China University of Geoscience Beijing, Beijing 100083, China; State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
International School of Beijing, AnHua Street, Shun Yi District, Beijing, China.
Mar Pollut Bull. 2023 Nov;196:115658. doi: 10.1016/j.marpolbul.2023.115658. Epub 2023 Oct 12.
Mangroves are vital components of coastal ecosystems. Due to the complex canopy morphology and dense distribution of mangroves, it is challenging to accurately estimate the density based on satellite data. In this study, a density regression-based mangrove mapping network is proposed. The network can capture the multi-scale characteristics of mangroves through the combination of an attention mechanism and a parallel segmentation path, and its performance is better than existing methods. We then apply it to mapping the Greater Bay Area (GBA) the number of mangrove trees. The results show about 2.55 million mangrove trees in the GBA, with an average density of 782 trees per hectare. The tree number of mangroves on the beach is significantly higher than those distributed along the riverbank. This study is the first to achieve mangrove tree count mapping, opening up new prospects for applying satellite-based mangrove monitoring.
红树林是沿海生态系统的重要组成部分。由于红树林树冠形态复杂且分布密集，因此基于卫星数据准确估算其密度具有一定的挑战性。本研究提出了一种基于密度回归的红树林制图网络。该网络通过注意力机制和并行分割路径的结合，可以捕捉红树林的多尺度特征，其性能优于现有方法。然后，我们将其应用于粤港澳大湾区（GBA）的红树林树木数量进行制图。结果表明，GBA 约有 255 万棵红树林，平均密度为每公顷 782 棵。海滩上的红树林树木数量明显高于分布在河岸上的红树林树木数量。本研究首次实现了红树林树木数量的制图，为基于卫星的红树林监测开辟了新的前景。
