Interannual variations of Sargassum blooms in the Yellow Sea and East China Sea during 2017–2021

Wang, Zongling; Zhang, Xuelei; Liu, Yongjuan; Fu, Mingzhu; Xiao, Jie; Yuan, Cuiling

doi:10.1016/j.hal.2023.102451

Cited by 11 publications

(2 citation statements)

References 42 publications

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“…In the coastal and nearshore waters of China, the green tide of U. prolifera produced as biofoulings in seaweed cultivation in the Yellow Sea have received world-wide concerns [3,10,11]. Additionally, floating Sargassum (Sargassum horneri) in the Yellow Sea and the East China Sea during the winter and spring seasons have also attracted attention since 2017 [12,13]. Different from the above-mentioned blooms caused by floating Water 2023, 15, 3080 2 of 14 macroalgae in surface water, coastal benthic green macroalgae [14,15] are relatively hard to monitor with satellite remote sensing; however, expanding blooms of benthic green macroalgae also pose threats to the environment and cause pollution [6,16] due to their wide distribution.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach of Monitoring Ulva pertusa Green Tide on the Basis of UAV and Deep Learning

Xing,

Liu,

et al. 2023

Water

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Ulva pertusa (U. pertusa) is a benthic macroalgae in submerged conditions, and it is relatively difficult to monitor with the remote sensing approaches for floating macroalgae. In this work, a novel remote-sensing approach is proposed for monitoring the U. pertusa green tide, which applies a deep learning method to high-resolution RGB images acquired with unmanned aerial vehicle (UAV). The results of U. pertusa extraction from semi-simultaneous UAV, Landsat-8, and Gaofen-1 (GF-1) images demonstrate the superior accuracy of the deep learning method in extracting U. pertusa from UAV images, achieving an accuracy of 96.46%, a precision of 94.84%, a recall of 92.42%, and an F1 score of 0.92, surpassing the algae index-based method. The deep learning method also performs well in extracting U. pertusa from satellite images, achieving an accuracy of 85.11%, a precision of 74.05%, a recall of 96.44%, and an F1 score of 0.83. In the cross-validation between the results of Landsat-8 and UAV, the root mean square error (RMSE) of the portion of macroalgae (POM) model for U. pertusa is 0.15, and the mean relative difference (MRD) is 25.01%. The POM model reduces the MRD in Ulva pertusa area extraction from Landsat-8 imagery from 36.08% to 6%. This approach of combining deep learning and UAV remote sensing tends to enable automated, high-precision extraction of U. pertusa, overcoming the limitations of an algae index-based approach, to calibrate the satellite image-based monitoring results and to improve the monitoring frequency by applying UAV remote sensing when the high-resolution satellite images are not available.

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Section: Introductionmentioning

confidence: 99%

A Novel Approach of Monitoring Ulva pertusa Green Tide on the Basis of UAV and Deep Learning

Xing,

Liu,

et al. 2023

Water

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“…Sargassum horneri is a major brown macroalga commonly found along the northwest coast of the Pacific Ocean [1]. Since its discovery in the eastern Pacific in 2003, the range of this macroalga has rapidly expanded [2].…”

Section: Introductionmentioning

confidence: 99%

The Draft Genome of the “Golden Tide” Seaweed, Sargassum horneri: Characterization and Comparative Analysis

Wang,

2023

Genes

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Sargassum horneri, a prevalent species of brown algae found along the coast of the northwest Pacific Ocean, holds significant importance as a valuable source of bioactive compounds. However, its rapid growth can lead to the formation of a destructive “golden tide”, causing severe damage to the local economy and coastal ecosystems. In this study, we carried out de novo whole-genome sequencing of S. horneri using next-generation sequencing to unravel the genetic information of this alga. By utilizing a reference-guided de novo assembly pipeline with a closely related species, we successfully established a final assembled genome with a total length of 385 Mb. Repetitive sequences made up approximately 30.6% of this genome. Among the identified putative genes, around 87.03% showed homology with entries in the NCBI non-redundant protein database, with Ectocarpus siliculosus being the most closely related species for approximately one-third of these genes. One gene encoding an alkaline phosphatase family protein was found to exhibit positive selection, which could give a clue for the formation of S. horneri golden tides. Additionally, we characterized putative genes involved in fucoidan biosynthesis metabolism, a significant pathway in S. horneri. This study represents the first genome-wide characterization of a S. horneri species, providing crucial insights for future investigations, such as ecological genomic analyses.

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Ecological function and interaction of different bacterial groups during alginate processing in coastal seawater community

Cha,

Liu,

Dang

et al. 2023

Environment International

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Interannual variations of Sargassum blooms in the Yellow Sea and East China Sea during 2017–2021

Cited by 11 publications

References 42 publications

A Novel Approach of Monitoring Ulva pertusa Green Tide on the Basis of UAV and Deep Learning

A Novel Approach of Monitoring Ulva pertusa Green Tide on the Basis of UAV and Deep Learning

The Draft Genome of the “Golden Tide” Seaweed, Sargassum horneri: Characterization and Comparative Analysis

Ecological function and interaction of different bacterial groups during alginate processing in coastal seawater community

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