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- W2053260479 abstract "There are increasingly more choices from a complex of data resources, classification algorithms, and methods of training sample selections. To increase the repeatability of digital classifications of remotely sensed data with consistently high accuracy, it is essential to use optimal classification options or factors. In this paper, two temporal sets of Landsat thematic mapper (TM) data, three classifiers and three approaches of training sample selections were tested for mapping deforestation. The use of these different factors can have significant effects on classification accuracy. The mixed effects of the three factors can also magnify the variations of classification accuracy. The use of bi-temporal data, a spatial‐spectral classifier, and hybrid training samples results in steadily higher classification accuracy than the combination of uni-temporal data, a spectral classifier, and image training samples. For the purpose of characterizing managed forest lands, even a small increase in overall accuracy of image classification is important because it may represent a large decrease in the variations of the producer's and user's accuracy, which in turn can reduce the uncertainties of area measurements for forest coverage." @default.
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- W2053260479 date "2002-01-01" @default.
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- W2053260479 title "Optimal combinations of data, classifiers, and sampling methods for accurate characterizations of deforestation" @default.
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- W2053260479 doi "https://doi.org/10.5589/m02-050" @default.
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