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Object-based image analysis for the impact of sewage pollution in Malad Creek, Mumbai, India

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Abstract

Today, object-based image analysis provides an option for integrating spatial information beyond conventional pixel-based classifications for high-resolution imagery. Due to its rare applicability in pollution assessment, an attempt has been made to assess the spatial extent of sewage pollution in Malad Creek, Mumbai, India. Based on multiresolution segmentation of an IRS P6 (LISS IV) image and the Normalized Difference Turbidity Index (NDTI), the various water quality regions in the creek were classified. The existing literature implies that the reflectance of turbid water is similar to that of bare soil which gives positive NDTI values. In contrast to this, negative values of NDTI are observed in the present study due to the presence of organic matter which absorbs light and imparts turbidity, which is supported by the significant correlation between NDTI and turbidity. A strong relationship is observed between turbidity and water quality parameters, implying the impact of organic matter through discharges of sewage in the creek. Based on the classified regions and the water quality parameters, the extent of pollution was ranked as high, moderate, low and least. The methodology developed in the present study was successfully applied on an IKONOS image for the same study area but a different time frame. The approach will help in impact assessment of sewage pollution and its spatial extent in other water bodies.

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References

  • Allee, R. J., & Johnson, J. E. (1999). Use of satellite imagery to estimate surface chlorophyll a and Secchi disc depth of Bull Shoals Reservoir, Arkansas, USA. International Journal of Remote Sensing, 20, 1057–1072.

    Article  Google Scholar 

  • APHA (2002). Standard methods for the examination of water and wastewater. NY, Washington DC: American Public Health Association, American Water Works Association, Water Environment Federation.

    Google Scholar 

  • Astrom, J., Pettersson, T. J. R., & Stenstrom, T. A. (2007). Identification and management of microbial contaminations in a surface drinking water source. Journal of Water and Health, 5(1), 67–79.

    Article  Google Scholar 

  • Bhandari, N. S., & Nayal, K. (2008). Correlation study on physicochemical parameters and quality assessment of Kosi river water, Uttarakhand. Electronic Journal of Chemistry, 5(2), 342–346.

    CAS  Google Scholar 

  • Blaschke, T., & Strobl, J. (2003). Defining landscape units through integrated morphometric characteristics. In E. Buhmann & S. Ervin (Eds.), Landscape modelling: digital techniques for landscape architecture (pp. 104–113). Heidelberg: Wichmann-Verlag.

    Google Scholar 

  • Borges, E. F., Anjos, C. S., & Pablo, S. S. (2011). Detection of suspended sediments in Grande River and Ondas River – Bahia/Brazil. Brazil: Federal University of Bahia, Institute of Environmental Sciences and Sustainable Development.

    Google Scholar 

  • Brezonik, P. L., Olmanson, L. G., Bauer, M. E., & Kloiber, S. M. (2007). Measuring water clarity and quality in Minnesota lakes and rivers: a census-based approach using remote-sensing techniques. CURA Reporter, 37(2), 3–13.

    Google Scholar 

  • Burnett, C., & Blaschke, T. (2003). A multi-scale segmentation/object relationship modelling methodology for landscape analysis. Ecological Modeling, 168, 233–249.

    Article  Google Scholar 

  • Chen, S., Fang, L., Zhang, L., & Huang, W. (2009). Remote sensing of turbidity in seawater intrusion reaches of Pearl River Estuary: a case study in Modaomen water way, China. Estuary Coastal and Shelf Science, 82, 119–27.

    Article  CAS  Google Scholar 

  • CPCB (1993). Criteria for classification and zoning of coastal waters (Sew Water-SW)—a coastal pollution control series: COPOCS/6/1993-CPCB. New Delhi: Central Pollution Control Board, India.

    Google Scholar 

  • Dambach, P., Machault, V., Lacaux, J. P., Vignolles, C., Sié, A., & Sauerborn, R. (2012). Utilization of combined remote sensing techniques to detect environmental variables influencing malaria vector densities in rural West Africa. International Journal of Health Geographics, 11, 8.

    Article  Google Scholar 

  • Ekercin, S. (2007). Water quality retrievals from high resolution IKONOS multispectral imagery: a case study in Istanbul, Turkey. International Journal of Environmental and Pollution, 183, 239–251.

    CAS  Google Scholar 

  • Fraser, R. N. (1998). Multispectral remote sensing of turbidity among Nebraska Sand Hill lakes. International Journal of Remote Sensing, 23, 15–35.

    Google Scholar 

  • Frohn, R. C., Feif, M., Lane, C., & Autrey, B. (2009). Satellite remote sensing of isolated wetlands using object-oriented classification of Landsat-7 data. Wetlands, 29(3), 931–941.

    Article  Google Scholar 

  • Gardelle, J., Hiernaux, P., Kergoat, L., & Grippa, M. (2010). Less rain, more water in ponds: a remote sensing study of the dynamics of surface waters from 1950 to present in pastoral Sahel (Gourma region, Mali). Hydrology and Earth System Sciences, 14, 309–324.

    Article  CAS  Google Scholar 

  • Gitas, I. Z., Mitri, G. H., & Ventura, G. (2004). Object-based image classification for burned area mapping of Creus Cape, Spain, using NOAA-AVHRR imagery. Remote Sensing and Environment, 92, 409–413.

    Article  Google Scholar 

  • Goetz, S. J., Wright, R. K., Smith, A. J., Zinecker, E., & Schaub, E. (2003). IKONOS imagery for resource management: tree cover, impervious surfaces, and riparian buffer analyses in the mid-Atlantic region. Remote Sensing and Environment, 88, 195–208.

    Article  Google Scholar 

  • Hildebrandt, G. (1996). Fernerkundung und Luftbildmessung für Forstwirtschaft, Vegetationskartierung und Landschaftsökologie (p. 676 S). Karlsruhe: Wichmann Verlag.

    Google Scholar 

  • Jensen, J. R. (2000). Remote sensing of the environment: an earth resource prospective (2nd edition), Prentice Hall Series in Geographic Information System.

  • Kondratyev, K. Y., Pozdnyakov, D. V., & Pettersson, L. H. (1998). Water quality remote sensing in the visible spectrum. International Journal of Remote Sensing, 19, 957–979.

    Article  Google Scholar 

  • Lacaux, J. P., Tourre, Y. M., Vignolles, C., Ndione, J. A., & Lafaye, M. (2006). Classification of ponds from high-spatial resolution remote sensing: application to rift valley fever epidemics in Senegal. Remote Sensing of Environment, 106(2007), 66–74.

    Google Scholar 

  • Li, R., & Li, J. (2004). Satellite remote sensing technology for lake water clarity monitoring: an overview. Environmental Informatics Archives, 2, 893–901.

    Google Scholar 

  • McFeeters, S. K. (1996). The use of Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7), 1425–1432.

  • Obi, C. L., Igumbor, J. O., Momba, M. N. B., & Samie, A. (2008). Interplay of factors involving chlorine dose, turbidity flow capacity and pH on microbial quality of drinking water in small water treatment plants. Water SA (Online), 34(5), 565–572.

    CAS  Google Scholar 

  • Parada, M., & Canton, M. (1998). Sea surface temperature variability in Alboran Sea from satellite data. International Journal of Remote Sensing, 19, 2439–2450.

    Article  Google Scholar 

  • Pattiaratchi, C. B., Lavery, P., Wyllie, A., & Hick, P. (1994). Estimates of water-quality in coastal waters using multi-date Landsat Thematic Mapper data. International Journal of Remote Sensing, 15, 1571–1584.

    Article  Google Scholar 

  • Ray, R., Mandal, S., Dhara, A. (2012). Characterization and mapping of inland wetland: a case study of selected Bils on Nadia district. International Journal of Scientific and Research Publications, 2(12).

  • Ritchie, C. J., Cooper, C. M., & Schiebe, F. R. (1990). The relationship of MSS and TM digital data with suspended sediment, chlorophyll and temperature in Moon Lake, Mississippi. Remote Sensing and Environment, 33, 137–148.

    Article  Google Scholar 

  • Saliu, J. K., & Ekpo, M. P. (2006). Preliminary chemical and biological assessment of Ogbe creek, Lagos, Nigeria. West African Journal of Applied Ecology, 9, 14–22.

    Google Scholar 

  • Sardar, V. K., Vijay, R., & Sohony, R. A. (2010). Water quality assessment of Malad Creek, Mumbai, India: an impact of sewage and tidal water. Water Science and Technology, 62(9), 2037–2043.

    Article  CAS  Google Scholar 

  • Satapathy, D. R., Vijay, R., Kamble, S. R., & Sohony, R. A. (2010). Remote sensing of turbidity and phosphate in creeks and coast of Mumbai: an effect of organic matter. Transactions in GIS, 14(6), 811–832.

    Article  Google Scholar 

  • Senay, G. B., Shafique, N. A., Autrey, B. C., Fulk, F., & Cormier, S. M. (2001). The selection of narrow wavebands for optimizing water quality monitoring on the Great Miami River, Ohio using hyperspectral remote sensor data. Journal of Spatial Hydrology, 1, 1–22.

    Google Scholar 

  • Somvanshi, S., Kunwar, P., Singh, N. B., Kachhwaha, T. S. (2011). Water turbidity assessment in part of Gomti River using high resolution Google Earth’s Quickbird satellite data. Geospatial World Forum, 18–21 January 2011, Paper Reference No.: PN-60.

  • Strasser, T., Lang, S., Pernkopf, L., Paccagnel, K. (2012). Object-based class modelling for assessing habitat quality in riparian forests. Proceedings of the 4th GEOBIA, May 7–9, 2012 - Rio de Janeiro - Brazil. 555–560.

  • Tassan, S. (1998). A procedure to determine the particulate content of shallow water from Thematic Mapper data. International Journal of Remote Sensing, 19, 557–562.

    Article  Google Scholar 

  • Thiemann, S., & Kaufmann, H. (2000). Determination of chlorophyll content and trophic state of lakes using field spectrometer IRS-1C satellite data in the Mecklenburg Lake District, Germany. Remote Sensing and Environment, 73, 227–235.

    Article  Google Scholar 

  • Trimble (2006). Trimble Juno ST, TerraSync Software, getting started guide. Westminster: Trimble Navigation.

    Google Scholar 

  • Vijay, R., Bhattacharyya, T., Sharma, R., Dhage, S. S., & Sohony, R. A. (2011). GIS based water quality indexing of Malad creek, Mumbai: an impact of sewage discharge. Journal of Environmental Science and Engineering, 53(2), 143–150.

    CAS  Google Scholar 

  • Vijay, R., Sardar, V. K., Dhage, S. S., Kelkar, P. S., & Gupta, A. (2010). Hydrodynamic assessment of sewage impact on water quality of Malad Creek, Mumbai, India. Environmental Monitoring and Assessment, 165(1–3), 559–571.

    Article  CAS  Google Scholar 

  • Vijay, R., Kushwaha, V., Pandey, N., Nandy, T., & Wate, S. R. (2015). Extent of sewage pollution in coastal environment of Mumbai, India: an object based image analysis. Water and Environment Journal, 29(3), 365–374.

    Article  CAS  Google Scholar 

  • Zhou, W., & Troy, A. (2008). An object-oriented approach for analysing and characterizing urban landscape at the parcel level. International Journal of Remote Sensing, 29(11), 3119–3135.

    Article  Google Scholar 

Download references

Acknowledgments

The authors are thankful to the Director, CSIR-NEERI, Nagpur, for providing encouragement, support and kind permission for publishing the research article.

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Correspondence to Ritesh Vijay.

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Shirke, S., Pinto, S.M., Kushwaha, V.K. et al. Object-based image analysis for the impact of sewage pollution in Malad Creek, Mumbai, India. Environ Monit Assess 188, 95 (2016). https://doi.org/10.1007/s10661-015-4981-9

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  • DOI: https://doi.org/10.1007/s10661-015-4981-9

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