Combining InSAR and GPS to Determine Transient Movement and Thickness of a Seasonally Active Low‐Gradient Translational Landslide

Hu, Xie; Lü, Zhong; Pierson, Thomas C.; Kramer, R.; George, David Lloyd

doi:10.1002/2017gl076623

Cited by 82 publications

(68 citation statements)

References 31 publications

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“…The cumulative downslope displacement of the slide is about 1.5 m from 2007 to 2011 and around 0.8 m from 2016 to 2018, and movement patterns resemble an annual cycle throughout the years: the sliding motion starts to accelerate after wet seasons arrive and decelerates substantially when summer comes; while a considerable amount of deformation occurs from mid-November to mid-May, few displacements appear during the dry seasons from mid-May to mid-November (Figure 5d,e). Nevertheless, the landslide movement does not totally stop even during dry summers; similar behaviors have been observed at other landslides over the pacific northwest [5,23].…”

Section: Time-series Displacements and Annual Deformation Ratessupporting

confidence: 76%

“…The surface velocity field can be derived from LOS observations of InSAR, yet reconstructing 3D surface velocity vectors requires at least three independent measurements. In this study, assuming that the sliding body only moves along the downslope direction on the slip plane (i.e., u = 0) [23,24], we construct a pseudo three-dimensional velocity field using the LOS velocities from the ALOS-2 ascending tracks T68 and T69, ALOS-2 descending track T171, and Sentinel-1A/B ascending track T35. Defining θ as the radar look angle, φ the satellite heading angle, α the slope angle, β the slope aspect, and w a vector perpendicular to the slope surface defined by vectors u and v, the surface velocity field…”

Section: Thickness Inversionmentioning

confidence: 99%

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Characterizing Seasonally Rainfall-Driven Movement of a Translational Landslide using SAR Imagery and SMAP Soil Moisture

Kim

George

et al. 2019

Remote Sensing

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Precipitation infiltrates into basal shearing zones, triggering seasonal landslide motion by increasing pore-pressure and reducing shear resistance. This process is jointly controlled by basal depth, rainfall intensity, soil moisture, and hydraulic conductivity/diffusivity. Using interferometric synthetic aperture radar (InSAR), we detected and mapped a slow-moving slide in the southwestern Oregon. Its basal depths are estimated using InSAR-derived surface velocity fields based on the mass conservation approach by assuming a power-law rheology. The estimated maximum thickness over the central region of the landslide is 6.9 ± 2.6 m. This result is further confirmed by an independent limit equilibrium analysis that solely relies on soil mechanical properties. By incorporating satellites-captured time lags of 27–49 days between the onset of wet seasons and the initiation of landslide motions, the averaged characteristic hydraulic conductivity and diffusivity of the landslide material is estimated as 1.2 × 10−5 m/s and 1.9 × 10−4 m2/s, respectively. Our investigation layouts a framework for using InSAR and satellite-sensed soil moisture to infer landslide basal geometry and estimate corresponding hydraulic parameters.

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Section: Time-series Displacements and Annual Deformation Ratessupporting

confidence: 76%

Section: Thickness Inversionmentioning

confidence: 99%

Characterizing Seasonally Rainfall-Driven Movement of a Translational Landslide using SAR Imagery and SMAP Soil Moisture

Kim

George

et al. 2019

Remote Sensing

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“…In many cases, the increase in pore water pressure caused by precipitation can reduce the landslide shear strength, which is an important external influencing factor for many landslides [27]. It has been found that there is a high correlation between the slip acceleration and the regional precipitation in many cases [7,9,27]. However, there is a time lag between the precipitation and the maximum deformation rate [6,7,28].…”

Section: Pre-collapse Time-series Deformationmentioning

confidence: 99%

“…As a technology that can accurately acquire ground deformation, interferometric synthetic aperture radar (InSAR) has been widely used to identify and monitor landslides [6][7][8][9][10]. In addition, InSAR can be used in landslide modeling based on the elastic dislocation models [11,12].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Xinmo (China) Landslide Based on Interferometric Synthetic Aperture Radar Observation and Modeling

et al. 2019

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The Xinmo landslide occurred on 24 June 2017 and caused huge casualties and property losses. As characteristics of spatiotemporal pre-collapse deformation are a prerequisite for further understanding the collapse mechanism, in this study we applied the interferometric synthetic aperture radar (InSAR) technique to recover the pre-collapse deformation, which was further modeled to reveal the mechanism of the Xinmo landslide. Archived SAR data, including 44 Sentinel-1 A/B data and 20 Envisat/ASAR data, were used to acquire the pre-collapse deformation of the Xinmo landslide. Our results indicated that the deformation of the source area occurred as early as 10 years before the landslide collapsed. The deformation rate of source area accelerated about a month before the collapse, and the deformation rate in the week before the collapse reached 40 times the average before the acceleration. Furthermore, the pre-collapse deformation was modeled with a distributed set of rectangular dislocation sources. The characteristics of the pre-collapse movement of the slip surface were acquired, which further confirmed that a locked section formed at the bottom of the slope. In addition, the spatial-temporal characteristics of the deformation was found to have changed significantly with the development of the landslide. We suggested that this phenomenon indicated the expansion of the slip surface and cracks of the landslide. Due to the expansion of the slip surface, the locked section became a key area that held the stability of the slope. The locked section sheared at the last stage of the development, which triggered the final run-out. Our study has provided new insights into the mechanism of the Xinmo landslide.

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“…Seasonal rainwater recharge will cause the water storage variation [37] and change the pore pressure, shear strength. As a result, seasonal accelerations will occur on slopes [38]. In general, the displacement signals are correlated with rainfall in the temporal dimension [39].…”

Section: Rainfall Induced Slope Displacement Modelingmentioning

confidence: 99%

Detection and Characterization of Active Slope Deformations with Sentinel-1 InSAR Analyses in the Southwest Area of Shanxi, China

et al. 2020

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A catastrophic landslide happened on 15 March 2019 in Xiangning County of Shanxi Province, causing 20 fatalities. Such an event makes us realize the significance of loess slope instability detection. Therefore, it is essential to identify the potential active landslides, monitor their displacements, and sort out dominant controlling factors. Synthetic Aperture Radar (SAR) Interferometry (InSAR) has been recognized as an effective tool for geological hazard mapping with wide coverage and high precision. In this study, the time series InSAR analysis method was applied to map the unstable areas in Xiangning County, as well as surrounding areas from C-band Sentinel-1 datasets acquired from March 2017 to 2019. A total number of 597 unstable sites covering 41.7 km2 were identified, among which approximately 70% are located in the mountainous areas which are prone to landslides. In particular, the freezing and thawing cycles might be the primary triggering factor for the failure of the Xiangning landslide. Furthermore, the nonlinear displacements of the active loess slopes within this region were found to be correlated significantly with precipitation. Therefore, a climate-driven displacement model was employed to explore the quantitative relationship between rainfall and nonlinear displacements.

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Combining InSAR and GPS to Determine Transient Movement and Thickness of a Seasonally Active Low‐Gradient Translational Landslide

Cited by 82 publications

References 31 publications

Characterizing Seasonally Rainfall-Driven Movement of a Translational Landslide using SAR Imagery and SMAP Soil Moisture

Characterizing Seasonally Rainfall-Driven Movement of a Translational Landslide using SAR Imagery and SMAP Soil Moisture

Diagnosis of Xinmo (China) Landslide Based on Interferometric Synthetic Aperture Radar Observation and Modeling

Detection and Characterization of Active Slope Deformations with Sentinel-1 InSAR Analyses in the Southwest Area of Shanxi, China

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