Advertisement

Sustainable development of land and water resources using geographic information system and remote sensing

  • R. S. DwivediEmail author
  • K. Sreenivas
  • K. V. Ramana
  • P. R. Reddy
  • G. Ravi Sankar
Article

Abstract

Realizing the potential of spaceborne multispectral measurements in providing spatial information on natural resources, and of Geographic Information System (GIS) in integrating such information with the socio-economic data and other collateral information to arrive at derivative information, we report here the results of a study which was taken up in a watershed in Charkhari block of Mahoba district, northern India, to generate the information on natural resources from Indian Remote Sensing Satellite (IRS-1B) Linear Imaging Self-scanning Sensor (LISS-II) images through a systematic visual interpretation, and its subsequent integration with the collateral information in a GIS environment to develop optimal land use plan/action plan for sustainable development of its land resources. Since permanent vegetation cover in the watershed has been dwindling due to population pressure, the need for establishing more vegetation cover has been stressed.

Keywords

Geographic Information System Optimal Land National Remote Sensing Agency Rabi Crop Basaltic Terrain 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. All India Soil and Land Use Survey (1970). Soil Survey Manual Publishers: All India Soil and Land Use Survey. Ministry of Agriculture, Government of India, New Delhi.Google Scholar
  2. All India Soil and Land Use Survey (1990). Watershed Atlas of India, Publishers: All India Soil and Land Use Survey. Ministry of Agriculture, Government of India, New Delhi.Google Scholar
  3. Anderson, J.R., Hardy, E.E., Roach, H.J. and Witmer, R.E. (1976). A land use and land cover classification system for use with remote sensor data. Geological Survey Professional Paper 964. United States Government Printing Office, Washington. D.C.Google Scholar
  4. Bahuguna, I.M., Nayak, S., Tamilarsan, V. and Moses, J. (2003). Ground water prospective zones in basaltic terrain using remote sensing.J. of Indian Society of Remote Sensing,31(2): 101–105.Google Scholar
  5. Bishop, Y., Fienberg, S. and Holland, P. (1975). Discrete multivariate analysis-theory and practices. MIT Press, Cambridge, Massachusetts, 575p.Google Scholar
  6. Congalton, R., Oderwald, R. and Mead, R. (1983). A quantitative method to test for consistency and correctness in photo interpretation.Photogrammetric Engineering on Remote Sensing. 49(1): 69–74.Google Scholar
  7. Das, D.C. (1985). Problem of soil erosion and land degradation in India. Lead paper, National Seminar on Soil Conservation and Watershed Management, New Delhi, Sep. 17–18.Google Scholar
  8. Dowdeswell, E. (1998). Extent and impacts of soil degradation on a world-wide scale. In H.P. Blumeet al. (eds.) Towards Sustainable Land Use: Furthering Co-operation between People and Institutions (Vol-1) Advances in Geo-ecology. A Cooperating Series of the International Society of Soil Science. Pp. xi–xv.Google Scholar
  9. Jayakumar, S. and Arockiasamy, D.I. (2003). Land use/ land cover mapping and change detection in part of Eastern Ghats of Tamil Nadu using remote sensing and GIS.J. of Indian Society of Remote Sensing,31(4): 261–270.Google Scholar
  10. Jones, C.B. Kidner, D.B., Luo, L.Q. Bundi, G.L. and Ware, J.M. (1996). Data base design for a multi- scale spatial information system.International J. of Geographic Information Systems,10(8): 901–920.Google Scholar
  11. Jothiprakash, V., Marimuthu, G., Muralidharan, R. and Senthilkumar, N. (2003). Delineation of potential zones for artificial recharge using GIS.J. of Indian Society of Remote Sensing,31(1): 37–47.Google Scholar
  12. Lal, R. and Pierce, F. J. (1991). The vanishing resource. In Soil Management for Sustainability. Soil and Water Conservation Society, U.S.A., pp. 1–5.Google Scholar
  13. Malczewski, J. (1996). A GIS - based approach to multiple criteria group decision-making.International J. of Geographic Information Systems,10(8): 955- 971.Google Scholar
  14. Martin, D. (1996). An assessment of surface and zonal models of population.International J. of Geographic Information Systems,10(8): 973–990.Google Scholar
  15. Nandy, S., Joshi, P.K. and Das, K.K. (2003). Forest canopy density stratification using biophysical modeling.J. of Indian Society of Remote Sensing,31(4): 291–298.Google Scholar
  16. National Commission on Agriculture (1976). Report of the National Commission on Agriculture, Parts V, IX and Abridged Report. Ministry of Agriculture and Irrigation, Govt. of India, New Delhi.Google Scholar
  17. National Remote Sensing Agency (1995). Integrated Mission for Sustainable Development: Technical Guidelines. Technical Report. National Remote Sensing Agency, Department of Space, Government of India, Hyderabad.Google Scholar
  18. Olson, G.W. (1981). Archaeology: Lessons on future soil use.J. Soil and Water Conservation,36: 261–264.Google Scholar
  19. Rao, D.P. (2000). Role of remote sensing and geographic information system in sustainable development. Proc. XIXth Congress of the International Society for Photogrammetry and Remote Sensing (ISPRS) on Geo-information for All. 16–23 July 2000, Amsterdam, The Netherlands.Google Scholar
  20. Rao, D.P. and Chandrasekhar, M.G. (1996). Integrated Mission for Sustainable Development (IMSD): A holistic approach to land and water resources development. Proc. 47th International Astronautical Congress. October, 7–11, 1996, Beijing, China.Google Scholar
  21. Reddy, M.G.R., Reddy, G.P.O., Maji, A.K. and Rao, N. (2003). Landscape analysis for pedo-geomorphological characterization in part of basaltic terrain, central India using remote sensing.J. of Indian Society of Remote Sensing,31(4): 271–282.CrossRefGoogle Scholar
  22. Rotmans, J. and Dowlatabadi, H. (1996). In Human and Choice and Climate Change: An International Social Science Assessment, S. Rayner and E. Malone, eds. Cambridge University Press, New York.Google Scholar
  23. Remillard, M.M. and Welch, R.A. (1993). GIS technologies for aquatic macrophyle studies: Modeling applications.Landscape Ecology,8(3):: 163–175.CrossRefGoogle Scholar
  24. U.S. Department of Agriculture (1975). Soil Taxonomy, A basic system for making and interpreting soil survey Government Printing Office, Washington D.C. U.S.A.Google Scholar
  25. Welch, R.A., Fernandes, N. and Jordan, T. (1993) GIS modelling of non-point source pollution with remotely sensed data. Proc. of the 1993 Georgia Water Resources Conference, April 20 and 21, 1993 at the University of Georgia, Kathryn, J. Hatcher. Editor. Institute of Natural Resources, the University of Georgia, Athens, Georgia.Google Scholar

Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  • R. S. Dwivedi
    • 1
    Email author
  • K. Sreenivas
    • 1
  • K. V. Ramana
    • 1
  • P. R. Reddy
    • 1
  • G. Ravi Sankar
    • 1
  1. 1.National Remote Sensing Agency, Department of SpaceGovernment of IndiaBalanagar, HyderabadIndia

Personalised recommendations