Water Quality Monitoring in Small and Medium-Scale Lakes of Sri Lanka Using UAV Multispectral Images
DOI:
https://doi.org/10.52151/jae2025624.1976Keywords:
lake water, nitrate, phosphate, total suspended solids, UAV-based multispectral imaging, water qualityAbstract
Monitoring water quality is essential for sustainable aquatic ecosystems management, and Unmanned Aerial Vehicles (UAVs) equipped with multispectral sensors offer an efficient, high-resolution alternative to traditional methods. This study evaluated UAV-based multispectral imagery for assessing nutrients, e.g. nitrate and phosphate and total suspended solids (TSS) in two Sri Lankan lakes, i.e., Mapalana Lake (small-scale), which is located at the Faculty of Agriculture, University of Ruhuna and Wilpita Lake (medium-scale), which is located in Akurassa, Matara. A total of 17 water samples were collected from both lakes during January 2024 and May 2024 and the collected samples were analyzed in the laboratory, while the UAV images were captured at 40 m altitude from the ground surface over 0.4-0.5 ha area of each lake. The UAV images were processed to generate index maps, including Normalized Difference Water Index (NDWI), Non-Linear Water Index (NLWI), Enhanced Water Index (EWI), Re-normalized Difference Water Index (RDWI), Red-Edge Normalized Difference Water Index (RENDWI), and Natural Logarithm Water Index (LWI). Among these, RENDWI was the best predictor for phosphate, with coefficient of determination (R²) values of 0.84 for Mapalana Lake and 0.92 for Wilpita Lake. The NDWI showed the best performance for nitrate monitoring, with R² values of 0.45 (Mapalana Lake) and 0.83 (Wilpita Lake) at two sites. In contrast, all other indices revealed poor correlations with TSS, indicating poor prediction of the suspended solids. These findings demonstrated that UAV-based multispectral imaging is a promising approach for rapid and accurate monitoring of the key nutrients in hydrological systems. Future work should focus on developing improved or novel indices to enhance prediction accuracy and extend applicability to a wider range of water quality parameters.
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References
Abu Sari, M. Y., Mohmad Hassim, Y. M., Hidayat, R., & Ahmad, A. (2021). Monitoring rice crop and paddy field condition using UAV RGB imagery. JOIV: International Journal on Informatics Visualization, 5(4), 469–474. https://doi.org/10.30630/joiv.5.4.742. DOI: https://doi.org/10.30630/joiv.5.4.742
Adjovu, G. E., Stephen, H., James, D., & Ahmad, S. (2023). Measurement of total dissolved solids and total suspended solids in water systems: A review of the issues, conventional, and remote sensing techniques. Remote Sensing, 15(14), 3534. https://doi.org/10.3390/rs15143534 DOI: https://doi.org/10.3390/rs15143534
APHA. (1998). Standard Methods for the Examination of Water and Wastewater (20th ed., pp. 10–161). American Public Health Association (AHA), American Water Works Association, and Water Environment Federation, Washington DC.
Asrori, M. R., Margaretha, M. A. H., Khoirunnisa, S., Suharti, S., & Wonorahardjo, S. (2025). Quantitative analysis of total solid of a river water using gravimetric method by paper tissue. Journal of Research and Education, 3(3), 01–04. https://doi.org/10.33140/JRE DOI: https://doi.org/10.21203/rs.3.rs-7267978/v1
Batina, A., & Krtalić, A. (2024). Integrating remote sensing methods for monitoring lake water quality: A comprehensive review. Hydrology, 11(7), 92. https://doi.org/10.3390/hydrology11070092 DOI: https://doi.org/10.3390/hydrology11070092
Chandrasekara, S. S. K., Pathmarajah, S., Obeysekera, J., & Vithanage, M. (2023). Water scarcity – a concealed phenomenon in Sri Lanka: A mini review. ACS ES&T Water, 3(6), 1454-1462. https://doi.org/10.1021/acsestwater.2c00398. DOI: https://doi.org/10.1021/acsestwater.2c00398
Chen, B., Mu, X., Chen, P., Wang, B., Choi, J., Park, H., Xu, S., Wu, Y., & Yang, H. (2021). Machine learning-based inversion of water quality parameters in a typical reach of an urban river using UAV multispectral data. Ecological Indicators, 133, 108434. https://doi.org/10.1016/j.ecolind.2021.108434 DOI: https://doi.org/10.1016/j.ecolind.2021.108434
Fitz, D., Buske, I., Walther, A., & Acosta, J. (2022). Multispectral optical signatures of micro UAV: acquisition and database for usage in a neural network. In J. Dijk (Ed.), Artificial Intelligence and Machine Learning in Defense Applications IV (p. 19). SPIE. https://doi.org/10.1117/12.2636145 DOI: https://doi.org/10.1117/12.2636145
Habibah, N., Dhyanaputri, I. G. A. S., Karta, I. W., Sundari, C. D. W. H., & Hadi, M. C. (2018). A simple spectrophotometric method for the quantitative analysis of phosphate in water samples. Jurnal Sains dan Teknologi, 7(2), 198–204. https://doi.org/10.23887/jstundiksha.v7i2.13940 DOI: https://doi.org/10.23887/jst-undiksha.v7i2.13940
Lakshmi, G. J., Bodapati, V., Baji, S. S., & Panuganti, T. S. (2023). Water quality monitoring using remote-sensing. 2023 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES), 624–628. https://doi.org/10.1109/CISES58720.2023.10183395 DOI: https://doi.org/10.1109/CISES58720.2023.10183395
Liu, H., Miliutina, E., Su, H., Beck, R., Shu, S., Lu, Y., Xu, M., Henry, J., Wang, L., & Cohen, S. (2024a). Spatial transferability and temporal repeatability of water quality remote sensing inversion models for inland lakes and rivers. IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, 5060–5062. https://doi.org/10.1109/IGARSS53475.2024.10642570 DOI: https://doi.org/10.1109/IGARSS53475.2024.10642570
Liu, X., Wang, Y., Chen, T., Gu, X., Zhang, L., Li, X., Tang, R., He, Y., Chen, G., & Zhang, B. (2024b). Monitoring water quality parameters of freshwater aquaculture ponds using UAV-based multispectral images. Ecological Indicators, 167, 112644. https://doi.org/10.1016/j.ecolind.2024.112644 DOI: https://doi.org/10.1016/j.ecolind.2024.112644
Logan, R. D., Torrey, M. A., Feijó-Lima, R., Colman, B. P., Valett, H. M., & Shaw, J. A. (2023). UAV-based hyperspectral imaging for river algae pigment estimation. Remote Sensing, 15(12), 3148. https://doi.org/10.3390/rs15123148. DOI: https://doi.org/10.3390/rs15123148
Meegahakumbura, S. P. S., & Nanayakkara, N. (2023). Assessment of groundwater quality in Vavuniya and Mullaitivu, Sri Lanka using multivariate statistical techniques and a Water Quality Index. Water Supply, 23(2), 867-883. https://doi.org/10.2166/ws.2023.028. DOI: https://doi.org/10.2166/ws.2023.028
Novković, M., Cvijanović, D., Mesaros, M., Pavić, D., Drešković, N., Milošević, D., Anđelković, A., Damnjanović, B., & Radulović, S. (2023). Towards UAV assisted monitoring of aquatic vegetation within large rivers – the middle Danube (Serbia). Carpathian Journal of Earth and Environmental Sciences, 18(2), 307-322. https://doi.org/10.26471/cjees/2023/018/261. DOI: https://doi.org/10.26471/cjees/2023/018/261
Oki, T., & Kim, H. (2016). Macroscale Hydrological Modeling and Global Water Balance. In: Q. Tang, & T. Oki (Eds), Terrestrial Water Cycle: Natural and Human-Induced Changes (pp. 3-16). American Geophysical Union and John Wiley & Sons, Inc., Hoboken, New Jersey. https://doi.org/10.1002/9781118971772.CH1. DOI: https://doi.org/10.1002/9781118971772.ch1
Pix4D. (n.d.). Professional photogrammetry and drone mapping. Retrieved from https://www.pix4d.com/
Roman, A., Tovar Sánchez, A., Gauci, A., Deidun, A., Caballero, I., Colica, E., D’Amico, S., Heredia, S., & Navarro, G. (2023a). High resolution UAV multispectral imagery for water quality monitoring in coastal regions. EGU General Assembly 2023, EGU23 8599. https://doi.org/10.5194/egusphere-egu23-8599 DOI: https://doi.org/10.5194/egusphere-egu23-8599
Román, A., Tovar-Sánchez, A., Gauci, A., Deidun, A., Caballero, I., Colica, E., D’Amico, S., & Navarro, G. (2023b). Water-quality monitoring with a UAV-mounted multispectral camera in coastal waters. Remote Sensing, 15(1), 237. https://doi.org/10.3390/rs15010237 DOI: https://doi.org/10.3390/rs15010237
Wei, L., Wang, Z., Huang, C., Zhang, Y., Wang, Z., Xia, H., & Cao, L. (2020). Transparency estimation of narrow rivers by UAV-borne hyperspectral remote sensing imagery. IEEE Access, 8, 168137–168153. https://doi.org/10.1109/ACCESS.2020.3023690 DOI: https://doi.org/10.1109/ACCESS.2020.3023690
Wu, H., Guo, Q., Zang, J., Qiao, Y., Zhu, L., & He, Y. (2021). Research on water quality parameter inversion based on Landsat 8 ad measured data. Remote Sensing Technology and Application, 36(4), 898–907. https://doi.org/10.11873/j.issn.1004-0323.2021.4.0898
Xiao, Y., Guo, Y., Yin, G., Zhang, X., Shi, Y., Hao, F., & Fu, Y. (2022). UAV multispectral image-based urban river water quality monitoring using stacked ensemble machine learning algorithms—A case study of the Zhanghe river, China. Remote Sensing, 14(14), 3272. https://doi.org/10.3390/rs14143272. DOI: https://doi.org/10.3390/rs14143272
Xu, H. (2021). Research progress on remote sensing index of water bodies. Journal of Fuzhou University (Natural Science Edition), 49, 613–625.
Yan, Y., Wang, Y., Yu, C., & Zhang, Z. (2023). Multispectral remote sensing for estimating water quality parameters: A comparative study of inversion methods using unmanned aerial vehicles (UAVs). Sustainability, 15(13), 10298. https://doi.org/10.3390/su151310298 DOI: https://doi.org/10.3390/su151310298
Zhang, Y., Jing, W., Deng, Y., Zhou, W., Yang, J., Li, Y., Cai, Y., Hu, Y., Peng, X., Lan, W., Peng, M., & Tang, Y. (2023). Water quality parameters retrieval of coastal mariculture ponds based on UAV multispectral remote sensing. Frontiers in Environmental Science, 11, 1079397. https://doi.org/10.3389/fenvs.2023.1079397. DOI: https://doi.org/10.3389/fenvs.2023.1079397
Zhao, X., Li, Y., Chen, Y., Qiao, X., & Qian, W. (2022). Water chlorophyll a estimation using UAV-based multispectral data and machine learning. Drones, 7(1), 2. https://doi.org/10.3390/drones7010002. DOI: https://doi.org/10.3390/drones7010002
Zheng, Y., Wang, J., Kondratenko, Y., & Wu, J. (2024). Research progress in surface water quality monitoring based on remote sensing technology. International Journal of Remote Sensing, 45(7), 2337–2373. DOI: https://doi.org/10.1080/01431161.2024.2327086
