Disaster risk reduction using image fusion of optical and SAR data before and after tsunami

This study applied supervised change detection to identify and estimate damaged urban surface conditions before and after a tsunami event in order to provide more accurate information for the implementation and enhancement of disaster risk reduction policies and strategies. Advanced remote sensing is crucial to support cost-effective emergency response activities for disaster risk assessment and management. This preliminary study, as an effort to propose a good case study in risk management, suggested that three main steps, i.e., filtering, fusing and classifying, should be adopted to perform change detection before and after a natural disaster. We fused very high-spatial-resolution multi-temporal optical images (0.6 m spatial resolution) and X-band SAR images (2.5 m spatial resolution). The study also revealed that the decision-level method, i.e. morphological transform, was the most promising in image fusion of filtered images to classify urban surfaces in tsunami damage assessment, compared with the pixel-level method, i.e. wavelet transform, and feature-level method, i.e. segmentation extraction. This paper reports, coupled with the results from the image fusion, that the preliminary results are good enough to obtain urban impervious surface estimation of a wide disaster risk area but not good enough to make clear amplitude images to identify individual buildings of dwelling zone. The proposed normalized change index (NCI), an important indicator for detecting changes, was found capable of providing better estimation of urban impervious surfaces, such as transport-related land (e.g., bridges and parking lots) and building roof tops in residential and industrial areas over a coastal zone in Rikuzen-takada City, devastated in the 2011 Great East Japan Earthquake.