Landslide Detection in the Linzhi–Ya’an Section along the Sichuan–Tibet Railway Based on InSAR and Hot Spot Analysis Methods

Abstract

Construction of the 998.64-km Linzhi–Ya’an section of the Sichuan‒Tibet Railway has been influenced by landslide disasters, threatening the safety of Sichuan‒Tibet railway projects. Landslide identification and deformation analysis in this area are urgently needed. In this context, it was the first time that 164 advanced land-observing satellite-2 (ALOS-2) phased array type L-band synthetic aperture radar-2 (PALSAR-2) images were collected to detect landslide disasters along the entire Linzhi‒Ya’an section. Interferogram stacking and small baseline interferometry methods were used to derive the deformation rate and time-series deformation from 2014‒2020. After that, the hot spot analysis method was introduced to conduct spatial clustering analysis of the annual deformation rate, and the effective deformation area was quickly extracted. Finally, 517 landslide disasters along the Linzhi‒Ya’an route were detected by integrating observed deformation, Google Earth optical images, and external geological data. The main factors controlling the spatial landslide distribution were analyzed. In the vertical direction, the spatial landslide distribution was mainly concentrated in the elevation range of 3000‒5000 m, the slope range of 10‒40°, and the aspect of northeast and east. In the horizontal direction, landslides were concentrated near rivers, and were also closely related to earthquake-prone areas, fault zones, and high-precipitation areas. In short, rainfall, freeze–thaw weathering, seismic activity, and fault zones are the main factors inducing landslides along this route. This research provides scientific support for the construction and operation of the Linzhi‒Ya’an section of the Sichuan‒Tibet Railway.

Detecting Long-Term Deformation of a Loess Landslide from the Phase and Amplitude of Satellite SAR Images: A Retrospective Analysis for the Closure of a Tunnel Event

“…In summary, influenced by a variety of factors, the Gaojiawan landslide influenced the safety of the Zhangjiazhuang tunnel. Moreover, geological disasters occur frequently along the "Belt and Road", "Sichuan-Tibet Railway", and other major national strategic projects [73][74][75]. The combined analysis of the phase and amplitude of satellite SAR images conducted in this study provides powerful support for the identification and prevention of landslide disasters.…”

“…Landslides can cause deformation, damage, or even destruction of underground structures [70][71][72][73][74]. The Zhangjiazhuang tunnel passes through the Gaojiawan landslide, and thus determining the relationship between the Gaojiawan landslide and the tunnel is of great importance.…”

An Interpretation Approach of Ascending–Descending SAR Data for Landslide Identification

“…The effective extraction of useful information for landslide identification from multiple MPs requires close collaboration between geologists and experts from remote sensing. In order to detect landslides automatically, various spatial analysis and statistics tools on the geographic information system (GIS) platform, such as buffer, hot spot analysis, and kernel density estimation, have been developed to analyze deformation rates of MPs in TS-InSAR analysis results [20,[30][31][32][33][34][35][36]. Among the various approaches, hot spot analysis and buffer are relatively popular.…”

SBAS-InSAR-Based Landslide Susceptibility Mapping Along the North Lancang River, Tibetan Plateau

“…The interferometric synthetic aperture radar (InSAR) technique serves as a reliable way to improve the identification and monitoring of landslides. Most especially, it can provide valuable information on the current activity of regional landslides (Dong et al, 2017;Introeri et al, 2017;Zhang et al, 2021). It has attracted increasing attention and has been applied to landslide identification (Bürgmann et al, 2000;Rott and Nagler,2006;Altamira 2017;Yao et al, 2017;Ge et al, 2019;Xu et al, 2019) and establish optimized landslide assessment models (Ciampalini et al, 2016;Zhao et al, 2019;Dimitris and Skevi Perdikou 2021) in the past 20 years.…”