Abstract
Monitoring slow-moving landslides in densely vegetated areas using X-band Synthetic Aperture Radar (SAR) data posed challenges due to the dramatic loss of coherence during SAR interferometry and the relative lower precision of sub-pixel offset tracking (SPOT). The mountainous Three Gorges Reservoir Area (TGRA) in China is a landslide-prone region with unique hydrogeological conditions, where riparian slopes are mostly covered with dense vegetation. Here, we explore the potential of utilizing temporal stacking to improve SPOT (TS-SPOT) for mitigating background noise and enhancing the continuous deformation signal of natural scatterers on densely vegetated slopes. By leveraging redundant information in multiple offset maps, TS-SPOT demonstrates enhanced measurement capability, offering more precise velocity estimations and extended velocity field coverage than single pair-wise SPOT. The ability of the proposed method is illustrated for two large-scale, slow-moving reservoir landslides in the TGRA, the Outang and Xinpu landslides, for which TerraSAR-X High-resolution Spotlight (TSX-HS) images and GNSS measurements, and ground truth data are available. The monitoring results revealed a maximum of 40 and 10 cm/year average deformation rates along the azimuth and range direction, respectively. This study demonstrates a powerful and efficient method for monitoring slow-moving landslides in vegetated terrain.
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SAR data sharing is not applicable to this article due to privacy. All processing and plotting codes will be made available from the authors upon reasonable request.
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Acknowledgements
We gratefully acknowledge the German Aerospace Centre (DLR) for providing TerraSAR-X High-resolution Spotlight images used in this study (under data grant GEO3873). The sub-pixel offset tracking was implemented by COSI-Corr software, which can be freely downloaded from http://www.tectonics.caltech.edu/slip_history/spot_coseis/download_software.html for non-commercial research. Figures were plotted with GMT software. This work additionally benefitted from email exchanges with Luyi Sun. Special thanks go to the editor Emanuele Intrieri and anonymous reviewers for their insightful and constructive comments on the manuscript.
Funding
This research was jointly supported by the International Research Center of Big Data for Sustainable Development Goals (No. CBAS2022GSP02), the National Natural Science Foundation of China (Nos. 42072320, 41972219, and 42372264), and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (No. KYCX23_0173).
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Chang, F., Dong, S., Yin, H. et al. Temporal stacking of sub-pixel offset tracking for monitoring slow-moving landslides in vegetated terrain. Landslides 21, 1255–1271 (2024). https://doi.org/10.1007/s10346-024-02227-7
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DOI: https://doi.org/10.1007/s10346-024-02227-7