Identification and Analysis of Landslides in the Ahai Reservoir Area of the Jinsha River Basin Using a Combination of DS-InSAR, Optical Images, and Field Surveys

Abstract

We employed ascending and descending Sentinel-1A, optical image data, and field investigation methods to identify and monitor landslides in the Jinsha River Basin to overcome the difficulties associated with the use of a single method and its inaccuracies in identifying landslides in the alpine and canyon areas. Using distributed scatterer-synthetic aperture radar interferometry (DS-InSAR), Sentinel-1A ascending and descending data were integrated to obtain surface deformation information within the study area from July 2017 to May 2019. Thereafter, high-resolution optical image data were introduced to interpret landslides, and field investigations were conducted to validate landslides. These combined methods enabled the assessment of spatiotemporal evolutionary characteristics, and their accuracy in identifying typical landslides was verified. The results showed that the use of both ascending and descending data effectively avoided certain problems, such as the inability to identify certain landslide hazards or the retrieval of incomplete identification results due to geometric distortion associated with single-track SAR imaging. The combined use of these methods effectively improves the timeliness and verification of the accuracy of landslides. Fifteen landslides were identified in the study area, which had different degrees of tension cracks, vertical dislocations, and slip marks that were verified in the field. Of these, two landslides show serious deformation characteristics that currently pose a serious threat to lives and infrastructure. Follow-up monitoring of these landslides is essential. These findings will assist in obtaining comprehensive information about the distribution of landslides and their deformation developmental trends in the Ahai Reservoir area of the Jinsha River Basin and show that the combined methods can be employed to prevent and control landslides in this area.

A Multi-Input Channel U-Net Landslide Detection Method Fusing SAR Multisource Remote Sensing Data

“…are widely distributed, forming a fragile and sensitive geological environment. At the same time, frequent seismic activities and deep valleys provide favorable conditions for the occurrence of landslide disasters, making landslide disasters frequent [45,46]. Affected by complex factors such as complex terrain, vegetation cover, and weather conditions in the basin, it is difficult to detect landslides only using optical remote sensing data.…”

Automatic Mapping of Potential Landslides Using Satellite Multitemporal Interferometry

“…The visibility of the target area to satellite sensors depends on the combination of local slope direction and the acquisition geometry and mode of satellite sensor LOS [109]. The caused geometric distortions, including layover, foreshortening, and shadow, can be improved using ascending and descending data monitoring [110]. Many advanced InSAR processing methods and other improved methods continue to emerge, such as distributed scatterer (DS-InSAR) technology [111], stacking InSAR [112], and phase-decomposition-based persistent scatterer (PS) InSAR (PD-PSInSAR) technology [113], which enabled a higher density of measurement points in areas with complex topographic environment conditions [114].…”

Time-Lag Response of Landslide to Reservoir Water Level Fluctuations during the Storage Period: A Case Study of Baihetan Reservoir

“…Reservoir bank landslides have been confirmed as a common geological disaster phenomenon during the construction and operation of water conservancy and hydropower facilities [1][2][3]. A wide variety of reservoir bank landslides, with varying degrees of sliding and diverse manifestations, have been reported in the reservoir areas of hydropower stations under construction or already built worldwide [2,4].…”