Ocean sand ridges in the Yellow Sea observed by Satellite Remote Sensing measurements

Abstract. Massive floating macroalgal blooms in the ocean have had an array of ecological consequences; thus, tracking their drifting pattern and predicting their biomass are important for their effective management. However, a high-resolution ecological dynamics model is lacking. In this study, a physical–ecological model, Floating Macroalgal Growth and Drift Model (FMGDM v1.0), was developed to determine the dynamic growth and drift pattern of floating macroalgal, based on the tracking, replication and extinction of Lagrangian particles. The position, velocity, quantity and represented biomass of particles are updated synchronously between the tracking module and the ecological module. The former is driven by ocean flows and sea surface wind, while the latter is controlled by the temperature, salinity, and irradiation. Based on the hydrodynamic models of the Finite-Volume Community Ocean Model and parameterized using a culture experiment of Ulva prolifera, which caused the largest bloom worldwide of the green tide in the Yellow Sea, China, this model was applied to simulate the green tides around the Yellow Sea in 2014 and 2015. The simulation result, distribution and biomass of green tides, was validated using remote sensing observation data and reasonably modeled the entire process of green tide bloom and its extinction from early spring to late summer. Given the prescribed spatial initialization from remote sensing observation, the model could provide accurate short-term (7–8 d) predictions of the spatial and temporal developments of the green tide. With the support of the hydrodynamic model and biological data of macroalgae, this model can forecast floating macroalgae blooms in other regions.

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Remote Sensing Retrieval of Turbidity Near Radial Sand Ridges Area in the South Yellow Sea Using HJ-1A/B CCD Imagery

Future Control and Automation - Lecture Notes in Electrical Engineering ◽

10.1007/978-3-642-31006-5_15 ◽

2012 ◽

pp. 121-127

Author(s):

Zhifeng Yu ◽

Xiaoling Chen ◽

Liqiao Tian ◽

Xiaohong Yuan ◽

Hai Liu ◽

...

Keyword(s):

Remote Sensing ◽

Yellow Sea ◽

South Yellow Sea ◽

The South ◽

Sand Ridges ◽

Radial Sand Ridges ◽

The South Yellow Sea

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The Use of Remote Sensing Analysis for Evaluating the Impact of Development Projects in the Yellow Sea Large Marine Ecosystem

Sustainability ◽

10.3390/su12093628 ◽

2020 ◽

Vol 12 (9) ◽

pp. 3628

Author(s):

Gabriel Sidman ◽

Sydney Fuhrig ◽

Geeta Batra

Keyword(s):

Remote Sensing ◽

Time Series ◽

Yellow Sea ◽

Marine Pollution ◽

Marine Ecosystem ◽

Environmental Indicators ◽

The Yellow Sea ◽

Series Data ◽

Large Marine Ecosystem ◽

The Impact

Remote sensing has long been valued as a data source for monitoring environmental indicators and detecting trends in ecosystem stress from anthropogenic causes such as deforestation, river dams and air and water pollution. More recently, remote sensing analyses have been applied to evaluate the impacts of environmental projects and programs on reducing environmental stresses. Such evaluation has focused primarily on the change in above-surface vegetation such as forests. This study uses remote sensing ocean color products to evaluate the impact on reducing marine pollution of the Global Environment Facility’s (GEF) portfolio of projects in the Yellow Sea Large Marine Ecosystem. Chlorophyll concentration was derived from satellite images over a time series from the 1990s, when GEF projects began, until the present. Results show a 50% increase in chlorophyll until 2011 followed by a 34% decrease until 2019, showing a potential delayed effect of pollution control efforts. The rich time series data is a major advantage to using geospatial analysis for evaluating the impacts of environmental interventions on marine pollution. However, one drawback to the method is that it provides insights into correlations but cannot attribute the results to any particular cause, such as GEF interventions.

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The Lagrangian-based Floating Macroalgal Growth and Drift Model (FMGDM v1.0): application to the Yellow Sea green tide

Geoscientific Model Development ◽

10.5194/gmd-14-6049-2021 ◽

2021 ◽

Vol 14 (10) ◽

pp. 6049-6070

Author(s):

Fucang Zhou ◽

Jianzhong Ge ◽

Dongyan Liu ◽

Pingxing Ding ◽

Changsheng Chen ◽

...

Keyword(s):

Remote Sensing ◽

Yellow Sea ◽

Surface Wind ◽

Late Summer ◽

Ocean Model ◽

The Yellow Sea ◽

Green Tide ◽

Observation Data ◽

Macroalgal Blooms ◽

Drift Model

Abstract. Massive floating macroalgal blooms in the ocean result in many ecological consequences. Tracking their drifting pattern and predicting their biomass are essential for effective marine management. In this study, a physical–ecological model, the Floating Macroalgal Growth and Drift Model (FMGDM), was developed. Based on the tracking, replication, and extinction of Lagrangian particles, FMGDM is capable of determining the dynamic growth and drift pattern of floating macroalgae, with the position, velocity, quantity, and represented biomass of particles being updated synchronously between the tracking and the ecological modules. The particle tracking is driven by ocean flows and sea surface wind, and the ecological process is controlled by the temperature, irradiation, and nutrients. The flow and turbulence fields were provided by the unstructured grid Finite-Volume Community Ocean Model (FVCOM), and biological parameters were specified based on a culture experiment of Ulva prolifera, a phytoplankton species causing the largest worldwide bloom of green tide in the Yellow Sea, China. The FMGDM was applied to simulate the green tide around the Yellow Sea in 2014 and 2015. The model results, e.g., the distribution, and biomass of the green tide, were validated using the remote-sensing observation data. Given the prescribed spatial initialization from remote-sensing observations, the model was robust enough to reproduce the spatial and temporal developments of the green tide bloom and its extinction from early spring to late summer, with an accurate prediction for 7–8 d. With the support of the hydrodynamic model and biological macroalgae data, FMGDM can serve as a model tool to forecast floating macroalgal blooms in other regions.

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