Displacement Characterization and Spatial-Temporal Evolution of the 2020 Aniangzhai Landslide in Danba County Using Time-Series InSAR and Multi-Temporal Optical Dataset

Abstract

On 17 June 2020, a large ancient landslide over the Aniangzhai (ANZ) slope, Danba County, Sichuan Province, China, was reactivated by a series of multiple phenomena, including debris flow triggered by heavy rainfall and flooding. In this study, Synthetic Aperture Radar (SAR) images acquired by the Sentinel-1A/B satellite and optical images captured by the PlanetScope satellites were jointly used to analyze and explore the deformation characteristics and the Spatial-Temporal evolution of the ANZ landslide before and after the multi-hazard chain. Several areas of pre-failure movements were found from the multi-temporal optical images analysis before the reactivation of the ANZ landslide. The large post-failure surface deformation over the ANZ slope was also retrieved by the optical pixel offset tracking (POT) technique. A major northwest movement with the maximum horizontal deformation of up to 14.4 m was found. A time-series InSAR technique was applied to analyze the descending and ascending Sentinel-1A/B datasets spanning from March 2018 to July 2020, showing that the maximum magnitudes of the Line of Sight (LoS) displacement velocities were −70 mm/year and 45 mm/year, respectively. The Spatial-Temporal evolution over the ANZ landslide was analyzed based on the time-series results. No obvious change in acceleration (precursory deformation) was detected before the multi-hazard chain, while clear accelerated deformation can be observed over the slope after the event. This suggested that heavy rainfall was the most significant triggering factor for the generation and reactivation of the ANZ landslide. Other preparatory factors, including the deformation behavior, the undercutting and erosion of the river and the outburst flood, the local terrain conditions, and earthquakes, might also have played an important role in the generation and reactivation of the landslide.

Robust Estimation of Landslide Displacement From Multitemporal UAV Photogrammetry-Derived Point Clouds

“…This, in turn, triggered the movement of the main body of the landslide. Coupled with other secondary hazards induced by the mudslide in Meilong Gully, it impacted 12 townships in the downstream area of the Xiaojinchuan River, affecting the life and property safety of more than 20,000 people [46], [47]. Shortly after the disaster, the emergency response agencies conducted on-site investigations, providing a substantial amount of valuable information for our subsequent study [48], [49].…”

Geodetic Monitoring for Land Deformation

“…Kuang et al [8] jointly used the Sentinel-1A/B satellite SAR data and the PlanetScope satellites optical images to investigate the deformation time series for the Aniangzhai (ANZ) landslide in Sichuan Province, China. Kuang et al [8] used optical pixel offset tracking (POT) and InSAR techniques to map the pre-and post-failure surface deformation over the ANZ slope.…”

“…Kuang et al [8] jointly used the Sentinel-1A/B satellite SAR data and the PlanetScope satellites optical images to investigate the deformation time series for the Aniangzhai (ANZ) landslide in Sichuan Province, China. Kuang et al [8] used optical pixel offset tracking (POT) and InSAR techniques to map the pre-and post-failure surface deformation over the ANZ slope. Kuang et al [8] was able to identify and characterize several areas of pre-failure movements before the re-activation of the ANZ landslide, and suggested that the most significant triggering factor for the landslide was because of the heavy rainfall.…”

“…Kuang et al [8] used optical pixel offset tracking (POT) and InSAR techniques to map the pre-and post-failure surface deformation over the ANZ slope. Kuang et al [8] was able to identify and characterize several areas of pre-failure movements before the re-activation of the ANZ landslide, and suggested that the most significant triggering factor for the landslide was because of the heavy rainfall. In another article, Jiao et al [13] collected multi-temporal remote sensing data to extract the deformation information of the landslides in Beijing, China from optical images and time-series InSAR.…”

Understanding the Spatiotemporal Characteristics of Land Subsidence and Rebound in the Lianjiang Plain Using Time-Series InSAR with Dual-Track Sentinel-1 Data

“…Interferometric Synthetic Aperture Radar (InSAR), an ideal and effective remote sensing method for monitoring ground deformation, has been widely utilized for this purpose. InSAR has already been successfully applied for monitoring a range of ground displacement applications over the past 20 years, including glacier motion [14], earthquake deformation [15,16], volcanic movement [17], fluid flow [18,19], slope stability monitoring [20], mining activities [21], etc. Compared to conventional geodetic monitoring techniques, InSAR offers a low-cost and low-labor way to measure surface displacement over large spatial coverages with a high accuracy of centimeters or even millimeters [22][23][24].…”