Water Quality Monitoring in Small and Medium-Scale Lakes of Sri Lanka Using UAV Multispectral Images

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.