Large-Scale Surface Deformation Monitoring Using SBAS-InSAR and Intelligent Prediction in Typical Cities of Yangtze River Delta

Abstract

Large-scale short-term monitoring and prediction of surface deformation are of great significance for the prevention and control of geohazards in rapidly urbanizing developing cities. Most studies focus on individual cities, but it would be more meaningful to address large urban agglomerations and consider the relevance of the regions within them. In addition, the commonly used linear fitting prediction methods cannot accurately capture the dynamic mechanisms of deformation. In this study, we proposed an automatic PS extraction method (named PS-SBAS-InSAR) that improves SBAS-InSAR to extract surface deformation and an Informer-based short-term surface deformation prediction method for case studies in 16 typical cities of the Yangtze River Delta (YRD). The results show that PS-SBAS-InSAR successfully extracted accurate surface deformation sequences of the YRD. During the period from January 2019 to January 2021, the YRD experienced a slight deformation with an average deformation rate within [−4, 4] mm/year. Geographically neighboring cities may have associated deformation distributions and similar deformation trends, as indicated by average deformation rate maps and landscape metrics. Both types of deformation (i.e., subsidence/uplift) tend to occur simultaneously, with specific areas of subsidence/uplift occurring in close proximity to areas of concentrated deformation. The Informer model effectively captured the time-series variation in surface deformation, suggesting a slowdown of deformation over the next two months (February 2021–March 2021). Our work contributes to a better understanding of changes and trends in large-scale surface deformation and provides useful methods for monitoring and predicting surface deformation in coastal areas.

Analysis and Prediction of Urban Surface Transformation Based on Small Baseline Subset Interferometric Synthetic Aperture Radar and Sparrow Search Algorithm–Convolutional Neural Network–Long Short-Term Memory Model

“…The SBAS-InSAR technique [ 21 , 22 , 23 , 24 ] performs deformation measurements by means of a small baseline differential interferometric atlas, which can reduce the influence of spatial de-correlation and terrain errors, and then applies the singular value decomposition (SVD) method based on the least-paradigm criterion of the deformation rate to obtain the deformation rate of coherent targets and their time series [ 25 ]. Its technological flow is shown in Figure 2 .…”

Research on Time Series Monitoring of Surface Deformation in Tongliao Urban Area Based on SBAS-PS-DS-InSAR

“…Subsequently, the SBAS-InSAR [ 18 , 19 ] technique was proposed, which employs small baseline combinations for measurements and uses the singular value decomposition (SVD) method to compute multiple small baseline combinations for atmospheric phase removal and deformation inversion in order to efficiently obtain surface deformation information on a time series. After more than a decade of development and application, currently, these two time-series InSAR techniques are commonly used in large-scale surface deformation monitoring [ 20 , 21 , 22 , 23 , 24 ]. Since the density of PS points in non-urban areas is generally less than 10 km 2 , which is much smaller than that in urban areas, it is difficult for PS-InSAR to obtain spatially continuous surface deformation information in areas with few artificial structures.…”

A Multi-Scale Remote Sensing for Heritage Monitoring Through Integration of Satellite Sar, Laser Scanning, and Drones

“…Zhao et al included temperature and precipitation as additional parameters in the SBAS-InSAR approach to enhance the estimation of surface deformation in a perpetual permafrost area (Zhao et al, 2016). Wang et al enhanced the SBAS-InSAR approach and the Informer-based short-term surface deformation prediction method to extract precise surface deformation sequences for 16 cities (Wang et al, 2023).…”

“…Drone photogrammetry is a rapid, easy-to-use, and versatile measurement approach that utilizes Structure from Motion (SfM) techniques for reconstructing 3D objects (Westoby et al, 2012, Jebara et al, 1999. Drone photogrammetry is low-cost and allows high-resolution orthoimages and point cloud models to be acquired with centimeter-level accuracy over the flight area.…”