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热带海洋学报

• • 上一篇    

基于Sentinel-2的平陆运河出海口红树林动态变化特征

梁喜幸 1,2,3，戴志军3*，李焰1，黎树式2，李为华3，王日明2，吴尔江1   

  1. 1. 广西壮族自治区海洋研究院，南宁530022;

    2. 北部湾大学，广西北部湾海洋环境变化与灾害研究重点实验室，钦州535011;

    3. 华东师范大学，河口海岸全国重点实验室，上海 200241



  • 收稿日期:2025-12-08 修回日期:2026-01-13 接受日期:2026-01-15
  • 通讯作者: 戴志军
  • 基金资助:
    国家重点研发计划政府间国际科技创新合作重点专项(2023YFE0121200)；上海市国际科技合作基金项目(23230713800)；国家自然科学基金(42366008)；2025年度广西科技界智库重点课题(桂科协〔2025〕J-004)

Spatiotemporal dynamics of mangroves at the Pinglu canal estuary based on sentinel-2

LIANG Xixing1,2,3, DAI Zhijun3*, LI Yan1, LI Shushi2, LI Weihua3, WANG Riming2, WU Erjiang1   

  1. 1. Guangxi Academy of Oceanography, Naning, 530022, China;

    2. Guangxi Key Laboratory of Marine Environmental Change and Disaster in Beibu Gulf /College of Resources and Environment, Beibu Gulf University, Qinzhou 535011, China;

    3. State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200041, China.



  • Received:2025-12-08 Revised:2026-01-13 Accepted:2026-01-15
  • Supported by:

    key special project for international cooperation in science and technology under the national key research and development program of China (2023YFE0121200), Shanghai international science and technology cooperation fund project (23230713800), national natural science foundation of China (42366008), key research topics for the Guangxi science and technology community in 2025 (Gui Kexie〔2025〕J-004).

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摘要： 平陆运河出海口位于茅尾海北部，是典型的强人类干扰型红树林潮滩区域。本文基于遥感影像和GeoAI深度学习框架，采用U-Net模型定量提取平陆运河出海口红树林的时空变化特征，并系统分析水动力作用、生蚝养殖、船舶密度及促淤消浪工程对红树林演变过程的综合影响。结果表明：2016~2025年期间平陆运河出海口红树林面积总体呈下降趋势，平均缩减速率为6.7 ha/yr，其中2021~2023年侵蚀速率明显加快，面积缩减速率由2021年之前的3.4 ha/yr增至16.3 ha/yr，其边缘以-4.89 m/yr的速率后退，而后2023~2025年面积损失速率减缓至5.75 ha/yr ，蚀退速率降至-3.69 m/yr ，侵蚀区域主要发生于河道两侧。研究表明，船舶密度增大、蚝排移除等人类活动叠加效应是驱动红树林退化的主要原因，而生态型促淤消浪工程可有效提升红树林岸线的稳定性，减缓红树林受损过程。研究成果为运河航道建设与红树林生态保护协调发展提供关键支撑。

关键词: U-Net, 深度学习, 红树林, 平陆运河出海口

Abstract: The Pinglu Canal estuary, located in the northern part of Maowei Sea, represents a typical mangrove tidal flat area subject to strong human disturbances. In this study, we employed remote sensing imagery and a GeoAI deep learning framework, using a U-Net model to quantitatively extract the spatiotemporal dynamics of mangroves in the estuary. We systematically analyzed the combined impacts of waves, oyster aquaculture, vessel traffic, and silt-promoting wave-dissipation engineering on mangrove evolution. The results show that between 2016 and 2025, the overall mangrove area in the Pinglu Canal estuary declined at an average rate of 6.7 ha/yr. The erosion rate accelerated markedly from 2021 to 2023, with the area reduction increasing from 3.4 ha/yr before 2021 to 16.3 ha/yr, and the shoreline retreating at a rate of -4.89 m/yr. Subsequently, from 2023 to 2025, the rate of area loss slowed to 5.75 ha/yr, with shoreline retreat decreasing to -3.69 m/yr; erosion primarily occurred along both sides of the river channel. The study indicates that increased vessel traffic and the removal of oyster rafts are major drivers of mangrove degradation, whereas ecological silt-promoting wave-dissipation structures effectively enhance shoreline stability and mitigate mangrove loss. These findings provide critical support for the coordinated development of canal navigation and mangrove ecosystem protection.

Key words: U-Net, deep learning, mangrove, Pinglu Canal estuary