The deformation monitoring of long-span railway bridges is significant to ensure the safety of human life and property. The interferometric synthetic aperture radar (InSAR) technology has the advantage of high accuracy in bridge deformation monitoring. This study monitored the deformation of the Ganjiang Super Bridge based on the small baseline subsets (SBAS) InSAR technology and Sentinel-1A data. We analyzed the deformation results combined with bridge structure, temperature, and riverbed sediment scouring. The results are as follows: (1) The Ganjiang Super Bridge area is stable overall, with deformation rates ranging from −15.6 mm/yr to 10.7 mm/yr (2) The settlement of the Ganjiang Super Bridge deck gradually increases from the bridge tower toward the main span, which conforms to the typical deformation pattern of a cable-stayed bridge. (3) The sediment scouring from the riverbed cause the serious settlement on the bridge's east side compared with that on the west side. (4) The bridge deformation negatively correlates with temperature, with a faster settlement at a higher temperature and a slow rebound trend at a lower temperature. The study findings can provide scientific data support for the health monitoring of long-span railway bridges.

Traditional inspection methods cannot quickly and accurately monitor tree barriers and safeguard the transmission lines. To solve these problems, in this study, we proposed a rapid canopy height information extraction method using optical remote sensing and LiDAR, and used UAV optical imagery with LiDAR to monitor the height of trees in a university and a high-voltage transmission line corridor in the Ningxia region. The results showed that the relative error of tree height extraction using UAV optical images was less than 5%, and the lowest relative error was 0.11%. The determination coefficient R² between the optical image tree height extraction results and the measured tree height was 0.97, thus indicating a high correlation for both. In the field of tree barrier monitoring, the determination coefficient R² of tree height extracted using airborne LiDAR point cloud, and canopy height model (CHM) and of the measured tree height were 0.947 and 0.931, respectively. The maximum and minimum relative error in tree height extraction performed using point cloud was 2.91% and 0.2%, respectively, with an extraction accuracy of over 95%. The experimental results demonstrated that it is feasible to use UAV optical remote sensing and LiDAR in monitoring tree barriers and tree height information extraction quickly and accurately, which is of great significance for the risk assessment and early warning of tree barriers in transmission-line corridors.