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 - Phuong, Nguyen Thi Binh; Tri, Van Pham Dang; Duy, Nguyen Ba; Nghiem, Nguyen Chanh (Vietnam Journal of Earth Sciences, 2017)
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. Available at http://linkinghub.elsevier.com/retrieve/pii/S0034425714004544. Casse C., Viet P. B., Nhung P.T.N., Phung H.P., and Nguyen L.D., 2012. Remote sensing application for coastline detection in Ca Mau, Mekong Delta. Proceeding of International Conferance on Geometics for spatial Infrastructure development in Earth and Allied Science-GIS IDEAS. Chavez, P. S., 1996. Image-based atmospheric corrections-revisited and improved. Photogrammetric engineering and remote sensing, 62(9), 1025-1035. Chebud, Y., Ghinwa M.N., Rosanna G.R., and Assefa M.M., 2012. Water Quality Monitoring Using Remote Sensing and an Artificial Neural Network. Water Air & Soil Pollution 223(8), 4875-4887. Available at http://link.springer.com/10.1007/s11270-012-1243-0. Gholizadeh M.H., Assefa M.M., and Lakshmi R., 2016. A Comprehensive Review on Water Quality Parameters Estimation Using Remote Sensing Techniques. Sensors (Basel, Switzerland) 16(8), 1298. Available at http://www.mdpi.com/1424-8220/16/8/1298/htm. Imen S., Ni-Bin C., and Y.J.Y., 2015. Developing the remote sensing-based early warning system for monitoring TSS concentrations in Lake Mead. Journal of Environmental Management 160, 73,89. Available at http://linkinghub.elsevier.com/retrieve/pii/S0301479715300943. Ines A.V.M., Peter D., Ian W.M., and Ashim G. D., 2001. Crop Growth and Soil Water Balance Water Modeling to Explore Water Management Water Options. Colombo. Kaur H., and Dalwinder S.S., 2013. Bayesian Regularization Based Neural Network Tool for Software Effort Estimation. Global Journal of Computer Science and Technology Neural & Artificial Intelligence 13(2), 44-50. Available at https://globaljournals.org/GJCST_Volume13/6-Bayesian-Regularization-Based-Neural.pdf. Lavery P., Charitha P., Alex W., and Peter H., 1993. Water quality monitoring in estuarine waters using the Landsat thematic mapper. Remote Sensing of Environment 46(3), 268-280. Le A.T., Du L.V., and Tristan S., 2014. Rapid integrated and ecosystem-based assessment of climate change vulnerability and adaptation for Ben Tre Province, Viet Nam. Journal of Science and Technology 52(3A), 287-293. Lim J. and Minha C., 2015. Assessment of water quality based on Landsat 8 operational land imager associated with human activities in Korea. Environmental monitoring and assessment 187(6), 4616. Available at http://www.scopus.com/inward/record.url?eid=2-s2.0-84930209268&partnerID=tZOtx3y1. Montanher O.C., Evlyn M.L.M.N., Claudio C.F.B., Camilo D.R., and Thiago S.F.S., 2014. Empirical models for estimating the suspended sediment concentration in Amazonian white water rivers using Landsat 5/TM. International Journal of Applied Earth Observation and Geoinformation 29(1), 67-77. Available at http://dx.doi.org/10.1016/j.jag.2014.01.001. Nas B., Semih E., Hakan K., Ali B., and David J.M., 2010. An application of landsat-5TM image data for water quality mapping in Lake Beysehir, Turkey. Water Air and Soil Pollution 212(1-4), 183-197. Nguyen D.D., Lam D.D., Ha V. Van, Tan N.T., Tuan D.M., Quang N.M., and Cuc N.T.T., 2010. New stratigraphic unit - The Early Holocene Binh Dai formation at the estuary and coastal area of Cuu Long delta. Vietnam Journal of Earth Sciences 32, 335-342. Pham B.T., Dieu T.B., Hamid R.P., Prakash I., and Dholakia M.B., 2015. Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods. Theoretical and Applied Climatology 128(1-2), 255-273. Pham Q.S. and Anh N.D., 2011. Evolution of the coastal erosion and accretion in the Hai Hau district (Nam Dinh province) and neighboring region over the last 100 years based on topographic maps and multi-temporal remote sensing data analysis. Vietnam Journal of Earth Sciences 311(2002), 82-85. PPC, 2016. Environmental Impacts Assessment (B-SWAMP). Ben Tre. Renaud F.G. and Claudia K., 2012. The Mekong Delta System: Interdisciplinary Analyses of a River Delta (FG Renaud and C Kuenzer, Eds.). Springer Dordrecht Heidelberg New York London. Sudheer K.P., Indrajeet C., and Vijay G., 2006. Lake water quality assessment from landsat thematic mapper data using neural network: An approach to optimal band combination selection. Journal of the American Water Resources Association 42(6), 1683-1695. Tien Bui D., Pradha B., Owe L., Inge R., and Oystein B.D., 2012. Landslide susceptibility assessment in the Hoa Binh province of Vietnam: A comparison of the Levenberg-Marquardt and Bayesian regularized neural networks. Geomorphology 171-172, 12-29Available at http://dx.doi.org/10.1016/j.geomorph.2012.04.023. Tien Bui D., Tuan T.A., Harald K., Biswajeet P., and Inge R., 2016. Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 13(2), 361-378. Wang Y., Hao X., Jiamo F., and Guoying S., 2004. Water quality change in reservoirs of Shenzhen, China: Detection using LANDSAT/TM data. Science of The Total Environment 328(1-3), 195-206. Available at http://linkinghub.elsevier.com/retrieve/pii/S0048969704001007. Wang J.P., Cheng S.T., and Jia H.F., 1977. Application of Artificial Neural Network Technology in Water Color Remote Sensing Inversion of Inland Water Body Using Tm Data. Waxter M.T., 2014. Analysis of Landsat Satellite Data to Monitor Water Quality Parameters in Tenmile Lake, Oregon. Were K., Dieu T.B., Øystein B.D., and Bal R.S., 2015. A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape. Ecological Indicators 52: 394-403. Available at http://dx.doi.org/10.1016/j.ecolind.2014.12.028. Wu J.L., Chung-Ru H., Chia-Ching H., Arun L.S., Jing-Hua T., and Yao-Tung L., 2014. Hyperspectral sensing for turbid water quality monitoring in freshwater rivers: Empirical relationship between reflectance and turbidity and total solids. Sensors (Switzerland) 14(12), 22670-22688. Yusop S.M., Abdullah K., Lim H.S., and Md N.A.B., 2011. Monitoring water quality from Landsat TM imagery in Penang, Malaysia. Proceeding of the 2011 IEEE International Conference on Space Science and Communication (IconSpace), 249-253. Zhu Z. and Curtis E.W., 2012. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment 118, 83-94. Zhu Z. Shixiong W., and Curtis E.W., 2015. Improvement and expansion of the Fmask algorithm: Cloud, cloud shadow, and snow detection for Landsats 4-7, 8, and Sentinel 2 images. Remote Sensing of Environment 159, 269-277.