Mapping Mangrove Using a Red-Edge Mangrove Index (REMI) Based on Sentinel-2 Multispectral Images
Zhaojun Chen, Meng Zhang, Huaiqing Zhang, Yang Liu
Abstract
Mangrove forests are among the most productive of coastal ecosystems, providing a variety of ecological functions and economic value to coastal areas around the world. Accurate identification of mangrove is of great importance for the restoration and conservation of mangrove ecosystems, and for promoting the development of a blue carbon economy and achieving carbon neutral strategies. In this study, a red-edge mangrove index (REMI) was proposed based on Sentinel-2 multispectral images, using red, green, red edge, and SWIR1 bands in the form of a (red edge-red)/(SWIR1-green) combination to highlight the unique green and moisture information of mangrove. Then, the REMI index was combined with the Otsu threshold segmentation algorithm (Otsu) to map the mangrove information in respect of Hainan Island, which has the most abundant mangrove species in China. The results indicate that, when compared with other vegetation indices, such as the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), mangrove index (MI), normalized difference mangrove index (NDMI), combined mangrove recognition index (CMRI), and mangrove vegetation index (MVI), the REMI showed greater proficiency in distinguishing mangrove from other vegetation. When the REMI was applied to mangrove mapping in Hainan Island, the overall accuracy and kappa coefficient were 95.68% and 0.92, respectively. In addition, the mangrove distribution ranges mapped in this study were compared with existing mangrove products (HGMF_2020 and China National Standard GB/T 7714-2015 (note)), and it was demonstrated that the mangrove distribution ranges identified based on the REMI had high coincidence with the above-mentioned mangrove products. This proves that the REMI has good potential for application in mangrove identification and mapping.