Identifying Potential Landslides by Stacking-InSAR in Southwestern China and Its Performance Comparison with SBAS-InSAR

Zhang, Lele; Dai, Keren; Deng, Jin; Ge, Daqing; Liang, Rubing; Li, Weile; Liu, Fangzhou

doi:10.3390/rs13183662

Cited by 103 publications

(41 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There is deformation with large magnitude and high coherence in the third area (Figure 9c,f,i,l), which can be detected by all methods, but the level of deformation is too large for the number and location of the subsidence basin center and cannot be determined by SBAS-InSAR and DInSAR with a long spatiotemporal baseline. As shown in Figure 10, we selected two typical subsidence areas near Jiangjiawan and Nanhewan from the study area for a comparative analysis and uniformly set the color bar of the SBAS-InSAR and stacking-InSAR monitoring results [55]. The maximum deformation rate of the LOS direction monitored by stacking-InSAR is −150 mm/a (Figure 10a).…”

Section: Discussionmentioning

confidence: 99%

Research on the Applicability of DInSAR, Stacking-InSAR and SBAS-InSAR for Mining Region Subsidence Detection in the Datong Coalfield

Tang

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

Intensive and large-scale underground coal mining has caused geological disasters such as local ground subsidence, cracks and collapse in the Datong coalfield, China, inducing serious threats to local residents. Interferometric synthetic aperture radar (InSAR) has the capability of surface deformation detection with high precision in vast mountainous areas. DInSAR, stacking-InSAR and SBAS-InSAR are commonly used InSAR-related deformation analysis methods. They can provide effective support for mine ecological security monitoring and prevent disasters. We use the three methods to conduct the deformation observation experiments in the Datong coalfield. Sentinel-1A data from November 2020 to October 2021 are used. As a result, a total of 256 deformations in the Datong coalfield were successfully detected by the three methods, of which 218 are mining deformations, accounting for 85% of the total deformations. By comparing the results of the three methods, we found that DInSAR, stacking-InSAR, and SBAS-InSAR detected 130, 256, and 226 deformations in the Datong coalfield, respectively, while the deformations caused by coal mining were 128, 218, and 190. DInSAR results with long spatiotemporal baselines are seriously incoherent. SBAS-InSAR results of displacement rate are more precise than stacking-InSAR, and the mean standard deviation is 1.0 mm/a. However, for areas with lush vegetation or low coherence, SBAS-InSAR has poor performance. The detection deformation area results of DInSAR and SBAS-InSAR are subsets of stacking-InSAR. The displacement rates obtained by stacking-InSAR and SBAS-InSAR are consistent; the mean difference in the displacement rate between the two methods is 2.7 mm/a, and the standard deviation is 5.1 mm/a. The mining deformation locations and their shapes in the study area can be identified with high efficiency and power by stacking-InSAR. Therefore, with a comprehensive understanding of the advantages and limitations of the three methods, stacking-InSAR can be an effective and fast method to identify the level, location and range of mining deformation in lush mountainous areas.

show abstract

Section: Discussionmentioning

confidence: 99%

Research on the Applicability of DInSAR, Stacking-InSAR and SBAS-InSAR for Mining Region Subsidence Detection in the Datong Coalfield

Tang

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…This technique can weaken the influence of orbital, atmospheric, and topographic errors in the unwrapped phase map. Benefiting from the open source of Sentinel-1 data, the role of this technology in the general surveys of land surface deformation is attracting more attention [44][45][46][47]. Since the atmospheric phase component has the characteristics of low frequency in the spatial domain and high frequency in the time domain, Stacking minimizes the atmospheric errors and improves the calculation accuracy of the deformation rate [48].…”

Section: Stackingmentioning

confidence: 99%

Efficient Identification and Monitoring of Landslides by Time-Series InSAR Combining Single- and Multi-Look Phases

et al. 2022

View full text Add to dashboard Cite

Identification and monitoring of unstable slopes across wide regions using Synthetic Aperture Radar Interferometry (InSAR) can further help to prevent and mitigate geological hazards. However, the low spatial density of measurement points (MPs) extracted using the traditional time-series InSAR method in topographically complex mountains and vegetation-covered slopes makes the final result unreliable. In this study, a method of time-series InSAR analysis using single- and multi-look phases were adopted to solve this problem, which exploited single- and multi-look phases to increase the number of MPs in the natural environment. Archived ascending and descending Sentinel-1 datasets covering Zhouqu County were processed. The results revealed that nine landslides could be quickly identified from the average phase rate maps using the Stacking method. Then, the time-series InSAR analysis with single- and multi-look phases could be used to effectively monitor the deformation of these landslides and to quantitatively analyze the magnitude and dynamic evolution of the deformation in various parts of the landslides. The reliability of the InSAR results was further verified by field investigations and Unmanned Aerial Vehicle (UAV) surveys. In addition, the precursory movements and causative factors of the recent Yahuokou landslide were analyzed in detail, and the application of the time-series InSAR method in landslide investigations was discussed and summarized. Therefore, this study has practical significance for early warning of landslides and risk mitigation.

show abstract

“…Stacking InSAR technology was developed based on conventional DInSAR technology, and it can improve the relative accuracy of deformation information from atmospheric disturbances through linear superposition of multiple differential interferograms [31]. The basic assumption of stacking InSAR technology is that the phase of atmospheric disturbances are random in time, while the ground deformation signal is time-dependent and can be approximately regarded to change linearly with time.…”

Section: Stacking Insar and Sbas Insar Technologymentioning

confidence: 99%

Mapping and Characterizing Displacements of Landslides with InSAR and Airborne LiDAR Technologies: A Case Study of Danba County, Southwest China

et al. 2021

Self Cite

View full text Add to dashboard Cite

Interferometric synthetic aperture radar (InSAR) technology is known as one of the most effective methods for active landslide identification and deformation monitoring in large areas, and thus it is conducive to preventing and mitigating the losses caused by landslides. However, great uncertainty inevitably exists due to influences of complex terrains, dense vegetations, and atmospheric interferences in the southwestern mountainous area of China, and this is associated with false or erroneous judgment during the process of landslide identification. In this study, a landslide identification method is put forward by integrating InSAR technology and airborne light detection and ranging (LiDAR) technology. Via this method, surface deformation characteristics detected by InSAR technology and micro-geomorphic features reflected by LiDAR technology were used to identify and map landslides of large areas. Herein, the method was applied to process 224 Sentinel-1 images covering Danba County and its surrounding areas (540 km2) from October 2014 to September 2020. Firstly, 44 active landslides with total areas of 59 km2 were detected by stacking InSAR technology. Then, major regions up to 135 km2 were validated by data gained from the airborne LiDAR technology. Particularly, several large landslides with lengths and/or widths of more than 2 km were found. Further, the precipitation data were integrated with the above results to analyze the temporal deformation characteristics of three typical landslides from major regions via SBAS InSAR technology. The key findings were as follows: (1) The combination of InSAR and LiDAR technologies could improve the accuracy of landslide detection and identification; (2) there was a significant correlation between temporal deformation characteristics of some landslides and monthly rainfall, with an obvious hysteretic effect existing between the initiation timing of rainfall and that of deformation; (3) the results of this study will be important guidance for the prevention and control of geological hazards in Danba County and areas with similar complex geomorphological conditions by helping effectively identify and map landslides.

show abstract

Identifying Potential Landslides by Stacking-InSAR in Southwestern China and Its Performance Comparison with SBAS-InSAR

Cited by 103 publications

References 47 publications

Research on the Applicability of DInSAR, Stacking-InSAR and SBAS-InSAR for Mining Region Subsidence Detection in the Datong Coalfield

Research on the Applicability of DInSAR, Stacking-InSAR and SBAS-InSAR for Mining Region Subsidence Detection in the Datong Coalfield

Efficient Identification and Monitoring of Landslides by Time-Series InSAR Combining Single- and Multi-Look Phases

Mapping and Characterizing Displacements of Landslides with InSAR and Airborne LiDAR Technologies: A Case Study of Danba County, Southwest China

Contact Info

Product

Resources

About