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Evaluation of Landsat TM data for forest cover type and landuse classification in subtropical forests of Kumaon Himalaya (U.P.)

  • Inderjit Singh
Article

Abstract

LANDSAT-TM has been evaluated for forest cover type and landuse classification in subtropical forests of Kumaon Himalaya (U.P.) Comparative evaluation of false colour composite generated by using various band combinations has been made. Digital image processing of Landsat-TM data on VIPS-32 RRSSC computer system has been carried out to stratify vegetation types. Conventional band combination in false colour composite is Bands 2, 3 and 4 in Red/Green/Blue sequence of Landsat TM for landuse classification. The present study however suggests that false colour combination using Landsat TM bands viz., 4, 5 and 3 in Red/Green/Blue sequence is the most suitable for visual interpretation of various forest cover types and landuse classes. It is felt that to extract full information from increased spatial and spectral resolution of Landsat TM, it is necessary to process the data digitally to classify land cover features like vegetation.

Supervised classification using maximum likelihood algorithm has been attemped to stratify the forest vegetation. Only four bands are sufficient enough to classify vegetaton types. These bands are 2,3,4 and 5. The classification results were smoothed digitaly to increase the readiability of the map.

Finally, the classification carred out using digital technique were evaluated using systematic sampling design. It is observed that forest cover type mapping can be achieved upto 80% overall mapping accuracy. Monospecies stand Chirpine can be mapped in two density classes viz., dense pine (<40%) with more than 90% accuracy. Poor accuracy (66%) was observed while mapping pine medium dense areas. The digital smoothening reduced the overall mapping accuracy. Conclusively, Landsat-TM can be used as operatonal sensor for forest cover type mapping even in complex landuse-terrain of Kumaon Himalaya (U.P.)

Keywords

Landsat Subtropical Forest Band Combination Landsat Thematic Mapper Grass Land 
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.

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References

  1. Champion H.G. and S.K. Seth, 1968. A Revised Survey of Forest Types of India. Government Publication, Delhi.Google Scholar
  2. Crist, E.P. 1983. The Thematic Mapper Tasseled Cap-A Preliminary Formulation. Proc. 9th Int. Symp. Machine Proceeding Remotely Sensed data. Prude University, W. Lafayette Ind. pp. 357–363.Google Scholar
  3. Curran, Paul J., 1985. Principles of Remote Sensing, Longman, London.Google Scholar
  4. Gupta, N.K., R.K., Lallianthanga & Beant Singh, 1987–88. Forest cover type and landuse mapping and assessment of growing stock of Ranikhet, using Remote Sensing Techniques, Project report.Google Scholar
  5. Jenson, John R., 1986. Introductory digital image processing. Prentice Hall Eaglewood New Jersey, pp. 117–129.Google Scholar
  6. Lilesand, Thomas M. & Kiefer, Ralph W., 1987. Remote Sensing & Image Interpretation. 2nd Ed., Johnwilley & Son, New York.Google Scholar
  7. Roy, P.S. 1988. Application of Remote Sensing Data for Forest Survey & Mapping, IIRS (NRSA), Technical Report.Google Scholar
  8. Roy, P.S. & N.V.M. Unni, 1980. The forest type clasification and shifting cultivation area mapping using Landsat (NRSA), Technical Report.Google Scholar
  9. Roy, P.S. & S.K. Bhan, 1988. Evaluation of SPOT-1 Multispectral Data for Forest/Vegetation Analysis. IIS (NRSA), Technical Report.Google Scholar
  10. Roy, P.S., 1987. Montane Vegetation Stratification through Digital. Processing of Landsat MSS Data GEOCARTO International Conf., Vol. I, pp. 19–26.Google Scholar
  11. Unni, N.V.M. 1984. Forest Survey & Management using Remote Sensing in Remote Sensing. Edt. by B.L. Deekshatulu & Y.S. Rajan. Indian Academy of Science, Bangalore, pp 115–138.Google Scholar

Copyright information

© Springer-Verlag 1988

Authors and Affiliations

  • Inderjit Singh
    • 1
  1. 1.Indian Institute of Remote Sensing (NRSA)Dehra Dun

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