Wide Area Detection and Distribution Characteristics of Landslides along Sichuan Expressways

Abstract

Wide area landslide detection is a major international research hotspot in the field of geological hazards, and the integration of multi-temporal optical satellite images and spaceborne interferometric synthetic aperture radar (InSAR) appears to be an effective way to realize this. In this paper, a technical framework is presented for wide area landslide detection: (i) multi-temporal satellite optical images are used to detect landslides with distinguishable geomorphological features; (ii) Generic Atmospheric Correction Online Service (GACOS) assisted InSAR stacking is employed to generate annual surface displacement rate maps in radar line of sight using satellite SAR images from both ascending and descending tracks, which are in turn utilized to automatically detect active landslides from ground motion using hotspot analysis, and (iii) the distribution characteristics of the detected landslides are investigated by examining their relationships with topographic and hydrological factors. Three expressways in Sichuan Province, China—namely the Yakang (Ya’an-Kangding), Yaxi (Ya’an-Xichang), and Lushi (Luding-Shimian) expressways—and their surrounding regions (a total area of approximately 20,000 square kilometers) were chosen as the study area. A total of 413 landslides were detected, among which 320 were detected using multi-temporal satellite optical images, and 109 were detected using GACOS-assisted InSAR stacking. It should be noted that only 16 landslides were detected by both approaches; these landslides all exhibited not only obvious geomorphological features but also ground motion. A statistical analysis of the topographic and hydrological factors shows that of the detected landslides: 81% are distributed at elevations of 1000–2500 m, over 60% lie within the elevation range of 100~400 m, and 90% present with medium and steep slopes (20°~45°), and 80% are located within areas seeing an annual rainfall of 950~1050 mm. Nine landslides were found to pose potential safety hazards to the expressways. The research findings in this paper have directly benefitted the Sichuan expressways; equally important, it is believed that the technical framework presented in this paper will provide guidance for hazard mitigation and the prevention of transportation hazards in the future.

Early detection of potential landslides along high‐speed railway lines: A pressing issue

“…We here used the Getis–Ord statistic, a local spatial statistical parameter that focuses on the analysis of location‐related trends between attributes of spatial data (points or regions) (Lu et al, 2012; Zhang et al, 2021). It can effectively remove errors that occur in individual pixels and distinguish clustered regions with high positive and low negative values from massive pixels (Chen et al, 2022; Shi et al, 2021): where X j is the LOS deformation velocity value derived by InSAR; is the spatial weight between elements i and j ; n is the total number of elements; is the mean deviation and is the standard deviation of LOS deformation velocity, which can be expressed as …”

“…We here used the Getis-Ord G Ã i statistic, a local spatial statistical parameter that focuses on the analysis of locationrelated trends between attributes of spatial data (points or regions) (Lu et al, 2012;Zhang et al, 2021). It can effectively remove errors that occur in individual pixels and distinguish clustered regions with high positive and low negative values from massive pixels (Chen et al, 2022;Shi et al, 2021):…”

“…Thus, remote sensing technology has become an irreplaceable method in landslide survey, as it can provide real‐time, high‐precision data to help researchers better understand the evolution of landslides and take timely and effective preventive measures (Carlà et al, 2019; Dai et al, 2020; Rehman et al, 2020). Among them, Interferometric Synthetic Aperture Radar (InSAR) technology has been widely applied in surface deformation studies (Dong et al, 2018; Zhang et al, 2020), particularly landslide detection (Chen et al, 2022; Zhang et al, 2021) and monitoring (Kang et al, 2021; Xie et al, 2020; Yan et al, 2023), owing to its high accuracy as well as all‐day and all‐weather availability (Chen et al, 2022; Dai et al, 2020). With the improvement of image quality acquired by Sentinel‐1 satellite that offers wider coverage, higher spatial resolution, and a shorter revisit period, InSAR evolved into a powerful tool in large‐scale landslide detection (Moretto et al, 2021; Zhang et al, 2022).…”

InSAR-Based Active Landslide Detection and Characterization Along the Upper Reaches of the Yellow River

“…Conventional field survey methods are difficult to be utilized for landslide detection and monitoring in river basins stretching hundreds or even thousands of kilometers because they are time-consuming and costly. In spaceborne remote sensing, high resolution optical imagery can be utilized to detect landslides with geomorphological features [12], but they are susceptible to weather (e.g., heavy clouds) and can hardly capture subtle landslide movements at the centimeter to millimeter scale, especially in the long term [13]. In the past decades, InSAR has emerged as an efficient technique for the identification of landslides and the exploration of the timing and magnitude of their motion [14], [15], [16], [17], [18].…”

Landslide Monitoring along the Dadu River in Sichuan Based on Sentinel-1 Multi-Temporal InSAR

“…Zhang et al [ 21 ] detected the landslide disasters in Wenchuan using stacking InSAR and small baseline subsets (SABS). Zhang et al [ 22 ] studied the spatial distribution and controlling factors of wide-area landslides along the Sichuan–Tibet railway based on SABS. Chen et al [ 23 ] used the GACOS-assisted stacking InSAR method to analyze landslides along Sichuan expressways with the combination of topographic and hydrological factors.…”