Analysis of environmental factors affecting the large-scale long-term sequence of green tide outbreaks in the Yellow Sea

Li, Dongxue; Gao, Zhiqiang; Song, Debin

doi:10.1016/j.ecss.2021.107504

Cited by 19 publications

(3 citation statements)

References 38 publications

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“…NDVI values typically range from −1 to 1. Areas with green vegetation cover are generally have higher NDVI values, typically exceeding 0.2 or 0.3 [35]. The NDVI formula is as follows:…”

Section: Gee Data Acquisition and Processingmentioning

confidence: 99%

Multi-Factors Synthetically Contribute to Ulva prolifera Outbreaks in the South Yellow Sea of China

Xue,

Wu,

Zheng

et al. 2023

Remote Sensing

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In recent years, the frequent outbreaks of Ulva prolifera in the South Yellow Sea have become the largest-scale green tide disasters in the world. The causes of its outbreaks have garnered widespread attention, particularly the coupled effects of multiple factors. Leveraging the Google Earth Engine (GEE) platform, this study conducted a long-term investigation of the Yellow Sea green tide disaster and the factors using multi-source satellite imagery. Finally, the combined effects of natural environmental changes and human activities on Ulva prolifera were analyzed by redundancy analysis (RDA) and variation partitioning analysis (VPA). The results indicate: (1) Since 2018, the scale of Ulva prolifera in the South Yellow Sea has shown a distinct “biennial” trend. (2) Regarding environmental factors, SST, PAR, precipitation, and windspeed have certain effects on the growth of Ulva prolifera. However, they cannot be considered as determining factors for the outbreak of Ulva prolifera (0.002 < R2 < 0.14). Regarding anthropogenic factors, the recycle time of Pyropia yezoensis culture rafts has a relatively minor influence on the extent of Ulva prolifera. There exists a certain positive correlation (R2 = 0.45) between the human footprint index (HFI) of Jiangsu Province and the annual variation in Ulva prolifera area in the South Yellow Sea. (3) The combined effects of multiple factors influence green tide outbreaks. The Ulvatotal explanatory power of SST, PAR, precipitation, windspeed, HFI, and the recycle time of Pyropia yezoensis culture rafts for the annual variation in the Ulva prolifera area is 31.8%, with these factors interweaving and mutually influencing each other. This study offers important insights into quantifying the driving forces behind Ulva prolifera in the South Yellow Sea, providing valuable information for a deeper comprehension of the complexity of marine ecosystems and sustainable management.

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“…NDVI values typically range from −1 to 1. Areas with green vegetation cover are generally have higher NDVI values, typically exceeding 0.2 or 0.3 [35]. The NDVI formula is as follows:…”

Section: Gee Data Acquisition and Processingmentioning

confidence: 99%

Multi-Factors Synthetically Contribute to Ulva prolifera Outbreaks in the South Yellow Sea of China

Xue,

Wu,

Zheng

et al. 2023

Remote Sensing

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“…Li et al found that the initial size of the green tides does not directly impact the outbreak scale. However, lower sea surface temperatures (SST) and adequate precipitation in the early stages might foster largescale occurrences [19]. Li et al assessed green tides from 2007 to 2021 and the long-term trends of various factors influencing them over the past 30 years.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Monitoring of Green Tide Disaster Using MODIS and GF-1 Data: A Case Study in the Yellow Sea

Men,

Liu,

et al. 2023

JMSE

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Satellites with low-to-medium spatial resolution face challenges in monitoring the early and receding stages of green tides, while those with high spatial resolution tend to reduce the monitoring frequency of such phenomena. This study aimed to observe the emergence, evolution, and migratory patterns of green tides. We integrated GF-1 and MODIS imagery to collaboratively monitor the green tide disaster in the Yellow Sea during 2021. Initially, a linear regression model was employed to adjust the green tide coverage area as captured using MODIS imagery. We jointly observed the distribution range, drift path, and coverage area of the green tide and analyzed the drift path in coordination with offshore wind field and flow field data. Furthermore, we investigated the influence of SST, SSS, and rainfall on the 2021 green tide outbreak. The correlations calculated between SST, SSS, and precipitation with the changes in the area of the green tide were 0.43, 0.76, and 0.48, respectively. Our findings indicate that the large-scale green tide outbreak in 2021 may be associated with several factors. An increase in SST and SSS during the initial phase of the green tide established the essential conditions, while substantial rainfall during its developmental stage provided favorable conditions. Notably, the SSS exhibited a close association with the outbreak of the green tide.

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“…However, due to the lack of a comprehensive and scientific understanding of the green tide bloom mechanism, the root cause of the "jumping" changes in green algae coverage area remains elusive. Particularly when various factors are interdependent and mutually influencing each other (Jin et al, 2018;Li et al, 2021a and2021b), the absence of collaborative joint analysis impedes accurate elucidation of the critical response mechanism and interactions between environmental factors and green tide outbreaks. Hence, it is imperative to investigate the interplay between green tide occurrences and the environment by examining the correlation between environmental factors and outbreaks of green algae.…”

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confidence: 99%

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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Analysis of environmental factors affecting the large-scale long-term sequence of green tide outbreaks in the Yellow Sea

Cited by 19 publications

References 38 publications

Multi-Factors Synthetically Contribute to Ulva prolifera Outbreaks in the South Yellow Sea of China

Multi-Factors Synthetically Contribute to Ulva prolifera Outbreaks in the South Yellow Sea of China

Remote Sensing Monitoring of Green Tide Disaster Using MODIS and GF-1 Data: A Case Study in the Yellow Sea

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

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