Werapong Koedsin1,, Wissarut Intararuang1, Raymond J. Ritchie2, Alfredo Huete3
1 Remote Sensing & Geo-Spatial Science Research Unit, Faculty of Technology and Environment, Prince of Songkla University, Phuket Campus, Phuket, Thailand; email@example.com, firstname.lastname@example.org
2 Tropical Environmental Plant Biology Unit, Faculty of Technology and Environment, Prince of Songkla University, Phuket Campus, Phuket, Thailand, E-mail: email@example.com
3 Plant Functional Biology and Climate Change Cluster (C3), University of Technology Sydney, NSW 2007, Australia, E-mail: Alfredo.Huete@uts.edu.au * Correspondence: firstname.lastname@example.org; Tel.: +66-803-297-155
Accurate and up-to-date maps of seagrass biodiversity are important for marine resource management but it is very challenging to test the accuracy of remote sensing techniques for mapping of seagrass in coastal waters with variable water turbidity. Seagrasses beds are typically very patchy and biomass is sometimes very low and so it is difficult to resolve seagrass bed from bare sand and mud. We used Worldview-2 (WV-2) imagery combined with field sampling to demonstrate the capability of mapping species type, percentage cover and above-ground biomass of seagrasses in monsoonal southern Thailand. A high accuracy positioning technique, involving the Real Time Kinematic (RTK) Global Navigation Satellite System (GNSS) was used to record field sample data positions and reduce uncertainties in matching locations between satellite and field data sets. Positional accuracy of less than 10 cm was needed to properly resolve seagrass from bare substrate. Our results showed high accuracy (90.67%) in mapping seagrass distribution and moderate accuracies for mapping percentage cover and species type (73.74% and 75.00%, respectively). Seagrass species type mapping was successfully achieved despite discrimination confusion among Halophila ovalis, Thalassia hemprichii and Enhalus acoroides species with greater than 50% cover. The green, yellow and near infrared spectral channels of WV-2 were used to estimate the above-ground biomass using a multiple linear regression model (RMSE of ±10.38 gDW/m2, R=0.68). The average total above ground biomass was 23.95±10.38gDW/m2. The seagrass maps produced in this study () are an important step towards measuring the attributes of seagrass biodiversity and as inputs to seagrass dynamic models.
Keywords: seagrass; remote sensing; percentage cover; species diversity; biomass; Worldview- 2