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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 FROM AUTHOR]

The "Two Mountains" concept of "green water and green mountains are gold and silver mountains" plays an important value-oriented role in the ecological transformation of land consolidation. In this study, Guangxi was divided into five consolidation zones in combination with relevant policies, and the evolution characteristics and change intensity of ecological-economic values before and after the three phases of land consolidation in Guangxi and each consolidation zone in 2010, 2015 and 2020 were explored by bivariate spatial autocorrelation, standard deviation ellipse, and linear regression equation. Finally, the ecological-economic values of each consolidation area, which were obtained separately, were standardized by z-score, and the standardized results were matched by dividing quadrants for analysis. The ecological-economic value matching states of each consolidation area are ecological-economic value coordinated development type (central karst basin area of Guangxi), ecological value imbalance type (southeast plain area and coastal hilly plain area of Guangxi), economic value imbalance type (northwest mountain area of Guangxi) and ecological-economic value low imbalance type (northeast hilly mountain area of Guangxi). The study aims to provide a theoretical basis for the planning and differentiated management of land consolidation in Guangxi and promote the ecological-economic value transformation of the region. [ABSTRACT FROM AUTHOR]