporal analysis of the indices essentially fill up data of phytoplankton monitoring within an aquatic ecosystem and reflect the phytoplankton spatial distribution. The range of the NDVI variation reflects the range of the phytoplankton primary biosynthesis intensity. The temporal dynamics of average, minimal, and maximal values of the index for all the researched water area as a whole reflect specificity of the phytoplankton development for the vegetation period, number of the phytoplankton intensive growth periods (algal blooms), and temporal localization and duration of the periods. Research of the NDVI values spatial variability allows ascertaining of a water area objective partition into ecological zones. Owing to parallel research of spatial and temporal variability of the NDVI and NDTI indices, and the correlation relationship between them, we can reveal the water turbidity influence on the primary biosynthesis and designate reasons for the turbidity alterations. A researcher obtains an opportunity of response for a question about the turbidity reason. It can be terrigenous suspended matter, particles of the bottom sediments, or plant pollen. In addition, it can take place the phytoplankton occultation by its cells, so-called self-shadowing. The presented methodology was applied for research of the Suuri Lake ecosystem. The lake is situated within the north part of the Karelian Isthmus, inside the area of the Training and Scientific Facility “Priladozhskaya” of St. Petersburg State University.

Keywords

phytoplankton biosynthesis intensity, Earth remote sensing, NDVI, NDTI

References

  1. Alexandrov S.V., Bukanova T.V., Semenova A.S. Dynamics of Bioproductivity of Ecosystems of the Baltic Sea Lagoons Under the Influence of Climate Change, Eutrophication and “Blooms” of Cyanobacteria. Dynamics of Ecosystems in the Holocene. Proceedings of the Scientific and Practical Conference, 2022. P. 20–25 (in Russian).
  2. Bashirova Ch.F. Index NDVI for Vegetation Remote Monitoring. Young Scientist, 2019. No. 31 (269). P. 30–31. Web resource: https://help.onesoil.ai/ru/articles/5237493-как-отслеживать-индексы-вегетации-ndvi-msavi-ndre-и-др (accessed 04.03.2025) (in Russian).
  3. Blaauboer M.C.T. The Phytoplankton Species Composition and the  easonal Periodicity in Lake Vechten from 1956–1979. Hydrobiologia, 1982. No. 95. P. 25–36.
  4. Gamier J., Billen G., Coste M. Seasonal Succession of Diatoms and Chlorophyceae in the Drainage Network of the Seine River: Observations and Modeling. Limnology and Oceanography, 1995. No. 40. P. 750–765.
  5. Jaworska B., Dunalska J., Górniak D. Bowszys M. Phytoplankton Dominance Structure and Abundance as Indicators of the Trophic State and Ecological Status of Lake Kortowskie (Northeast Poland) Restored with Selective Hypolimnetic Withdrawal. Archives of Polish Fisheries, 2014. No. 22. P. 7–15. DOI: 10.2478/aopf-2014-0002.
  6. Kudryavtseva E., Bukanova T., Kostianoy A., Melnik A., Alexandrov S. Krek A., Kanapatskiy T., Rusanov I., Ezhova E. Influence of Circulation Processes on Cyanobacteria Bloom and Phytoplankton Succession in the Baltic Sea Coastal Area. Ecologica Montenegrina, 2023. No. 70. P. 164–182. DOI: 10.37828/em.2023.70.18.
  7. Li Z., Wu H., Duan S., Zhao W., Ren H., Liu X., Leng P., Tang R., Ye X., Zhu J., Sun Y., Si M., Liu M., Li J., Zhang X., Shang G., Tang B., Yan G., Zhou C. Satellite Remote Sensing of Global Land Surface Temperature: Definition, Methods, Products, and Applications. Reviews of Geophysics, 2023. No. 61 (1). P. 1–18. DOI: 10.1029/2022RG000777.
  8. Lyashenko O.A., Radchenko A.P., Susloparova O.N. Monitoring of Phytoplankton Status in the Luga Bay of the Gulf of Finland Under Natural and Anthropogenic Influence. Trudy VNIRO. Habitat of Aquatic Biological Resources, 2020. V. 179. P. 149–163 (in Russian).
  9. Makarevich P., Druzhkova E., Larionov V. Primary Producers of the Barents Sea. Diversity of Ecosystems, 2012. P. 367–392. DOI: 10.5772/37512.
  10. Munawar M., Talling J.F. Seasonality of Freshwater Phytoplankton: A Global Perspective. Boston: Springer, 1986. 236 p.
  11. Poppeschi C., Charria G., Daniel A., Verney R., Retho M., Goberville E., Grossteffan E., Plus M. Interannual Variability of the Initiation of the Phytoplankton Growing Period in Two French Coastal Ecosystems. Biogeosciences Discussions, 2022. P. 1–2. DOI: 10.5194/bg-2022-86.
  12. Radchenko I., Aksenova V., Voronov D., Rostanets D., Krasnova E. Annual Dynamics of a Layered Phytoplankton Structure in a Meromictic Lagoon Partially Isolated from the White Sea. Diversity, 2023. V. 15. No. 1009. P. 1–28. DOI: 10.3390/d15091009.
  13. Rahuba A.V. Hydroecological Studies of Water Bodies Using the “Chiton-Wave” Measuring and Computing System. Information and Computing Technologies (ICT) and Their Applications. Penza: RIO PGSKHA, 2012. P. 64–68 (in Russian).
  14. Sommer U., Gliwicz Z., Lampert W., Duncan A. The PEG-Model of Seasonal Succession of Planktonic Events in Fresh Waters. Archiv Fur Hydrobiologie, 1986. No. 106. P. 433–471. DOI: 003-9136/86/0106-0433.
  15. Titlyanova A.A. Methodology and Methods for Evaluation of Net Primary Production and Building a Balance of Chemical Elements in Ecosystems. Theoretical Foundation and Experience of Ecological Monitoring. Moscow: Nauka, 1983. P. 63–76 (in Russian).
  16. Wang Q., Moreno-Martínez Á., Muñoz-Marí J., Campos-Taberner M., Camps-Valls G. Estimation of Vegetation Traits with Kernel NDVI. ISPRS Journal of Photogrammetry and Remote Sensing, 2023. V. 195. P. 408–417. DOI: 10.1016/j.isprsjprs.2022.12.019.
  17. Wang S., Zhang L., Huang C., Qiao N. An NDVI-Based Vegetation Phenology is Improved to be More Consistent with Photosynthesis Dynamics Through Applying a Light Use Efficiency Model Over Boreal High-Latitude Forests. Remote Sensing, 2017. No. 9 (695). DOI: 10.3390/rs9070695.
  18. Winder M., Cloern J. The Annual Cycles of Phytoplankton Biomass. Philosophical Transactions of the Royal Society of London. Series B. Biological Sciences, 2010. No. 365. P. 3215–3226. DOI: 10.1098/rstb.2010.0125.

For citation: Tretyakov V.Yu., Dmitriev V.V., Klubov S.M. Methodology for discovery of aquatic ecosystems primary biosynthesis intensity within-year dynamics on the base of the Earth remote sensing. InterCarto. InterGIS. Moscow: MSU, Faculty of Geography, 2025. V. 31. Part 2. P. 245–258. DOI: 10.35595/2414-9179-2025-2-31-245-258 (in Russian)