Two consecutive earthquakes with magnitudes exceeding 7 occurred in the Republic of Turkey on February 6, 2023, causing severe damage to cities along the causative fault lines. Many satellite images were provided soon after the earthquakes for supporting the emergency response. In this study, the authors conducted a quick extraction of collapsed individual buildings using two pre-event and one post-event TerraSAR-X intensity images. The differences of backscattering coefficients and the correlation coefficients were calculated for pre-event and co-event SAR data pairs. Collapsed buildings were then identified based on the thresholds defined by the pre-event pair. Compared with visual interpretation results from high-resolution optical images, 64% of the collapsed buildings were detected successfully, demonstrating the effectiveness of our proposed method at an early stage of disaster response.

Wide spread landslides were extracted from airborne LiDAR data acquired before and after the 6 September 2018 Hokkaido-Eastern-Iburi earthquake. By taking the difference of Digital Surface Models (DSMs) from the LiDAR datasets, the failed slopes and accumulated soils were recognized. Since the pre-event dataset was obtained 12 years before the earthquake, the growth of trees was estimated based on forest registry block data. The adjusted DSM difference was compared with the visual inspection result of aerial photos by the Geospatial Information Authority of Japan and the accuracy of the confusion matrix was discussed.

A series of earthquakes hit Kumamoto Prefecture, Japan, in April 2016. Numerous landslides occurred in the surroundings of the Aso volcano and in the areas within 10-km from the Futagawa fault. Two months after the earthquake, the stagnation of the rainy season front brought heavy rainfall to the Kyushu Island. The rainfall caused additional landslides and the expansion of the earthquake-induced landslides. In this study, three temporal Lidar data taken before the 2016 Kumamoto earthquake, after the earthquake and after the heavy rainfall, were used to estimate the earthquake-induced and rainfall-induced landslides in the outer ring of the Mt. Aso. The height difference between two temporal digital surface models (DSMs) and that of digital terrain models (DTMs) were calculated. They were combined to extract the collapse zones and sediments of landslides. The results were verified by comparing with the reports of visual interpretation of aerial optical images.

One span of an aqueduct bridge suddenly collapsed in Wakayama City in western Japan on October 3, 2021. This study investigates the use of remote sensing data for the assessment of bridge situations in the normal time and in accidents/disasters. A field survey was conducted by the authors with the aid of a small UA V. Google Street View photos taken before the accident were also used. Based on these data, it is estimated that more than 5 hangers out of 18 might have failed for the collapsed span when the bridge collapse occurred. The failure of 4 hangers was also confirmed in the adjacent surviving span from the UAV images. The pre- and post-event high-resolution TerraSAR- X intensity images were also introduced to extract the collapsed span from the SAR data.

Record-breaking heavy rainfall hit Japan from July 3 to 31, 2020. The water level of rivers rose rapidly, and many bridges were washed away. In this study, two temporal ALOS-2 PALSAR-2 intensity images were introduced to detect damaged bridges over the Kuma River in Yatsushiro City and Ashikita Town, Kumamoto Prefecture, Japan. First, the backscattering models of bridges were investigated. Then the damage conditions of twenty-five target bridges were examined by the difference and correlation coefficient in the backscatter intensity. In addition, the increases of water levels was estimated by the movements of bridges' SAR model. A field survey report and a post-event SPOT-7 satellite optical image were used to verify our results.