Synthetic Aperture Radar (SAR) can observe the earth surface in daytime and night-time regardless of weather conditions. Hence it is highly useful to capture damage distributions shortly after natural disasters strike. Detection of flooded areas is carried out using ALOS/PALSAR images acquired before and after the 2008 Iwate-Miyagi Inland earthquake. Because the backscattering echo of SAR shows the condition of the earth surface, the changes of the echo in two images are used to detect the areas covered by water and those dried up after the earthquake. Since many small noises are also extracted from the SAR images, an open-close-scale filter is employed to remove them. The extracted results are compared with the visual detection results from ALOS/AVNIR-2 images and then the accuracy of the proposed method is verified.

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ALOS/AVNIR-2 optical images were employed to extract slope failures which occurred in the 14 June 2008 Iwate-Miyagi Inland, Japan, earthquake. Since the earthquake occurred in the mountainous area, many landslides were caused. In this paper, landslide areas were extracted comparing the pre- and post-event ALOS/AVNIR-2 images by two methods. One method uses the difference of the normalized difference vegetation index (NDVI) obtained from the pre- and post-event images. Another method performs a supervised land-cover classification by maximum likelihood approach, and the difference of extracted bare-ground pixels between the two images was recognized as landslide. The digital elevation model (DEM) was also employed to reveal the relationship between the slope angle and the occurrence of landside, and to remove the extraction errors on non-sloping ground. Comparing with the visual inspection result, the accuracy of these image processing methods was demonstrated.

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In this study, the texture analysis of the high-resolution SAR image is conducted in order to extract areas of landslides produced by the 2008 Iwate-Miyagi-Nairiku, Japan earthquake. The variance and skewness of the data for different window sizes are calculated. The result shows that the area with the higher variance for the window size of 100 x 100 pixels or more shows better correlation with the landslide area.

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Remote sensing technology is effective to grasp the damage distributions from various natural disasters, such as earthquakes, tsunamis and volcanic eruptions. After the 2007 Niigata-ken Chuetsu-oki earthquake, aerial images were taken in the stricken area by several air survey companies. Airborne remote sensing is more suitable to collect detailed damage distribution because it provides higher resolution images than satellite remote sensing does. The pre- and post-event images taken by a digital aerial camera are employed in this study to detect building damages. Since visual damage inspection takes time to perform for the whole areas that are subjected to severe ground motion, an object-based technique is proposed to extract debris from buildings. The proposed method is expected to contribute for the damage assessment at an early stage after the occurrence of an earthquake.

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In order to evaluate distribution of building damage in the southeastern part of Beichuan county, severely damaged by the 2008 Sichuan, China, earthquake, visual damage detection technique is applied to pre- and post-event satellite optical images. The locations of about 5,000 buildings are identified from the pre-event image. The damage of each building is classified into three categories; complete collapse, severe damage, and less than moderate damage by comparing the pre- and post-event images. The damage ratio computed from the result of the visual detection in Qushan town, the capital of Beichuan county, is more than 80%. The distribution of the building damage shows that the damage ratios on the hanging wall of the earthquake fault is higher than those on the foot wall.

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In this paper, the authors develop a multiple regression model which estimates urban earthquake vulnerability (building collapse risk and conflagration risk) for different eras, and clarify the historical changes of urban risk in Marunouchi and Ginza Districts in Tokyo, Japan using old maps and contemporary geographic information data. Also, we compare the change of urban vulnerability of the districts with the significant historical events in Tokyo. Finally, the results are loaded onto Google Earth with timescale extension to consider the possibility of urban recovery digital archives in the era of the recent geoinformatic technologies.

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For quick and stable estimation of damaged buildings due to earthquakes all over the world using PALSAR (Phased Array type L-band Synthetic Aperture Radar) loaded on the ALOS satellite, a model using combined usage of satellite SAR imagery and seismic intensity is proposed. In order to expand the existing C-band SAR based damage estimation model into L-band SAR, this paper rebuilds a likelihood function for severe damage ratio on the basis of dataset from JERS-1/SAR (L-band SAR) images observed the 1995 Kobe earthquake and its detailed ground truth data. The model which integrates the fragility functions of building damage in terms of seismic intensity and proposed likelihood function is then applied to PALSAR images taken over the areas affected by the 2007 Pisco, Peru, and the 2008 Sichuan, China, earthquakes. The accuracy of the proposed damage estimation model is examined by comparing the results of the analyses with field investigations and/or interpretation of high-resolution satellite images.

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The authors proposed the tsunami fragility curves to estimate the structural damage by the 1993 Hokkaido Nansei-oki earthquake tsunami, by the integrated approach of damage inspection of aerial photographs of pre and post tsunami in Aonae town and the numerical modeling of tsunami inundation flow. The tsunami fragility curves are expressed as the structural damage probabilities with regard to the hydrodynamic features of tsunami inundation flow, such as inundation depth, inundation height, current velocity and hydrodynamic force, to expand the capability of estimating potential tsunami damage in a quantitative manner.

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The improvement of the recent information technologies has changed the ways of recording urban recovery. The authors report on the Urban Recovery Digital Archives for Chi-Chi City affected by the 1999 Taiwan Earthquake, which was constructed in order to record the post earthquake recovery condition based on the field surveys. This paper shows how the authors obtained the necessary information in Chi-Chi and how to develop the digital archives. It also presents the contents and functions including timescale expression, images of building conditions, and the recovery information of some important facilities using placemark.

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Many methods of automatic analysis of wide urban areas after a large-scale disaster using aerial images have been researched. In several of these methods, information is obtained by integrating aerial images taken before and after the disaster or by integrating the images and a map. For their automation, images must be registered automatically. In this paper, we propose a new method of automatic registration of urban aerial images taken before and after a disaster. First, this method detects feature points whose changes are few in the images taken before and after the disaster. Then, this method registers the images automatically by using the locations of the feature points and a_geometric transformation.

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