Abstract
The Brazilian Cerrado is threatened by agricultural land use conversion. Accurate quantification of overall and subtype Cerrado distributions is essential for regional monitoring. In this research, unsupervised fuzzy ARTMAP was compared against conventional k-means classification of Cerrado and agriculture, based on Hyperion satellite data. We systematically tested a range of fuzzy ARTMAP parameters, determining the best parameter combinations. The effect of an additional surface liquid-water input vector was also tested. Similar results were obtained when only Hyperion apparent surface reflectance data were used; fuzzy ARTMAP, however, was generally markedly more accurate than k-means when the additional surface liquid-water input was included.
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