TWO-DIMENSIONAL LOESS LANDSLIDE DEFORMATION MONITORING WITH MULTIDIMENSIONAL SMALL BASELINE SUBSET (MSBAS) – A CASE STUDY OF XINYUAN No.2 LANDSLIDE, GANSU, CHINA

The deformation monitoring of active landslides is of great importance for the safety of human lives and properties. And twodimensional deformation result can give us more thoughts on the landslide type and deformation process. In this study, multidimensional small baseline subsets (MSBAS) technique is introduced and tested to compute two-dimensional deformation rate and time series for both east-west and vertical deformation of Xinyuan No.2 landslide by simultaneously processing ascending and descending TerraSAR-X data acquired from January 2016 to November 2016. Results show not only the spatiotemporal characteristics of this landslides, but the retrogressive loess landslide failure mode is revealed for the first time with the two-dimensional deformation.

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Multidimensional Small Baseline Subset (MSBAS) for Two-Dimensional Deformation Analysis: Case Study Mexico City

Canadian Journal of Remote Sensing ◽

10.1080/07038992.2017.1344926 ◽

2017 ◽

Vol 43 (4) ◽

pp. 318-329 ◽

Cited By ~ 23

Author(s):

Sergey V. Samsonov ◽

Nicolas d'Oreye

Keyword(s):

Mexico City ◽

Deformation Analysis ◽

Two Dimensional ◽

Small Baseline Subset ◽

Small Baseline

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Multidimensional Small Baseline Subset (MSBAS) for volcano monitoring in two dimensions: Opportunities and challenges. Case study Piton de la Fournaise volcano

Journal of Volcanology and Geothermal Research ◽

10.1016/j.jvolgeores.2017.04.017 ◽

2017 ◽

Vol 344 ◽

pp. 121-138 ◽

Cited By ~ 15

Author(s):

Sergey V. Samsonov ◽

Wanpeng Feng ◽

Aline Peltier ◽

Halldor Geirsson ◽

Nicolas d’Oreye ◽

...

Keyword(s):

Two Dimensions ◽

Volcano Monitoring ◽

Piton De La Fournaise ◽

Small Baseline Subset ◽

Small Baseline

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Deformation monitoring of typical loess landslide case studies through combining InSAR and UAV

10.5194/egusphere-egu21-462 ◽

2021 ◽

Author(s):

Qingkai Meng ◽

Federico Raspini ◽

Pierluigi Confuorto ◽

Ying Peng ◽

Haocheng Liu

Keyword(s):

High Speed ◽

Slope Failure ◽

Deformation Gradient ◽

Regional Scale ◽

Deformation Characteristic ◽

Deformation Monitoring ◽

Gansu Province ◽

Landslide Monitoring ◽

Loess Landslide ◽

Landslide Deformation

<p>InSAR is an advanced earth observation (EO) technique for retrieving past, subtle (millimetre-level) and continuous surface movements over a long period, which has been widely applied in landslide deformation monitoring and detecting precursory signals of deformation. However, limited by the maximum detected deformation gradient from two consecutive scenarios, singular InSAR has hampered the recognition application for high-speed slides or earth flows, leading to a misleading understanding of slope evolution. Being a high-resolution photogrammetry technology, UAV represents a suitable tool to detect meter-level displacement rates and estimate ground detachment. Thus, InSAR and UAV's synergic analysis can detect the kinematic variation of geographical and geomorphological features, corresponding surface displacements to cross-validation. In the present work, two representative cases illustrated how the combination of InSAR and UAV could be applied in loess landslide deformation monitoring. One case, located in Hongheyan, Gansu Province, China, was selected to reconstruct landslide morphology, identify deformation evolution behaviour and produce dynamic deformation zonation maps using 85 Sentinel-1A SAR images and three UAV fight surveys from pre-sliding to post-sliding. The integrated deformation results illustrate the slide of theHongheyan slope was triggered by heavy rainfall, became suspended owing to the topography effect after the occurrence, and reactivated recently. Another case, located in Qinghai-Gansu province, calculated two-dimensional displacements (vertical-horizontal) by decomposing the ascending and descending Sentinel-1 images to reclassify the regional slope failure type into the translational slide, rotational slide and loess flow based on deformation characteristic. Overall, multi-source information fusion is a new approach for landslide monitoring from regional-scale failure classification to specific-scale slope deformation evolution, giving the comprehensive understanding for local government or Civil Protection to take sufficient precautions for risk mitigation.</p>

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APPLICATION OF TIME SERIES INSAR TECHNIQUE FOR DEFORMATION MONITORING OF LARGE-SCALE LANDSLIDES IN MOUNTAINOUS AREAS OF WESTERN CHINA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b1-89-2016 ◽

2016 ◽

Vol XLI-B1 ◽

pp. 89-91

Author(s):

T. Qu ◽

P. Lu ◽

C. Liu ◽

H. Wan

Keyword(s):

Time Series ◽

Large Scale ◽

Deformation Monitoring ◽

In Situ Monitoring ◽

Western China ◽

Surface Displacements ◽

Landslide Hazards ◽

Landslide Body ◽

Small Baseline Subset ◽

Small Baseline

Western China is very susceptible to landslide hazards. As a result, landslide detection and early warning are of great importance. This work employs the SBAS (Small Baseline Subset) InSAR Technique for detection and monitoring of large-scale landslides that occurred in Li County, Sichuan Province, Western China. The time series INSAR is performed using descending scenes acquired from TerraSAR-X StripMap mode since 2014 to get the spatial distribution of surface displacements of this giant landslide. The time series results identify the distinct deformation zone on the landslide body with a rate of up to 150mm/yr. The deformation acquired by SBAS technique is validated by inclinometers from diverse boreholes of in-situ monitoring. The integration of InSAR time series displacements and ground-based monitoring data helps to provide reliable data support for the forecasting and monitoring of largescale landslide.

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A NEW STRATEGY FOR PHASE UNWRAPPING IN INSAR TIME SERIES OVER AREAS WITH HIGH DEFORMATION RATE: CASE STUDY ON THE SOUTHERN TEHRAN SUBSIDENCE

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w18-35-2019 ◽

2019 ◽

Vol XLII-4/W18 ◽

pp. 35-40 ◽

Cited By ~ 1

Author(s):

P. Ajourlou ◽

S. Samiei Esfahany ◽

A. Safari

Keyword(s):

Deformation Gradient ◽

Small Deformation ◽

Phase Unwrapping ◽

Bayesian Estimator ◽

High Deformation ◽

New Strategy ◽

Small Baseline Subset ◽

Spatio Temporal ◽

Small Baseline

Abstract. The primary step in all timeseries interferometric synthetic aperture radar (T-InSAR) algorithms is the phase unwrapping step to resolve the inherent cycle ambiguities of interferometric phases. In areas with a high spatio-temporal deformation gradient, phase unwrapping fails due to the aliasing problem, and so it can result in an underestimation of deformation signal. One way to handle this problem is to use the so called Small-Baseline Subset (SBAS) algorithms; in these algorithms, by using only small-baseline interferograms – hence interferograms with small deformation gradients – the chance of unwrapping error gets reduced. However, due to more number of the used interferograms, SBAS method is computationally more expensive and more time-consuming compared to algorithms that exploit Single-Master (SM) stacks. Moreover, the existence of sufficiently small temporal baseline interferograms is not guaranteed in all SAR stacks. In this paper, we propose a new method to take advantage of short temporal baseline interferograms but effectively using SM approach. We treat the phase unwrapping step as a Bayesian estimation problem while the prior information, required by the Bayesian estimator, is extracted from few short coherent interferograms that are unwrapped separately. Results from the proposed approach and a case study over the southwest of Tehran, with a high subsidence rate (reaching to 25 cm/year), demonstrates that utilizing the proposed method overcomes the aliasing problem and produces the results equal to the conventional SBAS results, while the proposed method is computationally much more efficient than SBAS.

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