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Open AccessArticle

Landslide Deformation Analysis by Coupling Deformation Time Series from SAR Data with Hydrological Factors through Data Assimilation

by 1, 1,2,*, 3,†, 1,†, 1,2,† and 1
1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
2
Collaborative Innovation Center for Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China
3
Global Navigation Satellite System Research Centre, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Zhenhong Li, Roberto Tomas, Zhong Lu and Prasad S. Thenkabail
Remote Sens. 2016, 8(3), 179; https://doi.org/10.3390/rs8030179
Received: 2 December 2015 / Revised: 17 January 2016 / Accepted: 14 February 2016 / Published: 25 February 2016
(This article belongs to the Special Issue Earth Observations for Geohazards)
Time-series SAR/InSAR techniques have proven to be effective tools for measuring landslide movements over large regions. Prior studies of these techniques, however, have focused primarily on technical innovation and applications, leaving coupling analysis of slope displacements and trigging factors as an unexplored area of research. Linking potential landslide inducing factors such as hydrology to SAR/InSAR derived displacements is of crucial importance for understanding landslide deformation mechanisms and could support the development of early-warning systems for disaster mitigation and management. In this study, a sequential data assimilation method named the Ensemble Kalman filter (EnKF), is adopted to explore the response mechanisms of the Shuping landslide movement in relation to hydrological factors. Previous research on the Shuping landslide area shows that the reservoir water level and rainfall are the two main triggering factors in slope failures. To extract the time-series deformations for the Shuping landslide area, Pixel Offset Tracking (POT) technique with corner reflectors was adopted to process the TerraSAR-X StripMap (SM) and High-resolution Spotlight (HS) images. Considering that these triggering factors are the primary causes of displacement fluctuations in periodic displacement, time-series decomposition was carried out to extract the periodic displacement from the POT measurements. The correlations between the periodic displacement and the inducing factors were qualitatively estimated through a grey relational analysis. Based on this analysis, the EnKF method was adopted to explore the response relationships between the displacements and triggering factors. Preliminary results demonstrate the effectiveness of EnKF in studying deformation response mechanisms and understanding landslide development processes. View Full-Text
Keywords: landslide; pixel offset tracking; corner reflectors; displacement; time-series; triggering factors; data assimilation; Ensemble Kalman filter landslide; pixel offset tracking; corner reflectors; displacement; time-series; triggering factors; data assimilation; Ensemble Kalman filter
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MDPI and ACS Style

Jiang, Y.; Liao, M.; Zhou, Z.; Shi, X.; Zhang, L.; Balz, T. Landslide Deformation Analysis by Coupling Deformation Time Series from SAR Data with Hydrological Factors through Data Assimilation. Remote Sens. 2016, 8, 179.

AMA Style

Jiang Y, Liao M, Zhou Z, Shi X, Zhang L, Balz T. Landslide Deformation Analysis by Coupling Deformation Time Series from SAR Data with Hydrological Factors through Data Assimilation. Remote Sensing. 2016; 8(3):179.

Chicago/Turabian Style

Jiang, Yanan; Liao, Mingsheng; Zhou, Zhiwei; Shi, Xuguo; Zhang, Lu; Balz, Time. 2016. "Landslide Deformation Analysis by Coupling Deformation Time Series from SAR Data with Hydrological Factors through Data Assimilation" Remote Sens. 8, no. 3: 179.

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