Monitoring Large-scale Landslide Displacements In Complicated Mountainous Areas With Time Series InSAR

Landslide disaster is one of the most serious disasters to people’s life and property safety and public infrastructure due to its high frequency and wide influence,especially the landslide and collapse disasters widely existed in complex mountainous areas,with strong concealment and great harmfulness,and difficult to monitor and research.The traditional measuring tools,such as GPS and leveling has high monitoring accuracy,but their spatial density of the observation network is low.The coverage of the non-contact monitoring technologies such as unmanned aerial vehicle remote sensing(UAVRS),light detection and ranging(Li DAR)and ground based-synthetic aperture radar(GB-SAR)are greatly limited to investigate and monitor large-scale ground deformation.The optical remote sensing is greatly affected by weather conditions,and can not observe the small deformation signal.In contrast,synthetic aperture radar interferometry(InSAR)has been widely used in surface deformation monitoring due to its characteristics of high monitoring precision,high spatial resolution,high temporal repetition observation,wide coverage and small impact of climate conditions.High precision deformation monitoring is very important for the study of landslide disaster,but there are still many limitations in landslide monitoring in complex mountainous areas.First of all,one-dimensional line-of-sight(LOS)deformation monitoring ability of D-InSAR method and two-dimensional deformation measurement results in range and azimuth direction of offset tracking method limited a single satellite platform data to reflect the three-dimensional deformation characteristics of landslide surface.Secondly,it is difficult to detect enough measurement points(MP)with conventional time-series InSAR methods in mountainous areas with dense vegetation coverage,leading to underestimation or misestimating of deformation results.Thirdly,most landslide investigations focus on pre-disaster deformation signal extraction or co-disaster landslide-affected area estimation but ignore the stability analysis of landslides in post-disaster stage.However,the remaining part of the slope and the debris accumulation area might be still in high risk of secondary failure subject to triggering factors such as earthquakes and intensive precipitation.Finally,the Distributed Scatterers SAR Interferometry(DS-InSAR)time series analysis method adopt batch processing mode.When new observation data acquired,the entire archived data is reprocessed,completely ignoring the existing results,and difficult to realize the real-time updating of data processing.In terms of these problems,this paper proposed corresponding solutions with the main research contents as followed:(1)Due to the geometric distortion of SAR in complex mountainous areas,the available SAR data are limited.For slow deformation landslides,at least three independent observations are needed to retrieve the three-dimensional deformation of landslide surface when there are only LOS deformation measurements of Differential SAR interferometry(D-InSAR).In this paper,a surface-parallel flow model is proposed to reconstruct the landslide surface three-dimensional deformation field with two observation results from different geometric images based on the geological data and DEM slope information.Experiments were carried out on Jiaju landslide in Sichuan Province,and the effectiveness of the method and model was verified by GPS observation data.(2)Persistent Scatterers Interferometry(PSI)and DS-InSAR method is combined to solve the problem of insufficient measurement points in complex mountainous areas.Taking Jiaju landslide in Danba County,Xishancun landslide and Huangnibazi landslide in Lixian County in the southwest China and Sunkoshi landslide in the northern Nepal as typical cases,the application conditions of DS-InSAR technology for landslides monitoring with different bands SAR datasets are analyzed,and the availability of different InSAR methods and multi-source SAR data is discussed.(3)The deformation evolution of landslide is a long-term development process,and single satellite data can not cover the whole life cycle of landslide disaster.In this study,the evolution life cycle of the Sunkoshi landslide during different periods(pre-,co-and post-disaster stages)is characterized using various InSAR techniques with multi-source SAR data.The deformation pattern and possible driving factors in the pre-disaster stage are explored,the sliding area is determined and the collapse volume is estimated,and the post-disaster stability of the landslide is evaluated.(4)As DS-InSAR analysis method cannot update and process long time-series and large datasets in real-time,this paper proposed to divide the large dataset into several sub-datasets,and then a irregular blocked sequential estimation algorithm is utilized to link all the sub-datasets to get the long-time series results.This method effectively avoids reprocessing the entire archived data when new SAR data is acquired.The sequential estimation method is applied to Xishancun landslide in Lixian County and Sunkoshi landslide in Nepal to verify its reliability.