The Earth’s land-covers are exposed to several types of environmental changes, issued by either human activities or natural disasters. On 15 April 2017, severe precipitations in the west and southwest regions of Iran caused flooding in the rivers of Ilam, Lorestan and Khuzestan provinces. The peak of this rainfall was in the Karoon Basin and the Dez Dam, causing an unprecedented flood in recent years with an intensity of eight thousand cubic meters per second. The occurrence of this flood has led to damages to these villages and agricultural plains. As well as, on 11 March 2011, an earthquake occurred at about 130 km of the east coast of Sendai City in Japan. This earthquake has been followed by a huge tsunami, which caused devastating damages over the wide areas in the eastern coastlines of Japan. Due to the occurrence of natural disasters across the world, there is a strong need to develop an automated algorithm for fast and accurate extraction of changed landscapes within the affected areas. Such techniques can accelerate the process of strategic planning and primary services for people to move into shelters, damage assessment, as well as risk management during a crisis. Therefore, a variety of change detection (CD) techniques has been previously developed, based on various requirements and conditions. However, the selection of the most suitable method for change detection is not easy in practice. To our best of knowledge, there is no existing CD approach that is both optimal and applicable in the cases of using a variety of optics and radar remote sensing images. In order to resolve these problems, an automated CD method based on Support Vector Data Description (SVDD) classifier is proposed. This method used the information contents of radar and optical data simultaneously by decision level fusing of obtained change maps from these data. In order to evaluate the efficiency of the proposed method and extract the damaged areas, two case studies consist of Sendai 2011’s tsunami and Shoosh 2017’s flood were considered. Various optical and radar remote sensing images from before and after of Sendai 2011’s tsunami and Shoosh 2017’s flood, acquired by IKONOS, Radarsat-2 and Sentinel-1, 2 were used, respectively. The proposed CD approach leads to an acceptable level of accuracy for both optical and radar imagery. The results confirmed the fundamental role and potential of using both optical and radar data for natural hazard damage detection applications.