Remote sensing methods for biomass estimation of green algae attached to nursery-nets and raft rope. (January 2020)

• Record Type: Journal Article
• Authors: Jiang, Xiaopeng; Gao, Zhiqiang; Zhang, Qingchun; Wang, Yueqi; Tian, Xinpeng; Shang, Weitao; Xu, Fuxiang
• Is Part Of: Marine pollution bulletin. Volume 150(2020)
• Publication Date: 2020-01
• Subjects: Green tide -- Biomass estimation -- Unmanned aerial vehicle -- Attached algae -- Subei shoal; Marine pollution -- Periodicals; Marine Biology -- Periodicals; Water Pollution -- Periodicals; Mer -- Pollution -- Périodiques; Publications périodiques; Pollution des mers; Lutte antipollution; Electronic journals; 363.7394
• DOI: 10.1016/j.marpolbul.2019.110678
• Languages: English
• ISSNs: 0025-326X

Abstract

Abstract: Accurate estimation of the biomass of raft-attached green algae is important for predicting the scale of green-tides in the Yellow Sea, China. In this study, two different biomass estimation methods are proposed: green algae attached to nursery-net (GAAN) and green algae attached to rope (GAAR). The GAAN method involves the use of images obtained using an unmanned aerial vehicle (UAV), high-resolution satellite images, and data from a statistical yearbook. The GAAR method uses high-resolution satellite images and data from a field sample survey. The results showed that the biomass of GAAN and GAAR in the Subei Shoal during 2017 was 8868 tons and 2974 tons respectively. A longer-term study of the biomass of GAAN and GAAR could provide quantitative information for the earnings forecasts of Porphyra yezoensis and for green-tide prevention. Graphical abstract: Image 1 Highlights: Two methods were proposed for estimating raft-attached macroalgal biomass. Methods synthesized data from drone, satellite, field survey and statistics. Biomass of algae attached to nursery-nets and rope was estimated, respectively. Results showed potential for profit forecast of laver and green-tide prevention.