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Environmental Management

, Volume 15, Issue 6, pp 823–831 | Cite as

Decision-tree and rule-induction approach to integration of remotely sensed and GIS data in mapping vegetation in disturbed or hilly environments

  • Brian G. Lees
  • Kim Ritman
Research

Abstract

The integration of Landsat TM and environmental GIS data sets using artificial intelligence rule-induction and decision-tree analysis is shown to facilitate the production of vegetation maps with both floristic and structural information. This technique is particularly suited to vegetation mapping in disturbed or hilly environments that are unsuited to either conventional remote sensing methods or GIS modeling using environmental data bases.

Key words

Vegetation mapping Geographic information systems Decision-tree classifiers Artificial intelligence 

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Copyright information

© Springer-Verlag New York Inc. 1991

Authors and Affiliations

  • Brian G. Lees
    • 1
  • Kim Ritman
    • 1
  1. 1.Department of Geography School of Resource & Environmental Management Faculty of ScienceAustralian National UniversityCanberraAustralia

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