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利用无人机影像测量黄河河湾处的冰块流速

Ice velocity in the Yellow River bends using unmanned aerial vehicle imagery.

作者信息

Xue Zhongshu, Ji Honglan, Luo Hongchun, Liu Bin

机构信息

College of Water Resources and Civil Engineering, Inner Mongolia Agricultural University, Hohhot, 010018, Inner Mongolia, China.

Autonomous Region Collaborative Innovation Center for Integrated Management of Water Resources and Water Environment in the Inner Mongolia Reaches of the Yellow River, Hohhot, 010018, China.

出版信息

Sci Rep. 2025 Jul 2;15(1):22956. doi: 10.1038/s41598-025-07321-x.

DOI:10.1038/s41598-025-07321-x
PMID:40596591
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12215903/
Abstract

Ice velocity is a critical parameter in river ice monitoring, playing a vital role in analyzing ice blockages, the freeze-up process, and ice jam locations. It provides essential references for freeze-up forecasting. This study utilized UAV (Unmanned Aerial Vehicle) remote sensing imagery from December 2023, during the ice run period, combined with the Pyramidal Lucas-Kanade Optical Flow Algorithm to quantify ice velocity distribution across multiple bends during the Yellow River's ice run period. The study identified low ice velocity cross-sections at bend apexes, where velocity decreased sharply, leading to ice floe accumulation and an increased probability of ice jams. Significant spatial variations in ice velocity within bends were observed. Curvature and constriction ratios were identified as key factors influencing ice velocity distribution. Bends with high curvature and significant constriction should be prioritized for monitoring and early warning. The study also revealed a correlation between the low ice velocity cross-section at the apex of Shisifenzi Bend and the location of the initial ice jam during the ice run period, suggesting its potential as a scientific guide for ice jam risk forecasting. These findings provide valuable insights for freeze-up forecasting and flood prevention in the Yellow River.

摘要

冰速是河流冰情监测中的一个关键参数，在分析冰坝、封冻过程和冰塞位置方面起着至关重要的作用。它为封冻预报提供了重要参考。本研究利用了2023年12月冰期无人机遥感影像，结合金字塔式卢卡斯-卡纳德光流算法，对黄河冰期多个弯道的冰速分布进行了量化。研究发现弯道顶点处冰速较低的断面，此处流速急剧下降，导致冰块堆积，冰塞发生概率增加。观察到弯道内冰速存在显著的空间变化。曲率和收缩率被确定为影响冰速分布的关键因素。曲率大且收缩显著的弯道应优先进行监测和预警。研究还揭示了四分子弯道顶点处冰速较低的断面与冰期初始冰塞位置之间的相关性，表明其作为冰塞风险预报科学指南的潜力。这些发现为黄河的封冻预报和防洪提供了有价值的见解。

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