Coherence Difference Analysis of Sentinel-1 SAR Interferogram to Identify Earthquake-Induced Disasters in Urban Areas

Abstract

This study proposes a workflow that enables the accurate identification of earthquake-induced damage zones by using coherence image pairs of the Sentinel-1 satellite before and after an earthquake event. The workflow uses interferometric synthetic aperture radar (InSAR) processing to account for coherence variations between coseismic and preseismic image pairs. The coherence difference between two image pairs is useful information to detect specific disasters in a regional-scale area after an earthquake event. To remove background effects such as the atmospheric effect and ordinal surface changes, this study employs the two-step threshold method to develop the coseismic coherence difference (CCD) map for our analyses. Thirty-four Sentinel-1 images between January 2015 and February 2016 were collected to process 30 preseismic image pairs and two coseismic image pairs for assessing multiple types of disasters in Tainan City of southwestern Taiwan, where severe damages were observed after the Meinong earthquake event. The coseismic unwrapping phases were further calculated to estimate the surface displacement in east-west and vertical directions. Results in the CCD map agree well with the observations from post-earthquake field surveys. The workflow can accurately identify earthquake-induced land subsidence and surface displacements, even for areas with insufficient geological data or for areas that had been excluded from the liquefaction potential map. In addition, the CCD details the distribution of building damages and structure failures, which might be useful information for emergency actions applied to regional-scale problems. The conversion of 2D surface displacement reveals the complex behavior of geological activities during the earthquake. In the foothill area of Tainan City, the opposite surface displacements in local areas might be influenced by the axis activities of the Kuanmiao syncline.

UAV-Based Structural Damage Mapping: A Review

“…Damage detection in reality is then supported by the use of proxies, such as evidence of nearby debris or damage clues associated with particular shadow signatures [6,7]. There have been some notable successes in satellite-based damage mapping, especially related to cases where radar data have an advantage, in particular interferometric [8] and polarimetric synthetic aperture radar [9]. Where damage patterns are structurally characteristic, such as foundation walls remaining after the 2011 Tohoku (Japan) tsunami, simple backscatter intensity has also been used to detect damage [10].…”

Uav-Based Structural Damage Mapping – Results From 6 Years Of Research in Two European Projects

“…Damage detection in reality is then supported by use of proxies, such as evidence of nearby debris or damage clues associated with particular shadow signatures (Kerle and Hoffman 2013). There have been some notable successes in satellite-based damage mapping, in particular related to cases where radar data have an advantage, in particular interferometric (Lu et al 2018) and polarimetric synthetic aperture radar (Li et al 2018). Methods to process optical data using advanced machine learning algorithms, including deep learning, have recently started to emerge (e.g., Sublime and Kalinicheva 2019), though still tend to focus on disaster-related changes rather than specific building-level damage mapping.…”

Small Scale Landslide Detection Using Sentinel-1 Interferometric SAR Coherence

“…On the other hand, the temporal component of coherence depends on changes on the ground surface, which appears as variations in the scattering properties of the target pixels in the area of interest between the two SAR images of the interferometric pair [41][42][43]. To detect these changes, the two images must be taken of the same scene with the same incident angle, but at different times.…”

“…The Coherent Change Detection (CCD) technique has been used in several applications such as monitoring land use / land cover changes [44][45][46][47][48] as well as agricultural studies [49][50][51] and forestry applications [52][53][54][55][56], and, lately, in earthquake impact assessment studies [42,43,[57][58][59]. The CCD technique is less efficient in areas with high human activity, or where the underlying geological formations are too hard to record detectable surface modifications.…”