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遥感技术与应用  2018, Vol. 33 Issue (4): 600-611    DOI: 10.11873/j.issn.1004-0323.2018.4.0600
GEE专栏     
Google Earth Engine在地球科学与环境科学中的应用研究进展
郝斌飞1,2,韩旭军1,2,马明国1,2,刘一韬3,李世卫4
(1.西南大学遥感大数据应用重庆市工程研究中心,重庆 400715;
2.西南大学岩溶环境重庆市重点实验室,重庆 400715;
3.成都市国土资源信息中心成都市国土资源局,四川 成都 610042;
4.尚正(北京)信息技术有限公司,北京 100086 )
Research Progress on the Application of Google Earth Engine in Geoscience and Environmental Sciences
Hao Binfei1,2,Han Xujun1,2,Ma Mingguo1,2,Liu Yitao3,Li Shiwei4
(1.Chongqing Engineering Research Center for Remote Sensing Big Data Application,
School of Geographical Sciences,Southwest University,Chongqing 400715,China;
2.Chongqing Key Laboratory of Karst Environment,
School of Geographical Sciences,Southwest University,Chongqing 400715,China;
3.Chengdu Land and Resources Information Center,
The Bureau of Land and Resources Chengdu,Chengdu 610042,China;
4.Shang Zheng(Beijing) Information Technology Co.,Ltd,Beijing 100086,China)
 全文: PDF(4393 KB)  
摘要:
21世纪以来,随着全球信息化与工业化的高度集成发展,出现了物联网与云计算,人类进入大数据时代。在地学、环境科学及相关学科领域,海量地理、遥感及社会经济等数据产生,在本地平台存储、管理以及分析数据的传统方式已经较难满足当前需求。Google Earth Engine(GEE)云平台由Google云基建提供,是一个对海量地球科学数据集(尤其是遥感影像数据)进行全球尺度在线处理分析和可视化的云计算平台,它利用谷歌强大的计算能力,可以分析处理多种环境与社会问题,如气候变化、植被退化、粮食安全和水资源短缺等。首先对GEE云平台进行介绍,综述了近年来应用GEE云平台所做的相关研究,然后应用该平台及MODIS土地覆盖类型数据,研究了2002~2013年三峡库区主要土地覆盖类型的时空变化规律。结果表明:以林地、灌丛草地以及耕地变化最为明显。最后,经粗略统计得出GEE云平台无论在成本还是效益方面,其综合效率提升90%以上。GEE云平台不仅可以为地学及遥感领域专家提供强有力的支持,也能为相关学科领域人员进行科学研究提供帮助,是一个高效的科研工具。
关键词: Google Earth Engine(GEE)大数据云计算三峡库区时空变化趋势    
Abstract: With the rapid development and large integration of global informatization and industrialization since the 21st century,the Internet of things and cloud\|computing have emerged.The world has entered an era of big data.There are a huge amount geographical and remote sensing data generated every day in the field of geoscience,environmental science and related disciplines.However,the traditional approaches for storing,managing and analyzing massive data on the local platform,which take up lots of resources,time and energy,have been unable to meet the needs of the current researches.Google Earth Engine(GEE) cloud platform is powered by Google’s cloud infrastructure,and it combines a large number of geospatial datasets and satellite imagery,in which the datasets could be processing,analyzing as well as visualizing on a global scale.Meanwhile,it uses Google’s powerful computational capabilities to analyze and process a variety of environmental and social issues including climate change,vegetation degradation,food security and water resource shortages.Firstly,an introduction of GEE cloud platform has been given.Secondly,recent researches that using GEE cloud platform were reviewed.Thirdly,GEE cloud platform and MODIS land cover type data were used to analyze spatio\|temporal changes patterns of major land use and land cover type in Three Gorges Reservoir in the period of 2002~2013.The results indicate the largest changes occurring in forest lands,shrub grasslands and croplands.Finally,after a rough calculation,GEE cloud platform is superior to the traditional approaches in terms of both cost and economic efficiency,improving the overall efficiency by more than 90%.GEE cloud platform could not only provide powerful support to experts in the field of geosciences and remote sensing,but also offer valuable help to researchers in related disciplines.GEE cloud platform is an excellent tool for scientific research in geosciences,environment sciences and related disciplines.
Key words: Google Earth Engine(GEE);Big data;Cloud computing;Three Gorges Reservoir;Spatio\    temporal Changes trends
收稿日期: 2018-02-25 出版日期: 2018-09-08
:  P237  
基金资助: 国家自然科学基金项目(41771361,41771453),重庆市发改委2017年高技术产业重大产业技术研发项目,西南大学博士基金(含引进人才计划)项目(SWU 117035)。
作者简介: 郝斌飞(1994-),男,山西忻州人,硕士研究生,主要从事遥感大数据分析。Email:hbfnihao@email.swu.edu.cn。
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引用本文:

郝斌飞,韩旭军,马明国,刘一韬,李世卫. Google Earth Engine在地球科学与环境科学中的应用研究进展[J]. 遥感技术与应用, 2018, 33(4): 600-611.

Hao Binfei,Han Xujun,Ma Mingguo,Liu Yitao,Li Shiwei. Research Progress on the Application of Google Earth Engine in Geoscience and Environmental Sciences. Remote Sensing Technology and Application, 2018, 33(4): 600-611.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2018.4.0600        http://www.rsta.ac.cn/CN/Y2018/V33/I4/600

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