Abstract
The advanced interferometric synthetic aperture radar (InSAR) provides an effective tool to detect landslides over a large area. However, it is greatly affected by atmospheric delays and phase unwrapping errors in a complex environment and requires massive calculations and analysis. These factors hinder InSAR from reliably and rapidly identifying landslides. In this study, we propose an improved phase gradient stacking (IPGS) method, which effectively suppresses atmospheric delay disturbance, topographic residuals, and noise while enhancing local deformation signals. The temporally stacked phase gradients with a preset step along four directions are merged to form a phase gradient map. It avoids complicated unwrapping and massive time series analysis. The simulation experiment demonstrates the improvement to traditional methods by combining four directions and a specific step. The IPGS method achieves a comparative landslide detection as the classical SBAS method in terms of Sentinel-1 datasets covering Danba County. Even for some small-scale landslides that are difficult for SBAS to detect, the phase gradients are distinct. A field investigation validates the reliability of IPGS-detected landslides. It provides an effective tool for large-scale, rapid, and reliable detection of geological disasters.
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Data availability
The MATLAB code of the IPGS algorithm is available on demand (please contact: dongjie@whu.edu.cn).
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Acknowledgements
We thank Paul Wessel for sharing Generic Mapping Tools (GMT). We thank the European Space Agency (ESA) for providing the Sentinel-1 datasets under the framework of the Sino-EU Dragon Project (ID 59332) and the National Aeronautics and Space Administration (NASA) for providing the SRTM DEM.
Funding
This work was financially supported by the National Key Research and Development Program of China (Grant No. 2021YFC3000400), the National Natural Science Foundation of China (Grant No. 42374013), the Sichuan Province Science and Technology Support Program (Grant No. 2020JDR0394), and the LIESMARS Special Research Funding.
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Zhang, D., Zhang, L., Dong, J. et al. Improved phase gradient stacking for landslide detection. Landslides (2024). https://doi.org/10.1007/s10346-024-02263-3
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DOI: https://doi.org/10.1007/s10346-024-02263-3