An Improved Method for Automatic Identification and Assessment of Potential Geohazards Based on MT-InSAR Measurements

Abstract

Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) has been widely used for ground motion identification and monitoring over large-scale areas, due to its large spatial coverage and high accuracy. However, automatically locating and assessing the state of the ground motion from the massive Interferometric Synthetic Aperture Radar (InSAR) measurements is not easy. Utilizing the spatial-temporal characteristics of surface deformation on the basis of the Small Baseline Subsets InSAR (SBAS-InSAR) measurements, this study develops an improved method to locate potential unstable or dangerous regions, using the spatial velocity gradation and the temporal evolution trend of surface displacements in large-scale areas. This method is applied to identify the potential geohazard areas in a mountainous region in northwest China (Lajia Town in Qinghai province) using 73 and 71 Sentinel-1 images from the ascending and descending orbits, respectively, and an urban area (Dongguan City in Guangdong province) in south China using 32 Sentinel-1 images from the ascending orbit. In the mountainous area, 23 regions with potential landslide hazards have been identified, most of which have high to very high instability levels. In addition, the instability is the highest at the center and decreases gradually outward. In the urban area, 221 potential hazards have been identified. The moderate to high instability level areas account for the largest proportion, and they are concentrated in the farmland irrigation areas, and construction areas. The experiment results show that the improved method can quickly identify and evaluate geohazards on a large scale. It can be used for disaster prevention and mitigation.

A Strategy for Variable-Scale InSAR Deformation Monitoring in a Wide Area: A Case Study in the Turpan–Hami Basin, China

“…(2) We detect ROI from the deformation rates. Setting the threshold for the deformation rate, the extension radius, and the minimum clustering area, we calculate the spatial distribution and area of the ROI in the WSA using an adaptive deformation detection method [39]. (3) We obtain the high-spatio-temporal-resolution deformation result of ROI.…”

Landslide Identification and Gradation Method Based on Statistical Analysis and Spatial Cluster Analysis

“…Here, μ ± 6σ is set as the threshold of deformation gradation [61]; max refers to the maximum value for the positive value of LOS direction and the minimum value for the negative value of LOS direction. The gradation result of each landslide (Equation ( 5)) is obtained by superimposing graded labels corresponding to three deformation maps.…”

Monitoring Displacements and Damage Detection through Satellite MT-InSAR Techniques: A New Methodology and Application to a Case Study in Rome (Italy)

“…Among MT-InSAR techniques, Permanent Scatterers Interferometry (PS-InSAR) [14] and SBAS-DInSAR [15,16] methods have proven to be reliable in detecting ground motion and building displacements. During the last two decades, different authors [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] have extensively performed satellite interferometry analysis to assess geological ground deformations and displacements on structures. Referring to SHM applications, MT-InSAR methods have proven able to detect displacement phenomena on infrastructures [24,28,32,34], civil structures and monuments [1,11,12,22,35,36], or archeological sites [8,9,17].…”