Coherence and Intensity Statistical Mapping of Earthquake Affected Areas using Sentinel-1 Images

Abstract

Abstract. The January 1, 2024, Noto Peninsula earthquake left many devastated areas. Right after the event, aerial photography provided the initial assessment but covered only specific areas. Satellite images with optical sensors that are freely available are limited by spatial resolution and oftentimes covered by cloud. The Sentinel-1 Synthetic Aperture Radar (SAR) due to its all-weather sensing capability proved to be useful in mapping the extent of the affected areas. This study mapped Wajima City where earthquake aftereffects were severe as reported using coherence and intensity mapping. Pre- and post-event images were used to rapidly identify affected areas. Based on the result, taking the coherence of one pre- and one post-event images can give an impression of wide earthquake damage. The coherence of two post-event images compared with two pre-event images provides a better estimate of affected areas. Taking multiple pre- and post- event images improved the map because the large variation of the intensity is minimized through time-series averaging. Using a simple RGB composite, the affected areas were mapped. Threshold mapping was also used to extract collapsed buildings/houses using a training dataset. The mean coherence difference and mean intensity difference between pre- and post-event images were the two variables used. The computed minimum threshold for the mean coherence difference was 0.35. However, the average not the minimum of the mean intensity difference of 3.0 was used as initial value. Using mean coherence difference>=0.35 gave a high accurate prediction of collapsed buildings at 73.47% but also has false identification of 30.95%. To negate the overprediction, the mean intensity difference<=4.0 was integrated. The result lowered the overprediction to 20.75% but also lowered the accuracy to 64.97%. This assessment was only conducted in the fire-razed morning market in Wajima City.