Long-Term Monitoring of Landslide Activity in a Debris Flow Gully Using SBAS-InSAR: A Case Study of Shawan Gully, China
Abstract
:1. Introduction
2. Study Area and Datasets
2.1. Study Area
2.2. Datasets
3. Materials and Methods
3.1. Geometric Distortion Identification
3.2. InSAR Monitoring Sensitivity Analysis
3.3. Surface Deformation Measurements
3.3.1. PS-GCPs Selection
3.3.2. Displacement Components Estimation in Vertical and Horizontal Directions
4. Results
4.1. Suitability Assessment of InSAR Measurements
4.1.1. SAR Imagery Availability Analysis
4.1.2. Reliability Analysis of InSAR Measurements
4.2. Deformation Monitoring and Kinematic Patterns Assessment
4.2.1. PS-GCP Detection and Identification
4.2.2. Multi-Orbit SBAS-InSAR Surface Deformation Measurements
4.3. Regional Analysis of Landslide Dynamics
4.4. Rainfall-Induced Changes in Landslide Activity
5. Discussion
- (1)
- RMSE: This metric evaluates the phase quality of the interferograms, where a lower RMSE value indicates smaller phase errors and higher quality. Post-masking, the average interferometric phase error decreased from 1.55 rad to 1.41 rad, with the standard deviation reducing from 0.33 to 0.25, signifying a notable reduction in phase error.
- (2)
- 1/ADI: Higher 1/ADI values indicate better phase stability of the pixels and more reliable results. The masking process had minimal impact on the phase mean and standard deviation (1/ADI), with no significant difference observed before and after processing. This suggests that geometric distortions affect the magnitude of interferometric phase errors rather than the phase distribution itself. The result also indicates that reducing sources of phase error is crucial for improving InSAR monitoring quality.
- (3)
- Vprecision: This parameter assesses the precision of deformation measurements, with lower Vprecision values indicating higher measurement accuracy. After masking, the mean Vprecision value decreased from 1.45 mm/year to 1.18 mm/year, and the standard deviation dropped from 0.33 mm to 0.30 mm, demonstrating that masking reduces error and uncertainty, thereby enhancing the precision of deformation rate measurements.
- (4)
- Velocity: Masking reduced the variability in average rate measurements, with the standard deviation of mean velocity dropping from 13.38 mm/yr to 12.85 mm/yr, indicating more concentrated and stable measurement results.
6. Conclusions
- This study used 688 Sentinel-1 SAR scenes collected from three orbits: S1AP26, S1AP128, and S1DP62. Before SBAS-InSAR processing, we proposed a method to evaluate the suitability of the InSAR monitoring scheme based on geometric distortions and monitoring sensitivity. The results show that the SAR imagery from Sentinel-1 ascending path 128 is severely affected by layover and shadow, making it unsuitable for refined monitoring of the Nuole and Huajiaoshu landslides. Among the remaining two orbits, the descending path 62 provides higher reliability for surface deformation monitoring.
- After thinning the ground surface extent, we used SBAS-InSAR to process the refined SAR datasets. To eliminate residual fringes caused by topographical effects, we utilized PS-GCPs with stable phase and high coherence to refine the satellite orbit and re-flatten the interferograms. The refined SBAS-InSAR results of multi-orbits indicate significant surface deformation at the rear scarp and toe of the NL1 section on the Nuole landslide. Additionally, deformation features were observed in the BT2 collapse area, the NL2 section of the Nuole landslide, and the toe of the Huajiaoshu landslide using descending orbit 62 data. For detailed deformation monitoring and analysis of movement characteristics in different regions of the landslide, we estimated the surface deformation rates in both vertical and horizontal directions. The results show significant displacement in the NL1 area in both directions, with consistent movement in the main body of the landslide, while the northern scarp and the foot of the slope exhibited different movement characteristics. Time series displacement demonstrated that rainfall effectively triggers landslide instability and changes the slopes’ movement states, making it a significant factor influencing landslide activity.
- We discussed the role of suitability assessment in enhancing InSAR monitoring accuracy through RMSE, Vprecision, 1/ADI, and Velocity. The mean and standard deviation of RMSE and Vprecision decreased, indicating improved deformation measurement accuracy. The standard deviation of mean deformation velocity decreased from 13.3788 mm/yr to 12.8514 mm/yr, indicating more stable and consistent measurement results. This also confirms that reducing error sources is an effective way to improve InSAR monitoring accuracy. Future research could explore the applicability of these methods in larger and more diverse study areas and investigate other factors that may influence InSAR monitoring accuracy to further enhance the quality and reliability of surface deformation monitoring.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Path | 26 | 128 | 62 |
---|---|---|---|
Flight mode | ascending | ascending | descending |
Polarization | VV | VV | VV |
Heading angle (°) | 347.5028 | 347.4863 | 192.5084 |
Incidence angle (°) | Local incidence angle (LIA) | ||
Acquisition period | 19 October 2014 to 29 May 2023 | 26 November 2015 to 5 June 2023 | 9 October 2014 to 6 July 2023 |
Number | 223 | 210 | 255 |
Sensitivity | [min, −0.5) and (0.5, 1] | [−0.5, −0.3) and (0.3, 0.5] | [−0.3, −0.1) and (0.1, 0.3] | [−0.1, 0) and (0, 0.1] | 0 |
Classification | High | Middle | Low | Extremely low | insensitivity |
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Zhang, J.; Zuo, X.; Zhu, D.; Li, Y.; Liu, X. Long-Term Monitoring of Landslide Activity in a Debris Flow Gully Using SBAS-InSAR: A Case Study of Shawan Gully, China. Remote Sens. 2025, 17, 1580. https://doi.org/10.3390/rs17091580
Zhang J, Zuo X, Zhu D, Li Y, Liu X. Long-Term Monitoring of Landslide Activity in a Debris Flow Gully Using SBAS-InSAR: A Case Study of Shawan Gully, China. Remote Sensing. 2025; 17(9):1580. https://doi.org/10.3390/rs17091580
Chicago/Turabian StyleZhang, Jianming, Xiaoqing Zuo, Daming Zhu, Yongfa Li, and Xu Liu. 2025. "Long-Term Monitoring of Landslide Activity in a Debris Flow Gully Using SBAS-InSAR: A Case Study of Shawan Gully, China" Remote Sensing 17, no. 9: 1580. https://doi.org/10.3390/rs17091580
APA StyleZhang, J., Zuo, X., Zhu, D., Li, Y., & Liu, X. (2025). Long-Term Monitoring of Landslide Activity in a Debris Flow Gully Using SBAS-InSAR: A Case Study of Shawan Gully, China. Remote Sensing, 17(9), 1580. https://doi.org/10.3390/rs17091580