The species composition of mangrove forests significantly affects the biomass carbon stock of man-grove ecosystems,and thus the change in biomass carbon stock serves as an important indicator for assessing mangrove carbon sequestration.Utilizing Sentinel-2 multispectral imagery,this study constructed 11 vegeta-tion index combinations.A Support Vector Regression(SVR)-based classifier was trained for the dominant mangrove species,Sonneratia apetala and Aegiceras corniculatum,in the Maowei Sea area.Subsequently,the InVEST model was employed to calculate the changes in biomass carbon stock of mangroves in the study area from 2019 to 2023.The results indicated that the combination of Mangrove Recognition Index(MRI)with other vegetation indices significantly improved the identification accuracy for S.apetala and A.cornicula-tum.Furthermore,from 2019 to 2023,the overall biomass carbon stock of S.apetala and A.corniculatum in the study area kept increasing,with an average annual growth rate of 3.02％.Due to the limitations of optical imagery in accurately distinguishing between pure S.apetala stands and mixed S.apetala-A.corniculatum communities,this study may have underestimated the actual area occupied by A.corniculatum within mixed mangrove communities.Therefore,it is recommended that appropriate protective measures be implemented for the mangrove forests in the Maowei Sea area during the construction of the Pinglu Canal project.Further-more,drawing on the experience of blue carbon offset trading projects in other provinces can facilitate the de-velopment and utilization of blue carbon sequestration in Guangxi.
关键词
红树林/遥感/组合植被指数/物种分类/生物碳储量/InVEST模型
Key words
mangrove/remote sensing/combined vegetation indices/species identification/biomass carbon stock/InVEST model
维普
万方数据