MONITORING EUTROPHICATION OF WATER BODIES USING GEOINFORMATION TECHNOLOGIES: A CASE STUDY OF THE KARACHUNIVSKE RESERVOIR
Abstract
This paper proposes a methodology for monitoring eutrophication of water bodies using geoinformation technologies. The approach is based on the analysis of satellite multispectral indices, including NDCI, FAI, and NDTI, which respectively reflect chlorophyll-a concentration in the water column, the presence of surface phytoplankton accumulations, and turbidity levels. The study was conducted using the Karachunivske Reservoir as a case study. A spatiotemporal analysis of index variations was performed, cartographic models of their distribution were developed, and the features of their dynamics at different stages of eutrophication were identified. It was found that the NDCI index characterizes the diffuse development of phytoplankton within the water column, whereas FAI reflects the formation of surface accumulations with clear spatial localization, primarily in coastal and low-flow areas. It is shown that the highest spatial correspondence between the indices occurs during the peak stage of eutrophication, when a continuous surface biomass layer is formed. At the same time, the NDTI index demonstrates a relatively stable spatial pattern determined by hydrodynamic and morphological characteristics of the reservoir and is less sensitive to biological processes. A spatial correlation analysis revealed an unstable relationship between NDCI and FAI, with a sharp increase during peak eutrophication, as well as a stable negative relationship between indices representing organic and mineral components of turbidity. Based on the obtained results, an approach to identifying eutrophication stages through the integration of spatial characteristics and inter-index relationships is substantiated. The proposed methodology enhances the informativeness of remote monitoring and can be applied for assessing the ecological condition of water bodies and supporting environmental management decision-making.
References
2. Глух О.С., Заяць Я.Р., Симканич О.І., Мільович М.І., Шварц Р.Р. Молнар-Бабіля Д.І. (2025). Використання даних GOOGLE EARTH ENGINE для моніторингу евтрофікаційних процесів у водосховищах Закарпаття. Наук. вісник Ужгород. ун-ту (Сер. Хімія), № 2 (54), 120–125. [Hlukh O.S., Zaiats Ya.R., Symkanych O.I., Milovych M.I., Shvarts R.R., Molnar-Babilia D.I. (2025). Use of Google Earth Engine data for monitoring eutrophication processes in the reservoirs of Transcarpathia. Scientific Bulletin of Uzhhorod University (Series: Chemistry), No. 2 (54), 120–125. (In Ukrainian)]. DOI: 10.24144/2414-0260.2025.2.120-126
3. Глухота В., Шевчук С. (2023). Дослідження процесу цвітіння води в Кам'янському водосховищі з використанням методів ГІС і ДЗЗ. Вісник Київського національного університету імені Тараса Шевченка. Географія. 1/2(86/87), 56-59. [Hlukhota V., Shevchuk S. (2023). Study of water bloom processes in the Kamianske Reservoir using GIS and remote sensing methods. Bulletin of Taras Shevchenko National University of Kyiv. Geography, 1/2(86/87), 56–59. (In Ukrainian)]. DOI: 10.17721/1728-2721.2023.87.2.
4. Томченко О. В., Підгородецька Л. В., Федоровський О. Д. (2013). Комплексна оцінка екологічного стану водойм на основі космічної інформації дистанційного зондування Землі (на прикладі оз. Світязь та верхів'я Київського водосховища). Гідроакустичний журнал (Проблеми, методи та засоби досліджень Світового океану). № 10. 111–117. [Tomchenko O. V., Pidhorodetska L. V., Fedorovskyi O. D. (2013). Comprehensive assessment of the ecological state of water bodies based on Earth remote sensing data (a case study of Lake Svityaz and the upper reaches of the Kyiv Reservoir). Hydroacoustic Journal (Problems, Methods and Tools for Studying the World Ocean). V.10. 111–117. (In Ukrainian)].
5. Холошин І.В., Бондаренко О.В., Ганчук О.В., Мантуленко С.В., ,Варфоломєєва І.М. (2025). Геопросторовий аналіз у географічних дослідженнях. Прага: Oktan Print, 243 с. [Kholoshyn I. V., Bondarenko O. V., Hanchuk O. V., Mantulenko S. V., Varfolomieieva I. M. (2025). Geospatial Analysis in Geographic Research. Prague: Oktan Print, 243 p. (In Ukrainian)]. DOI 10.46489/GAUGD-25-38.
6. Шевчук С. А., Вишневський В. І., Шевченко І. А. (2014). Використання даних ДЗЗ для встановлення екологічного стану дніпровських водосховищ. Праці Центральної геофізичної обсерваторії. Центральна геофізична лабораторія. Вип. 10 (24). 72–78. [Shevchuk S. A., Vyshnevskyi V. I., Shevchenko I. A. (2014). Use of remote sensing data for assessing the ecological state of the Dnipro reservoirs. Proceedings of the Central Geophysical Observatory. Central Geophysical Laboratory, Issue 10 (24), 72–78. (In Ukrainian)].
7. Янко Н. В., Станкевич В. В., Коваль Н. М. (2014). Антропогенна евтрофікація Шацьких озер. Довкілля та здоров'я.№ 3, 51−55. [Yanko N. V., Stankevych V. V., Koval N. M. (2014). Anthropogenic eutrophication of the Shatsk Lakes. Environment and Health, No. 3, 51–55. . (In Ukrainian)].
8. Dogliotti A.I., Ruddick K., Nechad B., et al. (2015). A single algorithm to retrieve turbidity from remotely sensed data. Remote Sensing of Environment. Vol. 156. P. 157–168. https://doi.org/10.1016/j.rse.2014.09.020
9. Kokolakis S., Kokinou E., Karagiannidou M., Chronaki C. (2025). Remote Sensing of Urban Stream Eutrophication and Its Implications for Public Health. Global Healthcare Transformation in the Era of Artificial Intelligence and Informatics. DOI: 10.3233/SHTI250762/.
10. Mishra K., Choudhary B., Fitzsimmons K.E. (2024). Predicting seasonal water turbidity using remote sensing and GIS. Frontiers in Environmental Science. Vol. 12. https://doi.org/10.3389/fenvs.2024.1371759.
11. Moses, Wesley & Gitelson, Anatoly & Berdnikov, Sergey & Povazhnyi, Vasiliy. (2009). Satellite Estimation of Chlorophyll– a Concentration Using the Red and NIR Bands of MERIS − The Azov Sea Case Study. Geoscience and Remote Sensing Letters, IEEE. 6. 845−849. https://doi.org/10.1109/LGRS.2009.2026657.
12. Pahlevan N., Smith B., Schalles J., et al. (2020). Seamless retrievals of chlorophyll-a from Sentinel-2 Remote Sensing of Environment. Vol. 240. doi.org/10.1016/j.rse.2019.111604.
13. Page, B.P.; Olmanson, L.G.; Mishra, D. R., (2019) A harmonized image processing workflow using Sentinel-2/MSI and Landsat-8/OLI for mapping water clarity in optically variable lake systems. Remote Sens. Environ, 231, 111284.
14. Ruuhulhaq M. S., Setianingrum, S. (2025). Utilization of Sentinel-2 Imagery for Water Quality Analysis: A Case Study of Saguling Reservoir. International Journal for Disaster and Development Interface. 5(2), 83–95. https://doi.org/10.53824/ijddi.v5i2.71.
15. Schaeffer B.A., Schaeffer K.G., Keith D., Lunetta R.S., Conmy R., Gould R.W. (2018). Barriers to adopting satellite remote sensing for water quality monitoring Remote Sensing of Environment. Vol. 216. P. 618–628.
16. Wang X., Chen Y., Yuan Q., Xing X., Hu B., Gan J., Zheng Y., Liu Y. (2022). Effect of river damming on nutrient transport and transformation and its countermeasures. Front. Mar. Sci. 9. https://doi.org/10.3389/fmars.2022.1078216.
17. Wetzel R.G. (2001) Limnology: Lake and River Ecosystems. San Diego: Academic Press, 1006 p.
18. Yaqi Zhao, Xianqiang He, Shuping Pan, Yan Bai, Difeng Wang, Teng Li, Fang Gong, Xuan Zhang (2024). Satellite retrievals of water quality for diverse inland waters from Sentinel-2 images: An example from Zhejiang Province, China. International Journal of Applied Earth Observation and Geoinformation. V. 132. https://doi.org/10.1016/j.jag.2024.104048.
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