A novel index to detect green-tide using UAV-based RGB imagery. (30th October 2020)

• Record Type: Journal Article
• Authors: Jiang, Xiaopeng; Gao, Meng; Gao, Zhiqiang
• Is Part Of: Estuarine, coastal and shelf science. Volume 245(2020)
• Publication Date: 2020-10-30
• Subjects: Unmanned aerial vehicle (UAV) -- RGB-FAI -- Remote sensing -- Green tide -- Drift velocity estimation; Estuarine oceanography -- Periodicals; Coasts -- Periodicals; Estuarine biology -- Periodicals; Seashore biology -- Periodicals; Coasts; Estuarine biology; Estuarine oceanography; Seashore biology; Periodicals; 551.461805
• DOI: 10.1016/j.ecss.2020.106943
• Languages: English
• ISSNs: 0272-7714

Abstract

Abstract: Unmanned aerial vehicles (UAV) equipped with high-resolution camera have been increasingly applied in environment monitoring as an important complement to traditional satellite remote sensing. An accurate extraction of marine green-tide regions still faces many technological challenges, such as the absence of centimeter-level orthophoto maps and a dedicated green-tide index based on red-green-blue (RGB) bands. In this study, a new green-tide index, namely, the red-green-blue floating algae index (RGB-FAI) using RGB images captured by ship-borne UAV, is developed for green-tide detection in the Yellow Sea, China. Specifically, RGB-FAI is defined to measure the green-reflectance height by using the red and blue bands as the baselines. Our results show that the RGB-FAI performs well in the detection of green-tide and the accuracy is satisfactory (kappa = 0.95). It is worthy to note that RGB-FAI has the highest extraction accuracy among these competing indices for green-tide in the declining phase under a hazy atmospheric condition. In addition, by combining the bi-temporal UAV images with RGB-FAI, the drift velocity of green-tide has also been estimated as 0.26 m/s in a 17.1° east by north during aerial photography. In conclusion, the proposed RGB-FAI is effective for green-tide detection and has more potential usage in marine environment monitoring. Graphical abstract: Image 1 Highlights: A novel index to detect green-tide was proposed for UAV RGB images. First time used a ship-borne UAV to map green-tide. The extraction method worked well, especially for declining green-tides. The method helped estimate drift velocity and biomass of green-tide.