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应用生态学报 ›› 2026, Vol. 37 ›› Issue (9): 2929-2944.doi: 10.13287/j.1001-9332.202609.036

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遥感大数据在湿地监测中的应用:现状、挑战与展望

叶辉1,2, 李胭胭2, 王新新1*, 刘影4, 郑玉晗1, 张茜3, 陈鸿洋5, 刘永超6, 聂明1, 李博1,7   

  1. 1复旦大学生命科学学院, 湿地环境保护与生态修复全国重点实验室/上海长江河口湿地生态系统国家野外科学观测研究站/生物多样性与生态工程教育部重点实验室/崇明生态研究院, 上海 200438;
    2九江学院旅游与地理学院, 江西九江 332005;
    3中国科学院地理科学与资源研究所, 北京 100101;
    4江西师范大学鄱阳湖湿地与流域研究教育部重点实验室, 南昌 330022;
    5东北林业大学生态学院, 哈尔滨 150040;
    6宁波大学地理科学与遥感技术学院, 浙江宁波 315211;
    7云南大学生态与环境学院/生物多样性研究院/植被结构功能与建造全国重点实验室, 昆明 650504
  • 收稿日期:2026-05-15 接受日期:2026-07-06 出版日期:2026-09-18 发布日期:2026-10-12
  • 通讯作者: *E-mail: wangxinxin@fudan.edu.cn
  • 作者简介:叶 辉, 男, 1985年生, 博士, 讲师。主要从事湿地遥感与生态环境监测研究。E-mail: huiye.geo@jju.edu.cn
  • 基金资助:
    国家重点研发计划青年科学家项目(2023YFF0806900)、国家自然科学基金项目(32330065, 32430065, 32501456, 42201341)、湿地环境保护与生态修复全国重点实验室青年科学家专项(SDGZ-QN2025-01)和植被结构功能与建造全国重点实验室开放课题(VegLabOF2025004)

Applications of remote sensing big data in wetland monitoring:Current status, challenges, and prospects

YE Hui1,2, LI Yanyan2, WANG Xinxin1*, LIU Ying4, ZHENG Yuhan1, ZHANG Xi3, CHEN Hongyang5, LIU Yongchao6, NIE Ming1, LI Bo1,7   

  1. 1State Key Laboratory of Wetland Conservation and Restoration/National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary/Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering/Institute of Eco-Chongming, School of Life Sciences, Fudan University, Shanghai 200438, China;
    2College of Tourism and Geography, Jiujiang University, Jiujiang 332005, Jiangxi, China;
    3Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;
    4Ministry of Education Key Laboratory of Poyang Lake Wetland and Watershed Research, Jiangxi Normal University, Nanchang 330022, China;
    5School of Ecology, Northeast Forestry University, Harbin 150040, China;
    6School of Geography and Remote Sensing, Ningbo University, Ningbo 315211, Zhejiang, China;
    7State Key Laboratory for Vegetation Structure, Function and Construction (VegLab)/Institute of Biodiversity, School of Ecology and Environmental Science, Yunnan University, Kunming 650504, China
  • Received:2026-05-15 Accepted:2026-07-06 Online:2026-09-18 Published:2026-10-12

摘要: 湿地是维持水文调节、气候调节、生物多样性保护和生态安全的重要生态系统,但在气候变化和人类活动双重影响下,全球湿地普遍面临面积萎缩、功能退化和生境破碎化等问题。遥感大数据凭借其长时间序列、广域覆盖和多源观测的优势,为湿地退化识别、动态监测、恢复跟踪和风险预警提供了重要技术支撑。本文系统综述了遥感大数据在湿地监测中的应用现状、技术方法、评估体系及未来发展方向。首先,梳理了遥感大数据源的特征及其在湿地监测中的适用性;其次,对比分析了基于光谱特征、时序特征、机器学习、深度学习和多源融合的湿地信息提取技术进展;再次,从水文过程、植被功能、景观结构和时序稳定性等方面构建了湿地退化与恢复遥感监测评估框架,并探讨了其在退化诊断、恢复轨迹追踪、成效评估和失稳预警中的应用;最后,总结了当前应用中存在的海量异构数据整合困难、时空分辨率受限、湿地地表复杂性和生态解释不足等问题,并从多源传感器协同、复杂场景模型构建、多要素环境评估和治理模式创新等方面提出未来发展路径。本文旨在为湿地退化与恢复全过程监测、生态保护修复成效评估及智慧化治理提供理论参考和技术借鉴。

关键词: 湿地遥感, 湿地监测, 湿地制图, 遥感大数据, 湿地退化与恢复

Abstract: Wetlands are critical ecosystems that support hydrological and climate regulation, biodiversity conservation, and ecological security. However, under the combined pressures of climate change and human activities, global wetlands are increasingly facing severe area loss, functional degradation, and habitat fragmentation. With the advantages including long-term time-series records, broad spatial coverage, and multi-source observations, remote sensing big data provide essential technical support for identifying wetland degradation, monitoring dynamic changes, tracking restoration processes, and enabling risk early warning. We systematically reviewed the current applications, technical approaches, assessment frameworks, and future development directions of remote sensing big data in wetland monitoring. First, we sorted out the characteristics of remote sensing big data sources and their applicability in wetland monitoring. Second, we comparatively analyzed recent advances in wetland information extraction techniques based on spectral features, temporal characteristics, machine learning, deep learning, and multi-source data fusion. Third, from the perspectives of hydrological processes, vegetation function, landscape structure, and temporal stability, we constructed a remote sensing-based assessment framework for monitoring wetland degradation and restoration, and explored its applications in degradation diagnosis, restoration trajectory tracking, effectiveness evaluation, and instability early warning. Finally, we identified key challenges in current applications, including difficulties in integrating massive heterogeneous datasets, limitations in spatiotemporal resolution, the complexity of wetland surface conditions, and insufficient ecological interpretation. Accordingly, we proposed future development pathways involving multi-source sensor synergy, model construction for complex scenarios, multi-factor environmental assessment, and innovation in governance models. This review would provide theoretical references and technical guidance for whole-process monitoring of wetland degradation and restoration, evaluation of ecological conservation and restoration, and intelligent wetland governance.

Key words: wetland remote sensing, wetland monitoring, wetland mapping, remote sensing big data, wetland degradation and restoration