Global mapping of tidal wetlands and adjacent environments using tidal analysis and multi-source Earth observations

湿地 潮间带 环境科学 环境资源管理 遥感 土地覆盖 地球观测 气候变化 生态系统 采样(信号处理) 全球变暖 海岸管理 合成孔径雷达 地球系统科学 自适应采样 生态系统服务 选择(遗传算法) 陆地生态系统 不透水面 全球变化 选址 生态预报 自然地理学 气象学 气候学 激光雷达 雷达 时间尺度 海平面 土地利用
作者
Miao Li,Thomas Worthington,Bin Chen,Lindsey S. Smart,Mark Spalding,Tao Zhang,Sayam U. Chowdhury,Yuqi Bai,Dongmei Yan,S. Li,Bing Xu
出处
期刊:Isprs Journal of Photogrammetry and Remote Sensing [Elsevier BV]
卷期号:234: 293-306 被引量:1
标识
DOI:10.1016/j.isprsjprs.2026.02.014
摘要

Tidal wetlands, located at the dynamic land-sea interface, provide vital ecosystem services, yet face increasing threats from human activities and climate change. Accurate and up-to-date global mapping of tidal wetlands is essential for assessing their status and advancing conservation efforts. However, challenges such as tidal fluctuations and limited data availability lead to inconsistencies across existing datasets. Moreover, previous studies have largely overlooked the adjacent terrestrial environments of tidal wetlands, reducing our understanding of coastal dynamics and associated environmental drivers. To address these critical issues, this study proposes a novel global coastal mapping framework with three key components. First, using the ocean tide model EOT20, we systematically analyzed tidal variations observed in Sentinel-1 and Sentinel-2 imagery between 2019 and 2021, facilitating the development of an adaptive image selection strategy to ensure low-tide coverage. Second, we integrated multi-source global datasets and employed a knowledge-driven, semi-automatic sampling approach to generate training samples for tidal wetlands and adjacent land covers. Third, we iteratively trained and refined random forest models using tide-level and phenological features extracted from composite optical and radar imagery, producing a global coastal dataset centered on 2020 with 11 distinct land cover types. The mapping result was cross-compared with multiple global and regional coastal datasets and validated using the temporally cleaned external dataset, achieving an overall accuracy of 92.7%. By optimizing the selection of Sentinel scenes to approximately 10–50 in time-series composite mapping, this approach balances computational efficiency with intertidal classification accuracy. The dataset delineates the global distribution of tidal wetlands and their adjacent environments at a 10-m resolution; specifically, tidal wetlands–including mangroves, tidal marshes, and tidal flats, amount to 425,509 ± 932 km 2 . This fine-resolution and multi-category mapping framework enables a more precise identification of potential trade-offs between conservation and development, providing valuable references for sustainable coastal management.
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