Masashi Matsuoka

Use of Satellite SAR Intensity Imagery for Detecting Building Areas Damaged Due to Earthquakes

Synthetic aperture radar (SAR) is remarkable for its capability to record the backscattering coefficient, the physical value of the earths surface, regardless of weather condition or sun illumination. Therefore, SAR is a powerful tool that can be utilized to develop a universal method to comprehend damaged areas in disasters such as earthquakes, forest fires, and floods. We performed a feasibility study on backscattering characteristics of damaged areas in the 1995 Hyogoken-Nanbu (Kobe), Japan, earthquake using the pre- and post-event ERS images, revealing that the backscattering coefficient and intensity correlation between the two attained values were significantly lowered in hard-hit areas. The evaluation, however, was performed without speckle noise reduction. We also investigated the effects of speckle noise reduction and pixel-window size in evaluating building damage using the difference in the backscattering coefficient and correlation coefficient of the pre- and post-event ERS images. From the analysis, an optimum window size for the damage evaluation was obtained. It was also found that the accuracy of damage detection is not significantly improved for speckle-reduction filtering of window size larger than 21×21 pixels. We developed an automated method to detect hard-hit areas based on the discriminant analysis, and compared the detected distribution with a damage survey result.

Visual Damage Interpretation of Buildings in Bam City Using QuickBird Images Following the 2003 Bam, Iran, Earthquake

A strong earthquake struck the city of Bam in southeast Iran on 26 December 2003. The earthquake brought massive destruction to the city and its surrounding rural areas. QuickBird, a high-resolution satellite, captured a clear image of Bam on 03 January 2004, eight days after the event. The city was also observed by QuickBird on 30 September 2003, about three months before the event. In this paper, using the pre-event image, the location of individual buildings was registered on GIS and the city blocks surrounded by major roads were assigned. Then, the visual damage interpretation based on the European Macroseismic Scale (EMS-98) was carried out building by building, comparing the pre-event and post-event images. The result of the damage inspection was compared with field survey data, and the accuracy and usefulness of the high-resolution satellite images in damage detection was demonstrated.

Shadow detection and radiometric restoration in satellite high resolution images

In this paper a new transformation which enables us to detect boundaries of cast shadows in high resolution satellite images is introduced. The transformation is based on color invariant indices. Different radiometric restoration techniques such as gamma correction, linear-correlation correction and histogram matching are introduced in order to restore the brightness of detected shadow area

Building Damage Mapping of the 2003 Bam, Iran, Earthquake Using Envisat/ASAR Intensity Imagery

A strong earthquake occurred beneath the city of Bam, Iran, on 26 December 2003. High-resolution optical satellite images, such as Ikonos and QuickBird, obtained after the earthquake indicate that severely damaged areas were widely distributed in the city. A European radar satellite, Envisat, also captured the hard-hit areas on 07 January 2004. This paper introduces an automated damage detection technique that was developed based on the data set of the 1995 Kobe, Japan, earthquake and applied to Envisat/ASAR images of Bam. A detailed investigation of the characteristics of the areas damaged due to the Bam earthquake in terms of the differences in the backscattering coefficient and the correlation coefficient of the pre- and post-event Envisat/ASAR images was conducted in order to raise the precision of damage detection. Finally, the damage-mapping scheme was revised to present the distribution of damaged areas in Bam.

LIDAR-based change detection of buildings in dense urban areas

An automatic method for LIDAR-based (Light Detection And Ranging) change detection is proposed. Highly dense LIDAR point clouds are recommended as the most suitable gathered data for dense urban areas. The main goal is to develop an up-to-date building inventory database, which is in great demand for the earthquake-prone areas like Japan, using LIDAR as primary data. Two LIDAR surveying flights in 1999 and 2004 provide the test data over Roppongi, Tokyo, Japan. Detected results are visual evaluation using orthophoto produced by LIDAR surveying flights. The highly automated processing proved the efficiency of using LIDAR for a quick and reliable updating. Moreover, it also implies the feasibility for detection of damaged buildings due to earthquake.

REMOTE SENSING TECHNOLOGIES IN POST-DISASTER DAMAGE ASSESSMENT

This paper highlights the recent applications of remote sensing technologies in post-disaster damage assessment, especially in the 2004 Indian Ocean tsunami and the 2006 Central Java earthquake. After the 2004 Indian Ocean tsunami, satellite images which captured the affected areas before and after the event were fully employed in field investigations and in tsunami damage mapping. Since the affected areas are vast, moderate resolution satellite images were quite effective in change detection due to the tsunami. Using high-resolution optical satellite images acquired before and after the 2006 Central Java earthquake, the areas of building damage were extracted based on pixel-based and object-based land cover classifications and their accuracy was compared with visual inspection results. In the Central Java earthquake, ALOS/PALSAR captured a SAR image of the affected area one day after the event as well as pre-event times. Taking the difference of the pre-event correlation and the pre-and-post event correlation, the areas affected by the earthquake were also identified. From these examples, the use of proper satellite imagery is suggested considering the area to cover, sensor type, spatial resolution, satellites retake time etc., in post-disaster damage assessment.

Detection and Animation of Damage Using Very High-Resolution Satellite Data Following the 2003 Bam, Iran, Earthquake

The focus of this study was to thoroughly exploit the capability of very high-resolution (VHR) satellite imagery such as Ikonos and QuickBird for disaster mitigation. An efficient automated methodology that detects damage was implemented to derive the rich information available from VHR satellite imagery. Consequently, the detected results and the VHR satellite imagery are attractively presented through a fly-over animation and visualization. The aim is to assist the field-based damage estimation and to strengthen public awareness. The available Ikonos and QuickBird data captured after the Bam, Iran, earthquake in December 2003 was employed to demonstrate the competence of the automated detection algorithm and fly-over animation/visualization. These results are consistent with the field-based damage results.

Earthquake damage detection using high-resolution satellite images

QuickBird observed the city of Zemmouri, Algeria, before and after the May 21, 2003 Algeria earthquake. Using the pre-event and post-event pan-sharpened images, visual inspection of building damage was carried out by the five authors of this paper individually. A total 1,399 buildings were classified into five damage levels of European Micro-seismic Scale. The results from the different interpreters were reasonably close for collapsed buildings but the difference becomes larger for smaller damage levels. The locations of refugee tents in the two post-event images were also identified. These observations indicate that high-resolution satellite images can provide quite useful information to emergency management after natural disasters.

Building Damage Estimation by Integration of Seismic Intensity Information and Satellite L-band SAR Imagery

For a quick and stable estimation of earthquake damaged buildings worldwide, using Phased Array type L-band Synthetic Aperture Radar (PALSAR) loaded on the Advanced Land Observing Satellite (ALOS) satellite, a model combining the usage of satellite synthetic aperture radar (SAR) imagery and Japan Meteorological Agency (JMA)-scale 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 Japanese Earth Resource Satellite-1 (JERS-1)/SAR (L-band SAR) images observed during 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 the proposed likelihood function is then applied to PALSAR images taken over the areas affected by the 2007 earthquake in Pisco, Peru. 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.

Damage assessment after 2001 Gujarat earthquake using Landsat-7 satellite images

In this paper, we present a method of earthquake damage detection by comparing the optical images with panchromatic bands for the Gujarat, India earthquake, which occurred on January 26, 2001. The data used in this study are optical remote sensing images taken by Landsat-7 satellite on January 8 and February 29, 2001, before and after the earthquake. We have investigated the pre and post-earthquake satellite images calculating the differences in the reflection intensity (digital number) of the two images. The estimated affected area has been subtracted on a pixel unit based on the obtained frequency distributions of the differences in the optical sensor values, which show significant changes in the reflectance due to the earthquake disaster. We have investigated the accuracy of our analysis result using a classification method for the training areas with aerial photographs taken after the earthquake. The two damage detection methods show a very similar result.