A multi-module with a two-way feedback method for <i>Ulva</i> drift-diffusion

Hui Sheng; Jianmeng Li; Qimao Wang; Bin Zou; Lijian Shi; Mingming Xu; Shanwei Liu; Jianhua Wan; Zhe Zeng; Yanlong Chen

doi:10.1007/s13131-023-2165-y

Volume 42 Issue 12

Dec. 2023

Turn off MathJax

Article Contents

Article Navigation > Acta Oceanologica Sinica > 2023 > 42(12): 118-134

Hui Sheng, Jianmeng Li, Qimao Wang, Bin Zou, Lijian Shi, Mingming Xu, Shanwei Liu, Jianhua Wan, Zhe Zeng, Yanlong Chen. A multi-module with a two-way feedback method for Ulva drift-diffusion[J]. Acta Oceanologica Sinica, 2023, 42(12): 118-134. doi: 10.1007/s13131-023-2165-y

Citation:

Hui Sheng, Jianmeng Li, Qimao Wang, Bin Zou, Lijian Shi, Mingming Xu, Shanwei Liu, Jianhua Wan, Zhe Zeng, Yanlong Chen. A multi-module with a two-way feedback method for Ulva drift-diffusion[J]. Acta Oceanologica Sinica, 2023, 42(12): 118-134. doi: 10.1007/s13131-023-2165-y

Citation:

PDF( 5923 KB)

A multi-module with a two-way feedback method for Ulva drift-diffusion

doi: 10.1007/s13131-023-2165-y

1.
College of Oceanography Space Informatics, China University of Petroleum Huadong, Qingdao 266580, China
2.
National Satellite Ocean Application Service, Beijing 100081, China
3.
National Marine Environmental Monitoring Center, Dalian 116023, China

Funds: The Shandong Provincial Natural Science Foundation of China under contract No. ZR2019MD023; the National Natural Science Foundation of China under contract No. 41776182.

More Information

Corresponding author: shilj@mail.nsoas.org.cn; xumingming@upc.edu.cn
Received Date: 2022-10-18
Accepted Date: 2022-12-12

Available Online: 2023-12-25

Publish Date: 2023-12-01

Abstract

Abstract

The outbreak of Ulva in the Yellow Sea has seriously affected marine ecology and economic activities. Therefore, effective prediction of the distribution of Ulva is of great significance for disaster prevention and reduction. However, the prediction method of Ulva is mainly based on numerical simulation. There are two problems with these methods. First is that the initial distribution of Ulva is simulated using independent pixel-level particles. Besides, the influence of Ulva growth and diffusion on the drift is not considered. Therefore, this paper proposes a multi-module with a two-way feedback method (MTF) to solve the above problems. The main contributions of our approach are summarized as follows. First, the initialization module, the generation and elimination module, and the drive module are composed in our way. Second, we proposed an initialization method using rectangle objects to simulate the Ulva distribution extracted from remote sensing images. Thirdly, the drift and diffusion mechanism of the Ulva is considered to realize the two-way feedback between the generation and elimination module and the drive module. The results of our experiments show that the MTF performs better than the traditional method in predicting the drift and diffusion of Ulva. The code is already publicly available at https://github.com/UPCGIT/A-multi-module-with-a-two-way-feedback-method-for-Ulva-drift-diffusion.
- Ulva,
- remote sensing,
- drift,
- diffusion,
- feedback

FullText(HTML)

References(17)

References

Ahn J H, Park Y J, Ryu J H, et al. 2012. Development of atmospheric correction algorithm for Geostationary Ocean Color Imager (GOCI). Ocean Science Journal, 47(3): 247–259. doi: 10.1007/s12601-012-0026-2

An Deyu, Yu Dingfeng, Zheng Xiangyang, et al. 2021. Monitoring the dissipation of the floating green macroalgae blooms in the Yellow Sea (2007–2020) on the basis of satellite remote sensing. Remote Sensing, 13(19): 3811. doi: 10.3390/rs13193811

Bao Min, Guan Weibing, Yang Yang, et al. 2015. Drifting trajectories of green algae in the western Yellow Sea during the spring and summer of 2012. Estuarine, Coastal and Shelf Science, 163: 9–16

Cao Hongye, Han Ling. 2020. Drift path of green tide and the impact of typhoon “Chan-hom” in the Chinese Yellow Sea based on GOCI images in 2015. Ecological Informatics, 60: 101156. doi: 10.1016/j.ecoinf.2020.101156

Chassignet E P, Hurlburt H E, Smedstad O M, et al. 2007. The HYCOM (HYbrid Coordinate Ocean Model) data assimilative system. Journal of Marine Systems, 65(1–4): 60–83. doi: 10.1016/j.jmarsys.2005.09.016

He Enye, Ji Xuanliang, Gao Shan, et al. 2021. Numerical simulation and forecasting of drift, growth, and death of Ulva in the Yellow Sea. Oceanologia et Limnologia Sinica (in Chinese), 52(1): 39–50

Keesing J K, Liu Dongyan, Fearns P, et al. 2011. Inter- and intra-annual patterns of Ulva prolifera green tides in the Yellow Sea during 2007–2009, their origin and relationship to the expansion of coastal seaweed aquaculture in China. Marine Pollution Bulletin, 62(6): 1169–1182. doi: 10.1016/j.marpolbul.2011.03.040

Lou Xiulin, Hu Chuanmin. 2014. Diurnal changes of a harmful algal bloom in the East China Sea: Observations from GOCI. Remote Sensing of Environment, 140: 562–572. doi: 10.1016/j.rse.2013.09.031

Qiao Fangli, Dai Dejun, Simpson J, et al. 2009. Banded structure of drifting macroalgae. Marine Pollution Bulletin, 58(12): 1792–1795. doi: 10.1016/j.marpolbul.2009.08.006

Son Y B, Choi B J, Kim Y H, et al. 2015. Tracing floating green algae blooms in the Yellow Sea and the East China Sea using GOCI satellite data and Lagrangian transport simulations. Remote Sensing of Environment, 156: 21–33. doi: 10.1016/j.rse.2014.09.024

Son Y B, Min J E, Ryu J H. 2012a. Detecting massive green algae ( Ulva prolifera) blooms in the Yellow Sea and East China Sea using Geostationary Ocean Color Imager (GOCI) data. Ocean Science Journal, 47(3): 359–375. doi: 10.1007/s12601-012-0034-2

Sun Ke, Sun Junchuan, Liu Qing, et al. 2020. A numerical study of the Ulva prolifera biomass during the green tides in China—toward a cleaner Porphyra mariculture. Marine Pollution Bulletin, 161: 111805. doi: 10.1016/j.marpolbul.2020.111805

Taylor R, Fletcher R L, Raven J A. 2001. Preliminary studies on the growth of selected ‘green tide’ algae in laboratory culture: effects of irradiance, temperature, salinity and nutrients on growth rate. Botanica Marina, 44: 327–336

Xing Qianguo, Hu Chuanmin. 2016. Mapping macroalgal blooms in the Yellow Sea and East China Sea using HJ-1 and Landsat data: Application of a virtual baseline reflectance height technique. Remote Sensing of Environment, 178: 113–126. doi: 10.1016/j.rse.2016.02.065

Xu Qing, Zhang Hongyuan, Cheng Yongcun, et al. 2016. Monitoring and tracking the green tide in the Yellow Sea with satellite imagery and trajectory model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(11): 5172–5181. doi: 10.1109/JSTARS.2016.2580000

Zhang Xiaowen, Wang Hongxia, Mao Yuze, et al. 2010. Somatic cells serve as a potential propagule bank of Ulva prolifera forming a green tide in the Yellow Sea, China. Journal of Applied Phycology, 22(2): 173–180. doi: 10.1007/s10811-009-9437-6

Zheng Xiangyang, Xing Qianguo, Li Li, et al. 2011. Numerical simulation of the 2008 green tide in the Yellow Sea. Marine Science (in Chinese), 35(7): 82–87

Relative Articles

Supplements(0)

Cited By

Proportional views