Remote Sensing for Monitoring Surface Water Quality in the Vietnamese Mekong Delta: The Application for Estimating Chemical Oxygen Demand in River Reaches in Binh Dai, Ben Tre

Abstract
In this study, the method of Fault Movement Potential (FMP) proposed by Lee et al. (1997) is used to assess the Surface water resources played a fundamental role in sustainable development of agriculture and aquaculture. They were the main sectors contributing to economic development in the Vietnamese Mekong Delta. Monitoring surface water quality was also one of the essential missions especially in the context of increasing freshwater demands and loads of wastewater fluxes. Recently, remote sensing technology has been widely applied in monitoring and mapping water quality at a regional scale replacing traditional field-based approaches. The aims of this study were to assess the application of the Landsat 8 (OLI) images for estimating Chemical Oxygen Demand (COD) as well as detecting spatial changes of the COD concentration in river reaches of the Binh Dai district, Ben Tre province, a downstream area of the delta. The results indicated the significant correlation (R=0.89) between the spectral reflectance values of Landsat 8 and the COD concentration by applying the Artificial Neuron Network (ANN) approach. In addition, the spatial distribution of the COD concentration was found slightly exceeded the national standard for irrigation according to the B1 column of QCVN 08:2015. References Ackerman S., Richard F., Kathleen S., Yinghui L., Chris M., Liam G., Bryan B., and Paul M., 2010. Discriminating clear-sky from cloud with MODIS algorithm theoretical basis document (MOD35). Ali Sheikh A.A., Ghorbanali A., and Nouri N., 2007. Coastline change detection using remote sensing. International Journal of Environmental Science and Technology 4(1), 61-66. Bonansea M., María C. R., Lucio P., and Susana F., 2015. Using multi-temporal Landsat imagery and linear mixed models for assessing water quality parameters in Río Tercero reservoir (Argentina). Remote Sensing of Environment 158, 28-41. 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