Analysis of the Decadal Kinematic Characteristics of the Daguangbao Landslide Using Multiplatform Time Series InSAR Observations After the Wenchuan Earthquake

Abstract

The Daguangbao landslide was the largest landslide triggered by the "5.12" Wenchuan Mw 7.9 earthquake. This landslide is among the few superlarge-scale landslides of 1 billion m 3 magnitude worldwide. As the landslide accumulation represents a potential hazard, long-term monitoring and geological interpretation of the Daguangbao landslide is important to reveal the postsliding deformation characteristics and laws of this landslide type. To study the kinematic characteristics 10 years postearthquake, the landslide accumulation deformation was monitored in two stages using different data sets and processing methods. First, regarding the deformation 2 years postearthquake, we selected PALSAR-1 data (21 October 2008 to 14 March 2011) and obtained the LOS (line of sight) direction time series deformation of the landslide using the Small BAseline Subset (SBAS) algorithm. Second, for the last 4.5 years of deformation, Sentinel-1 data sets (26 November 2015 to 8 June 2020) in two ascending and one descending path are experimentally used. The vertical and east-west deformations during this period are obtained. Thickness, topography, precipitation, and other factors are combined to analyze the landslide accumulation deformation. The landslide accumulation volume decreases under gravitational influence, shrinkage is accelerated under precipitation influence, and periodic fluctuations occur, which lag precipitation changes. The maximum vertical direction deformation rate is below 80 mm/year, and the deformation magnitude in the east-west direction is approximately ±35 mm/year. Local deformation is still affected by the surface slope and aspect, and the overall landslide accumulation movement trend involves gathering of the eastern and western parts toward the middle. In the observation window, with the stabilization of accumulation, the deformation tends decrease.

Landslide Susceptibility Evaluation Based on Potential Disaster Identification and Ensemble Learning

“…Suppose in the study area, N SAR images were shot at the N times t 0 , t 1 , ⋯, t N-1 , respectively, and M ( ) interference image pairs are generated. The differential interference phase of the k th differential interferogram in Pixel ( x , y ) is generated from the SAR images shot at the times t a and t b ( t a < t b ) and is calculated in Equation (1) [ 70 , 72 , 73 ]. in which and are the differential interference phases at the times t a and t b , respectively; and are the displacements in the SAR line of sight (LOS) at the times t a and t b , respectively, which is relative to the displacement at the initial time t 0 ; , , and are the phases of topography, atmosphere, and noise, respectively; and represents the sensor wavelength.…”

A method for correcting InSAR interferogram errors using GNSS data and the K-means algorithm

“…SBAS processing can be used to obtain displacement time series, and it has been applied to monitor the deformation trends of geologic hazards such as earthquakes (Liu et al 2021;Jafari et al 2023), volcanoes (Doke et al 2021;Festa et al 2022), and landslides (Luo et al 2020;Khan et al 2023). The basic principle is to consider the deformation value as the product of the deformation rate and the time span (Berardino et al 2002;Lanari et al 2004).…”

Improving the Understanding of Landslide Development in Alpine Forest Regions Using the InSAR Technique: A Case Study in Xiaojin County China

“…The detailed information of the used Sentinel-1 images is presented in Table S2 and the spatial coverage of SLCs and the geometric relationship between the SLCs and the study area are shown in Figure S1. It is noteworthy that the combination of ascending and descending data can reduce the slope effects on the observed geometry [57]. The STRMGL1 digital elevation model with a resolution of 30 m was downloaded from the Land Processes Distributed Active Archive Center (https://lpdaac.usgs.gov (accessed on 12 October 2023)) and used for co-registration and flat-earth phase removal in the single interferogram generation processes of SLCs.…”

“…In addition, the combination of both ascending and descending orbit data sets can overcome the poor observation induced by the shadow and foreshortening effects of SAR images to increase the detection ratio of potential landslides and, therefore, improve the identification of potential landslides. Therefore, the combination of multisensor data with different paths would also be helpful for observations in all azimuths [57]. For landslide detection with InSAR techniques in alpine forest regions, some key points need to be stressed in future studies.…”