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热带海洋学报

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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, 烟台 山东 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, 广西 钦州 535011;

    3. 北部湾大学海洋学院广西北部湾海洋环境变化与灾害研究重点实验室, 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, 烟台 山东 264005



  • 收稿日期:2025-10-23 修回日期:2026-01-03 接受日期:2026-01-22
  • 通讯作者: 邹涛
  • 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)

A 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, 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, P. R. China;

    3. Guangxi Key Laboratory of Marine Environmental Change amd Disaster in Beibu Gulf, School of Marine Science, Beibu Gulf University, Qinzhou 535011, P. R. China;

    4. QinZhou Marine Environmental Forecasting Center, Qinzhou 535099, China;

    5. School of Civil Engineering of Yantai University，Yantai 264005, China



  • Received:2025-10-23 Revised:2026-01-03 Accepted:2026-01-22
  • 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，发展一种自适应阈值养殖筏架识别算法，刻画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分割聚类结果，根据大律法阈值提取影像中筏架。结果表明，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78609.6km2，筏架分布由龙门镇海域向钦州湾外海及茅尾海拓展。钦州湾养殖筏架面积与海水水质具有较好相关性，筏架面积与活性磷酸盐、综合污染物和富营养化指数，相关性分别为R2=0.73、0.5和0.36。研究结果对于理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 understanding its spatial expansion changes is the prerequisites of implementing dynamic marine monitoring, regional aquaculture management, layout optimization, and ecological protection. Our study chooses the Qinzhou Bay as the research area and introduces an adaptive threshold algorithm for identifying aquaculture raft frames to characterize their spatiotemporal changes in the bay. Significantly, a Raft Index is used into the Landsat series dataset. Then, the fuzzy clustering algorithm helps to enhance the raft frame structure features, and a chessboard segmentation algorithm is applied to partition the clustering Raft index images. The raft frames in the images are discerned based on the Otsu threshold method. Our results indicate that since 2000, aquaculture raft frames in the Qinzhou Bay have expanded significantly, with the maximum area reaching to 78,609.6km² in 2022. The distribution of raft frames has extended from the waters near Longmen Town toward the outer areas of the Qinzhou Bay and Maowei Sea. A notable correlation is established between the area of aquaculture raft frames and seawater quality in the Qinzhou Bay. The correlations between raft frame area and active phosphate, comprehensive pollution index, and eutrophication index are R² = 0.73, 0.5, and 0.36, respectively. The findings of this study provide valuable insights for understanding 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