This study discusses the versatility of a method developed by the authors that uses pre- and post-disaster SAR data to accurately assess the building damaged areas during a large-scale earthquake. After reviewing the outline of the method, we report the results of applying it to the Noto Peninsula earthquake that occurred in January 2024 and evaluating the extent of damage at the municipal level. Three combinations of paired data acquired by L-band ALOS-2 PALSAR-2 and C-band Sentinel-1 C-SAR were used to calculate the coherence. Seven municipalities near the epicenter in Ishikawa Prefecture were targeted, and building damage rate, which indicate the ratio of completely destroyed buildings to the total number of buildings, were calculated from data published by the prefecture. The urban areas were then extracted from the coherence using the land cover, and average coherence in each municipality was compared with the damage rate. The result showed that although the coherence was greater or less depending on the combination, there was a significant negative correlation between the average coherence and the damage rate in all combinations. Our finding indicated that the proposed coherence-based method was effective in assessing damage at the municipal level.
KEYWORDS: Earthquakes, Synthetic aperture radar, Data acquisition, Satellites, Land cover, Optical coherence, Physical coherence, Linear regression, L band, Interferometry
In a previous study, we developed a method for assessing building damage at the district level in markedly damaged areas using satellite-derived synthetic aperture radar (SAR) data and conducted a coherence analysis of urban areas. We previously evaluated the relationship between the average coherence within a 200-m mesh and degree of building damage in Mashiki, Kumamoto Prefecture, Japan, using L-band ALOS-2 PALSAR-2 data obtained before and after the Kumamoto earthquake. Our findings indicated a significant negative correlation between these parameters. Currently, C-band Sentinel-1 provides global coverage at a high observation frequency. However, previous studies have not sufficiently evaluated the effects of differences in acquisition conditions. Therefore, we aimed to investigate how acquisition conditions affect the relationship between coherence and the building damage rate using images captured by Sentinel-1 C-SAR during the Kumamoto earthquake. The results showed that coherence tended to decrease as the damage rate increased but the correlation was not significant. The difference in coherence per 10% of the building damage rate was less pronounced compared with the results obtained from ALOS-2. Our findings also indicated that the effects of the pre-earthquake acquisition dates and the baseline were less than those of the orbit direction and incidence angle.
High-resolution commercial synthetic aperture radar (SAR) satellites with resolutions of several meters have recently been used for effective disaster monitoring. One study reported the earthquake’s displacement using the pixel matching method with both pre- and postevent TerraSAR-X data, with a validated accuracy of ∼30 cm at global navigation satellite system (GNSS) Earth observation network (GEONET) reference points. However, it is insufficient to determine the accuracy using analysis of only a couple of data points per orbit. In addition, the errors were not reported because the number of data samples was too small to discuss the statistics. In order to better understand displacement accuracy, we analyzed displacement features using the pixel matching method to evaluate the relative geolocation accuracies of the TerraSAR-X product. First, we used fast Fourier transform oversampling 16 times to develop the pixel matching method for estimating the displacement at the subpixel level using the TerraSAR-X StripMap dataset. Second, we applied this methodology to 20 pairs of images from the Tokyo metropolitan area and calculated the displacement for each image pair. Third, we conducted spatial and temporal analyses in order to understand the displacement features. Finally, we evaluated the displacement accuracy by comparison with GEONET and solid earth tide data as a reference.
This paper presents a methodology that utilizes high-resolution optical satellite imagery, specifically GeoEye-1, and
airborne lidar data to detect disaster-related damaged buildings in order to conduct a case study on the 2011 Tohoku
earthquake. The methodology is based on change detection algorithms used in the field of image processing for remote
sensing. Specifically, we examine the use of the image algebra change detection algorithm. This algorithm identifies the
amount of change between two rectified images by band rationing or image differencing. On the other hand, it seems that
the results calculated are different depending on the calculation method used because the data type of satellite data is
different from that of the airborne lidar data. In this research, we propose three methods for creating a dataset used to
detect damaged buildings: the Difference method, the Ratio method, and the Normalized Difference method, which are
simply referred to as the D-method, R-method, and ND-method, respectively. The D-method is based on the difference
in the value of the post-event imagery compared to that of the pre-event imagery. The R-method is based on the quotient
of dividing the value of the pre-event imagery by that of the post-event imagery. The ND-method uses a calculation
formula that is similar to that used by the Normalized Difference Vegetation Index (NDVI). The experimental results
indicate that the dataset created using the ND-method has a higher sensitivity in the detection of damaged buildings than
that of other methods.
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