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热带海洋学报

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基于光谱占比指数的珊瑚白化程度高精度识别研究

黄微1, 2, 3, 陈启东1, 3, 4, 李祝理1, 3, 刘显傅1, 3, 黄晖2,5, 赵俊6, 孙绍杰6, 韩洪勇1, 3, 7, 李明杰1, 3, 4
  

  1. 1. 自然资源部南海遥感测绘协同应用技术创新中心, 广东 广州 510301;

    2. 中国科学院南海海洋研究所, 海南三亚海洋生态系统国家野外科学观测研究站, 中国科学院海南热带海洋生物实验站, 海南 三亚 572000; 3. 自然资源部南海发展研究院(自然资源部南海遥感技术应用中心), 广东 广州 510301;

    4. 海南南沙珊瑚礁生态系统国家野外科学观测研究站, 广东 广州 510301;

    5. 三亚海洋生态环境工程研究院, 海南省热带海洋生物技术重点实验室，三亚海洋科学综合（联合）实验室，海南 三亚 572000

    6. 中山大学海洋科学学院, 广东 珠海 519082;

    7. 北京邮电大学计算机学院(国家示范性软件学院), 北京 100876


  • 收稿日期:2026-04-22 修回日期:2026-08-07 接受日期:2026-08-19
  • 通讯作者: 李明杰
  • 基金资助:
    南海局科技发展基金项目(230208，23YD05); 广东省促进经济发展专项资金粤自然资合[2020]012号;

High-Precision Identification of Coral Bleaching Severity Based on Spectral Proportion Index

HUANG Wei1,2,3, CHEN Qidong1,3,4, LI Zhuli1,3, LIU Xianfu1,3, HUANG Hui2,5, ZHAO Jun6, SUN Shaojie6, HAN Hongyong1,3,7, LI Mingjie1,3,4    

  1. 1. Technology Innovation Center for South China Sea Remote Sensing, Surveying and Mapping Collaborative Application, Ministry of Natural Resources, Guangzhou 510300, China;

    2. National Field Observation and Research Station (Hainan Sanya) for Marine Ecosystem, Tropical Marine Biological Research Station in Hainan, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China;

    3. South China Sea Development Research Institute, Ministry of Natural Resources (Remote Sensing Technology Application Center of South China Sea, MNR), Guangzhou 510300, China;

    4. Nansha Islands Coral Reef Ecosystem National Observation and Research Station, Guangzhou 510300, China;

    5. Sanya Joint Laboratory of Marine Science Research, Key Laboratory of Tropical Marine Biotechnology of Hainan Province, Sanya Institute of Ocean Eco-Environmental Engineering, Sanya 572000, China;

    6. School of Marine Sciences, Sun Yat-sen University, Zhuhai 519082, China;

    7. Beijing University of Posts and Telecommunications, School of Computer Science (National Pilot Software Engineering School), Beijing 100876, China




  • Received:2026-04-22 Revised:2026-08-07 Accepted:2026-08-19
  • Supported by:

    Science and Technology Development Foundation of South China Sea Bureau, Ministry of Natural Resources (230208, 23YD05); Key Program of Marine Economy Development Special Foundation of Department of Natural Resources of Guangdong Province(GDNRC [2020]012)

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摘要： 高效、定量地识别珊瑚白化程度是珊瑚礁生态系统动态监测面临的关键技术瓶颈。现有方法多依赖人工判读，存在主观性强、量化不足等局限，难以满足精细化与长期监测的需求。为此，本研究创新性地提出一种基于固定端元反射率光谱占比指数（Spectral Proportion Index, SPI）的方法，对首次构建的珊瑚健康状况的四级分类体系（健康、白化Ⅰ-Ⅲ级）与健康/非健康二级分类进行识别研究。具体为：基于2024年原位采集1370条珊瑚样本光谱数据，通过将样本光谱以沙、珊瑚和大型海藻为三个固定端元进行光谱解混，计算出珊瑚端元的SPI指数，并据此实现四级分类判别。研究结果表明，珊瑚白化程度与珊瑚端元光谱占比具有高度一致性，分类模型总体精度达86%（Kappa系数为0.81），各级别分类精度分别为：健康80.6%、白化Ⅰ级94.1%、白化Ⅱ级75.0%、白化Ⅲ级93.5%；在“健康/非健康”二分类粗识别场景下，SPI方法的精度高达95.6%，显著优于遥感使用的珊瑚白化指数BCI（82.5%）、标准化红绿指数NRGI（80.3%）、标准化植被指数NDVI（89.8%）和标准化红蓝指数NRBI（32.1%）。研究进一步表明，SPI指数法能够实现珊瑚白化程度的有效量化判别，为珊瑚状态识别提供了一种简便、可靠的新工具，有助于推动珊瑚礁生态评估的定量化进程。

关键词: 珊瑚白化识别, 三端元光谱分解, 光谱占比指数, 珊瑚健康状态量化分级

Abstract: Efficient and quantitative identification of coral bleaching severity remains a critical technological bottleneck for dynamic monitoring of coral reef ecosystems. Current methods predominantly rely on manual interpretation, which suffers from strong subjectivity and insufficient quantification, failing to meet the demands for precise and long-term monitoring. To address this, this study innovatively proposes a method based on the fixed-endmember Spectral Proportion Index (SPI) for identifying coral health status using a newly constructed four-level classification system (Healthy, Bleaching Levels I-III) and a two-level (Healthy/Non-healthy) classification framework. Specifically, based on 1370 in-situ collected coral spectral data samples acquired in 2024, spectral unmixing was performed using sand, coral, and macroalgae as three fixed endmembers to calculate the coral endmember SPI, enabling four-level classification. Results demonstrate a high consistency between coral bleaching severity and the coral endmember spectral proportion. The overall classification accuracy reached 86% (Kappa coefficient = 0.81), with level-specific accuracies as follows: Healthy 80.6%, Level I 94.1%, Level II 75.0%, and Level III 93.5%. In the binary "Healthy/Non-healthy" coarse classification scenario, the SPI method achieved an accuracy of 95.6%, significantly outperforming commonly used remote sensing coral bleaching indices: the Bleaching Chromatic Index (BCI, 82.5%), the Normalized Red-Green Band Difference Index (NRGI, 80.3%), the Normalized Difference Vegetation Index (NDVI, 89.8%), and the Normalized Red-Blue Band Difference Index (NRBI, 32.1%). This study further indicates that the SPI method enables effective quantitative discrimination of coral bleaching severity, providing a simple and reliable new tool for coral status assessment and contributing to the advancement of quantitative coral reef ecological evaluation.

Key words: Coral bleaching detection, Three-endmember spectral unmixing, Spectral Proportion Index (SPI), Quantitative classification of coral health status