In this paper, a new approach for unsupervised change-detection using multitemporal InSAR data is proposed, of which the significant characteristics is joint use of backscattering temporal intensity and long-term coherence based on 2-D (two dimensional) Renyi's entropy. The proposed approach is made up of two steps: feature extraction and unsupervised 2-D thresholding. In the first step, two features are based on the concepts of backscattering intensity variation and long-term coherence variation respectively, and are defined according to the analysis of different signal behavior of interferometric SAR in the presence of land-cover classes within urban area. In the second step, an unsupervised 2-D thresholding technique based on maximum 2-D Renyi's entropy criterion is developed. The thresholding is performed on the two difference images derived from the two features to produce an accurate change-detection map with two classes: changed and no-changed. Primary experimental results, which were obtained from a set of six multitemporal ERS-1/2 SAR images within Shanghai city of China, show the effective of the proposed approach and that ERS-1/2 InSAR data could be exploited for detecting urban land-cover changes.