Abstract
Abstract. Remote sensing can make seagrass aboveground carbon stock (AGCseagrass) information spatially extensive and widely available. Therefore, it is necessary to develop a rapid approach to estimate AGCseagrass 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)-AGCseagrass relationship in seagrass at the species and community levels to estimate AGCseagrass from PCv and (2) perform AGCseagrass 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 AGCseagrass using deglint, water column corrected, and principle component bands. The results were a rapid AGCseagrass estimation using an easily measured parameter, the seagrass PCv. At the community level, the AGCseagrass 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 AGCseagrass 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 AGCseagrass in the study area was estimated at 13.39 t C.
Highlights
The integration of remote sensing and field data for mapping aboveground carbon stock of seagrass (AGCseagrass) requires corresponding field data to train the regression model and assess the accuracy of the resulting map (Hossain et al, 2015; Tamondong et al, 2018)
These findings provide the fundamental justification for predicting AGCseagrass using percent cover (PCv) and mapping it at the species level
Seagrass reflectance captured by remote sensing can correctly map AGCseagrass when it is in the range of 15‒20 g Cymodocea serrulata (Cs) m-2 for Enhalus acoroides (Ea), 10‒15 g C m-2 for EaThCr, and 4‒8 g C m-2 for ThCr
Summary
The integration of remote sensing and field data for mapping aboveground carbon stock of seagrass (AGCseagrass) requires corresponding field data to train the regression model and assess the accuracy of the resulting map (Hossain et al, 2015; Tamondong et al, 2018). In addition to developing a species-specific PCv-AGCseagrass equation, we integrate WorldView-2 multispectral remote sensing images with in situ AGCseagrass data, calculated using the PCv-AGCseagrass equation, to map AGCseagrass. To achieve these objectives, this study measured seagrass PCv in the field and AGCseagrass in the laboratory and analyzed their relationship. 50% PCv of Enhalus acoroides might consist of a few tens of shoots, while 40% PCv of Thalassia hemprichii might be populated by hundreds of shoots because of its smaller size These variations may lead to a weak correlation between seagrass biophysical properties, especially AGCseagrass, and seagrass reflectance
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