Resumen
The amount of suspended sediment transported from rivers to the ocean fluctuates over time, with a substantial increase occurring during storm events. This surge in sediment poses numerous challenges to coastal areas, highlighting the importance of accurately assessing the sediment load to address these issues. In this study, we developed and experimentally verified a novel method for suspended-sediment-discharge quantification in estuaries and coasts using underwater imaging. Specifically, red clay samples with different particle sizes were introduced into separate tanks containing clean water. After adequate mixing, the concentration, particle size, turbidity, and water quality were measured and analyzed using LISST-200x and EXO2 Multiparameter Sonde sensors. To maintain constant lighting conditions, a camera box was created for filming. Based on the experimental results, a turbidity?concentration relationship formula was derived. The proposed regression equation revealed that the relationship between the turbidity and estimated suspended-sediment concentration was significantly affected by the particle size, and the prediction results were underestimated under high-concentration conditions. Using blue, green, and gray band values, a multiple regression model for estimating suspended-sediment concentrations was developed; its predictions were better than those obtained from the turbidity?concentration relationship. Following efficiency improvements through additional approaches considering underwater-image filming conditions and characteristics of actual streams, estuaries, and coasts, this method could be developed into an easily usable technique for sediment-discharge estimation, helping address sediment-related issues in estuaries and coastal regions.