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Journal of Tropical Oceanography

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Spatiotemporal Dynamics of Mangroves in the Jinhaiwan Ramsar Wetland, Beihai, Based on Sentinel-2 Imagery and AI Algorithms

XIE Xiaokui1, WANG Riming1, DAI Zhijun2, ZHANG Chao3, HUANG Yuanjun4, GONG Shouji5    

  1. 1. Guangxi Key Laboratory of Marine Environmental Disaster Processes and Ecological Protection Technology /College of Resources and Environment, Beibu Gulf University, Qinzhou 535011, China;

    2.  State Key Laboratory of Estuarine and Costal Research, East China Normal University, Shanghai 200062, China;

    3.  Guangxi Key Laboratory of Marine Environmental Disaster Processes and Ecological Protection Technology / College of Marine Sciences, Beibu Gulf University, Qinzhou 535011, China

    4.  College of Resources and Environment, Beibu Gulf University, Qinzhou 535011, China;

    5.  College of Food Engineering, Beibu Gulf University, Qinzhou 535011, China;


  • Received:2026-05-29 Revised:2026-07-12 Accepted:2026-07-17
  • Supported by:

     Project of the Center for Degree and Graduate Education Development, Ministry of Education of China(ZT-2511607005); National Natural Science Foundation of China (42366009); Open Fund Project of the Guangxi Key Laboratory of Marine Environmental Change and Disaster in Beibu Gulf Research (2021KA02)

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Abstract: Mangroves are key coastal ecosystems that maintain ecological security and biodiversity. Understanding their spatiotemporal dynamics and driving mechanisms is essential for wetland conservation and adaptive management. Taking the mangroves of the Jinhai Bay Ramsar Wetland in Beihai, Guangxi, China, as the study area, this study employed the Google Earth Engine (GEE) platform to acquire 487 valid Sentinel-2 images with cloud cover below 30% during 2019–2025. Annual low-tide composite images were generated using the NDVI-P75 algorithm, and a ResNet34-UNet-based artificial intelligence (AI) model was developed for high-accuracy mangrove mapping. The mapping results were further interpreted and validated using Google high-resolution historical imagery, field surveys, and DJI unmanned aerial vehicle (UAV) observations. The results showed that: (1) from 2019 to 2025, the mangrove area exhibited an overall expansion trend, increasing from 146.86ha to 167.23ha, characterized by overall expansion with local fluctuations; (2) pronounced spatial heterogeneity was observed among different functional zones. The eastern core conservation zone expanded continuously at an average rate of 2.05ha·yr⁻¹, the central zone increased steadily at 1.19ha·yr⁻¹, whereas the western zone exhibited a fluctuation–recovery pattern with an average annual increase of only 0.16ha·yr⁻¹; and (3) the mangrove area reached a temporary low point in 2022, mainly due to the artificial removal of the invasive fast-growing species Laguncularia racemosa, resulting in a net loss of 6.13ha between 2021 and 2022. Subsequently, with the implementation of the pond-to-mangrove ecological restoration project, together with systematic invasive species control and differentiated functional zoning management following the designation of the Jinhai Bay Wetland as a Ramsar Wetland in 2022, the mangrove area gradually recovered and entered a phase of sustained growth. The observed dynamic pattern of local fluctuations but overall expansion indicates that the synergistic implementation of invasive species removal, wetland restoration, and zoned management can effectively mitigate anthropogenic disturbances, providing a scientific basis for the sustainable management of similar coastal wetlands.

Key words: Mangrove Forest, Deep Learning, Beihai Jinhai Bay, Ramsar Wetland, Remote Sensing