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Detection and Biomass Estimation of Phaeocystis globosa Blooms off Southern China From UAV-Based Hyperspectral Measurements

2021; Institute of Electrical and Electronics Engineers; Volume: 60; Linguagem: Inglês

10.1109/tgrs.2021.3051466

1558-0644

Xue Li, Shaoling Shang, Zhongping Lee, Gong Lin, Yongnian Zhang, Jingyu Wu, Zhenjun Kang, Xiangxu Liu, Cheng Yin, Yue Gao,

Marine Biology and Ecology Research

Phaeocystis globosa (P. globosa) is a unique causative species of harmful algal blooms, which can form gelatinous colonies. We, for the first time, used unmanned aerial vehicle (UAV) measurements to identify P. globosa blooms and to quantify the biomass. Based on in situ measured remote sensing reflectance ( ${R_{\mathrm{ rs}}}$ ), it is found that, for P. globosa blooms, the maximum of the second-derivative ( ${d\lambda ^{2}{R}_{\mathrm{ rs}}}$ ) of ${R_{\mathrm{ rs}}(\lambda)}$ in the 460–480-nm domain is beyond 466 nm. An analysis of the absorption properties from algal cultures suggested that this feature comes from the absorption of chlorophyll ${c_{3}}$ (Chl $-/{c_{3}}$ ) around 466 nm, a prominent feature of P. globosa. This position of $ {d\lambda ^{2}{R}_{\mathrm{ rs}}}$ maximum was, thus, selected as the criterion for P. globosa identification. The spatial extent of P. globosa blooms in two bays off southern China was then mapped by applying the criterion to UAV-measured ${R_{\mathrm{ rs}}}$ . Twelve out of 16 UAV and in situ match-up stations were consistently identified as dominated by P. globosa, indicating the accuracy of 75%. Furthermore, using localized empirical models, chlorophyll a (Chl $-/{a}$ ) concentration and colony numbers of P. globosa were estimated from UAV-derived ${R_{\mathrm{ rs}}}$ , where P. globosa colonies were found in a range of ~3–37 gel matrix/L, indicating the occurrence of weak to moderate P. globosa blooms during the surveys. The promising results suggest a high potential for detection and quantification of P. globosa blooms in near-shore bays or harbors using UAV-based hyperspectral remote sensing, where conventional ocean color satellite remote sensing runs into difficulties.