Synthetic aperture radar (SAR) is able to detect sur-face changes in urban areas with a short revisit time, showing its capability in disaster assessment and urbanization monitoring. Most presented change detection methods are conducted using couples of SAR amplitude images. However, a prior date of sur-face change is required to select a feasible image pair. We pro-pose an automatic spatio-temporal change detection me-thod by identifying the temporary coherent scatterers. Based on amplitude time series, x2-test and iterative single pixel change detection are proposed to identify all step-times: the moments of the surface change. Then the parameters, e.g., deformation velo-city and relative height, are estimated and corresponding cohe-rent periods are identified by using interferometric phase time series. With identified temporary coherent scatterers, different types of temporal surface changes can be classified using the location of the coherent periods and spatial significant changes are identified combining point density and F values. The main advantage of our method is automatically detecting spatio-tem-poral surface changes without prior information. Experimental results by the proposed method show that both appearing and disappearing buildings with their step-times are successfully identified and results by ascending and descending SAR ima-ges show a good agreement.