Статья

Characterization of Local and Long-Distance Ice Floe Motion in the Yellow River Using UAV–GPS Joint Observations

Chunjiang LiSchool of Energy and Environment, Inner Mongolia University of Science and Technology, Baotou 014010, ChinaJiaqi DaiSchool of Energy and Environment, Inner Mongolia University of Science and Technology, Baotou 014010, ChinaYupeng LengState Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, ChinaXiaohua HaoKey Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaW. LiSchool of Energy and Environment, Inner Mongolia University of Science and Technology, Baotou 014010, ChinaShamshodbek AkmalovInstitute of Agriculture and Agrotechnologies of Karakalpakstan, Nukus 230109, UzbekistanXiangqian LiCollege of Urban and Environmental Sciences, Shihezi University, Shihezi 832003, ChinaZhichao WangSchool of Energy and Environment, Inner Mongolia University of Science and Technology, Baotou 014010, ChinaHan GaoInner Mongolia Hydrology and Water Resources Center, Hohhot 010010, ChinaXiang FuState Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, ChinaShengbo HuState Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, ChinaYu ZhengSchool of Energy and Environment, Inner Mongolia University of Science and Technology, Baotou 014010, China

Remote Sensingjournal2026en

ABI

Аннотация

Understanding the motion parameters of floating ice is very important for characterizing the ice water dynamics of rivers during freezing periods. Due to the low spatiotemporal resolution of satellite images, limited observation range of unmanned aerial vehicles, and deformation of shore-based camera images, it is difficult to simultaneously quantify the translational and rotational motion characteristics of floating ice and long-distance transportation. This study used the unmanned aerial vehicle GPS joint observation method to observe and obtain various motion parameters such as local translation, rotation, and long-distance transportation in the curved section of the upper reaches of the Yellow River and the straight section of the middle reaches of the Yellow River during the winter of 2024–2025 under conditions of ice density of 50–90%. The velocity field obtained by the drone shows an average ice velocity of 1.27 m/s at the bend and 1.18 m/s in the straight section, with lateral velocity gradients of −0.245 to 0.050 s−1 and −0.141 to 0.222 s−1, respectively. The angular velocity of a single floating ice block is 0.008–0.016 rad/s at bends and 0.010–0.036 rad/s in straight sections. The angular velocity is positively correlated with the local shear strength, and the rotation direction is consistent with the sign of the velocity gradient. GPS tracking provides long-distance transportation trajectories, and the average difference between the speeds obtained by GPS and drones is 0.10 m/s, confirming the reliability of speed estimation based on drones. These results indicate that integrated unmanned aerial vehicle GPS observation can quantitatively characterize local floating ice movement and long-distance floating ice transport behavior, providing on-site parameters for river ice water dynamics research and hazard assessment, and has the potential to be applied to rivers in other cold regions.

Темы

Arctic and Antarctic ice dynamics Icing and De-icing Technologies Cryospheric studies and observations

Идентификаторы

DOI: 10.3390/rs18050823

Цитирования и источники

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