Pre- and post-failure spatial-temporal deformation pattern of the Baige landslide retrieved from multiple radar and optical satellite images

Xiong, Zhiqiang; Feng, Guangcai; Zhang, Feng; Lü, Miao; Wang, Yuedong; Yang, Dingjiang; Luo, Shuran

doi:10.1016/j.enggeo.2020.105880

Cited by 58 publications

(22 citation statements)

References 31 publications

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“…Liu et al (2020) found that the cumulative deformations of area B in the satellite line of sight direction and the azimuth direction reached −60.2 and 12.6 m, respectively. Xiong et al (2020) found that area B has the maximum average displacement velocity, and its largest horizontal deformation rate exceeds 5. 8 m/yr.…”

Section: Validation Of Vegetation Coverage Change Related To the Creepmentioning

confidence: 94%

Detecting the Vegetation Change Related to the Creep of 2018 Baige Landslide in Jinsha River, SE Tibet Using SPOT Data

Guo

Zhao

2021

Front. Earth Sci.

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It is vital to monitor the post-seismic landslides economically and effectively in high-mountain regions for the long term. The landslide creep could cause a subtle change of the overlying vegetation after the earthquake, which will lead to the change of vegetation spectral characteristics in optical remote sensing data. The optical remote sensing technique can be used to monitor the landslide creep areas with dense vegetation in a large range at a low cost because it is easy to obtain multi-temporal, multiple-scale, and multi-spectral information. We identified and extracted the vegetation change area before the 2018 Baige landslide by the high-resolution optical remote sensing data. Firstly, the image fusion method was used to improve the accuracy of change detection. Then, vegetation coverage before the landslide was calculated. The vegetation change was identified, and qualitative and quantitative methods were used to analyze the spatio-temporal changes of vegetation coverage. Our results indicate that the creep distance of the landslide is about 50 m and the vegetation in the back scarp area and the main sliding area display a significant downward trend with time closing to the landslide comparing with that in the reference area. The vegetation change in the remote sensing image has an excellent spatio-temporal correlation with the landslide creep. This study provides a possible way and perspective for monitoring post-seismic landslide disasters.

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Section: Validation Of Vegetation Coverage Change Related To the Creepmentioning

confidence: 94%

Detecting the Vegetation Change Related to the Creep of 2018 Baige Landslide in Jinsha River, SE Tibet Using SPOT Data

Guo

Zhao

2021

Front. Earth Sci.

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“…The fast development of the Synthetic Aperture Radar (SAR) technology provides vast amounts of SAR datasets with high spatial and temporal resolution to measure the ground deformation around the world [2]. Nowadays, the accuracy and efficiency of InSAR data processing have been greatly improved by the MT-InSAR technologies [3], such as Persistent Scatterer InSAR (PS-InSAR) [4,5], SBAS-InSAR [6,7] and SqueeSAR [8], which have overcome some limitations (temporal decorrelation, atmospheric delay, and ramp phases) inherent in Differential Interferometry SAR (DInSAR) [9,10], and have been extensively exploited to geological disaster monitoring, including urban subsidence [11,12], landslides [13,14], volcanoes [15] and earthquakes [16,17], and so on. Traditionally, the basic information (location, area, and deformation magnitude) of potential geohazards is obtained by visually interpreting the MT-InSAR measurement, which is inefficient, labor-intensive, and error-prone for large-scale area monitoring [18].…”

Section: Introductionmentioning

confidence: 99%

An Improved Method for Automatic Identification and Assessment of Potential Geohazards Based on MT-InSAR Measurements

et al. 2021

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Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) has been widely used for ground motion identification and monitoring over large-scale areas, due to its large spatial coverage and high accuracy. However, automatically locating and assessing the state of the ground motion from the massive Interferometric Synthetic Aperture Radar (InSAR) measurements is not easy. Utilizing the spatial-temporal characteristics of surface deformation on the basis of the Small Baseline Subsets InSAR (SBAS-InSAR) measurements, this study develops an improved method to locate potential unstable or dangerous regions, using the spatial velocity gradation and the temporal evolution trend of surface displacements in large-scale areas. This method is applied to identify the potential geohazard areas in a mountainous region in northwest China (Lajia Town in Qinghai province) using 73 and 71 Sentinel-1 images from the ascending and descending orbits, respectively, and an urban area (Dongguan City in Guangdong province) in south China using 32 Sentinel-1 images from the ascending orbit. In the mountainous area, 23 regions with potential landslide hazards have been identified, most of which have high to very high instability levels. In addition, the instability is the highest at the center and decreases gradually outward. In the urban area, 221 potential hazards have been identified. The moderate to high instability level areas account for the largest proportion, and they are concentrated in the farmland irrigation areas, and construction areas. The experiment results show that the improved method can quickly identify and evaluate geohazards on a large scale. It can be used for disaster prevention and mitigation.

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“…Analyzing the long-term spatiotemporal deformation characteristics of landslides helps improve landslide understanding and mitigate landslide disasters. However, using a single method could be insufficient for retrieving the deformation of a landslide since the entire movement process of a landslide may vary considerably [39,40]. Li et al [15] analyzed the pre-and post-failure evolution characteristics of the Huangnibazi landslide based on multi-temporal images.…”

Section: Introductionmentioning

confidence: 99%

“…Using the adaptive amplitude offset tracking method, the large displacement after the landslide failure was successfully retrieved with meter-level accuracy. Xiong et al [40] retrieved the pre-and post-failure spatiotemporal deformation history of the Baige landslide based on multi-satellite images and hybrid remote sensing technology, and obtained the surface displacement before the first failure event using cross-correlation technology and pixel offset tracking technology. Based on the Multi-temporal Synthetic Aperture Radar Interferometry (MT-InSAR) method, the displacement velocity map after the second failure was obtained.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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Information about the long-term spatiotemporal evolution of landslides can improve the understanding of landslides. However, since landslide deformation characteristics differ it is difficult to monitor the entire movement of a landslide using a single method. The Interferometric Synthetic Aperture Radar (InSAR) and pixel offset tracking (POT) method can complement each other when monitoring deformation at different landslide stages. Therefore, the InSAR and improved POT method were adapted to study the pre- and post-failure surface deformation characteristics of the Gaojiawan landslide to deepen understanding of the long-term spatiotemporal evolution characteristics of landslides. The results show that the deformation displacement gradient of the Gaojiawan landslide exhibited rapid movement that exceeded the measurable limit of InSAR during the first disaster. Moreover, the Gaojiawan landslide has experienced long-term creep, and while studying the post-second landslide’s failure stability, the acceleration trend was identified via time series analysis, which can be used as a precursor signal for landslide disaster warning. Our study aims to provide scientific reference for local governments to help prevent and mitigate geological disasters in this region.

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Pre- and post-failure spatial-temporal deformation pattern of the Baige landslide retrieved from multiple radar and optical satellite images

Cited by 58 publications

References 31 publications

Detecting the Vegetation Change Related to the Creep of 2018 Baige Landslide in Jinsha River, SE Tibet Using SPOT Data

Detecting the Vegetation Change Related to the Creep of 2018 Baige Landslide in Jinsha River, SE Tibet Using SPOT Data

An Improved Method for Automatic Identification and Assessment of Potential Geohazards Based on MT-InSAR Measurements

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

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