Decision-tree and rule-induction approach to integration of remotely sensed and GIS data in mapping vegetation in disturbed or hilly environments |
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| Authors: | Brian G Lees Kim Ritman |
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| Institution: | (1) Department of Geography School of Resource & Environmental Management Faculty of Science, Australian National University, Box 4, 2601 Canberra, ACT, Australia |
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| 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. |
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| Keywords: | Vegetation mapping Geographic information systems Decision-tree classifiers Artificial intelligence |
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