Due to the complexity inherent in river ice dynamics, the utilization of remote sensing imagery represents the most crucial and effective method currently available for monitoring changes in river ice. In the Inner Mongolia segment of the Yellow River during winter, two distinct types of ice surfaces are observed: juxtaposed ice and consolidated ice. Additionally, certain areas of open water remain unfrozen. Rapid identification and classification of extensive ice formations and open water zones along this lengthy river section constitute critical information for informed decision-making in ice prevention and management strategies within the Yellow River basin. This paper takes the formation and characteristic analysis of different types of ice in the Yellow River channels in Inner Mongolia as the starting point. It employs a support vector machine (SVM) as the classifier and introduces an optimized model for classifying river ice types using high-resolution Sentinel-2 optical imagery. The model utilizes multi-band spectral features, along with multi-spectral fusion indices such as the normalized difference snow index (NDSI) and the normalized difference frozen surface index (NDFSI), as feature vectors. This approach attains an overall accuracy of 94.91% in classifying different types of ice and can significantly contribute to river ice monitoring by offering robust theoretical support. In the winter of 2023–2024, the proportion of juxtaposed ice on the Yellow River section in Inner Mongolia changed from 45% to 55%, the proportion of consolidated ice changed from 30% to 40%, and the proportion of open water changed from 9% to 19%. This research investigates the characteristics of river ice formations and develops a classification methodology for river ice patterns utilizing high-resolution Sentinel-2 imagery in conjunction with a supervised classification algorithm. The findings of this study are intended to offer technical support for the expedited interpretation of ice conditions in the Yellow River, thereby serving as a scientific basis for precise monitoring and effective disaster prevention and management related to river ice phenomena.
Building similarity graph...
Analyzing shared references across papers
Loading...
Yupeng Leng
Dalian University of Technology
Chunjiang Li
Dalian University of Technology
Pai Lu
Beijing Institute of Technology
Remote Sensing
Chinese Academy of Sciences
Dalian University of Technology
Shihezi University
Building similarity graph...
Analyzing shared references across papers
Loading...
Leng et al. (Tue,) studied this question.
synapsesocial.com/papers/699f95841bc9fecf3dab35f5 — DOI: https://doi.org/10.3390/rs18050672