Mapping Sparse Vegetation Canopies

  • Milton O. Smith
  • John B. Adams
  • Don E. Sabol
Part of the Eurocourses: Remote Sensing book series (EURS, volume 4)


Mapping sparse vegetation communities is routinely applied using techniques such as band ratios, the normalized difference vegetation index (NDVI) and spectral mixture analysis (SMA). The uncertainty of these vegetative mapping techniques is examined using the soil spectral variability defined by the spectral reference endmembers from three Landsat Thematic Mapper images: Owens Valley, California, USA; Gran Desierto, Sonora, Mexico, and Fayyum, Egypt. We find that band ratios and NDVI are not optimized for detecting vegetation given soil spectral variability. For SMA, the detection of sparse vegetation is optimized when it is detected as a residual component. Depending on the uncertainty model utilized from two to four fold improvement in mapping sparse vegetation is possible compared to NDVI and band ratios.


Normalize Difference Vegetation Index Multispectral Image Sparse Vegetation Band Ratio Green Vegetation 
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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7. References

  1. Blount, G., M.O. Smith, J.B. Adams, R. Greeley, and P.R. Christensen (1990) ‘Regional aeolian dynamics and sand mixing in the Gran Desierto: evidence from Landsat thematic mapper images’, J. Geophys. Res., 95, 15463–15482.Google Scholar
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Copyright information

© ECSC, EEC, EAEC, Brussels and Luxembourg 1994

Authors and Affiliations

  • Milton O. Smith
    • 1
  • John B. Adams
    • 1
  • Don E. Sabol
    • 1
  1. 1.Department of Geological Sciences AJ-20University of Washington SeattleWashington

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