Skip to main content

Advertisement

Log in

A multi-temporal analysis for change assessment and estimation of algal bloom in Sambhar Lake, Rajasthan, India

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

Sambhar Lake in Rajasthan, India is the major inland salt water lake producing salt for centuries. The present study addresses the monitoring changes in and around the lake and its consequent effect on the lake water ecology. For this, satellite images of the years 1976, 1981, 1997, and 2013 are analyzed for land use land cover classes. Significant reduction in the water body is observed in contrast with the increase in salt pan around the periphery of lake and wetland classes. Further, the extent of water body and algae in the lake are delineated as per normalized difference water index and normalized difference vegetation index. Rainfall data do not indicate any major change in the pattern, but drastic decrease in the extent of water body and significant increase in algal bloom are serious concerns for the lake’s existence. This may be due to surrounding anthropogenic activities and construction of check dams and anicuts in the lake catchment which curtail the runoff into the lake and provide favorable growth of algae. Sambhar Lake, being declared as a wetland according to the Ramsar Convention, is necessary to protect and conserve the ecological importance of the lake through sustainable planning and management.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Anonymous. (1993). Directory of Indian wetlands, 1993. New Delhi, compiled by World Wildlife Federation - India, New Delhi and Asian Wetland Bureau, Kuala Lumpur.

  • Bhandari, A. K., Kumar, A., & Singh, G. K. (2014). Improved feature extraction scheme for satellite images using NDVI and NDWI technique based on DWT and SVD. Arabian Journal of Geosciences. doi:10.1007/s12517-014-1714-2.

    Google Scholar 

  • Batty, M. (2002). Thinking about cities as spatial events. Environment and Planning B, 29, 1–2.

    Article  Google Scholar 

  • Beck, P. S., Atzberger, C., Hogda, K. A., Johansen, B., & Skidmore, A. K. (2006). Improved monitoring of vegetation dynamics at very high latitudes: a new method using MODIS NDVI. Remote Sensing of Environment, 100(3), 321–334.

    Article  Google Scholar 

  • Bolstad, P. V., & Lillesand, T. D. (1991). Rapid maximum likelihood classification. Photogrammetric Engineering and Remote Sensing, 57, 67–74.

    Google Scholar 

  • Chuvieco, E., Martin, M. P., & Palacios, A. (2002). Assessment of different spectral indices in the red-near-infrared spectral domain for burned land discrimination. International Journal of Remote Sensing, 23(23), 5103–5110.

    Article  Google Scholar 

  • Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35–46.

    Article  Google Scholar 

  • Devranche, A., Lefebvre, G., & Poulin, B. (2010). Wetland monitoring using classification trees and SPOT-5 seasonal time series. Remote Sensing of Environment, 114(3), 552–562.

    Article  Google Scholar 

  • Deer, J. P. (1995). Digital change detection techniques: civilian and military applications, technical report. Salisbury, SA: Department of Defence, Defence Science and Technology Organization.

  • Dessi, F. G., & Niang, A. J. (2008). Thematic mapping using Quickbird multispectral imagery in Oung el-Jemel area, Tozeur (SW Tunisia). In A. Marini & M. Talbi (Eds.), Desertification and risk analysis using high and medium resolution satellite data (pp. 207–212). Dordrecht: Springer.

    Google Scholar 

  • Foody, G. M. (2002). Status of land covers classification accuracy assessment. Remote Sensing of Environment, 80, 185–201.

    Article  Google Scholar 

  • Gong, P., Pu, R., Biging, G. S., & Larriew, M. R. (2003). Estimation of forest lead area index using vegetation indices derived from Hyperion hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 41(6), 1355–1362.

    Article  Google Scholar 

  • Gross, J. E., R., N. R., Turner, W., & Melton, F. (2006). Remote sensing for the national parks. Park Science, 24, 30–36.

    Google Scholar 

  • Herrera, L. P., Hermida, V. G., Martinez, G. A., Laterra, P., & Maceira, N. (2005). Remote sensing assessment of Paspalum quadrifarium grasslands in the flooding pampa, Argentina. Rangeland Ecology and Management, 58(4), 406–412.

    Article  Google Scholar 

  • Ikiel, C., Dutucu, A. A., Ustaoglu, B., & Kilic, D. E. (2012). Land use and land cover classification using spot-5 image in the Adapazari plain and its surroundings, Turkey. The online Journal of Science and Technology, 2(2), 37–42.

    Google Scholar 

  • Jain, A. K. (2005). Conservation planning of Sambhar Lake, Rajasthan using satellite Remote Sensing and GIS. A thesis for M.Tech, Indian Institute of Remote Sensing National Remote Sensing Agency, Department of Space, Government of India. Submitted to Andhra University, Andhra Pradesh, India.

  • Jackson, R. D., & Huete, A. R. (1991). Interpreting vegetation indexes. Preventive Veterinary Medicine, 11, 185–200.

    Article  Google Scholar 

  • Jensen, J. R. (1996). Introductory digital image processing: a remote sensing perspective (2nd ed.pp. 247–251). Upper Saddle River: Prentice Hall.

    Google Scholar 

  • Joshi, R. R., Warthe, M., Dwivedi, S., Vijay, R., & Chakrabartie, T. (2011). Monitoring changes in land use land cover of Yamuna riverbed in Delhi: a multi-temporal analysis. International Journal of Remote Sensing, 32(24), 9547–9558.

    Article  Google Scholar 

  • Kerr, J. T., & Ostrovski, M. (2003). From space to species: ecological application for remote sensing. Trends in Ecology and Evolution, 18, 299–305.

    Article  Google Scholar 

  • Kumar, S. (2008). Conservation of Sambhar Lake—an important waterfowl habitat and Ramsar site in India. In Sengupta and R. Dalwani (Eds.), Proceedings of Taal 2007: The 12th World Lake Conference (pp. 1509–1517) Jaipur, India.

  • Lall, S. B. (1987). Note on Sambhar salt works and algae problem. In P. Sorgeloos (Ed.) Artemia Newsletter, 5, 5–6.

  • Lillesand, T. M., Keifer, R. W., & Chipman, J. W. (2004). Remote sensing and image Interpretation (5th ed.). New York: Wiley Chapter 7.

    Google Scholar 

  • Lu, D., Mausel, P., Brondízio, E., & Moran, E. (2004). Change detection techniques. International Journal of Remote Sensing, 25(12), 2365–2407.

    Article  Google Scholar 

  • Mathur, L. N. (2008). Geoscientific studies and managing lakes of arid and semi-arid regions of Rajashtan. In M. Sengupta and R. Dalwani (Eds.), Proceedings of Taal 2007: The 12th World Lake Conference (pp.1928–1932), Jaipur, India.

  • Mathur, R. P., & Mathur, L. N. (2007). Conservation of Sambhar wetland in Rajasthan. Journal of Social Policy Research Institute, 2(2), 122–129.

    CAS  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.

    Article  Google Scholar 

  • Mengistu, D. A., & Salami, A. T. (2007). Application of remote sensing and GIS inland use/land cover mapping and change detection in a part of south western Nigeria, African. Journal of Environmental Science and Technology, 1(5), 099–109.

    Google Scholar 

  • NWA (2010). National Wetland Atlas: Rajasthan, SAC/EPSA/AFEG/NWIA/ATLAS/31/2010 (p. 214). Ahmedabad: Space Applications Centre (ISRO).

    Google Scholar 

  • Pettorelli, N., Vik, J. O., Mysterud, A., Gaillard, J. M., Tucker, C. J., & Stenseth, N. C. (2005). Using the satellite-derived normalized difference vegetation index (NDVI) to assess ecological effects of environmental change. Trends in Ecology and Evolution, 20, 503–510.

    Article  Google Scholar 

  • Philipson, P., & Lindell, T. (2003). Can coral reefs be monitored from space? Ambio: A Journal of the Human Environment, 32(8), 586–593.

    Article  Google Scholar 

  • Rai, B., & Nair, S. S. (2013). Change detection of Barkhal Lake in Faridabad District of Haryana using geo-informatic techniques. International Journal of Remote Sensing & Geoscience (IJRSG), 2(2), 38–41.

    Google Scholar 

  • Reis, S. (2008). Analyzing land use/land cover changes using remote sensing and GIS in Rize, north-east Turkey. Sensors, 8, 6188–6202.

    Article  Google Scholar 

  • Rokni, K., Ahmad, A., Selamat, A., & Hazini, S. (2014). Water feature extraction and change detection using multi-temporal Landsat imagery. Remote Sensing, 6, 4173–4189.

    Article  Google Scholar 

  • Rouse, J. W., Haas, R. H., Schell, J. A. & Deering, D. W. (1973). Monitoring vegetation systems in the Great Plains with ERTS (Earth Resources Technology Satellite). In S. C. Freden, E. P. Mercanti, M. A. Becker (Eds.), Proceedings of Third Earth Resources Technology Satellite Symposium (pp. 309–317). Greenbelt, ON, Canada, 10–14 December, 351.

  • Singh, A. (1989). Review article digital change detection techniques using remotely-sensed data. International Journal of Remote Sensing, 10, 989–1003.

    Article  Google Scholar 

  • Shirke, S., Pinto, S. M., Kushwaha, V. K., Mardikar, T. & Vijay, R. (2016). Object-based image analysis for the impact of sewage pollution in Malad creek, Mumbai, India. Environmental Monitoring and Assessment, 188(2) Article no. 95 (online).

  • Solaimani, K., Arekhi, M., Tamartash, R., & Miryaghobzadeh, M. (2010). Land use/cover change detection based on remote sensing data (a case study; Neka Basin). Agriculture and Biology Journal of North America, 1(6), 1148–1157.

    Article  Google Scholar 

  • Stow, D. A., Hope, A., McGuire, D., Verbyla, D., Gamon, J., Huemmrich, F., et al. (2004). Remote sensing of vegetation and land-cover change in Arctic tundra ecosystems. Remote Sensing of Environment, 89, 281–308.

    Article  Google Scholar 

  • Sundaresan, S., Ponnuchamy, K., & Rahaman, A. A. (2006). Biological management of Sambhar Lake saltworks (Rajasthan, India), Proceedings of the 1st International Conference on the Ecological Importance of Solar Saltworks (CEISSA 06) (pp. 199–207). Santorini Island: Global Network for Environmental Science and Technology.

    Google Scholar 

  • Vijay, R., Kushwah, V. K., Chaudhary, A. S., Naik, K., Gupta, I., Kumar R. & Wate, S. R. (2016). Assessment of tourism impact on land use land cover and natural slope in Manali, India: a geospatial analysis. Environmental Earth Sciences, 75(20) (online).

  • 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), 3119–3313.

    Article  Google Scholar 

Web references

Download references

Acknowledgments

The authors gratefully acknowledge the Environment Department, Government of Rajasthan for providing financial support to carry out this study. Authors are also thankful to Director, CSIR-NEERI for providing encouragement, necessary infrastructural support, and kind permission for publishing the research article.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ritesh Vijay.

Electronic supplementary material

ESM 1

(PNG 26 kb)

ESM 2

(KML 1.38 mb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vijay, R., Pinto, S.M., Kushwaha, V.K. et al. A multi-temporal analysis for change assessment and estimation of algal bloom in Sambhar Lake, Rajasthan, India. Environ Monit Assess 188, 510 (2016). https://doi.org/10.1007/s10661-016-5509-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-016-5509-7

Keywords

Navigation