Landslide detection based on height and amplitude differences using pre- and post-event airborne X-band SAR data

The recognition of landslides and making their inventory map are considered to be urgent tasks not only for damage estimation but also for planning rescue and restoration activities. Owing to the capability of synthetic aperture radar (SAR) for day-and-night and all-weather imaging, various studies utilizing SAR data for landslide detection have been reported to date. Among the detection methods utilizing SAR data, those based on height differences accompanying landslides are attractive and should be further improved, since they can directly contribute to damage estimation through a volumetric estimation of landslides. In this context, we propose in this paper a landslide detection method utilizing height differences derived from pre- and post-event SAR digital elevation models (DEMs) combined with amplitude differences. The proposed method was applied to the landslides triggered by the 2016 Kumamoto earthquake. The application results demonstrate that SAR DEMs with a high altitudinal resolution can improve the detection ability and that the incorporation of the amplitude differences is effective for decreasing the number of false detections. Although the reliability of the proposed method is deemed moderate when evaluated on the basis of the kappa coefficients derived through an accuracy assessment, most of the outliers are correctly filtered out and large- and medium-scale landslides are detected. Therefore, the inventory maps derived from the proposed method are thought to be effective at the initial stage of planning rescue and restoration activities.