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.
Keywords: Deep learning; Mangroves; Remote sensing; Tree counting.
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