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Monitoring of large-scale landslides in Zongling, Guizhou, China, with improved distributed scatterer interferometric SAR time series methods

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Abstract

The Zongling landslide (Nayong, Guizhou, China) is dominated by a unique karst landscape area with many landslide masses. In this paper, an improved Interferometric Point Target Analysis (IPTA) method is proposed to identify and monitor the Zongling landslide. In this method, the Anderson-Darling test is applied to distributed scatterer (DS) selection, and DS and persistent scatterer (PS) are combined to improve the density of measurement points in vegetation area. Moreover, this method is also characterized by the appropriate combination of differential interferograms produced by a small baseline subsets and the employment of the phase triangulation algorithm to estimate the optimal phase. Combining 105 scenes of C-band Sentinel-1A ascending and descending data acquired during 2014–2018, the method is applied to retrieve time series displacement for the large-scale landslide in Zongling Town. Finally, three accelerating landslides are identified from our result, which is consistent with ALOS PALSAR differential interferometry synthetic aperture radar (DInSAR) results and field investigation. The influencing factors and deformation mechanism of the Zongling landslide are also analysed. Our monitoring results will help the local government to conduct regular inspections and strengthen disaster prevention in mountain areas.

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Acknowledgements

The authors would like to thank Copernicus programme for the free access to Sentinel-1 images processed in this analysis and ALOS/PALSAR data are provided by the Third Institute of Surveying and Mapping in Guizhou Province.

Funding

This research was funded by the National Natural Science Foundation of China under Grants 41930110 and 41801348.

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Correspondence to Chao Wang.

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Wang, J., Wang, C., Xie, C. et al. Monitoring of large-scale landslides in Zongling, Guizhou, China, with improved distributed scatterer interferometric SAR time series methods. Landslides 17, 1777–1795 (2020). https://doi.org/10.1007/s10346-020-01407-5

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