Land Use/Land Cover mapping and change detection using space Borne data

  • M Palaniyandi
  • V Nagarathinam
Article

Abstract

Land Use/Land Cover classes in Thiruvallur area of Chengai_— MGR district in Tamil Nadu during the years 1986–90 were mapped through visual interpretation of LANDSAT 5 TM and IRS 1A LISS II images, over space and time. In the study area, it is observed that the built-up area and the agricultural land use extensions are on the upward trend, whereas the area under forest and wasteland has shown a declining trend, caused by both increasing population and related trends in other parameters. The system devised through the study has thus been able to detect the changes in the land uses and cover classes during the selected time periods.

References

  1. Adeniyi P O (1980). Land use change analysis using sequential aerial photography and computer techniques, Photogrammetric Engineering and Remote Sensing, Vol. 46, No. 11, pp. 1147–1464.Google Scholar
  2. Dhinwa P S, Pathan S K, Sastry S V C, Mukund Rao, Majumder K L, Chotani M L, Premnath Singh J and Sinha R L P (1992). Land use change analysis of Bharatpur District using GIS, Journal of the Indian Society of Remote Sensing, Vol. 20, No. 4, pp. 237–250.CrossRefGoogle Scholar
  3. Pant D N, Das K K and Roy P S (1992). Mapping of tropical dry deciduous forest and land use in part of Vindhyan range using satellite remote sensing, Journal of the Indian Society of Remote Sensing, Vol. 20, No. 1, pp. 9–20.Google Scholar
  4. Sudhakar S, Das R K, Chakraborty D, Bardhan Roy B K, Raha A K and Shukla P (1994). Stratification approach for forest cover type and land use mapping using IRS-1A LISS-II data_— A case study, Journal of the Indian Society of Remote Sensing, Vol. 22, No. 1, pp. 21–29.Google Scholar

Copyright information

© Springer-Verlag 1997

Authors and Affiliations

  • M Palaniyandi
    • 1
  • V Nagarathinam
    • 2
  1. 1.Department of GeographyMadurai Kamaraj UniversityMadurai
  2. 2.Institute of Remote SensingAnna UniversityChennaiIndia

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