Evaluating the Effect of Sediment Size on Estimation of Suspended Sediment Concentration from Digital Imagery

Abstract:

An accurate estimation of suspended sediment concentrations (SSC) is often required for water resources management projects. Excessive sediment load may impair water quality, destroy food sources or habitats of aquatic life, reduce the useful life of reservoirs, and pose a risk to human health. Remote sensing has been used for estimating SSC in coastal areas, but its use in narrow waterbodies such as rivers is limited due to the coarse spatial resolution of satellite images. Capturing spectral information on surface water quality with digital photography provides an alternative solution for estimating SSC in river systems. However, the complexity of water surface optical properties caused by sediment size and color makes this approach challenging. The goal of this research is to investigate relationships among digital imagery data collected from the surface of turbid waters, SSC, and sediment size. For this purpose, a series of laboratory experiments were conducted for turbid waters with different sizes of sediment in suspension, including clay, silt, and very fine sand. Linear regressions were developed to describe the co-variability of SSC, particle sizes, and spectral reflectance of the water surface. The relationships were used to evaluate the effectiveness of the digital imagery method in predicting SSC in rivers. The results of this study indicate that reflectance values calculated from visible wavebands of digital images increase with SSC regardless of the sediment size. A significant increase in reflectance values was observed in tests with clay material compared to of other two sediment sizes. It was concluded that the spectral response of smaller particles is more correlated with SSC than of the larger particles. This variability in the reflectance-SSC relationship was also observed in the field data due to sediment size variation. The correlation between the reflectance-SSC in the regression models suggests that this method could be used to estimate SSC in complex river settings carrying suspended sediment of various sizes.