Fine-scale spatiotemporal land use changes is a prerequisite for understanding and predicting the effects of urbanization on the ecosystem. Land use changes are frequently examined using vegetation indices (VIs), although the validation of these indices has not been conducted at a high resolution. Therefore, a hierarchical classification was constructed to obtain accurate land use types at a fine scale. The characteristics of normalized difference VI (NDVI), green-red VI (GRVI), enhanced VI (EVI), and two-band EVI (EVI2) were investigated prior to examining the hierarchical classification. The reflectance data were obtained by the IKONOS and WorldView-2 sensor in 2001 and 2015 respectively. The hierarchical classification of land use types was constructed using a decision tree (DT) utilizing all four of the examined VIs. The accuracy of the classification was evaluated using ground truth data with multiple comparisons and kappa (k coefficients. The DT showed overall accuracies of 96.1% and 97.8% in 2001 and 2015, respectively, while the accuracies of the VIs were less than 91.2%.  These results indicate that each VI exhibits unique advantages. In addition, the DT was the best classifier of land use types, particularly for native ecosystems represented by Shorea forests and homestead vegetation, at the fine scale.