A comprehensive review of remote sensing techniques for monitoring Ulva prolifera green tides
In recent years, Ulva prolifera green tide, as a large-scale marine ecological phenomenon, has occurred frequently in coastal areas such as the Yellow Sea and the East China Sea, significantly affecting marine ecosystems and fishery resources. With the continuous advancement of remote sensing techno...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Marine Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmars.2025.1546289/full |
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| author | Xiaomeng Geng Xiaomeng Geng Xiaomeng Geng Huiru Li Huiru Li Le Wang Weidong Sun Yize Li |
| author_facet | Xiaomeng Geng Xiaomeng Geng Xiaomeng Geng Huiru Li Huiru Li Le Wang Weidong Sun Yize Li |
| author_sort | Xiaomeng Geng |
| collection | DOAJ |
| description | In recent years, Ulva prolifera green tide, as a large-scale marine ecological phenomenon, has occurred frequently in coastal areas such as the Yellow Sea and the East China Sea, significantly affecting marine ecosystems and fishery resources. With the continuous advancement of remote sensing technologies, these technologies have become indispensable tools for monitoring Ulva prolifera green tides. This review provides a comprehensive overview of the advances in remote sensing band indices for detecting green tides, including spatiotemporal distribution analysis, area and biomass estimation, drift trajectory modeling, and investigations of their driving mechanisms. Additionally, it identifies the limitations and unresolved challenges in current approaches, such as constraints on data resolution, algorithmic biases, and environmental variability. The potential for integrating multi-source remote sensing data with marine environmental parameters and deep learning techniques is discussed, emphasizing their roles in improving the accuracy and reliability of monitoring and predicting Ulva prolifera green tides. This review aims to guide future research efforts and technological innovations in this field. |
| format | Article |
| id | doaj-art-67fa8bf3c0c240ef872009a6e18d654f |
| institution | Kabale University |
| issn | 2296-7745 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj-art-67fa8bf3c0c240ef872009a6e18d654f2025-01-28T05:10:30ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452025-01-011210.3389/fmars.2025.15462891546289A comprehensive review of remote sensing techniques for monitoring Ulva prolifera green tidesXiaomeng Geng0Xiaomeng Geng1Xiaomeng Geng2Huiru Li3Huiru Li4Le Wang5Weidong Sun6Yize Li7School of Geographical Sciences, Hebei Normal University, Shijiazhuang, ChinaKey Laboratory of Marine Ecological Monitoring and Restoration Technologies, Ministry of Natural Resources (MNR), Shanghai, ChinaHebei Technology Innovation Center for Remote Sensing Identification of Environmental Change, Hebei Normal University, Shijiazhuang, ChinaSchool of Geographical Sciences, Hebei Normal University, Shijiazhuang, ChinaHebei Technology Innovation Center for Remote Sensing Identification of Environmental Change, Hebei Normal University, Shijiazhuang, ChinaKey Laboratory of Marine Ecological Monitoring and Restoration Technologies, Ministry of Natural Resources (MNR), Shanghai, ChinaState Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan, ChinaCollege of Information Engineering, Inner Mongolia University of Technology, Hohhot, ChinaIn recent years, Ulva prolifera green tide, as a large-scale marine ecological phenomenon, has occurred frequently in coastal areas such as the Yellow Sea and the East China Sea, significantly affecting marine ecosystems and fishery resources. With the continuous advancement of remote sensing technologies, these technologies have become indispensable tools for monitoring Ulva prolifera green tides. This review provides a comprehensive overview of the advances in remote sensing band indices for detecting green tides, including spatiotemporal distribution analysis, area and biomass estimation, drift trajectory modeling, and investigations of their driving mechanisms. Additionally, it identifies the limitations and unresolved challenges in current approaches, such as constraints on data resolution, algorithmic biases, and environmental variability. The potential for integrating multi-source remote sensing data with marine environmental parameters and deep learning techniques is discussed, emphasizing their roles in improving the accuracy and reliability of monitoring and predicting Ulva prolifera green tides. This review aims to guide future research efforts and technological innovations in this field.https://www.frontiersin.org/articles/10.3389/fmars.2025.1546289/fullUlva prolifera green tideYellow SeaEast China Searemote sensingdeep learning |
| spellingShingle | Xiaomeng Geng Xiaomeng Geng Xiaomeng Geng Huiru Li Huiru Li Le Wang Weidong Sun Yize Li A comprehensive review of remote sensing techniques for monitoring Ulva prolifera green tides Frontiers in Marine Science Ulva prolifera green tide Yellow Sea East China Sea remote sensing deep learning |
| title | A comprehensive review of remote sensing techniques for monitoring Ulva prolifera green tides |
| title_full | A comprehensive review of remote sensing techniques for monitoring Ulva prolifera green tides |
| title_fullStr | A comprehensive review of remote sensing techniques for monitoring Ulva prolifera green tides |
| title_full_unstemmed | A comprehensive review of remote sensing techniques for monitoring Ulva prolifera green tides |
| title_short | A comprehensive review of remote sensing techniques for monitoring Ulva prolifera green tides |
| title_sort | comprehensive review of remote sensing techniques for monitoring ulva prolifera green tides |
| topic | Ulva prolifera green tide Yellow Sea East China Sea remote sensing deep learning |
| url | https://www.frontiersin.org/articles/10.3389/fmars.2025.1546289/full |
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