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

Author:

Zhang Baowei1ORCID,Guo Jianzhong2,Li Ziwei3ORCID,Cheng Yi1,Zhao Yao1,Boota Muhammad Waseem34ORCID,Zhang Yaonan5,Feng Liqiang6

Affiliation:

1. PLA Strategic Support Force Information Engineering University, No. 62, Science Avenue, Zhengzhou, Henan Province 450001, China

2. College of Environment and Planning, Henan University, North Section of Jinming Avenue, Longting District, Kaifeng, Henan Province 475004, China

3. School of Water Conservancy Science and Engineering, Zhengzhou University, No. 100, Science Avenue, Zhengzhou, Henan Province 450001, China

4. Mott MacDonald MM Pakistan (Pvt.) Ltd, Lahore, Pakistan

5. Northwest Institute of Eco-Environment and Resources, CAS, No. 320, Donggang West Road, Lanzhou, Gansu Province 730000, China

6. Center for Ocean Mega-Science, Chinese Academy of Sciences, No. 7, Nanhai Road, Qingdao, Shandong Province 266071, China

Abstract

Abstract 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.

Funder

the project of Major scientific and technological innovation in Shandong Province

Publisher

IWA Publishing

Subject

Management, Monitoring, Policy and Law,Atmospheric Science,Water Science and Technology,Global and Planetary Change

Reference51 articles.

1. Spatiotemporal patterns and morphological characteristics of Ulva prolifera distribution in the Yellow Sea, China in 2016–2018;Journal of Remote Sensing,2019

2. The spatial and temporal distribution of floating green algae in the Subei Shoal in 2018 retrieved by Sentinel-2 images;Acta Oceanologica Sinica,2020

3. Remote-sensing monitoring of green tide and its drifting trajectories in Yellow Sea based on observation data of geostationary ocean color imager;Journal of Acta Optica Sinica,2020

4. Satellite monitoring of massive green macroalgae bloom (GMB): imaging ability comparison of multi-source data and drifting velocity estimation;Journal of Remote Sensing,2012

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