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Global mapping of the landside clustering of aquaculture ponds from dense time-series 10 m Sentinel-2 images on Google Earth Engine

文献类型：期刊论文

作者Wang, Zhihua1,2; Zhang, Junyao1,2; Yang, Xiaomei1,2,3; Huang, Chong1,2; Su, Fenzhen1,2; Liu, Xiaoliang1,2; Liu, Yueming1,2; Zhang, Yuanzhi4,5
刊名INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
出版日期2022-12-01
卷号115页码:12
ISSN号1569-8432
关键词Aquaculture Google Earth Engine Sentinel-2 Image Time Series Edge Detection Morphology
DOI10.1016/j.jag.2022.103100
通讯作者Yang, Xiaomei(yangxm@lreis.ac.cn) ; Zhang, Yuanzhi(yuanzhizhang@cuhk.edu.hk)
英文摘要Spatial distribution information offers a valuable resource for the growing research on evaluating and managing aquaculture and the mechanism of its interaction with the environment, especially concerning landside clustering aquaculture ponds (LCAP). However, studies aimed at obtaining data on global LCAP distribution remains limited. This study combined edge detection and the morphology implemented on Google Earth Engine (GEE) platform to produce the first global spatial distribution data of LCAP using 4,015,054 tiles of the 10-m Sentinel-2 time-series images collected throughout 2020. The study findings showed a total area of global LCAP of 55,337.03 km2. Asia, led by China, had the highest distribution area of LCAP, accounting for 89.12 % of global LCAP. The main LCAP distribution area was the landside 30-km buffer region of coastline, accounting for 75.57 % of global LCAP. Meanwhile, the greatest proportion of LCAP (69.63 %) was located in China. Accuracy verification revealed that the precision and recall error of the results were 83.91 % and 92.49 % respectively, and F1 score was 0.88. A comparison of China, Vietnam, and India, based on data drawn from most of the existing local studies that used remote sensing methods, showed that these countries' aquaculture area differs by less than 10 %. Despite some variation from FAO statistics or official national statistics, the results of our remote sensingbased method show promise in such aspects as global coverage and temporal coherency when compared with the classical statistical method.
WOS关键词WATER ; GIS
资助项目Earth Big Data Science Project of CAS[XDA19060303] ; National Natural Science Foundation of China[41901354] ; National Natural Science Foundation of China[41890854] ; National Natural Science Foundation of China[U1901215] ; Innovation Project of LREIS[O88RAA01YA]
WOS研究方向Remote Sensing
语种英语
出版者ELSEVIER
WOS记录号WOS:000891776900001
资助机构Earth Big Data Science Project of CAS ; National Natural Science Foundation of China ; Innovation Project of LREIS
源URL[http://ir.igsnrr.ac.cn/handle/311030/187861]  
专题中国科学院地理科学与资源研究所
通讯作者Yang, Xiaomei; Zhang, Yuanzhi
作者单位1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
3.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing 210023, Peoples R China
4.Nanjing Univ Informat Sci & Technol, Sch Marine Sci, Nanjing 210044, Peoples R China
5.Chinese Univ Hong Kong, Dept Geog & Resource Management, Shatin, Hong Kong, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Zhihua,Zhang, Junyao,Yang, Xiaomei,et al. Global mapping of the landside clustering of aquaculture ponds from dense time-series 10 m Sentinel-2 images on Google Earth Engine[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2022,115:12.
APA Wang, Zhihua.,Zhang, Junyao.,Yang, Xiaomei.,Huang, Chong.,Su, Fenzhen.,...&Zhang, Yuanzhi.(2022).Global mapping of the landside clustering of aquaculture ponds from dense time-series 10 m Sentinel-2 images on Google Earth Engine.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,115,12.
MLA Wang, Zhihua,et al."Global mapping of the landside clustering of aquaculture ponds from dense time-series 10 m Sentinel-2 images on Google Earth Engine".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 115(2022):12.

入库方式： OAI收割

来源：地理科学与资源研究所

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