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热带海洋学报

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基于多光谱卫星遥感影像的印度尼西亚Manado典型区域海草床时空分布监测

陈菲1, 李一琼2, 刘志松1, 杜建国3, Calvin Fredrik Aldus Sondak4   

  1. 1. 浙江海洋大学信息工程学院, 浙江 舟山 316000;

    2. 苏州科技大学地理科学与测绘工程学院, 江苏 苏州 215009;

    3. 自然资源部第三海洋研究所APEC海洋可持续发展研究中心，福建 厦门 361000;

    4. 萨姆拉图兰吉大学海洋与渔业学院, 印度尼西亚 万鸦老 95115


  • 收稿日期:2026-03-09 修回日期:2026-04-22 接受日期:2026-04-22
  • 通讯作者: 李一琼
  • 基金资助:

    亚洲合作资金项目(HX08-231401)

Monitoring the Spatiotemporal Distribution of Seagrass Beds in Typical Areas of Manado, Indonesia Based on Multispectral Satellite Remote Sensing Images

CHEN Fei1, LI Yiqiong2, LIU Zhisong1, DU Jianguo3, CALVIN Fredrik Aldus Sondak4   

  1. 1. College of Information Engineering, Zhejiang Ocean University, Zhoushan 316000, China;

    2. School of Geographic Science and Geomatics Engineering ,Suzhou University of Science and Technology, Suzhou 215009, China;

    3. APEC Marine Sustainable Development Center, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361000, China;

    4. Faculty of Fisheries and Marine Science，Sam Ratulangi University, Manado 95115, Indonesia


  • Received:2026-03-09 Revised:2026-04-22 Accepted:2026-04-22
  • Supported by:
    Asia Cooperation Fund Project (HX08-231401)

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摘要： 海草具有极高的生态价值。为揭示典型热带海域海草床的动态分布规律, 本研究以印度尼西亚Manado海域为研究区, 基于 Sentinel-2 L2A级多光谱影像卫星数据, 构建了融合时序特征的综合数据集。通过时空滤波、云掩膜、光谱指数计算等预处理, 结合海草像元的光谱响应特征, 采用分类技术实现海草床分布的高精度提取与动态监测。在分类过程中, 首先采用递归特征消除(Recursive Feature Elimination, RFE)方法对特征指数进行优化筛选, 进而运用轻量级梯度提升机(Light Gradient Boosting Machine,  LightGBM)实现海草床的精确分类。实验结果表明：基于本文方法提取研究区海草床分布信息的精度较高, 研究区海草床面积在2020—2025年间有一定程度波动(变幅< 13%), 海草生态系统总体稳定。本研究验证了采用光学卫星遥感影像分类技术在复杂滨海环境下开展海草床监测的可行性和有效性, 为热带海域海草生态系统保护与蓝碳资源管理提供科学支撑。

关键词: 海草, Sentinel-2遥感监测, 特征融合, 分类技术, 时空分布变化, 印度尼西亚Manado地区

Abstract: Seagrass possesses extremely high ecological value. To reveal the dynamic distribution patterns of seagrass beds in typical tropical marine areas, this study took the coastal waters of Manado, Indonesia as the research area and constructed a comprehensive dataset integrating temporal features based on Sentinel-2 Level-2A multispectral satellite imagery data. Through preprocessing steps including spatiotemporal filtering, cloud masking and spectral index calculation, combined with the spectral response characteristics of seagrass pixels, classification technology was adopted to achieve high-precision extraction and dynamic monitoring of seagrass bed distribution. In the classification process, the Recursive Feature Elimination (RFE) method was first used to optimize and screen feature indices, and then the Light Gradient Boosting Machine (LightGBM) was applied to realize the accurate classification of seagrass beds. The experimental results showed that the method proposed in this study achieved high precision in extracting the distribution information of seagrass beds in the research area; the area of seagrass beds in the study area fluctuated to a certain extent from 2020 to 2025 (variation < 13%), indicating the overall stability of the seagrass ecosystem. This study verified the feasibility and effectiveness of using optical satellite remote sensing image classification technology to monitor seagrass beds in complex coastal environments, and provided scientific support for the protection of seagrass ecosystems and the management of blue carbon resources in tropical marine areas.

Key words: seagrass, Sentinel-2 satellite remote sensing monitoring, feature fusion, classification technique, temporal and spatial distribution change, Manado region, Indonesia