Abstracts

Quantitative Estimation of Suspended Sediments and Associated Mercury Concentration in Enid Lake Using Remote Sensing Techniques

Author(s): Hossain, A.; Chao, X.; Cizdziel, J.; Jia, Y.

The streams, lakes, and reservoirs in the Yazoo River Basin provide significant natural and recreational resources in Mississippi. However, since the soils in this region are highly erodible, large amount of sediments are discharged into the water bodies. Sediments are often associated with pollutants, which cause many water bodies in this region to be impaired due to the contaminated sediments. Mercury is one of the widely distributed and persistent pollutants in this environment. Mississippi currently has 11 water bodies under fish consumption advisories for mercury, including Enid Lake. To study the mercury contamination issue in the Enid Lake, the National Center for Computational Hydroscience and Engineering at the University of Mississippi has an on-going research project funded by the Mississippi Water Resources Research Institute and USGS to study the transport, fate, and risk of mercury in Enid Lake. As one of the tasks of this project the potential of the remote sensing techniques were explored to estimate the mercury concentration associated with suspended sediments in Enid Lake. Suspended sediment concentration has been estimated and mapped successfully using remote sensing for the last three decades. Different approaches and algorithms had been developed over time for SSC estimation using optical satellite data. Several studies had success in estimating total suspended sediments (TSS) using simple linear regression techniques involving the Moderate-resolution Imaging Spectroradiometer (MODIS) visible and near infra red (VNIR) data and in situ measurements. Similar approach was used in this study to estimate TSS and associated mercury concentration in Enid Lake, MS. The correlation coefficients of the regression equations were obtained using in situ measurements of TSS and mercury from two field campaigns, and near-real time reflectance values of the VNIR bands of MODIS imagery. Preliminary results indicate that these regression equations can be used for quantitative estimation of TSS and associated mercury in Enid Lake with reasonable accuracy

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