Remote-Sensing Monitoring of Green Tide and Its Drifting Trajectories in Yellow Sea Based on Observation Data of Geostationary Ocean Color Imager

Ying, 陈莹 Chen; Deyong, 孙德勇 Sun; Hailong, 张海龙 Zhang; Shengqiang, 王胜强 Wang; Zhongfeng, 丘仲锋 Qiu; Yijun, 何宜军 He

doi:10.3788/aos202040.0301001

Cited by 7 publications

(5 citation statements)

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“…However, these algorithms require complex and accurate atmospheric correction procedures, which can increase the complexity of interpretation. To address this, Zhang Hailong et al developed the Floating Algae Index (FGTI) based on the DN values of different satellite data, and Chen Ying et al [89] proposed the Green Tide Index (TCT-GTI) algorithm based on the Tassel Cap Transformation method, which requires no atmospheric correction. The effect of atmospheric molecular scattering is removed, making the NDAI more sensitive to the radiance signal from the marine surface [85].…”

Section: Semi-analytical Modelmentioning

confidence: 99%

A Global Review of Progress in Remote Sensing and Monitoring of Marine Pollution

Ma,

Pan

et al. 2023

Water

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With the rapid development of urbanization and industrialization, human activities have caused marine pollution in three ways: land source, air source, and sea source, leading to the problem of marine environments. Remote sensing, with its wide coverage and fast and accurate monitoring capability, continues to be an important tool for marine environment monitoring and evaluation research. This paper focuses on the three types of marine pollution, namely marine seawater pollution, marine debris and microplastic pollution, and marine air pollution. We review the application of remote sensing technology methods for monitoring marine pollution and identify the limitations of existing methods. Marine seawater pollution can be effectively monitored by remote sensing technology, especially where traditional monitoring methods are inadequate. For marine debris and microplastic pollution, the monitoring methods are still in the early stages of development and require further research. For marine air pollution, more air pollution parameters are required for accurate monitoring. Future research should focus on developing marine remote sensing with data, technology, and standard sharing for three-dimensional monitoring, combining optical and physical sensors with biosensors, and using multi-source and multi-temporal monitoring data. A marine multi-source monitoring database is necessary to provide an immediately available basis for coastal and marine governance, improve marine spatial planning, and help coastal and marine protection.

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Section: Semi-analytical Modelmentioning

confidence: 99%

A Global Review of Progress in Remote Sensing and Monitoring of Marine Pollution

Ma,

Pan

et al. 2023

Water

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“…Unlike previous studies (Hu 2009;Chen et al 2020), this study realized the quantitative division of the life cycle of U. prolifera based on the five characteristic points in its life cycle curve. We obtained the time nodes of each stage of U. prolifera.…”

Section: Staged Characteristics Of U Prolifera In the Spatio-temporal Distributionmentioning

confidence: 99%

“…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%

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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“…The diurnal variation in the Ulva prolifera coverage area tended to increase and then decrease. Based on the band characteristics of the GOCI sensor, Chen et al (2020) designed a new green tides index algorithm based on the tasseled cap transformation method. They proved that the new method has high reliabilityand also explained that the monitored green tides coverage area reached a maximum expansion at noon, which may be affected by photosynthesis.…”

Section: Introductionmentioning

confidence: 99%

A novel quantitative analysis for diurnal dynamics of Ulva prolifera patch in the Yellow Sea from Geostationary Ocean Color Imager observation

Chen

Jiang

et al. 2023

Front. Mar. Sci.

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IntroductionIn the last decade, the outbreak of large-scale green tides caused by Ulva prolifera has continuously occurred in the Yellow Sea. Satellite remote sensing techniques have been widely used to monitor the distribution area and duration of green tides due to their advantages of their large-area synchronous observation. Ulva prolifera in the Yellow Sea is mainly distributed in bands or large patches during its flourishing stage. Previous studies have rarely reported the quantitative analysis of a single Ulva prolifera patch and its changes in the short term.MethodsConsidering the high temporal resolution of the Geostationary Ocean Color Imager (GOCI) sensor and the patchy distribution of Ulva prolifera floating on the sea surface, we developed a feasible method for monitoringUlva prolifera by performing clustering analysis with density-based spatial clustering of applications with noise (DBSCAN) to capture the diurnal variation characteristics of a single Ulva prolifera patch.ResultsThis new approach was used to extract informationfrom a single Ulva prolifera patch in the Yellow Sea in 2012 and 2017. The results showed that during the time of GOCI imaging, the tidal current was the main factor driving the drift of Ulva prolifera, and the drifting direction of Ulva prolifera was consistent with the direction of the local tidal current, with a coefficient of determination of 0.94.DiscussionBy changing the normalized difference vegetation index (NDVI) threshold, further more accurate atmospheric correction (AC) of GOCI data during the twilight periods was indirectly achieved. By comparing the areal change in the single patch before and after AC, we speculated that the daily change in signal intensity received by the GOCI sensor may be the main reason for the diurnal variation in the Ulva proliferacoverage area. The results showed the details of the diurnal variation in Ulvaprolifera patches in the dynamic marine environment, and the main reason that may cause this variation was speculated.

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Remote-Sensing Monitoring of Green Tide and Its Drifting Trajectories in Yellow Sea Based on Observation Data of Geostationary Ocean Color Imager

Cited by 7 publications

References 0 publications

A Global Review of Progress in Remote Sensing and Monitoring of Marine Pollution

A Global Review of Progress in Remote Sensing and Monitoring of Marine Pollution

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

A novel quantitative analysis for diurnal dynamics of Ulva prolifera patch in the Yellow Sea from Geostationary Ocean Color Imager observation

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