Species-based aboveground mangrove carbon stock estimation using WorldView-2 image data

The carbon stocks of a mangrove ecosystem depend to some extent on the diversity of its constituent species, which have different biophysical characteristics. The high-resolution remote sensing imagery from WorldView-2 (2 m pixel size) provides a data source for estimating aboveground mangrove carbon (mangrove AGC) stocks across species. This study was designed to (i) map mangrove species by applying the GEOBIA method, (ii) calculate mangrove AGC using species-specific allometric equations, and (iii) map the spatial distribution of mangrove AGC at the species level. It combined the species map from GEOBIA with carbon stock estimation by generating a model based on the vegetation index (spectral transformation of several WorldView-2 bands) over the Clungup Mangrove Conservation (CMC) area in Malang, East Java, Indonesia. GEOBIA was used to classify mangrove species based on the homogeneous segments of each species depicted in the image, while the carbon stocks in the field were calculated using species-specific allometric equations. A regression function between the carbon stocks and the vegetation index value stored in each pixel was applied to the derived mangrove species map. The GEOBIA species mapping using the nearest neighbor algorithm identified six dominant species in the CMC area, namely Bruguiera gymnorrhiza, Ceriops tagal, Rhizophora apiculata, Rhizophora mucronata, Sonneratia alba, and Nypa fruticans, with an 84 overall accuracy. Based on the regression analysis results, the vegetation index values varied across species. The mangrove AGC estimation and mapping at the species level produced favorable results, with a maximum accuracy of 54.94 for B. gymnorrhiza, 55.55 for C. tagal, 41.43 for R. apiculata, 51.38 for R. mucronata, and 54.87 for S. alba. Also, the range of the estimated carbon stocks differed for each species. This research contributes an innovative development by combining methods of mangrove species mapping with mangrove AGC estimation at the species level. © 2023 Elsevier B.V.