Home > Application > Natural Hazard Management > Earthquake

Overview | Earthquake | Drought | Fire | Flood & Cyclones | Landslide & Soil Erosion | Volcano


Printer Friendly Format

Page 1 of 3
| Next |


Earthquake loss estimation using high resolution satellite imagery

L. Chiroiu1,2, G. André1,2, F. Bahoken2
1PhD Candidate, Université Paris-7 Denis Diderot
GHSS, CNRS UMR 8586 PRODIG, Case 7001, 2 place
Jussieu, 752251 Paris Cedex 05; France
lucian_chiroiu@yahoo.fr , gilles_p_andre@yahoo.fr

2Géosciences Consultants, 157 rue des Blains, 92220 Bagneux; France
geosciences.consultants@wanadoo.fr; frcse.bahoken@worldonline.fr


Abstract
The recent progresses of remote sensing in terms of spatial resolution and data processing open new possibilities concerning the natural hazard assessment. Using a high resolution optical imagery available today, a damage detection could be performed inclusively in urban areas. A multidisciplinary approach based on high resolution satellite data and earthquake engineering was applied in order to estimate the damage after the Bhuj, (India), Earthquake of January 26 th , 2001. The study provide a fast loss estimation, in terms of physical damage and human casualties. A GIS has been used in order to display the spatial distribution of damages. The results could be very useful for the rescue teams deployed immediately after the catastrophe.

Introduction
A new generation of high resolution optical imagery is provided today by commercial satellites such IKONOS, launched in 1999, with 1 meter resolution in panchromatic mode and 4 meters in multispectral, or EROS A1, launched in 2000, with 1.8 meters resolution in panchromatic mode. Features like buildings, streets or cars become visible with a high ground resolution. In the recent future, an incredible under 1m resolution of Quickbird * satellite will be available for the civil applications (0.61 meters in panchromatic mode, 2.8 meters in multispectral). The high level of details makes possible a reliable damage detection to the buildings or to other structures.

This study is proposing to asses damages in the urban area of the city of Bhuj after the January 26 th earthquake by high resolution optical imagery. Using a 1 meter image taken after the event and a 2 meters image acquired before the earthquake, losses were recognized by mono and multi temporal approaches. The damaged area was analyzed in terms of surface, and the results were integrated into a GIS database.

Bhuj Earthquake
On January 26 th , 2001, at approximately 8:46 a.m. local time, a Mw 7.7 earthquake occurred in western India, where around 20 million people live and work. While the earthquake was felt as far as Nepal and in Pakistan, its most heavy destruction was in the state of Gujarat. The death toll stands at over 20,000 and about 167,000 people have been injured. It is estimated that nearly one million homes were damaged or destroyed. Some cities were completely destroyed, like Anjar or Bachau. The city of Bhuj, located at around 20 km from the epicenter, suffered important losses. A maximum intensity of X (MSK) was assigned by the local authorities.

Damage detection
There are two possibilities to detect damages using photo interpretation analysis: a mono temporal technique based on a post event image, and a multi temporal approach, where a before event scene is compared with an after event scene. The mono temporal procedure consists in the visual recognition of the damaged elements, and it is directly related with the image resolution. With a medium resolution (around 10 meters) only larges zones completely destroyed can be observed. The 1 meter resolution allows the detection of damaged buildings one by one, the building size being considerably greater than the pixel size. In this study it was applied a classical mono temporal photo interpretation method, the damage being detectable by a visual analysis. The results were verified by a multi temporal change detection approach.

In the south part of the town, the recognition of destruction was facilitated by the regular distribution of buildings (Figs 1 and 2).

    


Page 1 of 3
| Next |