Abstract
Landslide is the second most frequent geological disaster after earthquake, which causes a large number of casualties and economic losses every year. China frequently experiences devastating landslides in mountainous areas. Interferometric Synthetic Aperture Radar (InSAR) technology has great potential for detecting potentially unstable landslides across wide areas and can monitor surface displacement of a single landslide. However traditional time series InSAR technology such as persistent scatterer interferometry (PSI) and small-baseline subset (SBAS) cannot identify enough points in mountainous areas because of dense vegetation and steep terrain. In order to improve the accuracy of landslide hazard detection and the reliability of landslide deformation monitoring in areas lacking high coherence stability point targets, this study proposes an adaptive distributed scatterer interferometric synthetic aperture radar (ADS-InSAR) method based on the spatiotemporal coherence of the distributed scatterer (DS), which automatically adjusts its detection threshold to improve the spatial distribution density and reliability of DS detection in the landslide area. After time series network modeling and deformation calculation of the ADS target, the displacement deformation of the landslide area can be accurately extracted. Shuibuya Town in Enshi Prefecture, Hubei Province, China, was used as a case study, along with 18 Sentinal-1A images acquired from March 2016 to April 2017. The ADS-InSAR method was used to obtain regional deformation data. The deformation time series was combined with hydrometeorological and related data to analyze landslide deformation. The results show that the ADS-InSAR method can effectively improve the density of DS distribution, successfully detect existing ancient landslide groups and determine multiple potential landslide areas, enabling early warning for landslide hazards. This study verifies the reliability and accuracy of ADS-InSAR for landslide disaster prevention and mitigation.
Highlights
Landslides are a common geological disaster typically induced by persistent heavy rainfall, human activities and earthquakes
Monitoring and analysis of the landslide deformation area can provide an important basis for landslide early warnings; this is key research area for geological disaster prevention and mitigation
International landslide monitoring methods predominantly include absolute deformation measurements based on global positioning systems (GPS), electronic total stations, levels and so forth [2,3,4], relative deformation measurements based on displacement meters, crack meters or fiber sensors and so forth [5,6] and quantitative analysis based on light detection and ranging (LIDAR) technology [7,8]
Summary
Landslides are a common geological disaster typically induced by persistent heavy rainfall, human activities and earthquakes. International landslide monitoring methods predominantly include absolute deformation measurements based on global positioning systems (GPS), electronic total stations, levels and so forth [2,3,4], relative deformation measurements based on displacement meters, crack meters or fiber sensors and so forth [5,6] and quantitative analysis based on light detection and ranging (LIDAR) technology [7,8] These methods, which are based on ground point observations, have the advantage of high precision, the number of monitoring points on the landslide body is limited by the large amounts of field work and economic costs involved. In order to improve the spatial resolution and success rate of large-scale landslide hazard detection and landslide displacement monitoring, an adaptive distributed scatterer InSAR (ADS-InSAR) method is proposed. In order to verify the validity and reliability of this method, this study uses Shuibuya Town in Hubei Province, China, as the research area. 18 C-band Sentinel-1A images are employed to detect the spatial distribution of landslide points in the area and extract and analyze surface deformation data
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