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Remote Sens. 2016, 8(4), 292; doi:10.3390/rs8040292

An Integrated Field and Remote Sensing Method for Mapping Seagrass Species, Cover, and Biomass in Southern Thailand

1
Remote Sensing & Geo-Spatial Science Research Unit, Faculty of Technology and Environment, Prince of Songkla University, Phuket Campus, Phuket 83120, Thailand
2
Tropical Environmental Plant Biology Unit, Faculty of Technology and Environment, Prince of Songkla University, Phuket Campus, Phuket 83120, Thailand
3
Plant Functional Biology and Climate Change Cluster (C3), University of Technology Sydney, Sydney, NSW 2007, Australia
*
Author to whom correspondence should be addressed.
Academic Editors: Javier Bustamante, Patricia Kandus, Ricardo Díaz-Delgado, Clement Atzberger and Prasad S. Thenkabail
Received: 30 December 2015 / Revised: 20 March 2016 / Accepted: 22 March 2016 / Published: 30 March 2016
(This article belongs to the Special Issue What can Remote Sensing Do for the Conservation of Wetlands?)
View Full-Text   |   Download PDF [11337 KB, uploaded 30 March 2016]   |  

Abstract

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 seagrass in coastal waters with variable water turbidity. In this study, Worldview-2 (WV-2) imagery was 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. 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 g·DW/m2, R = 0.68). The average total above-ground biomass was 23.95 ± 10.38 g·DW/m2. The seagrass maps produced in this study are an important step towards measuring the attributes of seagrass biodiversity and can be used as inputs to seagrass dynamic models and conservation efforts. View Full-Text
Keywords: seagrass; remote sensing; percentage cover; species diversity; biomass; Worldview-2 seagrass; remote sensing; percentage cover; species diversity; biomass; Worldview-2
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Koedsin, W.; Intararuang, W.; Ritchie, R.J.; Huete, A. An Integrated Field and Remote Sensing Method for Mapping Seagrass Species, Cover, and Biomass in Southern Thailand. Remote Sens. 2016, 8, 292.

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