Abstract
In this study, we used fifty-six synthetic aperture radar (SAR) images acquired from the Sentinel-1 C-band satellite with a regular period of 12 days (except for one image) to produce sequential phase correlation (sequential coherence) maps for the town of Sarpole-Zahab in western Iran, which experienced a magnitude 7.3 earthquake on 12 November 2017. The preseismic condition of the buildings in the town was assessed based on a long sequential SAR coherence (LSSC) method, in which we considered 55 of the 56 images to produce a coherence decay model with climatic and temporal parameters. The coseismic condition of the buildings was assessed with 3 later images and normalized RGB visualization using the short sequential SAR coherence (SSSC) method. Discriminant analysis between the completely collapsed and uncollapsed buildings was also performed for approximately 700 randomly selected buildings (for each category) by considering the heights of the buildings and the SSSC results. Finally, the area and volume of debris were calculated based on a fusion of a discriminant map and a 3D vector map of the town.
Highlights
One of the necessities of disaster management before or during a natural or anthropogenic event is the inexpensive monitoring of buildings in urban areas
The purpose of this study is to demonstrate the feasibility of using synthetic aperture radar (SAR) data for rapid monitoring of urban regions, large-scale damage monitoring of the stricken area using SAR data is not currently possible because auxiliary data about building heights from commercial sources, such as digital surface models (DSMs), are not available
The number of misclassified buildings in the collapsed category was higher than that of the uncollapsed category. This was probably caused by two reasons: (1) because of the coarse resolution of the Sentinel-1 dataset, the pixel size is larger than the size of the buildings, so most buildings near collapsed buildings cannot be distinguished; and (2) for large buildings that cover more than one coherence pixel, we used only the pixel value that covers the centroid of the buildings, which increases the uncertainty of the building classification
Summary
One of the necessities of disaster management before or during a natural or anthropogenic event is the inexpensive monitoring of buildings in urban areas. The intensity information (e.g., difference and correlation of the backscattering coefficient) of 5 pre-event and 4 post-event SAR images acquired by the ERS-1 satellite were used to assess damaged and undamaged buildings after the 1995 Kobe earthquake [3]. This method was used with the ENIVSAT satellite’s SAR images and the Bam earthquake (2003) [4]. The retired SAR missions that were mentioned previously (i.e., ERS, ENVISAT, JERS-1, ALOS-1) had longer revisit intervals over specific areas, which made accurate and timely damage assessment difficult Their spatial resolution was not sufficient for building-by-building damage assessments. In the era of SAR dataset abundance, the monitoring of stationary (e.g., seasonal effects) and nonstationary (e.g., earthquake-related) changes on the Earth is easier than it was in previous years
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