Integrating Satellite InSAR and Topographic Data for Long-Term Displacement Monitoring of Bridge Crossing Slow-Moving Landslides

This study investigates the effectiveness of different monitoring strategies for estimating bridge displacement trends induced by landslides, with a focus on addressing three key questions: (i) Can bridge displacement trends induced by a landslide be monitored using only 1D displacement time series along the satellite line of sight (LOS), as provided by InSAR? (ii) How do InSAR-derived displacement trend estimates differ from those obtained through traditional topographic monitoring? (iii) Can a data fusion approach, integrating both InSAR and topographic data, provide more accurate results than using either method alone? Topographic monitoring, which offers direct three-dimensional measurements, is used as the “ground truth” for evaluating the accuracy of InSAR and data fusion methods. The results show that, even though only SAR images from a single orbital geometry are available, InSAR can provide reasonably accurate estimates along the slope-aligned direction, while it is less effective in capturing transverse displacements due to the limitations of measuring along the satellite’s LOS. However, when combined with prior knowledge of landslide behavior, InSAR still provides valuable insights. Bayesian data fusion, which integrates topographic and InSAR measurements, significantly reduces uncertainties, particularly in short monitoring periods, offering a cost-effective alternative to continuous topographic monitoring. Additionally, this study explores two alternative strategies: limiting topographic measurements to the first year and spreading sparse topographic measurements over several years and relying on satellite data thereafter. While both approaches yield satisfactory results in the slope direction, they show higher uncertainties in the transvers direction, particularly as the frequency of topographic measurements decreases. The findings suggest that a combined monitoring approach, integrating satellite and topographic data, as well as a prior knowledge of landslide behavior, provides an accurate and cost-effective solution for long-term monitoring of infrastructure in landslide-prone areas.