Compared with optical sensors, Synthetic Aperture Radar (SAR) can provide important damage information due to its ability to map areas affected by earthquakes independently from weather conditions and solar illumination. In 2013, a new TerraSAR-X mode named staring spotlight (ST), whose azimuth resolution was improved to 0.24 m, was introduced for various applications. This data source made it possible to extract detailed information from individual buildings. In this paper, we present a new concept for individual building damage assessment using a post-event sub-meter very high resolution (VHR) SAR image and a building footprint map. With the building footprint map, the original footprints of buildings can be located in the SAR image. Based on the building imaging analysis of a building in the SAR image, the features in the building footprint can be extracted to identify standing and collapsed buildings. Three machine learning classifiers, including random forest (RF), support vector machine (SVM) and K-nearest neighbor (K-NN), are used in the experiments. The results show that the proposed method can obtain good overall accuracy, which is above 80% with the three classifiers. The efficiency of the proposed method is demonstrated based on samples of buildings using descending and ascending sub-meter VHR ST images, which were all acquired from the same area in old Beichuan County, China.

A Mw 6.6 earthquake struck Menyuan, Qinghai, China, on 7 January 2022. To determine the rupture parameters of this event, the coseismic InSAR deformation fields were mapped and further employed to estimate the focal mechanism. The best-fitting solution emphasized that the 2022 Menyuan earthquake ruptured at the junction of the Tuolaishan fault and the Lenglongling fault. Both rupturing faults were dominated by sinistral strike-slip, and the main slip was concentrated on the shallow part of the rupture plane. The latter was the main rupture segment with a strike of 106° and a dip of 86°. The slip mainly occurred at depths of 0–8 km, and the rupture was exposed at the surface. The maximum slip reached ~3.5 m, which occurred mainly at a depth of 4 km. Joint analysis of the optimal slip model, relocated aftershocks, Coulomb stress change, and field observation suggested that the strain energy in the Tuolaishan fault may not have been fully released and needs further attention. Moreover, the 2022 Mw6.6 Menyuan earthquake caused a significant stress loading effect on the western Tuolaishan fault and eastern Lenglongling fault, which implies that the 2022 event increased the seismic hazard in these regions.

Synthetic Aperture Radar (SAR) data are used to map affected urban areas after an earthquake generally exploiting change detection techniques. In this paper, a novel change detection approach is proposed for multitemporal (SAR) images. The approach is based on two different parts, which are constructed through principal components (PCs) of multiple textural features and correlation analysis image, respectively. In the extraction of the PCs of texture, the principal component analysis technique is used to separate irrelevant and noisy elements. Then, correlation change detection (CCD) is carried out to detect the changes associated with the building collapse. Considering the earthquake that hit L’Aquila city (Italy) on 6 April 2009, the effectiveness of the proposed method is proved by the experiment results obtained on real SAR images data sets collected before and after the seismic event.

Indirect seismic economic loss not only impacts regional economic recovery policies but is also related to economic assistance at the national level. Due to cross-regional economic activities and the difficulty of obtaining data, it is difficult for the indirect economic loss survey to cover all economic activities. However, nighttime light in an area can reflect the economic activity of the region. This article focuses on the indirect economic losses caused by the 2008 Wenchuan earthquake and evaluation of the progress of restoration and reconstruction based on nighttime light images. First, the functional relationship between GDP and nighttime light parameters is established based on pre-earthquake data. Next, the indirect loss of the earthquake is evaluated by the nighttime light attenuation in the disaster area after the earthquake. Then, the capacity recovery, which is characterized by the brightness recovery process of the light area, is evaluated. Lastly, the process of light expansion in the disaster area is analyzed to evaluate the speed and efficiency of economic expansion.

After an earthquake, efficiently and accurately acquiring information about damaged buildings can help reduce casualties. Earth observation data have been widely used to map affected areas after earthquakes. However, fine post-earthquake assessment results are needed to manage recovery and reconstruction and to estimate economic losses. In this paper, for quantification and precision purposes, a method of earthquake-induced building damage information extraction incorporating multi-source remote sensing data is proposed. The method consists of three steps： (1) Analysis of multisource features that describe texture, colour, and geometry, (2) rough set theory is carried out to further determine the feature parameters, (3) Logistic regression model (LRM) was built to describe the relationship between the occurrence and absence of destroyed buildings within an individual object. Old Beichuan County (centered at approximately 31.833︒N, 104.459° E), China, the area most devastated by the Wenchuan earthquake on May 12, 2008, is used to test the proposed hypothesis. Multi-source remote sensing imagery include optical data, synthetic aperture radar (SAR) data, and digital surface model (DSM) data generated by interpolating light detection and ranging (LiDAR) point cloud data. Through comparison with the ground survey, the experimental results show that the detection accuracy of the proposed method is 94.2 %; the area under the receiver operating characteristic (ROC) curve is 0.827. The efficiency of the proposed method is demonstrated using 6 modes of data combination acquired from the same area in old Beichuan County. The approach is one of the first attempts to extract damaged buildings through the fusion of three types of data with different features. The approach addresses multivariate regression methodologies and compares the potential of features for application in the damage detection field.