ANALYSIS OF FIELD SEAGRASS PERCENT COVER AND ABOVEGROUND CARBON STOCK DATA FOR NON-DESTRUCTIVE ABOVEGROUND SEAGRASS CARBON STOCK MAPPING USING WORLDVIEW-2 IMAGE

Abstract. Remote sensing can make seagrass aboveground carbon stock (AGC_seagrass) information spatially extensive and widely available. Therefore, it is necessary to develop a rapid approach to estimate AGC_seagrass in the field to train and assess its remote sensing-based mapping. The aim of this research is to (1) analyze the Percent Cover (PCv)-AGC_seagrass relationship in seagrass at the species and community levels to estimate AGC_seagrass from PCv and (2) perform AGC_seagrass mapping at both levels using WorldView-2 image and assess the accuracy of the resulting map. This research was conducted in Karimunjawa and Kemujan Islands, Indonesia. Support Vector Machine (SVM) classification was used to map seagrass species composition, and stepwise regression was used to model AGC_seagrass using deglint, water column corrected, and principle component bands. The results were a rapid AGC_seagrass estimation using an easily measured parameter, the seagrass PCv. At the community level, the AGC_seagrass map had 58.79% accuracy (SEE = 5.41 g C m⁻²), whereas at the species level, the accuracy increased for the class Ea (64.73%, SEE = 6.86 g C m⁻²) and EaThCr (70.02%, SEE = 4.32 g C m⁻²) but decreased for ThCr (55.08%, SEE = 2.55 g C m⁻²). The results indicate that WorldView-2 image reflectance can accurately map AGC_seagrass in the study area in the range of 15–20 g C m⁻² for Ea, 10–15 g C m⁻² for EaThCr, and 4–8 g C m⁻² for ThCr. Based on our model, the AGC_seagrass in the study area was estimated at 13.39 t C.