Carbon Capture, Utilization and Storage, also referred to as Carbon Capture, Utilization and Sequestration (CCUS), is one of the novel climate mitigation technologies by which CO₂ emissions are captured from sources, such as fossil power generation and industrial processes, and further either reused or stored with more attention being paid on the utilization of captured CO₂. In the whole CCUS process, the dominant migration pathway of CO₂ after being injected underground becomes very important information to judge the possible storage status as well as one of the essential references for evaluating possible environmental affects. Interferometric Synthetic Aperture Radar (InSAR) technology, with its advantages of extensive coverage in surface deformation monitoring and all-weather traceability of the injection processes, has become one of the promising technologies frequently adopted in worldwide CCUS projects. In this study, taking the CCUS sequestration area in Shizhuang Town, Shanxi Province, China, as an example, unmanned aerial vehicle (UAV) photography measurement technology with a 3D surface model at a resolution of 5.3 cm was applied to extract the high-resolution digital elevation model (DEM) of the study site in coordination with InSAR technology to more clearly display the results of surface deformation monitoring of the CO₂ injection area. A 2 km surface heaving dynamic processes before and after injection from June 2020 to July 2021 was obtained, and a CO₂ migration pathway northeastward was observed, which was rather consistent with the monitoring results by logging and micro-seismic studies. Additionally, an integrated monitoring scheme, which will be the trend of monitoring in the future, is proposed in the discussion.

The deep-learning-based approach has drawn significant attention in glacier extraction due to its advantages over traditional techniques. In this study, to verify the feasibility and effectiveness of LandsNet architecture for glacier extraction, we applied a modified LandsNet (M-LandsNet) to extract the glacier outlines in the Three-Rivers Headwater Region. The band ratio method, U-Net, U-Net++, GlacierNet, SaU-Net, U-Net+cSE, and LandsNet, and two scenes were used for comparison. Analysis of the two scenes indicated that the M-LandsNet had the best performance and generalization ability among the eight methods. Weather conditions had the greatest negative impact on the eight methods, followed by geographic environment and geographic location. We further extracted the glacier outlines in the Three-Rivers Headwater Region in 1986−2021 in a total of 12 periods using the M-LandsNet and through manual adjustments. The glacier area in the Three-Rivers Headwater Region has decreased by 416.40 ± 102.71 km ² (16.53 ± 4.08%) in 1986−2021. The reduction rate (16.13 ± 5.63 km ² a ⁻¹ ) in 2003−2021 was almost twice that (7.42 ± 5.97 km ² a ⁻¹ ) in 1986−2003. The reduction rate of the glacier area varied among different periods and areas. Comparison with previous results indicated that the obtained glacier outline dataset in this study is reliable, and can effectively reflect the glacier area and spatio-temporal glacier changes in the Three-Rivers Headwater Region. A long time-series dataset of glacier outlines in the Three-Rivers Headwater Region in 1986−2021 is available at https://doi.org/10.5281/zenodo.5512064 . This study can provide data support for the estimation of regional water resources storage.