Geoscience Data Journal (Jun 2022)
A detailed mangrove map of China for 2019 derived from Sentinel‐1 and ‐2 images and Google Earth images
Abstract
Abstract Mangroves straddle terrestrial and marine ecosystems, thus are relevant to many United Nations Sustainable Development Goals (SDG), especially to SDG‐13 and SDG‐14, as they are linked to blue carbon strategies and fisheries. To better achieve SDGs, China has been restoring mangroves for their ecological and societal values through strict protection and afforestation, even in areas beyond their natural northern limits. However, small fragmented mangrove patches, especially those in marginal climates, have been historically neglected by existing mangrove maps, which, because they were derived from 30‐m‐resolution Landsat data, are too coarse to resolve them. To overcome this problem, we applied a classification to 10‐m‐resolution Sentinel‐1 and ‐2 images for 2019 and then an enhanced post‐processing to identify missing mangrove patches by utilizing Google Earth images. Thus, we produced the first publicly accessible detailed mangrove map supplementing the main 10‐m‐resolution results with submetre data from Google Earth images. According to general quantitative evaluations, the produced map achieved an overall accuracy of 96.4 ± 0.3% based on validation data sets, an accuracy of 96.2% based on a total of 1,096 field sample plots. By evaluations in marginal climates, the produced map identified six areas with a total of seven mangrove planting areas, which is twice the number of identified areas by current publicly accessible mangrove maps. The produced map also outperformed the competitors in the identification of small mangrove patches in suitable climates, and in the determination of neat and tidy boundaries of large mangrove patches. The map paid attention to these small mangrove patches because they can provide substantial ecosystem services, such as increasing the community diversity and providing habitats for local species. The map does not only serve as a basis for mangrove species mapping, leaf area index estimation and carbon stock assessment, but also improves other mangrove maps by mutual corrections.
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