Mangroves play a substantial role in the global carbon cycle and are highly productive. To evaluate the effectiveness of a remote-sensing image in mangrove-species classification and carbon stock assessment, we utilized Worldview-3 images to map the mangrove species in Qi’ao Island, Guangdong Province, China, using a Random Forest classifier. We compared the contribution of spectral features, derivation features, and textural features to the classification accuracy and found that textural features significantly improved the overall accuracy, achieving 92.44% with all features combined. According to field-survey results, the main mangrove species in Qi’ao Island were Sonneratia apetala (SA), Acanthus ilicifolius (AI), Kandelia candel (KC), Acrostichum aureum (AA), Aegiceras corniculatum (AC), and Heritiera littoralis (HL); there are also many reeds mixed with mangroves. According to classification results, the total area of the mangroves and reeds is about 451.86 ha; the SA was the dominant species with an area of 393.90 ha. We calculated the total carbon stock of mangroves on Qi’ao Island by integrating the area of different species and their average total carbon density for the first time. The total carbon stock of mangroves in Qi’ao Island is between 147.78–156.14 kt, which demonstrates the significant potential of mangroves in carbon sequestration.
Full Text Finder