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Water Quality Assessment of River Ganga and Chilika Lagoon using AVIRIS-NG Hyperspectral Data


Affiliations
1 Space Applications Centre, Indian Space Research Organisation (ISRO), Ahmedabad 380 015, India
2 National Remote Sensing Centre, ISRO, Hyderabad 500 037, India
3 Indian Institute of Remote Sensing, ISRO, Dehradun 248 001, India
 

Remote sensing is a vital tool to assess water quality parameters in water bodies like rivers, lakes, estuaries and lagoons. All these fall under the category of optically complex waters (case 2), where water-leaving radiance is affected by optically active water constituents and bottom substrate. The present study estimates water quality parameters, viz. turbidity, suspended sediment concentration and chlorophyll in River Ganga in Buxar (Bihar), and Howrah (West Bengal) and Chilika lagoon (Odisha) using hyperspectral reflectance data of AVIRIS-NG. Concurrent ground-truth data of water samples were collected and simultaneous spectro-radiometer measurements were made in synchronous with the AVIRIS-NG flight over the study area. Semi-analytical simulation modelling followed by inversion and contextual image analysis-based methods were used for estimating the water quality parameters. Water turbidity maps were generated for both the study sites. Over Ganga river, water was relatively clear in Buxar (6.87–20 NTU, TSS 42–154 mg/l), while it was extremely turbid in Howrah (50–175 NTU, TSS 75–450 mg/l). In Chilika lagoon, water was more turbid in the northern sector, which may be due to the river input and resuspension from shallow bathymetry. The results suggest that the small-scale changes in turbidity due to point sources like river tributaries or sewerage discharges can be identified using hyperspectral data. The imaging spectroscopy data over water are a key source to find out potential locations of water contamination.

Keywords

Hyperspectral Data, Remote Sensing Reflectance, Semi-Analytical Algorithms, Spectroradiometer.
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  • Ritchie, J. C., Zimba, P. V. and Everitt, J. H., Remote sensing techniques to assess water quality. Photogram. Eng. Remote Sensing, 2003, 69, 695–704.

  • Agrawal, A., Pandey, R. S. and Sharma, B., Water pollution with special reference to pesticide contamination in India. J. Water Resour. Prot., 2010, 2, 432–448.

  • Lee, Z., Carder, K. L., Mobley, C. D., Steward, R. G. and Patch, J. S., Hyperspectral remote sensing for shallow waters: 2. Deriving bottom depths and water properties by optimization. Appl. Opt., 1999, 38, 3831–3843.

  • Trivedi R. C., Water quality of the Ganga River – an overview. Aquat. Ecosyst. Health Manage., 2010, 13(4), 347–351.

  • Garg, V. et al., Spectral similarity approach for mapping turbidity of an Inland waterbody. J. Hydrol., 2017, 550, 527–537.

  • Brando, V. E. and Dekker, A. G., Satellite hyperspectral remote sensing for estimating estuarine and coastal water quality. IEEE Trans. Geosci. Remote Sensing, 2003, 41(6), 1378–1387.

  • Van der Meer, F., The effectiveness of spectral similarity measures for the analysis of hyperspectral imagery. Int. J. Appl. Earth Observ. Geoinform., 2006, 8, 3–17.

  • Van der Meer, F. and Bakker, W., Cross correlogram spectral matching (CCSM): application to surface mineralogical mapping using AVIRIS data from Cuprite, Nevada. Remote Sensing Environ., 1997, 61(3), 371–382.

  • Kruse, F. A., Lefkoff, A. B., Boardman, J. W., Heidebrecht, K. B., Shapiro, A. T., Barloon, P. J. and Goetz, A. F. H., The spectral image processing system (SIPS) – interactive visualization and analysis of imaging spectrometer data. Remote Sensing Environ., 1993, 44, 145–163.

  • Chang, C. I., An information theoretic-based approach to spectral variability, similarity and discriminability for hyperspectral image analysis. IEEE Trans. Inf. Theory, 2000, 46(5), 1927–1932.

  • Green, A. A. and Craig, M. D., Analysis of aircraft spectrometer data with logarithmic residuals. In Proceedings of AIS Workshop, JPL Publication 85–41, 8–10 April 1985, Jet Propulsion Laboratory, Pasadena, California, 1985, pp. 111–119.

  • Clark, R. N., King, T. V. V. and Gorelick, N. S., Automatic continuum analysis of reflectance spectra. In Proceedings of Third AIS Workshop (2–4 June 1987), JPL Publication 87–30, Jet Propulsion Laboratory, Pasadena, California, 1987, pp. 138–142.

  • Kokaly, R. F., Investigating a physical basis for spectroscopic estimates of leaf nitrogen concentration. Remote Sensing Environ., 2001, 75, 153–161.

  • Lee, Z., Carder, K. L., Mobley, C. D., Steward, R. G. Patch, J. S., Hyperspectral remote sensing for shallow waters: I. A semianalytical model. Appl. Opt., 1998, 37, 6329–6338.

  • Lee, Z., Carder, K. L., Chen R. F. and Peacock, T. G., Properties of the water column and bottom derived from Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data. J. Geophys. Res., 2001, 106, 11639–11651.

  • Gege, P., The water colour simulator WASI: an integrating software tool for analysis and simulation of in situ spectra. Comput. Geosci., 2004, 30, 523–532.

  • Pope, R. M. and Fry, E. S., Absorption spectrum (380–700 nm) of pure water. II. Integrating cavity measurements. Appl. Opt., 1997, 36(33), 8710–8723.

  • Bricaud, A., Morel, A. and Prieur, L., Absorption by dissolved organic matter of the sea (yellow substance) in the UV and visible domains. Limnol. Oceanogr., 1981, 26(1), 43–53.

  • Morel, A. and Prieur, L., Analysis of variations in ocean colour. Limnol. Oceanogr., 1977, 22, 709–722.

  • Mahapatro, D., Panigrahy, R. C., Panda, S. and Mishra, R. K., Checklist of intertidal benthic macrofauna of a brackish water coastal lagoon on east coast of India: the Chilika lake. Int. J. Mar. Sci., 2015, 5(33), 1–13.

  • Sahoo, R. K., Mohanty, P. K., Pradhan, S., Pradhan, U. K. and Samal, R. N., Bed sediment characteristics and transport processes along the inlet channel of Chilika Lagoon (India). Indian J. Geo.Mar. Sci., 2018, 47(2), 301–307.

  • Lobo, F. L., Costa, M., Phillips, S., Young, E. and McGregor, C., Light backscattering in turbid freshwater: a laboratory investigation. J. Appl. Remote Sensing, 2014, 8, doi:10.1117/1.jrs.8.083611.

  • Lee, Z., Visible-infrared remote-sensing model and applications for ocean waters, Ph D thesis, University of South Florida, 1994.

  • Prieur, L. and Sathyendranath, S., An optical classification of coastal and oceanic waters based on the specific spectral absorption curves of phytoplankton pigments, dissolved organic matter, and other particulate materials. Limonol. Oceanogr., 1981, 2, 671– 689.


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  • Water Quality Assessment of River Ganga and Chilika Lagoon using AVIRIS-NG Hyperspectral Data

Abstract Views: 354  |  PDF Views: 148

Authors

S. Chander
Space Applications Centre, Indian Space Research Organisation (ISRO), Ahmedabad 380 015, India
Ashwin Gujrati
Space Applications Centre, Indian Space Research Organisation (ISRO), Ahmedabad 380 015, India
K. Abdul Hakeem
National Remote Sensing Centre, ISRO, Hyderabad 500 037, India
Vaibhav Garg
Indian Institute of Remote Sensing, ISRO, Dehradun 248 001, India
Annie Maria Issac
National Remote Sensing Centre, ISRO, Hyderabad 500 037, India
Pankaj R. Dhote
Indian Institute of Remote Sensing, ISRO, Dehradun 248 001, India
Vinay Kumar
Indian Institute of Remote Sensing, ISRO, Dehradun 248 001, India
Arvind Sahay
Space Applications Centre, Indian Space Research Organisation (ISRO), Ahmedabad 380 015, India

Abstract


Remote sensing is a vital tool to assess water quality parameters in water bodies like rivers, lakes, estuaries and lagoons. All these fall under the category of optically complex waters (case 2), where water-leaving radiance is affected by optically active water constituents and bottom substrate. The present study estimates water quality parameters, viz. turbidity, suspended sediment concentration and chlorophyll in River Ganga in Buxar (Bihar), and Howrah (West Bengal) and Chilika lagoon (Odisha) using hyperspectral reflectance data of AVIRIS-NG. Concurrent ground-truth data of water samples were collected and simultaneous spectro-radiometer measurements were made in synchronous with the AVIRIS-NG flight over the study area. Semi-analytical simulation modelling followed by inversion and contextual image analysis-based methods were used for estimating the water quality parameters. Water turbidity maps were generated for both the study sites. Over Ganga river, water was relatively clear in Buxar (6.87–20 NTU, TSS 42–154 mg/l), while it was extremely turbid in Howrah (50–175 NTU, TSS 75–450 mg/l). In Chilika lagoon, water was more turbid in the northern sector, which may be due to the river input and resuspension from shallow bathymetry. The results suggest that the small-scale changes in turbidity due to point sources like river tributaries or sewerage discharges can be identified using hyperspectral data. The imaging spectroscopy data over water are a key source to find out potential locations of water contamination.

Keywords


Hyperspectral Data, Remote Sensing Reflectance, Semi-Analytical Algorithms, Spectroradiometer.

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DOI: https://doi.org/10.18520/cs%2Fv116%2Fi7%2F1172-1181