Journal of the Indian Society of Remote Sensing

, Volume 37, Issue 4, pp 681–692 | Cite as

Spatial analysis of groundwater potential using remote sensing and GIS in the Kanyakumari and Nambiyar basins, India

Research Article

Abstract

Remotely sensed data can provide useful information in understanding the distribution of groundwater, an important source of water supply throughout the world. In the present study, the modern geomatic technologies, namely remote sensing and GIS were used in the identification of groundwater potential zones in the Kanyakumari and Nambiyar basins of Tamil Nadu in India. The multivariate statistical technique was used to find out the relationship between rainfall and groundwater resource characteristics. It has been found out that groundwater not only depends upon rainfall, but various other factors also influence its occurrence. Eight such parameters were considered and multi criterion analysis has been carried out in order to find out the potential zones. Accordingly, it had been concluded that the Kanyakumari river basin has more ground water potential, whereas the Nambiyar basin has less potential. Thus surface investigation of groundwater has proved to be easier, time consistent and cheaper using the geomatic technologies.

Keywords

Ground water potential Inter-correlation Matrix Multi-criterion 

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References

  1. Das S, Behera SC, Kar A, Narendra P and Guha S (1997) Hydrogeomorphological mapping in groundwater exploration using remotely sensed data — A case study in Keonjhar district in Orissa. J Indian Soc. Remote Sen. 25(4): 247–259CrossRefGoogle Scholar
  2. Eastman JR (1996) Multi-criteria Evaluation and Geographical Information Systems Eds. Longley PA, Goodchild MF, Magurie DJ and Rhind DW, Second edition, 1 (John Wiley and sons, New York): 493–502Google Scholar
  3. Eastman JR, Jin W, Kyemi PAK and Toledano J (1995) Raster procedure for multi-criteria/multi-objective decisions. Photogrammetric Engineering and Remote Sensing 61: 539–547Google Scholar
  4. Goyal R and Arora AN (2004) Use of remote sensing in groundwater modeling. Map Asia 2004. Beijing International Convention Centre, Beijing, China. www.GIS@development.netGoogle Scholar
  5. Haan CT (2002) Statistical Methods in Hydrology. Second Edition, Iowa State University Press, 2121 State Avenue, Ames, Iowa: 378Google Scholar
  6. Jayakumar R (1996) Groundwater level fluctuation analysis using trend surface in Upper Vellar Basin BHU-JAL News: 5–10Google Scholar
  7. Krishnamurthy J, Manavalan P and Saivasan V (1992) Application of digital enhancement techniques for ground water exploration in a hard rock terrain. International Journal of Remote Sensing 13: 2925–2942CrossRefGoogle Scholar
  8. Kumar A and Tomas S (1998) Groundwater assessment through hydrogeomorphological and geophysical survey — A case study in Godaveri sub-watershed, Giridih, Bihar. J Indian Soc. Remote Sen. 26(4): 177–184CrossRefGoogle Scholar
  9. Navalgund RR (1997) Revised Development plan of Ahmedabad Urban Development Authority Area — 2011. Ahmedabad Urban Development Authority and Space Applications Centre, Ahmedabad, India, Technical Report 1 SAC/RSAG/TR/12/AUG/1997:142Google Scholar
  10. Obi Reddy GP, Suresh BR and Rao Sambasiva M (1994) Hydrogeology and hydrogeo-morphological conditions of Anantapur district using remote sensing data. Indian Geographical Journal, 69(2): 128–235Google Scholar
  11. Pratap K, Ravindran KV and Prabakaran B (2000) Groundwater prospect zoning using remote sensing and geographical information system — A case study in dala-Renukoot area, Sonbhadra district, Uttarpradesh. J Indian Soc. Remote Sen. 28(4): 249–263CrossRefGoogle Scholar
  12. Raghuvanshi R, Bharathi R, Padmakar C and Yadava RN (2001) Morphometric analysis of Dudhi river basin, Begamganj block, Raisen district, Madhya Pradesh, India using remote sensing techniques. Journal of Hydrology: 330–337Google Scholar
  13. Ramasamy SM, Athithan DSP, Jayakumar R and Vasudevan S (1995) Groundwater quality monitoring through remote sensing — A mathematical approach. Journal of Applied Hydrology 8(1–4):19–27Google Scholar
  14. Rao DP, Bhattacharya A and Reddy PR (1996) Use of IRS- 1C data for geological and geographical studies. Current Science, IRS 1C, 70(7): 619–623Google Scholar
  15. Reddy PR, Vinod Kumar K and Seshadri K (1996) Use of IRS-1C data in ground water studies. Current Science Special session, IRS 1C 70(7) 600-605Google Scholar
  16. Goyal S, Bhardwaj RS and Jugran DK (1999) Multicriteria analysis using GIS for groundwater resources evaluation in Rawasen and Pili watershed. U.P. Proc. Map India 99, New Delhi, IndiaGoogle Scholar
  17. Saaty TL, (1980) The Analytic Hierarchy Process. (Mc Graw Hill, New York)Google Scholar
  18. Sarkar BC and Deota BS (2000) A geographic information system approach to ground water potential of shamri micro-watershed in the Shimla Taluk, Himachal Pradesh. Unpublished Project Report submitted for NNRMS sponsored course on GIS; Technology and Applications, IIRS, NRSA, Dehradun, India, 24Google Scholar
  19. Sawant PT (1992) Ground water potential of Bhagawati river basin. Solapur district, Maharastra, India. Journal of Applied Hydrology 12(1) 1–9Google Scholar
  20. Sawant PT and Suryawanshi RA (1998) Hydrological studies of Terna river basin, Latur-Osmanabad districts, Maharastra. Journal of Applied Hydrology 11(1) 36-48Google Scholar
  21. Shrivastava VK and Mitra D (1995) Study of drainage pattern of Raniganj coal field (Burdwan district) as observed on Landsat — TM/IRS LISS II Imagery. J Indian Soc. Remote Sen. 23(4) 225-235Google Scholar
  22. Sikdar PK, Chakraborty S, Enakshi A and Paul PK (2004) Land use/Land cover changes and groundwater potential zoning in and around Raniganj coal mining area, Bardhaman District, West Bengal-A GIS and Remote Sensing Approach. Journal of Spatial Hydrology, 4 (2 Fall 2004): 1–24Google Scholar
  23. Subba Rao N, Chakradhar GKJ and Srinivas V (2001) Identification of groundwater potential using remote sensing techniques in and around Guntar town, Andhra pradesh, India. J Indian Soc. Remote Sen. 29(1 & 2): 69–78Google Scholar
  24. Tomas A, Sharma AK, Manoj KS and Sood, A. (1999) Hydrogeomorphological mapping in assessing groundwater by using remote sensing data — A case study in Lehra Ganga block, Sangrur district, Punjab. J. Soc. Remote Sens. 27(1): 31–42CrossRefGoogle Scholar
  25. Vaidyanathan R (1964) Geomorphology of Cuddapah basin. Journal of Indian Geosciences Association 4: 29–36Google Scholar
  26. Voogd H (1983) Multi-criteria Evaluation for Urban and Regional Planning. Pion, LondonGoogle Scholar
  27. Yeats MH (1968) An introduction to quantitative analysis in human geography. Mc Graw Hill, New YorkGoogle Scholar

Copyright information

© Indian Society of Remote Sensing 2009

Authors and Affiliations

  1. 1.Department of Environmental Remote Sensing and CartographyMadurai Kamaraj UniversityMaduraiIndia

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