Water Quality in Landsat OLI Images

2011

Rapid development of countries can cause many environmental problems. One of the problem is water pollution which is the result of urban runoff from industrial and resident area. It has become a major global problem as it can lead to many deaths and diseases among human being. One of the main sources of water pollution is the suspended or colloidal particles which commonly referred as total suspended solids (TSS). Before the launch of satellite imagery technology, the biological parameters of ocean is obtained by using traditional sampling method which is time consuming and expensive. With remote sensing technique, water quality monitoring can be carried out by using a large coverage of satellite images analysis for water quality mapping. It is proven to be effective as it has been widely used in many environmental studies such as environmental pollution monitoring and oceanography. The purpose of this study is to obtain the water quality over Penang Straits using remote sensing imagery data. By using four scenes of Landsat TM images and in-situ data sets of particular dates, the new algorithm for water quality mapping which is based on optical model of water is developed. First, the images were atmospherically corrected and filtered using PCI Geomatica software as it gave better results compared to raw data especially when performing multi-date analysis. The digital numbers of each images were extracted and regressed using Excel. The efficiency of the algorithm was determined based on the observations of correlation coefficient (R) and root-mean-square (RMS) deviations with in-situ data. The accuracy of the algorithm, in comparison of other algorithm was also investigated. Finally, the calibrated TSS algorithm was used to generate the water quality table of difference and this study shows that the research of water quality can be carried out using remote sensing technique.

2017

This study was undertaken by analyzing data from satellite image (Landsat-8 OLI) and geographical information system (GIS) to find the relationship between water parameters and water indices of spectral images. The main purpose of this research was to develop a model for the physical and chemical parameters of Gharraf stream in Iraq. The water parameters used in this study included: acidity (PH), Total Dissolved Solids (T.D.S), Alkalinity(ALK), Electrical Conductivity (E.C), Calcium(Ca), Chloride (CL), Sodium (Na), Sulfate (SO4), Potassium (k), Total suspended solid (T.S.S), Total Hardness (TH).Where the samples were taken to seventeen stations with two seasons and at the same time took a satellite image on 4/FEB, 11 / MAY.GIS techniques were used in the beginning to project the coordinates of seventeen stations along the stream in Landsat-8 satellite image for extract data. Then, these data are treated in SPSS software for purpose finding correlation and regression equations. Pos...

It is impractical to monitor water quality more than a small fraction of lakes by conventional field methods because of expense and time requirements. High resolution satellite image is more convenient to be applied to collect the required data for monitoring and assessing water quality in the lakes. Therefore, this study aims to estimate the water quality indices and concentration of some parameters (Temperature, DO, BOD, pH, Turbidity, TSS, TDS, EC, NO3, PO4 and E. coli) through applying developed water quality estimation models based on the remote sensing and GIS techniques on the Landsat 8 OLI satellite image using twenty points in Dokan Lake, Kurdistan Region, Iraq at two different seasons. Four standard mathematical methods (NSFWQI, CCMEWQI, OWQI and AWWQI) are used to find the water quality indices at the twenty stations in Dokan Lake. Results of NSFWQI method are found as medium class for all stations except station 11, 12 and 13 which are classified as bad class for Spring season. The second method (CCMEWQI), all stations are classified as good for Autumn Season except station 1 and 2 as marginal and 16 as fair. In third method (OWQI) are classified as good for all stations except station 1 and 8 which classified as fair. However, it has shown very poor for Spring season. Finally, in the fourth method (AWWQI), all stations are classified as poor except station 15 and 19 which classified as very poor while station 16 is classified as unsuitable for drinking. The Secchi Disk Transparency (SDT) and the Trophic State Index (TSI) was come up with high variance for all stations. Multiple linear regression is used to obtain mathematical models for estimating the water quality indices and concentration of some parameters depending on spectral reflectance of Landsat 8 OLI. In this study, new band (coastal blue) of Landsat 8 OLI has been undertaken in developing of models. Moreover, new Independent Component Analysis (ICA) and new 7 band ratios with 16 band combinations have been used. The best model is the AWWQI which has the highest coefficient of determination (R2) of 0.993 for Autumn season and slightly low (0.612) for Spring season. The highest determination coefficient for SDT and TSI is 0.982 and 0.873 for Autumn Season and (0.951, 0.973) for Spring season respectively. However, high R2 of 0.982, 0.982, 0.832 for TSS, Turbidity and DO are resulted respectively. Generally, for Spring season, the performance of all models is reduced due to seasonal change, variance of parameters and other factors. However, high R2 of 0.862 has been shown for Temperature. Once the developed models applied in order to have maps with a variation of colors. This facilitates to predict how the results of WQPs can be distributed within the lake and all the results are reasonable. The conclusions present that correlation of all bands of Landsat 8 OLI is appropriate to water quality indices and parameters. This study suggests further researching about how remote sensing of water quality index and parameters at different depths in Dokan Lake can be detected.

Geocarto International, 2016

Journal of Water Resource and Protection, 2019

2011 IEEE International Conference on Imaging Systems and Techniques, 2011

Environmental Monitoring and Assessment, 2007

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