The use of multispectral images to assess lake overgrowth

DOI: 10.35595/2414-9179-2025-2-31-274-286

View or download the article (Rus)

About the Authors

Asel B. Alexandrova

Institute for Environmental Problems and Subsoil Use of the Academy of Sciences of the Republic of Tatarstan,
28, Daurskaya str., Kazan, 420087, Russia,

Kazan (Volga Region) Federal University,
18, Kremlyovskaya str., Kazan, 420008, Russia,

E-mail: adabl@mail.ru

Irek I. Ziganshin

Institute for Environmental Problems and Subsoil Use of the Academy of Sciences of the Republic of Tatarstan,
28, Daurskaya str., Kazan, 420087, Russia,

Kazan (Volga Region) Federal University,
18, Kremlyovskaya str., Kazan, 420008, Russia,

E-mail: irek_ziganshin@mail.ru

Rustam R. Khasanov

Institute for Environmental Problems and Subsoil Use of the Academy of Sciences of the Republic of Tatarstan,
28, Daurskaya str., Kazan, 420087, Russia,
E-mail: rustamkhasanov88@gmail.com

Stanislav S. Ryazanov

Institute for Environmental Problems and Subsoil Use of the Academy of Sciences of the Republic of Tatarstan,
28, Daurskaya str., Kazan, 420087, Russia,
E-mail: rstanislav.soil@yandex.ru

Almaz M. Sabirzyanov

Kazan (Volga Region) Federal University,
18, Kremlyovskaya str., Kazan, 420008, Russia,
E-mail: sabiralmaz@mail.ru

Elena M. Pudovik

Kazan (Volga Region) Federal University,
18, Kremlyovskaya str., Kazan, 420008, Russia,
E-mail: epudovic@mail.ru

Abstract

The study is devoted to the analysis of overgrowing of 12 lakes in the protected area of Laishevsky Municipal District of the Republic of Tatarstan for 2003–2023 using geoinformation technologies and spectral indices (NDVI, NDWI, MNDWI, WRI) based on Sentinel-2A satellite images. It was found that the overgrowing rate varies from 0.03 to 0.7 ha per year, and its area reaches up to 83 %. The greatest changes were noted at the lakes Freshie, Stolbishche, Chistoe and Sapugoli, which is associated with anthropogenic stress. Overgrowth diagnostics is most effective when using the NDVI, NDWI and MNDWI vegetation indices, while WRI showed low accuracy for small lakes with a water surface area less than 1 ha. For a large lake (area of more 0.7 km2) with overgrowth of less than 10 %, the indicator error is not 2 %, which proves the reliability of observing the specified indices when conducting lake overgrowth. For small lakes with an area less than 0.2 km2 and overgrowth of more than 20 % of the area, the error reaches 10–21 %, which requires supplementing with Google Earth Pro data. It is recommended to use NDVI, NDWI and MNDWI indices for large lakes; and to use a combination of methods, including manual digitization, for small lakes with a high degree of overgrowth. The results show the need to adapt remote monitoring methods depending on the size and anthropogenic load on water bodies.

Keywords

vegetation indices, overgrowth, lake, remote sensing of the Earth, satellite images

References

  1. Aleksandrova A.B. Space Technologies in Assessing the State of Water Resources of Regions. Kazan: Kazan (Volga Region) Federal University, 2025. 111 p. (in Russian).
  2. Ermolaev O.P., Igonin M.E., Bubnov A.Yu., Pavlova S.V. Landscapes of the Republic of Tatarstan: Regional Landscape-Ecological Analysis. Kazan: Slovo, 2007. 411 p. (in Russian).
  3. Flerko T.G., Valova Z.G., Pavlovsky A.I. Hydrology of Lakes and Reservoirs: Terminological Dictionary. Gomel: Francysk Skaryna Gomel State University, 2015. 44 p. (in Russian).
  4. Kataev M.Yu., Bekerov A.A. Methodology for Detecting Water Bodies Using Multispectral Satellite Measurements. Proceedings of TUSUR University, 2017. V. 20. No. 4. P. 105–108 (in Russian). DOI: 10.21293/1818-0442-2017-20-4-105-108.
  5. Morozova V.A. Calculation of Indices for Identification and Analysis of Characteristics of Water Bodies Using Remote Sensing Data. Current Problems of Territorial Development, 2019. No. 2. Web resource: https://www.terjournal.ru (accessed 03.04.2025) (in Russian).
  6. Nikitin O.V., Stepanova N.Yu., Kondratieva T.A., Kuzmin R.S., Latypova V.Z. Spatio-Temporal Dynamics of Phytoplankton “Blooming” in the Kuibyshev Reservoir Based on Satellite Sensing Data. Current Problems in Remote Sensing of the Earth from Space, 2024. V. 21. No. 6. P. 284–293 (in Russian). DOI: 10.21046/2070-7401-2024-21-6-284-293.
  7. Perevedentsev Yu.P., Sherstyukov B.G., Naumov E.P., Vereshchagin M.A., Shantalinsky K.M. Climatic Conditions and Resources of the Republic of Tatarstan. Kazan: Publishing House of Kazan (Volga Region) Federal University, 2008. 288 p. (in Russian).
  8. Sabirzyanov A., Panasyuk M., Trofimov N., Sochneva S. GIS-technology and Data of Earth Remote Sensing to Identify and Predict Ravine Erosion Development. Bio Web of Conferences. International Scientific and Practical Conference “Agriculture and Food Security: Technology, Innovation, Markets, Human Resources” (FIES 2020). EDP Sciences, 2020. P. 00113.
  9. Vasiliev K.K., Dementiev V.E. Presentation and Processing of Satellite Multispectral Images. Ulyanovsk: Ulyanovsk State Technical University, 2017. 254 p. (in Russian).
  10. Ziganshin I.I., Ivanov D.V., Khasanov R.R. Analysis of the Dynamics of Morphometric Indicators of Lakes-Natural Monuments in the Republic of Tatarstan. Russian Journal of Applied Ecology, 2018. No. 2. P. 17–20 (in Russian).

For citation: Alexandrova A.B., Ziganshin I.I., Khasanov R.R., Ryazanov S.S., Sabirzyanov A.M., Pudovik E.M. The use of multispectral images to assess lake overgrowth. InterCarto. InterGIS. Moscow: MSU, Faculty of Geography, 2025. V. 31. Part 2. P. 274–286. DOI: 10.35595/2414-9179-2025-2-31-274-286 (in Russian)