Satellite radar and optical remote sensing for earthquake damage detection: results from different case studies

TLDR

Fast damage maps after seismic events are valuable, especially when epicenters are remote or communications are down, and both Izmit and Bam suffered heavy urban damage. The study aims to evaluate remote sensing techniques for urban earthquake damage detection and to assess combined SAR and optical satellite data. The authors applied pixel‑by‑pixel and homogeneous‑area classification procedures to two case studies, Izmit and Bam, comparing change‑detection features from optical and SAR data and exploring multi‑frequency data fusion. In Izmit, fusion of SAR and optical data achieved 89 % correct pixel classification versus 70 % and 82 % for SAR and optical alone, while in Bam fusion reached 76 % compared to 61 % and 70 %; these results correlated with ground survey data.

Abstract

In case of a seismic event, a fast and draft damage map of the hit urban areas can be very useful, in particular when the epicentre of the earthquake is located in remote regions, or the main communication systems are damaged. Our aim is to analyse the capability of remote sensing techniques for damage detection in urban areas and to explore the combined use of radar (SAR) and optical satellite data. Two case studies have been proposed: Izmit (1999; Turkey) and Bam (2003; Iran). Both areas have been affected by strong earthquakes causing heavy and extended damage in the urban settlements close to the epicentre. Different procedures for damage assessment have been successfully tested, either to perform a pixel by pixel classification or to assess damage within homogeneous extended areas. We have compared change detection capabilities of different features extracted from optical and radar data, and analysed the potential of combining measurements at different frequency ranges. Regarding the Izmit case, SAR features alone have reached 70% of correct classification of damaged areas and 5 m panchromatic optical images have given 82%; the fusion of SAR and optical data raised up to 89% of correct pixel‐to‐pixel classification. The same procedures applied to the Bam test case achieved about 61% of correct classification from SAR alone, 70% from optical data, while data fusion reached 76%. The results of the correlation between satellite remote sensing and ground surveys data have been presented by comparing remotely change detection features averaged within homogeneous blocks of buildings with ground survey data.