Rapid urbanization dramatically changes the local environment. A hybrid classification method is designed and applied to multi-temporal Landsat images and ancillary data to obtain land cover change datasets. A support vector machine (SVM) classifier is used to classify multi-temporal Landsat Enhanced Thematic Mapper Plus (ETM+) images that were collected in 2000 at the pixel level. These images are also segmented with the mean shift method. The impervious surface is refined based on a combination of the segmented objects and the SVM classification results. The changed areas in 1990 and 2010 are determined by comparing the Thematic Mapper (TM) and ETM+ images via the re-weighted multivariate alteration detection transformation method. The TM images that were masked as changed areas in 1990 and 2000 are input into the SVM classifier. Land cover maps for 1990 and 2010 are produced by combining the unchanged area in 2000 with the new classes of the changed areas in 1990 and 2010. Land cover change has continuously accelerated since 1990. Remarkably, arable land decreased, while the impervious surface area significantly increased.