Mangrove wetlands are hotspots of global biodiversity and blue carbon reserves in coastal wetlands, with unique ecological functions and significant socioeconomic value. Annual fine-scale monitoring of mangroves is crucial for evaluating national conservation programs and implementing sustainable mangrove management strategies. However, annual fine-scale mapping of mangroves over large areas using remote sensing remains a challenge due to spectral similarities with coastal vegetation, tidal periodic fluctuations, and the need for consistent and dependable samples across different years. In previous research, there has been a lack of strategies that simultaneously consider spatial, temporal, and methodological aspects of mangrove extraction. Therefore, based on an approach that considers mangrove habitat, tides, and a semantic segmentation approach, we propose a method for fine-scale mangrove mapping suitable for long time-series data. This is an optimized hybrid model that integrates spatial, temporal, and methodological considerations. The model uses five sensors (GF-1, GF-2, GF-6, ZY-301, ZY-302) to combine deep learning U-Net models with mangrove habitat information and algorithms during low-tide periods. This method produces a mangrove map with a spatial resolution of 2 m. We applied this algorithm to three typical mangrove regions in the Beibu Gulf of Guangxi Province. The results showed the following: (1) The model scored above 0.9 in terms of its F1-score in all three study areas at the time of training, with an average accuracy of 92.54% for mangrove extraction. (2) The average overall accuracy (OA) for the extraction of mangrove distribution in three typical areas in the Beibu Gulf was 93.29%. When comparing the validation of different regions and years, the overall OA accuracy exceeded 89.84% and the Kappa coefficient exceeded 0.74. (3) The model results are reliable for extracting sparse and slow-growing young mangroves and narrow mangrove belts along roadsides. In some areas where tidal flooding occurs, the existing dataset underestimates mangrove extraction to a certain extent. The fine-scale mangrove extraction method provides a foundation for the implementation of fine-scale management of mangrove ecosystems, support for species diversity conservation, blue carbon recovery, and sustainable development goals related to coastal development.

Abstract: Mangroves are crucial ecosystems with significant ecological and economic roles, providing habitat for diverse species, storm protection, and carbon sequestration. While many mangroves worldwide have faced substantial losses, China has seen notable recovery. Understanding the expansion patterns of mangroves necessitates high-resolution, large-scale monitoring of their coverage over time. In this study, we divided coastal zones based on geomorphic and sedimentary environments and generated annual mangrove coverage from 2016 to 2022 using multi-source high-resolution data and a U-Net model. By analyzing morphological changes of mangrove patches in different coastal zones of the Guangxi Beibu Gulf, we summarized expansion patterns and calculated expansion and degradation rates from area changes. Our findings reveal a 1,413 ha increase in mangrove area from 2016 to 2022. Estuarine mangroves experienced an initial increase followed by a decrease, while gulf mangroves showed an initial decrease followed by an increase. Three distinct expansion patterns were identified: (1) regular mangroves expanding parallel to coastlines; (2) mangroves expanding in estuarine areas where freshwater and seawater meet; and (3) independent mangrove patches expanding into surrounding wetlands. In summary, stable and changing mangrove areas were in a ratio of 7:3, with expansion areas at 70.5% and degradation areas at 29.5%. The loss of mangroves in Guangxi Beibu Gulf is primarily due to intense human activities, with minimal contributions from sea level rise and marine dynamics.