Improved Small Baseline Subset InSAR Deformation Monitoring Method for the Great Wall Using UAV LiDAR DEM Constraints

To support the long-term monitoring and preventive conservation of linear cultural heritage, this study proposes a UAV-LiDAR DEM-constrained SBAS-InSAR long-term time-series monitoring method to identify the spatiotemporal deformation patterns and risk-sensitive segments of the near-field ground surface along the Huairou Great Wall. Unlike traditional methods, this research is the first to apply high-resolution UAV-derived DEM for topographic correction and phase modeling in the Huairou Great Wall, aiding in long-term ground deformation monitoring. By integrating multi-scale meteorological data such as precipitation, temperature, and humidity, the study systematically analyzes their impact on deformation. The results reveal significant heterogeneity in ground deformation along the Huairou Great Wall, with the Jiankou section identified as a sensitive area. The study shows a clear event-scale correspondence between rainfall and short-term deformation fluctuations, while air temperature and relative humidity exhibit statistical consistency with cumulative deformation, serving as perturbation cues for sensitivity screening but not direct causal attribution. Compared to traditional ground-based monitoring methods, this approach significantly reduces labor and time costs, enabling large-scale, high-precision, long-term monitoring in a shorter period. It provides a technical basis for identifying risk-prone segments along the Great Wall and conducting post-rainfall inspections, providing a reference for the long-term monitoring and preventive protection of linear cultural heritage.