Detecting the terrain surface cracks caused by earthquakes, which are termed coseismic ruptures, has important significance for discovering concealed faults, monitoring their movements, and forecasting possible follow-on earthquakes. On May 22, 2021, Maduo County in Qinghai province, China, suffered an earthquake with a magnitude of 7.4, which created densely distributed cracks. In this study, we designed an automatic crack detection framework based on remote sensing technology. With the use of low-altitude unmanned aerial vehicles (UAVs), we obtained very high-resolution aerial images of the area affected by the earthquake, which were further processed by photogrammetric software to produce digital orthophoto maps (DOMs). We then designed a novel terrain surface crack detection neural network, which differs from the previous methods that focus on detecting cracks in man-made object surfaces such as flat roads. We investigated the spatial property of the sinuous linear cracks and handled this by introducing adaptive deformable convolutions with a context-channel-space boosted mechanism. The feature extraction stage, feature optimization stage, and upsampling stage were embedded with the deformable convolutions to form a compact and powerful crack detector, named Crack-CADNet [the Context-chAnnel-space boosted Deformable convolutional neural network (CNN) for crack detection]. The postprocessing included filtering out the nontectonic cracks, aided by annotations from experts, and grouping and vectorizing the generated binary segmentation map as crack polygons, which were evaluated at the instance level. In addition to the first in-depth investigation of detecting earthquake cracks with aerial remote sensing and a deep learning-based process, the crack detection network we propose outperformed the recent CNN-based methods designed for general semantic segmentation and crack detection. Source code and the Maduo earthquake crack dataset will be available at http://gpcv.whu.edu.cn/data/ .

Time series monitoring of earthquake-stricken areas is significant in evaluating post-disaster reconstruction and recovery. The time series of nighttime light (NTL) data collected by the defense meteorological satellite program-operational linescan system (DMSP/OLS) sensors provides a unique and valuable resource to study changes in human activity (HA) because of the long period of available data. In this paper, the DMSP/OLS NTL images’ digital number (DN) is used as a proxy for the intensity of HA since there is a high correlation between them. The purpose of this study is to develop a methodology to analyze the changes of intensity and distribution of HA in different areas affected by a 2008 earthquake in Wenchuan, China. In order to compare the trends of HA before and after the earthquake, the DMSP/OLS NTL images from 2003 to 2013 were processed and analyzed. However, their analysis capability is greatly limited owing to a lack of in-flight calibration. To improve the continuity and comparability of DMSP/OLS NTL images, this study developed an automatic intercalibration method to systematically correct NTL data. The results reveal that: (1) compared with the HA before the earthquake, the reconstruction and recovery of the Wenchuan earthquake have led to a significant increase of HA in earthquake-stricken areas within three years after the earthquake; (2) the fluctuation of HA in a severely-affected area is greater than that in a less-affected area; (3) recovery efforts increase development in the most affected areas to levels that exceeded the rates in similar areas which experienced less damage; and (4) areas alongside roads and close to reconstruction projects exhibited increased development in regions with otherwise low human activity.

The Luojia1-01 (LJ1-01) satellite launched on 2 June 2018 provides a new option for nighttime light (NTL) application research. In this paper, four types of human settlements, such as cities, counties, towns and villages, are sampled to evaluate the potential of LJ1-01 to detect feeble NTL by comparing with the NTL images from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-Orbiting Partnership Satellite. First, the landscape indices and cutoff threshold method are applied to enhance signal-noise ratio (SNR). Then, the detection accuracy of samples is evaluated to determine the optimal cutoff threshold for each NTL data source. After that, the spatial correspondence of different NTL images and the area consistency between the samples and NTL footprints are compared. Finally, after the discussion of feeble NTL detection and the influence of clouds, moonlight and image composites, it can be concluded that LJ1-01 is more suitable for detection feeble NTL objects, while great importance should be attached to the measures to eliminate the noise in LJ1-01 image and make LJ1-01 more widely used: (1) In the study area, a suitable cutoff threshold of LJ1-01 image can be set to 0.1 nano-Wcm⁻²sr⁻¹, which is lower than that of VIIRS image (0.3 nano-Wcm⁻²sr⁻¹), and this enables LJ1-01 to reserve more information of NTL, especially the feeble NTL. Moreover, the minimum area that can be identified by NTL footprints from LJ1-01 is 0.02 km², while that of VIIRS and DMSP are 0.3 km² and 4.5 km², respectively. (2) The cutoff threshold method can identify the range of NTL with more noise, but cannot eliminate the noise separately. The filtering method and the image composition method may play more important role in the applications of LJ1-01 data.

This article demonstrates the potential of Defense Meteorological Satellite Program-Operational Linescan System stable nighttime light imagery for county-level earthquake resilience analysis. In this article, we firstly intercalibrated the nighttime light data and calculated the total sum of stable lights for each of the 261 counties affected by the Mw 7.9 Wenchuan earthquake from 1992 to 2013; secondly established an earthquake resilience model based on a nighttime light index to analyze the static and dynamic resilience of different counties; and lastly discussed the possible influencing factors from geographic, disaster-related, political and socioeconomic perspectives. The results first show that the static resilience of the extremely hard-hit counties in the Wenchuan earthquake-affected area increased from south to north along the long axis of the intensity zone, and plain and hilly counties exhibited faster short-term economic recovery than plateau and mountainous counties. Moreover, all extremely hard-hit counties except Wenchuan, Dujiangyan and Pingwu recovered to the normal level before 2011, whereas 52% of the counties in the generally affected areas recovered within three years. Through linear regression analysis, we also found that the different earthquake resilience capabilities across counties are most likely related to the soil liquefaction risk, average elevation, land-use degree, and socioeconomic factors, such as the industrial structure, the population age distribution, and social welfare.

<!-- *** Custom HTML *** --> Earthquake disasters and postdisaster reconstruction have profound impacts on human society. We use time series nighttime light images collected by the defense meteorological satellite program-operational linescan system sensors as a proxy data source for human activities (HAs). After calibration, a time series analysis method is used to study the distribution and intensity of the changes in HAs after an earthquake. We consider the Haiti earthquake an example to analyze the HA sequence patterns and the aggregation patterns of the HA centers. The results show the following: (1) postearthquake recovery and reconstruction efforts led to increases in the long-term HAs, but the level of increased HA was usually low. (2) The level of postearthquake HAs in the most affected areas (seismic intensity zone VIII+) increased, whereas the postearthquake HAs in severely affected areas (seismic intensity zones VI and VII) were more fragmented. (3) The recovery of HAs in seismic intensity zone VIII+ (mainly Port-au-Prince) required 2 years, but the actual time may be far longer.

Earthquakes will significantly impact the economic development of earthquake-affected areas and the regional post-earthquake economic recovery trajectory. We presented an explorative analysis of disaster resilience and introduced a quantitative framework to model recovery patterns of earthquake disasters. Four resilience metrics, namely instant disturbance, recovery rate, recovery time, and accumulated loss, were calculated to measure resilience as a dynamic process. Furthermore, we applied random forests to explore the potential factors influencing recovery patterns. The selected factors fell into three categories: 1) disaster factors, 2) environmental factors, and 3) socioeconomic factors. The framework’s utility was demonstrated in a retrospective study of the 2008 Wenchuan earthquake, which uncovered the differences in the long-term recovery trajectory of earthquake-affected areas and their influencing factors. The county-level analyses indicated a substantial spatial and temporal variation in the recovery patterns. Earthquake disaster resilience was related to socioeconomic factors and had an association with environmental and disaster factors. Our study not only helped to enrich the understanding of earthquake disaster resilience but also provided a basis for disaster mitigation strategies.