Unprecedent green macroalgae bloom: mechanism and implication to disaster prediction and prevention

Cao, Mengmeng; Li, Xuyan; Cui, Tingwei; Pan, Xinliang; Li, Yan; Chen, Yanlong; Wang, Ning; Xiao, Yanfang; Song, Xingai; Xu, Yuzhu; A, Runa; Mu, Bing; Qing, Song; Liu, Rongjie; Zhao, Wenjing; Bao, Yuhai; Zhang, Jie; Wei, Lan

doi:10.1080/17538947.2023.2257658

Cited by 2 publications

(6 citation statements)

References 76 publications

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“…Based on daily and weekly green tide datasets derived from optical and SAR images, we analyzed to determine the annual maximum area coverage of algae observed by both satellites. The resulting maximum coverage time series aligns with findings from Cao et al (2023) and Hu et al (2023). As shown in Fig.…”

Section: Temporal Characteristics Of Green Tide Coveragesupporting

confidence: 72%

“…1a-b), the green tide drifts from south to north into the central Yellow Sea driven by currents and wind fields, corroborated by dynamic time series data from optical MODIS and Sentinel-1 SAR (see supplementary animation). Previous research indicates the scale of green tide outbreaks is linked to Porphyra mariculture along the Subei Shoal coast (Xing et al, 2019;Cao et al, 2023). Green algae spores released from mariculture rafts can trigger large-scale green tide events under favorable conditions.…”

Section: The Mechanism and Impact Of Green Tide Outbreaksmentioning

confidence: 99%

“…Earlier observations of green algae by satellites correspond to larger expected outbreak scales. Additionally, the final scale of the green tide outbreak is also directly proportional to the initial outbreak scale (Cao et al, 2023).…”

Section: Temporal Characteristics Of Green Tide Coveragementioning

confidence: 99%

“…Consequently, manual labeling and sharing representative sample labels have posed persistent challenges. Moreover, despite the plethora of green algae extraction algorithms proposed in previous studies, the time series of historical green tide coverage datasets, dating back to the inception of green tide records, have yet to be made publicly available and shared (Hu et al, 2019;Hu et al, 2023;Cao et al, 2023). These datasets serve as the foundation of green tide research and provide essential data for the mutual comparison and verification of various extraction algorithms.…”

mentioning

confidence: 99%

“…Recently, through in-depth research efforts, there has been some preliminary understanding of the mechanisms underlying green tide outbreaks (Zhang et al, 2019;Feng et al, 2020;Cao et al, 2023). The genesis of green tide is commonly attributed to Porphyra mariculture in the Subei Shoal, as depicted in Fig.…”

mentioning

confidence: 99%

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Weekly Green Tide Mapping in the Yellow Sea with Deep Learning: Integrating Optical and SAR Ocean Imagery

Gao,

Guo,

2024

Preprint

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Abstract. Since 2008, the Yellow Sea has experienced a world's largest-scale marine disasters, known as the green tide, marked by the rapid proliferation and accumulation of large floating algae. Leveraging advanced AI models, namely AlgaeNet and GANet, this study comprehensively extracted and analyzed green tide occurrences using optical Moderate Resolution Imaging Spectroradiometer (MODIS) images and microwave Sentinel-1 Synthetic Aperture Radar (SAR) images. Most importantly, this study presents a continuous and seamless weekly average green tide coverage dataset with the resolution of 500 m, by integrating high precise daily optical and SAR data during each week during the green tide breakout. The uncertainty assessment of this weekly product shows it is completely consistent with the overall direct average of the daily product (R2=1 and RMSE=0). Additionally, the individual case verification in 2019 also shows that the weekly product conforms to the life pattern of green tide outbreaks and exhibits parabolic curve-like characteristics, with an low uncertainty (R2=0.89 and RMSE=275 km2).This weekly dataset offers reliable long-term data spanning 15 years, facilitating research in forecasting, climate change analysis, numerical simulation and disaster prevention planning in the Yellow Sea. The dataset is accessible through the Oceanographic Data Center, Chinese Academy of Sciences (CASODC), along with comprehensive reuse instructions provided at http://dx.doi.org/10.12157/IOCAS.20240410.002 (Gao et al., 2024).

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Section: Temporal Characteristics Of Green Tide Coveragesupporting

confidence: 72%

Section: The Mechanism and Impact Of Green Tide Outbreaksmentioning

confidence: 99%

Section: Temporal Characteristics Of Green Tide Coveragementioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Weekly Green Tide Mapping in the Yellow Sea with Deep Learning: Integrating Optical and SAR Ocean Imagery

Gao,

Guo,

2024

Preprint

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Automatic Detection of Floating Ulva prolifera Bloom from Optical Satellite Imagery

Zhang,

Qin,

Sun

et al. 2024

JMSE

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Annual outbreaks of floating Ulva prolifera blooms in the Yellow Sea have caused serious local environmental and economic problems. Rapid and effective monitoring of Ulva blooms from satellite observations with wide spatial-temporal coverage can greatly enhance disaster response efforts. Various satellite sensors and remote sensing methods have been employed for Ulva detection, yet automatic and rapid Ulva detection remains challenging mainly due to complex observation scenarios present in different satellite images, and even within a single satellite image. Here, a reliable and fully automatic method was proposed for the rapid extraction of Ulva features using the Tasseled-Cap Greenness (TCG) index from satellite top-of-atmosphere reflectance (RTOA) data. Based on the TCG characteristics of Ulva and Ulva-free targets, a local adaptive threshold (LAT) approach was utilized to automatically select a TCG threshold for moving pixel windows. When tested on HY1C/D-Coastal Zone Imager (CZI) images, the proposed method, termed the TCG-LAT method, achieved over 95% Ulva detection accuracy though cross-comparison with the TCG and VBFAH indexes with a visually determined threshold. It exhibited robust performance even against complex water backgrounds and under non-optimal observing conditions with sun glint and cloud cover. The TCG-LAT method was further applied to multiple HY1C/D-CZI images for automatic Ulva bloom monitoring in the Yellow Sea in 2023. Moreover, promising results were obtained by applying the TCG-LAT method to multiple optical satellite sensors, including GF-Wide Field View Camera (GF-WFV), HJ-Charge Coupled Device (HJ-CCD), Sentinel2B-Multispectral Imager (S2B-MSI), and the Geostationary Ocean Color Imager (GOCI-II). The TCG-LAT method is poised for integration into operational systems for disaster monitoring to enable the rapid monitoring of Ulva blooms in nearshore waters, facilitated by the availability of near-real-time satellite images.

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Unprecedent green macroalgae bloom: mechanism and implication to disaster prediction and prevention

Cited by 2 publications

References 76 publications

Weekly Green Tide Mapping in the Yellow Sea with Deep Learning: Integrating Optical and SAR Ocean Imagery

Weekly Green Tide Mapping in the Yellow Sea with Deep Learning: Integrating Optical and SAR Ocean Imagery

Automatic Detection of Floating Ulva prolifera Bloom from Optical Satellite Imagery

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