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Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) has been widely used for ground motion identification and monitoring over large-scale areas, due to its large spatial coverage and high accuracy. However, automatically locating and assessing the state of the ground motion from the massive Interferometric Synthetic Aperture Radar (InSAR) measurements is not easy. Utilizing the spatial-temporal characteristics of surface deformation on the basis of the Small Baseline Subsets InSAR (SBAS-InSAR) measurements, this study develops an improved method to locate potential unstable or dangerous regions, using the spatial velocity gradation and the temporal evolution trend of surface displacements in large-scale areas. This method is applied to identify the potential geohazard areas in a mountainous region in northwest China (Lajia Town in Qinghai province) using 73 and 71 Sentinel-1 images from the ascending and descending orbits, respectively, and an urban area (Dongguan City in Guangdong province) in south China using 32 Sentinel-1 images from the ascending orbit. In the mountainous area, 23 regions with potential landslide hazards have been identified, most of which have high to very high instability levels. In addition, the instability is the highest at the center and decreases gradually outward. In the urban area, 221 potential hazards have been identified. The moderate to high instability level areas account for the largest proportion, and they are concentrated in the farmland irrigation areas, and construction areas. The experiment results show that the improved method can quickly identify and evaluate geohazards on a large scale. It can be used for disaster prevention and mitigation.

This study monitors the land subsidence of the whole Pearl River Delta (PRD) (area: ~40,000 km2) in China using the ALOS1/PALSAR data (2006-2011) through the SBAS-InSAR method. We also analyze the relationship between the subsidence and the coastline change, river distribution, geological structure as well as the local terrain. The results show that (1) the land subsidence with the average velocity of 50 mm/year occurred in the low elevation area in the front part of the delta and the coastal area, and the area of the regions subsiding fast than 30 mm/year between 2006 and 2011 is larger than 122 km2; (2) the subsidence order and area estimated in this study are both much larger than that measured in previous studies; (3) the areas along rivers suffered from surface subsidence, due to the thick soft soil layer and frequent human interference; (4) the geological evolution is the intrinsic factor of the surface subsidence in the PRD, but human interference (reclamation, ground water extraction and urban construction) extends the subsiding area and increases the subsiding rate.

This study monitors the land subsidence of the whole Pearl River Delta (PRD) (area: ~40,000 km2) in China using the ALOS1/PALSAR data (2006-2011) and the SBAS-InSAR method. We also analyze the relationship between the subsidence and the coastline change, river distribution, geological survey data as well as the geomorphology data. The results show that (1) the land subsidence with the average velocity of 50 mm/year occurred in the low elevation area in the front part of the delta and the coastal area; (2) the areas along rivers also suffered from surface subsidence, due to the fragile geological structure and frequent human interference; (3) the geological evolution is the intrinsic factor of the surface subsidence in the PRD, but human interference (reclamation, ground water extraction and urban construction) extend the subsiding area and accelerate the subsiding rate. This study would provide technique supports for the surface deformation and disaster warning in delta regions.

The Baige landslide in southwest China has been active for at least 50 years, and experienced two failure events on Oct. 10 and Nov. 3, 2018. The rock mass and debris of the slope failure formed a landslide dam. The discharge of the dammed lake plus the failure events caused significant socio-economic losses. Many slow-moving areas appeared after the two failure events and potential landslide risk still exists. Retrieving the long-term spatial-temporal pattern of landslide activity can help us to understand and mitigate landslide disasters. However, it is difficult to monitor all movement episodes of some landslides using single satellite dataset and method, due to the different deformation characteristics. Employing multiple radar and optical images may solve this problem. In this study, the Landsat 8, the ALOS PALSAR and ALOS-2 PALSAR-2 images are used to obtain the surface displacements before the first failure event by using the cross-correlation technique (for Landsat 8 images) and the pixel offset tracking technique. The displacement velocity map after the second failure is derived by the Multi-temporal Synthetic Aperture Radar Interferometry (MT-InSAR) method from the ALOS-2 PALSAR-2 and the Sentinel-1 images. The maximum radar line of sight (LOS) average velocity is 3 m/yr between Jan. 2007 and Feb. 2011, and 12 m/yr between Jul. 2015 and Jul. 2018. From Oct. 2013 to Feb. 2018, the maximum horizontal average velocity is 5.8 m/yr. The movement accelerated from Jul. 2017. A large deformation area appeared on the south of the failed slope after the second failure. The unstable area observed by the MT-InSAR is much larger than that obtained through the field measurements. In the first month after the second failure, the maximum LOS displacements are more than 20 cm. The depletion area and the neighboring zones of the landslide are unstable even one year after the second failure. This study shows how to use multiple satellite images and hybrid remote sensing techniques to investigate the landslide deformation history, and illustrates the benefits of landslide monitoring based on images from multiple satellite sensors.