The seasonal dissipation of <i>Ulva prolifera</i> and its effects on environmental factors: based on remote sensing images and field monitoring data

Zhang, Guangzong; Wu, Mengquan; Zhou, Min; Zhao, Lianjie

doi:10.1080/10106049.2020.1745301

Cited by 10 publications

(6 citation statements)

References 41 publications

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“…Moreover, the difference between ATSYS and ATAA was the largest. When discussing the influence of SST on U. prolifera, the SST in 2017 was found to be slightly different from that in the other years, consistent with the results obtained by Zhang et al (2020a). However, the temperature difference between the area where U. prolifera may occur and the area where U. prolifera occurs is the highest.…”

Section: Sea Surface Temperaturesupporting

confidence: 84%

“…The key to control such disasters is to monitor the spatio-temporal distribution characteristics and drift trajectory accurately. Existing studies have made significant achievements in terms of understanding the origin of U. prolifera disaster (Qing et al 2018), its spatio-temporal distribution (Qi et al 2016;Yang et al 2017), drift monitoring (Zhang et al 2018a(Zhang et al , 2018bChen et al 2020), biomass estimation (Hu et al 2017;Xiao et al 2017), and environmental factors (Feng et al 2012;Zhang et al 2020a). Among the representative studies, Gower et al (2006) used 300 m resolution data from MERIS to monitor a large area of Sargasso seaweed in the Gulf of Mexico, laying a foundation for the monitoring of U. prolifera disasters based on remote sensing data.…”

Section: Introductionmentioning

confidence: 99%

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Identifying the spatio-temporal variations of Ulva prolifera disasters in all life cycle

Zhang

Guo

et al. 2021

Journal of Water and Climate Change

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Since 2007, Ulva prolifera disasters have occurred every year in the South Yellow Sea of China, the largest green tide disaster in the world. The inter-annual differences make monitoring and early warning for such disasters difficult. This study used remote sensing data (2015–2019) to determine its spatio-temporal variations in all life cycles. The results showed a lay effect between the NDVI-mean and the coverage area of U. prolifera. The spatio-temporal distribution of U. prolifera showed stages and regional differences. From late April to early May, U. prolifera first emerged near the Subei Shoal. After development in the middle of the Yellow Sea, U. prolifera broke out in the eastern sea area of Shandong and Jiangsu, declined in the Shandong sea area, and disappeared near Qingdao. The cycle lasted for approximately 90 days. The sea surface temperature was the necessary condition for the disaster, and the sea wind field was the main driving force for its horizontal drift. This study overcomes the poor timing and continuity of remote sensing data in the monitoring of U. prolifera. It provides a theoretical reference for forecasting the outbreak period of U. prolifera and can aid policy-makers to avert such disasters in advance.

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Section: Sea Surface Temperaturesupporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Identifying the spatio-temporal variations of Ulva prolifera disasters in all life cycle

Zhang

Guo

et al. 2021

Journal of Water and Climate Change

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“…Simultaneously, the growth of marine heterotrophic bacteria also depends on the concentrations and properties of inorganic nutrients. Heterotrophic bacteria can use DIN and dissolved inorganic phosphorus as nitrogen and phosphorus sources (G. Z. Zhang et al, 2022 ). Only when heterotrophic planktonic bacteria in the water are not limited by dissolved organic matter do inorganic nutrients have an important effect on their growth (G. Z. Zhang et al, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

“…Heterotrophic bacteria can use DIN and dissolved inorganic phosphorus as nitrogen and phosphorus sources (G. Z. Zhang et al, 2022 ). Only when heterotrophic planktonic bacteria in the water are not limited by dissolved organic matter do inorganic nutrients have an important effect on their growth (G. Z. Zhang et al, 2022 ). Meanwhile, the change of marine bacteria also affects the green tide outbreak and environmental factors.…”

Section: Discussionmentioning

confidence: 99%

Effects of green tide on microbial communities in waters of the Jiangsu coastal area, China

Yuan

Luo

et al. 2022

Water Environment Research

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Recently, green tide outbreaks have resulted in severe coastal ecology and economic effects in China. Jiangsu coastal areas are usually the site of early green tide outbreaks. To clarify the effects of green tide outbreaks in Jiangsu coastal areas, this study analyzed microbial communities during green tide‐free and green tide outbreak periods (May and July, respectively) through 16S rDNA sequencing. Sequences were clustered into 4117 operational taxonomic units (OTUs), 1044 and 3834 of which were obtained from the May and July groups, respectively. Redundancy analysis indicated that green tide occurrence was closely associated with the temperature, pH, and concentrations of various nutrients. Diversity analysis revealed that the July group had a richer microbial community than the May group, in agreement with the results of propagule culture. Moreover, comparative analysis revealed that samples in the May and July groups clustered together. According to Megan analysis, the May group had much more Psychrobacter, Sulfitobacter, and Marinomonas than the July group, whereas the other genera were predominantly found in July, such as Ascidiacerhabitans, Synechococcus Hydrotalea, and Burkholderia‐Paraburkholderia. These findings suggest that green tide outbreaks affect marine microbial communities, and detecting the changes in the identified genera during green tide outbreaks may contribute to green tide forecasting. Practitioner Points Jiangsu coastal areas are usually the site of early green tide outbreaks. Green tide occurrence was related to the concentrations of various nutrients. Microbial species and community structure significantly changed after green tide outbreak.

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“…Influenced by the environment and physiological characteristics, the spectral reflectance of U. prolifera is different in different periods [12,15,53]. In this study, we selected some images of U. prolifera at different scales and dates to verify the accuracy of the model.…”

Section: Accuracy Comparison Based On Cross-sensormentioning

confidence: 99%

Adaptive Threshold Model in Google Earth Engine: A Case Study of Ulva prolifera Extraction in the South Yellow Sea, China

Zhang

Wei

et al. 2021

Remote Sensing

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An outbreak of Ulva prolifera poses a massive threat to coastal ecology in the Southern Yellow Sea, China (SYS). It is a necessity to extract its area and monitor its development accurately. At present, Ulva prolifera monitoring by remote sensing imagery is mostly based on a fixed threshold or artificial visual interpretation for threshold selection, which has large errors. In this paper, an adaptive threshold model based on Google Earth Engine (GEE) is proposed and applied to extract U. prolifera in the SYS. The model first applies the Floating Algae Index (FAI) or Normalized Difference Vegetation Index (NDVI) algorithm on the preprocessed remote sensing images and then uses the Canny Edge Filter and Otsu threshold segmentation algorithm to extract the threshold automatically. The model is applied to Landsat8/OLI and Sentinel-2/MSI images, and the confusion matrix and cross-sensor comparison are used to evaluate the accuracy and applicability of the model. The verification results show that the model extraction of U. prolifera based on the FAI algorithm has higher accuracy (R2 = 0.99, RMSE = 5.64) and better robustness. However, when the average cloud cover is more than 70% in the image (based on the statistical results of multi-year cloud cover information), the model based on the NDVI algorithm has better applicability and can extract the algae distributed at the edge of the cloud. When the model uses the FAI algorithm, it is named FAI-COM (model based on FAI, the Canny Edge Filter, and Otsu thresholding). And when the model uses the NDVI algorithm, it is named NDVI-COM (model based on NDVI, the Canny Edge Filter, and Otsu thresholding). Therefore, the final extraction results are generated by supplementing NDVI-COM results on the basis of FAI-COM extraction results in this paper. The F1-score of U. prolifera extracted results is above 0.85. The spatiotemporal distribution of U. prolifera in the South Yellow Sea from 2016 to 2020 is obtained through the model calculation. Overall, the coverage area of U. prolifera shows a decreasing trend over the five years. It is found that the delay in recovery time of Porphyra yezoensis culture facilities in the Northern Jiangsu Shoal and the manual salvage and cleaning-up of U. prolifera in May are among the reasons for the smaller interannual scale of algae in 2017 and 2018.

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The seasonal dissipation of Ulva prolifera and its effects on environmental factors: based on remote sensing images and field monitoring data

Cited by 10 publications

References 41 publications

Identifying the spatio-temporal variations of Ulva prolifera disasters in all life cycle

Identifying the spatio-temporal variations of Ulva prolifera disasters in all life cycle

Effects of green tide on microbial communities in waters of the Jiangsu coastal area, China

Adaptive Threshold Model in Google Earth Engine: A Case Study of Ulva prolifera Extraction in the South Yellow Sea, China

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