, Volume 13, Issue 3, pp 437–450 | Cite as

Using wavelet tools to analyse seasonal variations from InSAR time-series data: a case study of the Huangtupo landslide

  • R. Tomás
  • Z. Li
  • J. M. Lopez-Sanchez
  • P. Liu
  • A. Singleton
Original Paper


Synthetic aperture radar interferometry (InSAR) has proven to be a powerful tool for monitoring landslide movements with a wide spatial and temporal coverage. Interpreting landslide displacement time-series derived from InSAR techniques is a major challenge for understanding relationships between triggering factors and slope displacements. In this study, we propose the use of various wavelet tools, namely, continuous wavelet transform (CWT), cross wavelet transform (XWT) and wavelet coherence (WTC) for interpreting InSAR time-series information for a landslide. CWT enables time-series records to be analysed in time-frequency space, with the aim of identifying localized intermittent periodicities. Similarly, XWT and WTC help identify the common power and relative phase between two time-series records in time-frequency space, respectively. Statistically significant coherence and confidence levels against red noise (also known as brown noise or random walk noise) can be calculated. Taking the Huangtupo landslide (China) as an example, we demonstrate the capabilities of these tools for interpreting InSAR time-series information. The results show the Huangtupo slope is affected by an annual displacement periodicity controlled by rainfall and reservoir water level. Reservoir water level, which is completely regulated by the dam activity, is mainly in ‘anti-phase’ with natural rainfall, due to flood control in the Three Gorges Project. The seasonal displacements of the Huangtupo landslide is found to be ‘in-phase’ with respect to reservoir water level and the rainfall towards the front edge of the slope and to rainfall at the higher rear of the slope away from the reservoir.


InSAR Wavelet analysis Continuous wavelet transform Cross wavelet transform Wavelet coherence Time-series Time-frequency space Landslide Triggering factors 



R. Tomás was supported by the Generalitat Valenciana fellowship BEST-2011/225 and by the Ministry of Education, Culture and Sport trough the project PRX14/00100. Part of this work is also supported by the Spanish Ministry of Economy and Competitiveness and EU FEDER funds under project TEC2011-28201-C02-02, by the Natural Environmental Research Council (NERC) through the GAS and LICS projects (ref. NE/H001085/1 and NE/K010794/1, respectively) as well as the ESA-MOST DRAGON-3 projects (ref. 10607 and 10665). We thank JPL/Caltech for the use of ROI_PAC, TU-Delft for DORIS and Andy Hooper for StaMPS in our data processing and analysis. The authors also acknowledge A. Grindsted, J.C. Moore and S. Jevrejeva for the MatLab package for CWT, XWT and WTC analysis and two anonymous reviewers and F. Raspini (University of Florence) for their constructive suggestions and comments, which have been carefully incorporated into the revised manuscript.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • R. Tomás
    • 1
  • Z. Li
    • 2
  • J. M. Lopez-Sanchez
    • 3
  • P. Liu
    • 4
  • A. Singleton
    • 2
  1. 1.Departamento de Ingeniería Civil, Escuela Politécnica SuperiorUniversidad de AlicanteAlicanteSpain
  2. 2.COMET, School of Civil Engineering and GeosciencesNewcastle UniversityNewcastle upon TyneUK
  3. 3.Instituto Universitario de Investigación InformáticaUniversidad de AlicanteAlicanteSpain
  4. 4.Key Laboratory for Geo-Environment Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and Geo-Information, College of Information EngineeringShenzhen UniversityShenzhenChina

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