用深度学习和卫星数据，监测阿联酋2017-2024年红树林动态变化。
Artificial Intelligence Mangrove Monitoring System Based on Deep Learning and Sentinel-2 Satellite Data in the UAE (2017-2024)
- 基于UNet++模型与哨兵2号遥感影像，实现高精度红树林识别。
- 2024年红树林面积达9142.21公顷，较2017年增加2061.33公顷。
- 成果可为生态修复政策制定提供数据支持，适合环境监测研究者参考。
红树林在维持海岸生态系统健康和保护生物多样性方面具有关键作用，持续的红树林制图对理解其动态变化至关重要。地球观测影像通常为监测红树林动态提供了成本效益高的手段。然而，目前缺乏针对阿联酋区域红树林的研究。本研究结合UNet++深度学习模型、哨兵2号多光谱数据及人工标注标签，对2017至2024年间阿联酋密集分布（覆盖率高于70%）的红树林进行时空动态监测，在验证集上达到87.8%的mIoU。结果表明，2024年阿联酋红树林总面积约为9,142.21公顷，相比2017年增加了2,061.33公顷，碳汇量增加约194,383.42吨，相当于固定二氧化碳约713,367.36吨。阿布扎比拥有最大红树林面积，在2017至2024年间增长了1,855.6公顷，其他酋长国也通过稳定可持续的增长共同推动了整体扩张。这一全面增长趋势反映了各酋长国在红树林恢复方面的协同努力。
原文摘要 · Abstract (English)
Mangroves play a crucial role in maintaining coastal ecosystem health and protecting biodiversity. Therefore, continuous mapping of mangroves is essential for understanding their dynamics. Earth observation imagery typically provides a cost-effective way to monitor mangrove dynamics. However, there is a lack of regional studies on mangrove areas in the UAE. This study utilizes the UNet++ deep learning model combined with Sentinel-2 multispectral data and manually annotated labels to monitor the spatiotemporal dynamics of densely distributed mangroves (coverage greater than 70%) in the UAE from 2017 to 2024, achieving an mIoU of 87.8% on the validation set. Results show that the total mangrove area in the UAE in 2024 was approximately 9,142.21 hectares, an increase of 2,061.33 hectares compared to 2017, with carbon sequestration increasing by approximately 194,383.42 tons, equivalent to fixing about 713,367.36 tons of carbon dioxide. Abu Dhabi has the largest mangrove area and plays a dominant role in the UAE's mangrove growth, increasing by 1,855.6 hectares between 2017-2024, while other emirates have also contributed to mangrove expansion through stable and sustainable growth in mangrove areas. This comprehensive growth pattern reflects the collective efforts of all emirates in mangrove restoration.
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