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Research Article

Assessment of Pollution Status of a Coastal Lake System using Satellite Imagery

Sheela AM1*, Letha J2, Sabu Joseph3, Ramachandran KK4 and Justus J5
1Directorate of Environment and Climate Change, Thiruvananthapuram, Kerala, India
2Director of Technical Education, Thiruvananthapuram, Kerala, India
3Department of Environmental Sciences, University of Kerala, Kariavattom P. O., Thiruvananthapuram, Kerala, India
4Centre for Earth Sciences, Akkulam, Thiruvananthapuram, Kerala, India
5St.Xavier’s College, Thumba, Thiruvananthapuram, Kerala, India
Corresponding Author : Sheela AM
Directorate of Environment and Climate Change
Thiruvananthapuram, Kerala, India
Tel: 9446373462
E-mail: [email protected]
Received May 11, 2013; Accepted July 02, 2013; Published July 15, 2013
Citation: Sheela AM, Letha J, Joseph S, Ramachandran KK, Justus J (2013) Assessment of Pollution Status of a Coastal Lake System using Satellite Imagery. J Geophys Remote Sensing 2:110. doi:10.4172/2169-0049.1000110
Copyright: © 2013 Sheela AM, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Water pollution is a major threat to the existence of living beings. The restoration measures can be taken by assessing the extent of pollution in water bodies using various water quality indices. National Sanitation Federation Water Quality Index (NSFWQI) is the most widely used index. Usually NSFWQI is determined by collecting and analyzing water samples from various locations and it is a tedious and expensive process. Trophic status is usually ascertained from satellite imagery of Landsat TM. Here attempt has been made to quickly assess the pollution status in a vast area (Akkulam-Veli Lake, Kerala, India) directly from the satellite imagery (IRS P6- LISSIII) using NSFWQI. It is also attempted to calculate the pH, dissolved oxygen (DO), biochemical oxygen demand (BOD) and fecal coliforms (FC) in the Lake system. Regression equations for the prediction of NSFWQI, pH, DO, BOD and FC from radiance values from green, red, NIR and SWIR bands of satellite imagery were developed. The study reveals that the simple regression equation formed by the ratio of radiance in the green and the red bands, which yields a strong correlation coefficient for the prediction of NSFWQI. For the prediction of DO, the best equation is the simple regression equation formed by the ratio of radiance in green and red bands with a strong correlation. For BOD, multiple regression equation was formed by the radiance in the red and SWIR bands with a strong correlation. The best equation for predicting pH is the regression equation with the ratio of green and red bands with a strong correlation. But for fecal coliform, multiple regression equation is the best equation formed by the ratio of radiance in the green and SWIR bands with a low correlation coefficient. The performance of this model can be improved by using a large set of data. The spatial variation of these utmost important water quality characteristics is derived from imagery using remote sensing techniques. It is also found out whether the water quality is conforming to the standards or not for envisaging control measures. IRS P6-LISSIII imagery can give a quick assessment of the pollution status of the Lake system using water quality index (NSFWQI). Control measures can accordingly be adopted on priority basis. Satellite imagery can be used for the quick assessment of urban pollution status of water bodies all over the world.

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