Document Type : Original Article
Authors
1
Geology Department,Faculty of Science,Beni Suef University,Beni Suef
2
Prof., Engineering Applications Department, National Authority for Remote Sensing and Space Sciences (NARSS), Cairo, Egypt
3
Faculty of Science, Beni-suef University, Beni-Suef, 62511, Egypt
4
Geology Department, Faculty of Science, Cairo University
5
Geology Department, Faculty of Science,Beni-Suef University
6
Central Laboratory for Environmental Quality Monitoring (CLEQM), NWRC, Egypt.
Abstract
Beni Suef governorate is one of highly population density areas in northern Upper Egypt. The surface water in Beni Suef district suffer from pollution due to the impact of anthropogenic activities such as usage of fertilizers and pesticide, waste disposal, industrial wastes and wastewater. The present study aims to estimate the Water Quality Index (WQI) and concentration of some other parameters through applying water quality estimation models based on Remote Sensing techniques applied on Landsat 8 OLI satellite images. Thirty-four points distributed among the study area are used for the analysis and to compare the data with the results obtained from analyzing water samples tested in the laboratory to investigate the feasibility of utilizing remote sensing data to identify water quality. In the present study, integrated technologies of remote sensing and water quality have been successfully utilized to assess water pollution in the study area and give an explanation for the influence of urbanization, cultivation and other human activities on water quality. The results showed that there are four classes of WQI for surface water samples, about 6 % belong to the excellent class, 65% to good class, 20.6% to poor class and the rest of samples belong to very poor class, while no samples belong to unsuitable class. It could be noted that the very poor category of WQI belongs to Bahr Yusef Canal. on other hand the main classes obtained from the supervised classification show that the agricultural land dominated the whole study area with 75%, which might have an impact on water quality in the study area. The second dominant class is represented by urban areas with 11%. The third class was bare land with 10 %, which are distributed in several separate parts in the study area. The fourth class represented by water bodies with 4%. The most appropriate models which calculated from water indices, band ratios and combination bands of the satellite image for the study area are very significant to detect water quality parameters. They also show the same results as the set of field data which measured from the laboratory tests. It is recommended to continue in the study of water quality by remote sensing techniques.
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