Eddy Detection of Lakes in the Qinghai–Tibet Plateau Based on Optical Remote Sensing and Deep Learning
Lake eddies are dynamic phenomena prevalent in large lake systems, playing a critical role in affecting lake physics, nutrient transport, and ecological balance. Efficient and precise detection of these features is essential for advancing scientific understanding of lake dynamics and for improving e...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10971228/ |
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| _version_ | 1850132952669749248 |
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| author | Bingxin Bai Lixia Mu Jie Yang Baoxiang Huang Chunyong Ma Ge Chen |
| author_facet | Bingxin Bai Lixia Mu Jie Yang Baoxiang Huang Chunyong Ma Ge Chen |
| author_sort | Bingxin Bai |
| collection | DOAJ |
| description | Lake eddies are dynamic phenomena prevalent in large lake systems, playing a critical role in affecting lake physics, nutrient transport, and ecological balance. Efficient and precise detection of these features is essential for advancing scientific understanding of lake dynamics and for improving environment management strategies. Despite their importance, the study of lake eddies remains under-explored, and current detection methods are insufficient. This study addresses this gap by proposing a lake eddy identification framework based on the YOLOv7 deep learning model, utilizing Landsat 8/9 satellite imagery acquired from April 2013 to December 2023. Qinghai Lake, Selin Co, and Nam Co on the Qinghai–Tibet Plateau were selected as the study areas. The method involves the development of an image enhancement process to optimize the visibility of lake eddies in remote sensing imagery, followed by the implementation of the YOLOv7 model for detection. The framework identified 912 cyclonic eddies and 102 anticyclonic eddies on the three aforementioned lakes, providing detailed information on their spatial distribution and morphology. This approach demonstrates significant potential for advancing the study of lake hydrodynamics and energy transport substances in aquatic ecosystems. |
| format | Article |
| id | doaj-art-e4e26560c48d4559b8f992e397ade0f1 |
| institution | OA Journals |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-e4e26560c48d4559b8f992e397ade0f12025-08-20T02:32:05ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118119961200710.1109/JSTARS.2025.356277710971228Eddy Detection of Lakes in the Qinghai–Tibet Plateau Based on Optical Remote Sensing and Deep LearningBingxin Bai0https://orcid.org/0000-0002-7608-4779Lixia Mu1https://orcid.org/0009-0003-3472-8286Jie Yang2Baoxiang Huang3Chunyong Ma4https://orcid.org/0000-0003-0588-1591Ge Chen5https://orcid.org/0000-0003-4868-5179Faculty of Information Science and Engineering, Ocean University of China, Qingdao, ChinaCollege of Marine Technology, Ocean University of China, Qingdao, ChinaFaculty of Information Science and Engineering, Ocean University of China, Qingdao, ChinaCollege of Computer Science and Technology, Qingdao University, Qingdao, ChinaFaculty of Information Science and Engineering, Ocean University of China, Qingdao, ChinaFaculty of Information Science and Engineering, Ocean University of China, Qingdao, ChinaLake eddies are dynamic phenomena prevalent in large lake systems, playing a critical role in affecting lake physics, nutrient transport, and ecological balance. Efficient and precise detection of these features is essential for advancing scientific understanding of lake dynamics and for improving environment management strategies. Despite their importance, the study of lake eddies remains under-explored, and current detection methods are insufficient. This study addresses this gap by proposing a lake eddy identification framework based on the YOLOv7 deep learning model, utilizing Landsat 8/9 satellite imagery acquired from April 2013 to December 2023. Qinghai Lake, Selin Co, and Nam Co on the Qinghai–Tibet Plateau were selected as the study areas. The method involves the development of an image enhancement process to optimize the visibility of lake eddies in remote sensing imagery, followed by the implementation of the YOLOv7 model for detection. The framework identified 912 cyclonic eddies and 102 anticyclonic eddies on the three aforementioned lakes, providing detailed information on their spatial distribution and morphology. This approach demonstrates significant potential for advancing the study of lake hydrodynamics and energy transport substances in aquatic ecosystems.https://ieeexplore.ieee.org/document/10971228/Image enhancementlake eddieslandsat 8/9Qinghai–Tibet plateauYOLOv7 model |
| spellingShingle | Bingxin Bai Lixia Mu Jie Yang Baoxiang Huang Chunyong Ma Ge Chen Eddy Detection of Lakes in the Qinghai–Tibet Plateau Based on Optical Remote Sensing and Deep Learning IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Image enhancement lake eddies landsat 8/9 Qinghai–Tibet plateau YOLOv7 model |
| title | Eddy Detection of Lakes in the Qinghai–Tibet Plateau Based on Optical Remote Sensing and Deep Learning |
| title_full | Eddy Detection of Lakes in the Qinghai–Tibet Plateau Based on Optical Remote Sensing and Deep Learning |
| title_fullStr | Eddy Detection of Lakes in the Qinghai–Tibet Plateau Based on Optical Remote Sensing and Deep Learning |
| title_full_unstemmed | Eddy Detection of Lakes in the Qinghai–Tibet Plateau Based on Optical Remote Sensing and Deep Learning |
| title_short | Eddy Detection of Lakes in the Qinghai–Tibet Plateau Based on Optical Remote Sensing and Deep Learning |
| title_sort | eddy detection of lakes in the qinghai x2013 tibet plateau based on optical remote sensing and deep learning |
| topic | Image enhancement lake eddies landsat 8/9 Qinghai–Tibet plateau YOLOv7 model |
| url | https://ieeexplore.ieee.org/document/10971228/ |
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