With the improvement of remote sensing technology, the spatial, structural and texture information of land covers are
present clearly in high resolution imagery, which enhances the ability of crop mapping. Since the satellite RapidEye was
launched in 2009, high resolution multispectral imagery together with wide red edge band has been utilized in vegetation
monitoring. Broad red edge band related vegetation indices improved land use classification and vegetation studies.
RapidEye high resolution imagery was used in this study to evaluate the potential of red edge band in agricultural land
cover/use mapping using an objected-oriented classification approach. A new object-oriented decision tree classifier was
introduced in this study to map agricultural lands in the study area. Besides the five bands of RapidEye image, the
vegetation indexes derived from spectral bands and the structural and texture features are utilized as inputs for
agricultural land cover/use mapping in the study. The optimization of input features for classification by reducing
redundant information improves the mapping precision about 18% for AdaTree. WL decision tree, and 5% for SVM, the
accuracy is over 90% for both classifiers.