Expert system classifies eucalypt forest types using thematic mapper data and a digital terrain model

Andrew K. Skidmore*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

121 Citations (Scopus)

Abstract

Landsat Thematic Mapper digital data were classified into seven native eucalypt forest type classes using a nonparametric classifier that also calculated the probability of correct classification for each pixel. A digital elevation model, spaced on a 30-m grid, was generated and used to derive terrain features of gradient, aspect, and topographic position, which were geometrically co-registered with the TM thematic images. The thematic maps of forest type, probability of correct classification, and terrain features provided data for the expert system to infer the most likely forest species occurring at any given pixel. The modified thematic map output by the expert system had a higher mapping accuracy than the thematic map produced by the supervised nonparametric, the maximum likelihood, and the Euclidean distance classifier.

Original languageEnglish
Pages (from-to)1449-1464
Number of pages16
JournalPhotogrammetric Engineering and Remote Sensing
Volume55
Issue number10
Publication statusPublished - Oct 1989
Externally publishedYes

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