Analysis of remote sensing images and Persistent Scatterer Interferometry (PSI) analysis of Synthetic Aperture Radar (SAR) data could provide early warning of landslide risk, according to new research.
The paper published in the Journal of the International Consortium on Landslides by Ouyang et al provides insight into ground movement before the fatal Su Villages landslide in Zhejiang Province, China in April last year, which killed 27 people.
The paper states: “Ridge-top rockslides frequently cause huge property losses and casualties due to the difficulties involved in detecting their precursors by means of manual surveys. Their early identification and the surrounding area’s evaluation in terms of potential danger are essential for preventing disasters.”
The research team used data related to pre-failure images, real-time video, and post-failure boreholes to gain new insight which is presented in the paper.
According to the paper abstract, the early identification of the hazard associated with rockfalls, which were the main cause of death in the Su Village incident, can be made by the analysis of remote sensing images and PSI analysis of SAR data.
The paper states: “The depth-integrated continuum method, including taking the entrainment effect into account, was adopted to analyse the dynamic processes and to identify the areas at risk. The computational results show that the evaluated runout distance and extent match well with the field investigation results. The parameter sensitivity surrounding cohesion, coefficients of lateral earth pressure, and volume amplification were analysed. It is demonstrated that the cohesion plays a significant role in the dynamic processes and the deposited area. However, the effects from the earth pressure coefficient and volume bulking are comparatively weaker.”
Landslide specialist Dave Petley, who is also Sheffield University pro vice chancellor (research and innovation) welcomed the research but cautioned the industry in relying on such techniques without further studies.
“This paper is another very useful contribution towards the use of InSAR data for rock slope failure detection in natural slopes,” he said. “But, once again, it shows that this is a very complex and challenging problem, and that our satellite based systems do not yet have the maturity to be used operationally in many cases as yet.
“The good news is that statistically significant movement was detected in the areas that failed, whilst the unfailed areas displayed no trend (with some noise). But the detected movement is linear with time in all three points with above-error deformation, meaning that no inferences could be drawn to indicate time of failure. Thus, while the analysis shows that the slope was creeping, it could not be used as a warning system.”