{"title":"Framework for predicting the spatial distribution of green algae blooms utilizing historical GOCI and MODIS imagery","authors":"Zhongyuan Wang , Zhixiang Fang , Rui Feng","doi":"10.1016/j.marenvres.2025.107016","DOIUrl":null,"url":null,"abstract":"<div><div>Green algal blooms (GABs) have become a global concern and are particularly serious in the southern Yellow Sea (SYS). Remote sensing (RS) images are the most popular and effective data source for detecting GABs; however, in case of unfavorable weather conditions (e.g., cloud cover), extracting GABs from RS images is impossible. To compensate for the shortage of RS images for GAB detection, this study proposes an effective GAB prediction framework based on cellular automata (CA-GPM) that utilizes historical Geostationary Ocean Color Imager (GOCI) and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. CA-GPM first extracts the GAB coverage and chlorophyll-a (Chl-a) concentration from the last available RS images and establishes a competitive association between GAB events and Chl-a concentration. This study used structural equation model analysis to evaluate the impact weights of all marine environment data (MED) on GAB growth and drift. Eventually, the cellular automata method was used to predict GABs based on the competition pattern, drift pattern, and growth-and-decline rules. Through the comparison of the CA-GPM results with GABs identified in RS images from 2011 to 2022, the precision, missing alarm rate, and false alarm rate were found to be 0.6909 ± 0.5001, 0.2980 ± 0.6975, and 0.3091 ± 0.5001 respectively. The designed CA-GPM can be effectively applied for emergency management of GABs. In addition, based on CA-GPM, this study revealed the competition, physical, and biological factors affecting GABs in the SYS, with the biological and physical conditions being equally important during 2011–2020. Since 2021, the physical conditions have had a negative effect on GABs. In summary, the proposed method can facilitate the detection of GABs when RS images cannot be used.</div></div>","PeriodicalId":18204,"journal":{"name":"Marine environmental research","volume":"205 ","pages":"Article 107016"},"PeriodicalIF":3.0000,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Marine environmental research","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S014111362500073X","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Green algal blooms (GABs) have become a global concern and are particularly serious in the southern Yellow Sea (SYS). Remote sensing (RS) images are the most popular and effective data source for detecting GABs; however, in case of unfavorable weather conditions (e.g., cloud cover), extracting GABs from RS images is impossible. To compensate for the shortage of RS images for GAB detection, this study proposes an effective GAB prediction framework based on cellular automata (CA-GPM) that utilizes historical Geostationary Ocean Color Imager (GOCI) and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. CA-GPM first extracts the GAB coverage and chlorophyll-a (Chl-a) concentration from the last available RS images and establishes a competitive association between GAB events and Chl-a concentration. This study used structural equation model analysis to evaluate the impact weights of all marine environment data (MED) on GAB growth and drift. Eventually, the cellular automata method was used to predict GABs based on the competition pattern, drift pattern, and growth-and-decline rules. Through the comparison of the CA-GPM results with GABs identified in RS images from 2011 to 2022, the precision, missing alarm rate, and false alarm rate were found to be 0.6909 ± 0.5001, 0.2980 ± 0.6975, and 0.3091 ± 0.5001 respectively. The designed CA-GPM can be effectively applied for emergency management of GABs. In addition, based on CA-GPM, this study revealed the competition, physical, and biological factors affecting GABs in the SYS, with the biological and physical conditions being equally important during 2011–2020. Since 2021, the physical conditions have had a negative effect on GABs. In summary, the proposed method can facilitate the detection of GABs when RS images cannot be used.
期刊介绍：
Marine Environmental Research publishes original research papers on chemical, physical, and biological interactions in the oceans and coastal waters. The journal serves as a forum for new information on biology, chemistry, and toxicology and syntheses that advance understanding of marine environmental processes.
Submission of multidisciplinary studies is encouraged. Studies that utilize experimental approaches to clarify the roles of anthropogenic and natural causes of changes in marine ecosystems are especially welcome, as are those studies that represent new developments of a theoretical or conceptual aspect of marine science. All papers published in this journal are reviewed by qualified peers prior to acceptance and publication. Examples of topics considered to be appropriate for the journal include, but are not limited to, the following:
– The extent, persistence, and consequences of change and the recovery from such change in natural marine systems
– The biochemical, physiological, and ecological consequences of contaminants to marine organisms and ecosystems
– The biogeochemistry of naturally occurring and anthropogenic substances
– Models that describe and predict the above processes
– Monitoring studies, to the extent that their results provide new information on functional processes
– Methodological papers describing improved quantitative techniques for the marine sciences.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
