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热带海洋学报 ›› 2026, Vol. 45 ›› Issue (4): 104-115.doi: 10.11978/2025200CSTR: 32234.14.2025200

• 海洋遥感 • 上一篇    下一篇

基于遥感技术的2000年以来钦州湾筏架养殖空间扩展模式分析

金松1(), 邹涛1(), 廖日权2,3, 陈超豪2,4, 米环5, 唐建辉1,2,3   

  1. 1 山东省海岸带环境过程与生态安全重点实验室(中国科学院烟台海岸带研究所), 山东 烟台 264003
    2 平陆运河河口海湾生态系统广西野外科学观测研究站(北部湾大学), 广西 钦州 535011
    3 北部湾大学海洋学院, 广西海洋环境灾害过程与生态保护技术重点实验室, 广西 钦州 535011
    4 钦州市海洋环境监测预报中心, 广西 钦州 535099
    5 烟台大学土木工程学院, 山东 烟台 264005
  • 收稿日期:2025-10-23 修回日期:2025-11-24 出版日期:2026-07-10 发布日期:2026-07-31
  • 通讯作者: 邹涛。email:
  • 作者简介:

    金松(1990—), 山东省青岛市人, 助理研究员, 从事水色遥感应用研究。email:

  • 基金资助:
    国家自然科学基金项目(4240176); 山东省自然科学基金项目(ZR2022QD123)

Study on the spatial expansion mode of raft aquaculture in Qinzhou Bay since 2000 using remote sensing technology

JIN Song1(), ZOU Tao1(), LIAO Riquan2,3, CHEN Chaohao2,4, MI Huan5, TANG Jianhui1,2,3   

  1. 1 Shandong Key Laboratory of Coastal Zone Environmental Processes and Ecological Security, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China
    2 Pinglu Canal and Beibu Gulf Coastal Ecosystem Observation and Research Station of Guangxi, Beibu Gulf University, Qinzhou 535011, China
    3 Guangxi Key Laboratory of Marine Environmental Disaster Processes and Ecological Protection Technology, College of Marine Science, Beibu Gulf University, Qinzhou 535011, China
    4 Qinzhou Marine Environmental Monitoring and Forecasting Center, Qinzhou 535099, China
    5 School of Civil Engineering of Yantai University, Yantai 264005, China
  • Received:2025-10-23 Revised:2025-11-24 Online:2026-07-10 Published:2026-07-31
  • Contact: ZOU Tao. email:
  • Supported by:
    National Natural Science Foundation of China(4240176); Natural Science Foundation of Shandong Province(ZR2022QD123)

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摘要：

掌握浅海海洋养殖规模及其空间扩展变化, 有助于精准实施海域动态监测、区域养殖管理、格局优化与生态保护。本文以钦州湾为研究区, 发展了一种自适应阈值养殖筏架识别算法, 刻画了钦州湾养殖筏架时空变化, 即基于Landsat 系列影像构建筏架指数, 结合模糊聚类算法增强筏架特征, 并使用棋盘分割算法对聚类结果进行分割, 再根据大津法(Otsu's method, OTSU)阈值提取影像中的筏架。自2000年以来钦州湾养殖筏架大面积增长, 最大面积出现在2022年, 为7860.96hm2, 筏架分布由龙门镇海域向钦州湾外海及内湾茅尾海拓展。钦州湾养殖筏架面积与海水水质的相关性(r2值)不等, 筏架面积与活性磷酸盐浓度、综合污染物指数和富营养化指数均呈正相关关系, r2分别为0.73、0.5和0.36。研究结果对于厘清钦州湾养殖筏架时空演变规律及与海洋环境响应关系具有积极意义。

关键词: 养殖筏架提取, 自适应阈值算法, Landsat系列影像, 钦州湾

Abstract:

Mapping the distribution of shallow-sea aquaculture and monitoring its spatial expansion are essential for implementing dynamic marine monitoring, improving regional aquaculture management, optimizing spatial layout, and promoting ecological conservation. This study selects Qinzhou Bay as the research area and proposes an adaptive threshold algorithm for extracting aquaculture raft frames to characterize their spatiotemporal dynamics. A raft index (RI) is constructed based on Landsat series imagery. A fuzzy clustering algorithm was employed to enhance structural features of the raft frames, and a chessboard segmentation algorithm was applied to partition the clustered RI image. The Otsu’s threshold method (OTSU) is then used to identify raft frames in the segmented images. The results reveal a significant expansion of aquaculture raft frames in Qinzhou Bay since 2000, with the maximum area reaching 7860.96 hm2 in 2022. The distribution of raft frames has expanded from the coastal waters near Longmen Town toward the outer parts of Qinzhou Bay and into the Maowei Sea (inner bay). A strong correlation was observed between the raft frame area and seawater quality in Qinzhou Bay, with coefficients of determination (r2) of 0.73 for active phosphate, 0.50 for the composite pollution index, and 0.36 for the eutrophication index. The findings of this study offer valuable insights into the spatiotemporal evolution of aquaculture raft frames in Qinzhou Bay and their relationship with the marine environment.

Key words: raft extraction, adaptive threshold algorithm, Landsat images, Qinzhou Bay

中图分类号: 

  • P714