Machine Learning-Enhanced River Ice Identification in the Complex Tibetan Plateau
Accurate remote sensing identification of river ice not only provides scientific evidence for climate change but also offers early warning information for disasters such as ice jams. Currently, many researchers have used remote sensing index-based methods to identify river ice in alpine regions. How...
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MDPI AG
2025-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/11/1889 |
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| author | Xin Pang Hongyi Li Hongrui Ren Yaru Yang Qin Zhao Yiwei Liu Xiaohua Hao Liting Niu |
| author_facet | Xin Pang Hongyi Li Hongrui Ren Yaru Yang Qin Zhao Yiwei Liu Xiaohua Hao Liting Niu |
| author_sort | Xin Pang |
| collection | DOAJ |
| description | Accurate remote sensing identification of river ice not only provides scientific evidence for climate change but also offers early warning information for disasters such as ice jams. Currently, many researchers have used remote sensing index-based methods to identify river ice in alpine regions. However, in high-altitude areas, these index-based methods face limitations in recognizing river ice and distinguishing ice-snow mixtures. With the rapid advancement of machine learning techniques, some scholars have begun to use machine learning methods to extract river ice in northern latitudes. However, there is still a lack of systematic studies on the ability of machine learning to enhance river ice identification in high-altitude, complex terrains. The study evaluates the performance of machine learning methods and the RDRI index method across six aspects: river type, altitude, river width, ice periods, satellite data, and snow cover interference. The results show that machine learning, particularly the RF method, demonstrates superior generalization ability and higher recognition accuracy for river ice in the complex high-altitude terrain of the Tibetan Plateau by leveraging a variety of input data, including spectral and topographical information. The RF model performs best under all types of test conditions, with an average Kappa coefficient of 0.9088, outperforming other machine learning methods and significantly outperforming the traditional exponential method, demonstrating stronger recognition capabilities. Machine learning methods are adaptable to different types of river ice, showing particularly improved recognition of river ice in braided river systems. RF and SVM exhibit more accurate river ice recognition across different altitudinal gradients, with RF and SVM significantly improving the identification accuracy of river ice (0–90 m) on the plateau. RF and SVM methods offer more precise boundary recognition when identifying river ice across different ice periods. Additionally, RF demonstrates better generalization in the transfer of multisource satellite data. RF’s performance is outstanding under different snow cover conditions, overcoming the limitations of traditional methods in identifying river ice under thick snow. Machine learning methods, which are well suited for large sample learning and have strong generalization capabilities, show significant potential for application in river ice identification within high-altitude, complex terrains. |
| format | Article |
| id | doaj-art-44a582cd40594ce4aee06d25b4b66a56 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-44a582cd40594ce4aee06d25b4b66a562025-08-20T02:33:08ZengMDPI AGRemote Sensing2072-42922025-05-011711188910.3390/rs17111889Machine Learning-Enhanced River Ice Identification in the Complex Tibetan PlateauXin Pang0Hongyi Li1Hongrui Ren2Yaru Yang3Qin Zhao4Yiwei Liu5Xiaohua Hao6Liting Niu7College of Geological and Surveying Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaState Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaCollege of Geological and Surveying Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaState Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaState Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaState Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaState Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaState Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, ChinaAccurate remote sensing identification of river ice not only provides scientific evidence for climate change but also offers early warning information for disasters such as ice jams. Currently, many researchers have used remote sensing index-based methods to identify river ice in alpine regions. However, in high-altitude areas, these index-based methods face limitations in recognizing river ice and distinguishing ice-snow mixtures. With the rapid advancement of machine learning techniques, some scholars have begun to use machine learning methods to extract river ice in northern latitudes. However, there is still a lack of systematic studies on the ability of machine learning to enhance river ice identification in high-altitude, complex terrains. The study evaluates the performance of machine learning methods and the RDRI index method across six aspects: river type, altitude, river width, ice periods, satellite data, and snow cover interference. The results show that machine learning, particularly the RF method, demonstrates superior generalization ability and higher recognition accuracy for river ice in the complex high-altitude terrain of the Tibetan Plateau by leveraging a variety of input data, including spectral and topographical information. The RF model performs best under all types of test conditions, with an average Kappa coefficient of 0.9088, outperforming other machine learning methods and significantly outperforming the traditional exponential method, demonstrating stronger recognition capabilities. Machine learning methods are adaptable to different types of river ice, showing particularly improved recognition of river ice in braided river systems. RF and SVM exhibit more accurate river ice recognition across different altitudinal gradients, with RF and SVM significantly improving the identification accuracy of river ice (0–90 m) on the plateau. RF and SVM methods offer more precise boundary recognition when identifying river ice across different ice periods. Additionally, RF demonstrates better generalization in the transfer of multisource satellite data. RF’s performance is outstanding under different snow cover conditions, overcoming the limitations of traditional methods in identifying river ice under thick snow. Machine learning methods, which are well suited for large sample learning and have strong generalization capabilities, show significant potential for application in river ice identification within high-altitude, complex terrains.https://www.mdpi.com/2072-4292/17/11/1889river ice remote sensingmachine learningLandsat 8Tibetan Plateau |
| spellingShingle | Xin Pang Hongyi Li Hongrui Ren Yaru Yang Qin Zhao Yiwei Liu Xiaohua Hao Liting Niu Machine Learning-Enhanced River Ice Identification in the Complex Tibetan Plateau Remote Sensing river ice remote sensing machine learning Landsat 8 Tibetan Plateau |
| title | Machine Learning-Enhanced River Ice Identification in the Complex Tibetan Plateau |
| title_full | Machine Learning-Enhanced River Ice Identification in the Complex Tibetan Plateau |
| title_fullStr | Machine Learning-Enhanced River Ice Identification in the Complex Tibetan Plateau |
| title_full_unstemmed | Machine Learning-Enhanced River Ice Identification in the Complex Tibetan Plateau |
| title_short | Machine Learning-Enhanced River Ice Identification in the Complex Tibetan Plateau |
| title_sort | machine learning enhanced river ice identification in the complex tibetan plateau |
| topic | river ice remote sensing machine learning Landsat 8 Tibetan Plateau |
| url | https://www.mdpi.com/2072-4292/17/11/1889 |
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