A new method of vegetation mapping by object-based classification using high resolution satellite data

概要

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The effectiveness of object-based classification using high resolution satellite data was examined to establish in applying to vegetation mapping. We compared object-based and pixel-based classifiers for secondary forests in a rural area in the east part of Chiba prefecture. The minimum distance classifier as the object-based classification, and the maximum likelihood classifier and the ISODATA classifier as the pixel-based classification were applied. The results showed that the overall classification accuracy and Kappa statistics of object-based classification were higher than those of pixel-based, ISODATA and maximum likelihood classifications (overall classification accuracy of object-based : 64.17%, maximum likelihood : 60.17%, ISODATA : 53.64% and Kappa statistics of object-based : 0.551, maximum likelihood : 0.497, ISODATA : 0.388, respectively) . Boundaries of each plant community were well extracted by object-based classification. This research clarified that the object-based classification method is useful and has high potentiality in vegetation mapping.
社団法人日本写真測量学会の論文