Landslide Deformation Detection And Identification Based On Insar Technology

Landslides are one of the disasters that cause huge economic losses.With the rapid increase in population and the increasing intensity of human engineering activities,the destruction of the natural environment began to deteriorate and the frequency of landslide disasters increased.In order to mitigate and avoid the losses caused by landslide hazards,the geological environment and slope structure of the landslide should be investigated,and the landslide body should be monitored to obtain data on the changes in its physical quantity,and then its formation mechanism,stability state,and precipitating factors should be analyzed.Commonly used landslide monitoring technologies(GPS,leveling instruments,total stations,close-range photogrammetry and displacement meters)have gained rich experience in the research of landslide hazard technology,which has made landslide warnings in China greatly improved,however,traditional measurement techniques still have some limitations.The newly developed InSAR technology can overcome the defects of traditional measurement technology to a certain extent and can monitor a wide range of surface deformation activities.Therefore,this paper takes the urban area of Danba County as the research area,and uses the time series SBAS-InSAR technology to detect the landslide deformation and identify the hidden hazards.First select a landslide with GPS monitoring point--yakaze landslide to perform the deformation detection of InSAR technology in a small area.By adjusting the parameters in the SBAS-InSAR technical process,the InSAR monitoring results are consistent with the GPS monitoring results.Then,the same parameters were used to develop landslide deformation detection and hidden hazard identification research in the urban area of Danba County.The main research content has the following aspects:(1)Considering the Consider the topography of the urban area of Danba County,the imaging geometry of SAR images and the advantages and disadvantages of commonly used InSAR technologies.It is most suitable to use the SBAS-InSAR technology for the landslide deformation detection and hidden hazard detection in the urban area of Danba County with the use of ascending and descending SAR image data.(2)The image data of the ascending SAR monitored that the landslide deformation in yakze is: the cumulative deformation in the middle of the landslide is 80 mm,98mm in the right front,89 mm in the left front.On the landslide body,four time-sequence analyses were performed on the area with larger deformation rate,and the cumulative displacement curve showed a linear change as a whole,and annual precipitation caused the time-series curve to exhibit a regular “concave” change.(3)Sentinel ascending and descending SAR images are used to monitor the deformation of the yakaze landslide.yakaze landslide shows that the longitudinal deformation has the front edge> middle>back edge.Horizontally,deformation in the middle of the landslide is large and small on both sides.The overall deformation of the landslide shows a stepped feature.The InSAR monitoring results are consistent with our on-site surveys and the data collected.InSAR technology has achieved centimeter accuracy in deformation monitoring in the urban area of Danba county.(4)Combining Google Earth imagery and SAR imagery of ascending and descending rail sentinels,the SBAS-InSAR technique was used to obtain the annual mean deformation rate and cumulative displacement,and finally 20 deformation areas were identified.Deformation was detected in SAR images of two orbits in five deformation regions.In addition,there are 8 deformation areas that have been monitored by deformation in the ascending SAR image and 7 deformed areas in which only descending SAR images are monitored for deformation.The recognition of landslides in the urban area of Danba through the two orbital SAR images to a certain extent has reached the goal of mutual confirmation and mutual remedy.(5)20 sites deformation areas have been compared with existing data and 12 landslides have been verified.There are 4 landslides with large deformation and no corresponding historical survey data has been found.In combination with the Google Earth image,they are classified as landslides.There are six landslides that require our site investigation or continued monitoring to further confirm whether it is a landslide.