Effects of Epiphytes and Depth on Seagrass Spectral Profiles: Case Study of Gulf St. Vincent, South Australia

Charnsmorn Hwang, Chih-Hua Chang, Michael Burch, Milena Fernandes and Tim Kildea
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Charnsmorn Hwang: Department of Environmental Engineering, National Cheng Kung Chung University, Tainan City 701, Taiwan
Chih-Hua Chang: Department of Environmental Engineering, National Cheng Kung Chung University, Tainan City 701, Taiwan
Michael Burch: Department of Ecology & Evolutionary Biology, The University of Adelaide, Adelaide, South Australia 5005, Australia
Milena Fernandes: Australian Water Quality Centre, SA Water, Adelaide, South Australia 5000, Australia
Tim Kildea: Australian Water Quality Centre, SA Water, Adelaide, South Australia 5000, Australia

IJERPH, 2019, vol. 16, issue 15, 1-16

Abstract: Seagrasses are a crucial indicator species of coastal marine ecosystems that provide substratum, shelter, and food for epiphytic algae, invertebrates, and fishes. More accurate mapping of seagrasses is essential for their survival as a long-lasting natural resource. Before reflectance spectra could properly be used as remote sensing endmembers, factors that may obscure the detection of reflectance signals must be assessed. The objectives in this study are to determine the influence of (1) epiphytes, (2) water depth, and (3) seagrass genus on the detection of reflectance spectral signals. The results show that epiphytes significantly dampen bottom-type reflectance throughout most of the visible light spectrum, excluding 670–679 nm; the depth does influence reflectance, with the detection of deeper seagrasses being easier, and as the depth increases, only Heterozostera increase in the exact “red edge” wavelength at which there is a rapid change in the near-infrared (NIR) spectrum. These findings helped improve the detection of seagrass endmembers during remote sensing, thereby helping protect the natural resource of seagrasses.

Keywords: seagrass; reflectance; epiphytes; growing depth; optically shallow coastal waters; remote sensing; benthic bottom type (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
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