Earthquakes that have caused large-scale damage in developed areas, such as the 1994 Northridge and 1995 Kobe events, remind us of the importance of making quick damage assessments in order to facilitate the resumption of normal activities and restoration planning. Synthetic aperture radar (SAR) can be used to record physical aspects of the Earth's surface under any weather conditions, making it a powerful tool in the development of an applicable method for assessing damage following natural disasters. Detailed building damage data recorded on the ground following the 1995 Kobe earthquake may provide an invaluable opportunity to investigate the relationship between the backscattering properties and the degree of damage. This paper aims to investigate the differences between the backscattering coefficients and the correlations derived from pre- and post-earthquake SAR intensity images to smoothly detect areas with building damage. This method was then applied to SAR images recorded over the areas affected by the 1999 Kocaeli earthquake in Turkey, the 2001 Gujarat earthquake in India, and the 2003 Boumerdes earthquake in Algeria. The accuracy of the proposed method was examined and confirmed by comparing the results of the SAR analyses with the field survey data.
The building damage detection technique which we have developed has been successfully applied to past events such as
the earthquakes in Kobe in 1995, India in 2001, and Bam in 2003 by using the compound index, z-value, a value derived
from the correlation and difference in intensities between pre- and post-event SAR images. This technique was applied to
the areas affected in the Niigata-ken Chuetsu earthquake of October 23, 2004 by using one pair of Radarsat images taken
before and after the earthquake. However, it was not possible to identify any significant distribution of damaged
buildings. In our study, we examined the reasons for that and proposed a new technique that uses two pairs (pre-seismic
and co-seismic) of SAR images to identify smaller building damage ratios in less densely built-up areas as compared to
the previous technique. The main idea is to minimize the effects of signal noise and temporal changes of the earth's
surface on building damage estimation by calculating the difference values of the two pre-event images and one postevent
image. From a macroscopic point of view, the distributions of both difference values of the z-value and the
correlation coefficient in built-up areas in Niigata-ken Chuetsu region were in good agreement with damage reported in
survey reports. In former Yamakoshi village, located in the highlands, we could also identify large-scale landslides with
accuracy as good as that of interpretation from aerial photos.
Synthetic aperture radar (SAR) has the remarkable ability to examine the Earth's surface, regardless of weather or
sunlight conditions. A SAR-based remote sensing system can assess the damage to areas affected by large-scale
disasters at an early stage. This can aid in recovery planning. On May 27, 2006 an earthquake struck Yogyakarta,
Central Java, Indonesia, causing human suffering and severe building damage. PALSAR (Phased Array Type L-band
Synthetic Aperture Radar) onboard the Japanese ALOS (Advanced Land Observing Satellite) imaged the affected areas
on the morning following the earthquake. The European satellite, Envisat, also imaged a wider area of central Java two
days after the event. This paper applies a damage detection technique based on three time-series images from the SAR
dataset covering the Mid Java earthquake. From a macroscopic point of view, the estimated damage distribution closely
matched damage assessment derived from high-resolution satellite images and field surveys.
This paper presents a newly developed multi-level detection methodology using high-resolution optical satellite images.
It aims to balance the quick response requirement and the details of detected results and hence, to satisfy various user
demands. Damage extent is firstly detected from only post-disaster image on the first level, texture-based processing.
This level quickly maps the damage extent and damage distribution but not in details. In some focused areas, the second
level with object-based processing will derive further details of the damage using both pre- and post- data. The
methodology is demonstrated on QuickBird images acquired over the damage areas of Bam, Iran, which was extensively
devastated by the December 2003 earthquake. The detected results show a good agreement with the ones by visual
detection and field survey.