Huwaida Nur Salsabila, Pramaditya Wicaksono, Projo Danoedoro
Mapping aboveground carbon stock (AGC) in seagrass beds through remote sensing is challenging and requires integration with field data. Acquiring field data for seagrass AGC is resource-intensive, time-consuming, expensive, and potentially damaging to the environment. Consequently, there is a need to develop an approach that enables non-destructive mapping of seagrass AGC, encompassing both field surveys and image processing. The objective of this study is to establish an equation that can predict seagrass AGC based on their percent cover (PC) for various seagrass species, including Enhalus acoroides (Ea), Thalassia hemprichii (Th), Thalassodendron ciliatum (Tc), Cymodocea rotundata (Cr), Halophila ovalis (Ho), and Syringodium isoetifolium (Si). The derived species-specific PC to AGC equations are then applied to transform field PC values into AGC. These modelled AGC values are utilized to train a PlanetScope 8-bands SuperDove imagery using a stepwise regression model in the Nemberala region of Rote Island. Our findings reveal a strong relationship between seagrass PC and laboratory-measured AGC, with R-squared values higher than 0.5 for all six species, with the highest at 0.84 for Cr. The regression function for estimating seagrass AGC from the PlanetScope imagery incorporates four spectral bands: Green I, Coastal blue, Yellow, and NIR, with a correlation coefficient of 0.59 and an R-squared value of 0.34. Based on these estimations, the Root Mean Square Error (RMSE) value for seagrass AGC mapping is calculated as 1.03 grams of carbon per square meter, and the total seagrass AGC in the study area is projected to be 0.97 tons. © 2025 IEEE.
Universitas Negeri Makassar, Faculty of Mathematics and Natural Sciences, Department of Geography, Makassar, Indonesia; Universitas Gadjah Mada, Faculty of Geography, Department of Geographic Information Science, Sleman, Indonesia
