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  4. 中科院海岸带环境过程与生态修复重点实验室
Mapping Ulva prolifera green tides from space: A revisit on algorithm design and data products

文献类型：期刊论文

作者Hu, Chuanmin1; Qi, Lin2,3; Hu, Lianbo4; Cui, Tingwei5; Xing, Qianguo6; He, Mingxia4; Wang, Ning7; Xiao, Yanfang8; Sun, Deyong9; Lu, Yingcheng10
刊名INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
出版日期2023-02-01
卷号116页码:18
关键词Ulva prolifera Coverage Biomass Remote sensing MODIS VIIRS OLCI GOCI MSI OLI GaoFen CZI FAI AFAI NDVI DVI VB-FAH EVI
ISSN号1569-8432
DOI10.1016/j.jag.2022.103173
英文摘要Since the first report in 2008, macroalgal blooms of Ulva prolifera (often called green tides) in the Yellow Sea have occurred every year, with their origins, transport pathways, temporal changes, as well as causes and consequences studied extensively. Of these studies, satellite remote sensing has been used widely to detect the bloom presence and quantify the bloom size (i.e., U. prolifera coverage in km2 or biomass in kilotons). However, substantial variability has been found in the refereed literature in the remote sensing methodology, results, and interpretation of the U. prolifera coverage, especially in the attempts to study inter-annual changes or long-term trends. There are often inconsistent or contradicting results even from the same satellite sensor. Such in-consistencies or contradictions create difficulty not only within the remote sensing community when presenting new methodology or results, but also to researchers when attempting to use the remote sensing results to make predictions or perform impact assessments. Here, we review the literature on the remote sensing methodology to detect and quantify U. prolifera blooms, and make recommendations based on physical principles. Specifically, we propose the following conceptual guidelines: 1) a reliable index or algorithm should be relatively tolerant to perturbations by non-optimal observing conditions (thick aerosols, thin clouds, moderate sun glint, cloud-adjacent straylight, which can all be found frequently in the study region) for presence/absence detection, as well as to small errors in the selected thresholds to quantify U. prolifera; 2) a reliable index or algorithm should also make it relatively easy to account for variability in subpixel coverage of U. prolifera (i.e., through pixel unmixing) in order to obtain an accurate estimate of total U. prolifera coverage from an image; 3) a reliable data product (i.e., U. prolifera maps) should be able to account for the variable clouds when interpreting spatial patterns or temporal changes, with uncertainty estimates provided whenever possible; and 4) both the algorithm and the data product should minimize manual work in order to make them more objective and repeatable by other researchers. Finally, we show different types of time series of U. prolifera amounts in the Yellow Sea using
WOS关键词SOUTHERN YELLOW SEA ; LARGEST MACROALGAL BLOOM ; FLOATING-MACROALGAE ; INTERANNUAL VARIABILITY ; SEAWEED AQUACULTURE ; REMOTE ESTIMATION ; ALGAE BLOOMS ; COVERAGE ; EXPANSION ; COASTAL
WOS研究方向Remote Sensing
语种英语
WOS记录号WOS:000918428300001
源URL[http://ir.yic.ac.cn/handle/133337/37038]  
专题烟台海岸带研究所_中科院海岸带环境过程与生态修复重点实验室
烟台海岸带研究所_海岸带信息集成与综合管理实验室
通讯作者Hu, Chuanmin
作者单位1.Univ S Florida, Coll Marine Sci, St Petersburg, FL 33620 USA
2.NOAA Ctr Satellite Applicat & Res, College Pk, MD USA
3.Global Sci & Technol Inc, Greenbelt, MD USA
4.Ocean Univ China, Ocean Remote Sensing Inst, Qingdao, Shandong, Peoples R China
5.Sun Yat Sen Univ, Sch Atmospher Sci, Key Lab Trop Atmosphere Ocean Syst, Southern Marine Sci & Engn Guangdong Lab, Zhuhai, Guangdong, Peoples R China
6.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Yantai, Shandong, Peoples R China
7.North China Sea Marine Forecasting Ctr State Ocean, Qingdao, Shandong, Peoples R China
8.Minist Nat Resources, Inst Oceanog 1, Qingdao, Shandong, Peoples R China
9.Nanjing Univ Informat Sci & Technol, Sch Marine Sci, Nanjing, Peoples R China
10.Nanjing Univ, Int Inst Earth Syst Sci, Nanjing, Peoples R China
推荐引用方式
GB/T 7714		 Hu, Chuanmin,Qi, Lin,Hu, Lianbo,et al. Mapping Ulva prolifera green tides from space: A revisit on algorithm design and data products[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2023,116:18.
APA Hu, Chuanmin.,Qi, Lin.,Hu, Lianbo.,Cui, Tingwei.,Xing, Qianguo.,...&Wang, Menghua.(2023).Mapping Ulva prolifera green tides from space: A revisit on algorithm design and data products.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,116,18.
MLA Hu, Chuanmin,et al."Mapping Ulva prolifera green tides from space: A revisit on algorithm design and data products".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 116(2023):18.

入库方式： OAI收割

来源：烟台海岸带研究所

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