A fast and robust method for detecting trend turning points in InSAR displacement time series

Abstract

Ground deformation monitoring is a crucial task in geohazard management to ensure the safety of lives and infrastructure. Persistent scatterer interferometric synthetic aperture radar (PS-InSAR) is an advanced technique for measuring small displacements on the Earth’s surface. Estimated PS-InSAR time series acquired by Sentinel-1 satellites provide a great opportunity for effective monitoring of ground deformation in recent years. However, challenges arise when processing these time series due to their non-uniform sampling, noise from atmosphere and preprocessing issues including phase unwrapping and others. Therefore, estimating the location and direction of trend turning in such time series, as an indicator of ground deformation, is not an easy task. In this work, a sequential turning point detection method (STPD) is proposed and compared with other change point detection methods. Using a large set of simulated time series with various noise types, it is shown that STPD outperforms other methods in terms of overall accuracy and root mean square error for location and direction of trend turnings. As a case study, STPD is applied to detect turning points within PS-InSAR time series for the province of Frosinone in Italy and classified using topography and land cover/use. In addition, an area susceptible to landslides is selected to estimate the starting dates of potential slow-moving landslides. It is also shown that the turning points in the local precipitation time series have a high correlation with the ones in the PS-InSAR time series, indicating that precipitation is a major triggering factor of the displacements in the area. The STPD can rapidly and effectively detect locations and directions of trend turnings and is freely available online in both MATLAB and python.