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Journal of the Indian Society of Remote Sensing

, Volume 19, Issue 3, pp 163–174 | Cite as

An evaluation of landsat thematic mapper data for reforestation monitoring in British Columbia

  • Arun K Bansal
  • Peter A Murtha
  • Raoul J Wiart
Article
  • 32 Downloads

Abstract

Renewal of forests is important for continued wood supply and for other benefits. Consequently, restocking of forest cut-overs is a major forest management activity. Effective planning and successful implementation of reforestation programmes require efficient techniques for obtaining timely and accurate information regarding restocking status over clearcut forest lands. The purpose of this paper is to investigate the potential of Landsat Thematic Mapper (TM) data for reforestation monitoring. B-distance, a multivariation distance measure, has been used to measure spectral separability. Attempt has been made to discriminate five restocking classes (with percent canopy classes of 0,10 -12,15 -18, 43 - 47 and 100). Finally selection has been made for the optimum multiband subset from the six reflective TM bands. The results indicate that the combinations of TM bands 3-4-5, 4-5-7,1-4-5, and 2-4-5 were most useful for discriminating various restocking classes. Overall classification accuracies are estimated to be approximately 90 percent using these three-band subsets.

Keywords

Remote Sensing Landsat Thematic Mapper Landsat Thematic Mapper Spectral Separability 
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. Ahern F J and Archibald P D (1986). Thematic mapper information contents about Canadian forests; early results from across the country,Proc. 10th Canadian Symposium on Remote Sensing, pp 683-697.Google Scholar
  2. Anuta P E, Bartolucci L A, Dean M E, Lozano D F, Malaret E, McGillem C D, Valdes J A and Valenzuela C R (1984). Landsat-4 MSS and TM data quality and information content-analysis,IEEE Transaction on Geoscience and Remote Sensing, Vol. GE-22, No. 3, pp 222–236.CrossRefGoogle Scholar
  3. Bansal A K and Murtha P A (1988). Use of normal colour aerial photographs in collection of data for reforestation assessment.Indian Forester, Vol. 115, No. 10, pp 724–732.Google Scholar
  4. Butera M K (1986). A correlation and regression analysis of percent canopy closure versus TMS spectral response for selected forest sites in the San Juan national forest, Colorado,IEEE Transactions on Geoscience and Remote Sensing, Vol. GE-24, No. 1, pp 122–129.CrossRefGoogle Scholar
  5. Card D H and Angelici G L (1983). A minicomputer-based software system for the selection of optimal subsets of thematic mapper channels,Proc. International Geoscience and Remote Sensing Symposium, Vol. 2, TP-4, pp 6.1–6.6.Google Scholar
  6. Chavez P S Jr (1984). Digital processing techniques for image mapping with Landsat TM and SPOT simulator data,Proc. Eighteenth International Symposium on Remote Sensing of Environment, pp 101–116.Google Scholar
  7. Chavez P S Jr, Berlin G L and Sowers L B (1982). Statistical method for selecting Landsat MSS ratios,Journal of Applied Photogrammetric Engineering, Vol. 8, No. 1, pp 23–30.Google Scholar
  8. Coist E P and Cione R C (1984). A Physically Based Transformation of TM Data - the Tasseled Cap.,IEEE Transaction on Geoscience and Remote Sensing, GE-22(3): 256–263.CrossRefGoogle Scholar
  9. Dean M E and Hoffer R (1982). An evaluation of thematic mapper simulator data for mapping forest cover,Proc. Eighth International Symposium on Machine Processing of Remotely Sensed Data, pp 300–307.Google Scholar
  10. DeGloria S D (1984). Spectral variability of Land-sat-4 TM and MSS data for selected crop and forest cover types,IEEE Transactions on Geoscience. Google Scholar
  11. Derengi E, Carlin D, Dillons M, Morgan D, Peters L and Yardani R (1984). Change Detection Projects in New Bnunswico,Proc. 9th Canadian Symposium on Remote Sensing; 799–806.Google Scholar
  12. Graetz R D and Gentle M R (1982). The relationships between reflectance in the Landsat wavebands and the composition of an Australian semi-arid shrub rangeland.Photogrammetric Engineering and Remote Sensing, Vol. 48, No. 11, pp 1721–1730.Google Scholar
  13. Hegyi F and Quenet R V (1982). Updating the forest inventory data base in British Columbia,Remote Sensing for Resource Management (Ed. J Johannsen and J L Sanders), pp 512–518.Google Scholar
  14. Hopkins P F, Maclean A L and Lillesand T M (1988). Assessment of thematic mapper imagery for forestry application under lake states conditions,Photogrammetric Engineering and Remote Sensing, Vol. 54, No. 1, pp 61–68.Google Scholar
  15. Horler D N H and Ahern F J (1986). Forestry information content of thematic mapper data,International Journal of Remote Sensing, Vol. 7, pp 405- 428.CrossRefGoogle Scholar
  16. Irons J R, Markhan B L, Nelson R F, Toll D T, Williams D L, Latty R S and Stauffer M L (1985). The effects of spatial resolution on the classification accuracy of TM data,International Journal of Remote Sensing, Vol. 6, No. 8, pp 1385–1403.CrossRefGoogle Scholar
  17. Murtha P A and Watson E K (1975). Mapping of forest-clearcutting, south Vancouver island, from Landsat imagery,Proc. 3rd Canadian Symposium on Remote Sensing, pp 257–264.Google Scholar
  18. Nelson R F, Latty R S and Mott G (1984). Classifying northern forests using thematic mapper simulator data.Photogrammetric Engineering and Remote Sensing, Vol. 50, No. 5, pp 607–617.Google Scholar
  19. Polar J, Klinka K and Meidinger D V (1986). Biogeoclimatic ecosystem classification in British Columbia, Province of British Columbia, Ministry of Forests, 21 p.Google Scholar
  20. Rencz A N (1985). Multitemporal Analysis of Landsat Imagery for Monitoring forest cutovers in Novascotia,Canadian Journal of Remote Sensing, 11(2): 188–194.Google Scholar
  21. Swain P H and King R C (1973). Two effective feature selection criteria for multi-spectral remote sensing, LARS Information Note 042673, Purdue University, West Lafayette, Indiana, 5 p.Google Scholar
  22. Swain P H (1978). Fundamentals of pattern recognition in remote sensing, pp 136–187,In Swain P H and Davis S M (eds),Remote Sensing: The Quantitative Approach, McGraw Hill International Book Company, New York, 396 p.Google Scholar
  23. Werle D, Lee Y J and Brown R J (1986). The use of multi-spectral and RADAR remotely sensed data for monitoring forest clearcuts and regeneration sites on Vancouver island,Proc. 10th Canadian Symposium on Remote Sensing, pp 319–326.Google Scholar
  24. Wilkinson L (1988a). SYGRAPH. Evanston, IL, Systat Inc., 922 p.Google Scholar
  25. Wilkinson L (1988b). SYSTAT: The System for Statistics. Evanston, IL, Systat Inc., 822 p.Google Scholar
  26. Williams D L, Irons J R, Markhan B L, Nelson R F, Toll D L, Latty R S and Stauffer M L (1984). A statistical evaluation of the advantages of Landsat TM data in comparison to MSS data.IEEE Trans. on Geoscience and Remote Sensing, Vol. GE-22, No. 3, pp 294–301.CrossRefGoogle Scholar

Copyright information

© Springer 1991

Authors and Affiliations

  • Arun K Bansal
    • 2
  • Peter A Murtha
    • 1
  • Raoul J Wiart
    • 3
  1. 1.FIRMS, Faculty of ForestryUniversity of British ColumbiaVancouverCanada
  2. 2.Office of the Principal Chief Conservator of ForestsOrissa, Bhubaneswar
  3. 3.B.C. Ministry of ForestsVictoriaCanada

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