A global land cover map produced through integrating multi-source datasets

查看全文 作　　者：Min [1,2]Feng;Yan [3,4]Bai

机构地区：[1]Center of Three Poles Observation and Big Data,CAS Center for Excellence in Tibetan Plateau Earth Sciences and Institute of Tibetan Plateau Research,Chinese Academy of Sciences,Beijing,China;[2]University of Chinese Academy of Sciences,Beijing,China;[3]State Key Laboratory of Resources and Environmental Information System,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing,China;[4]Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application,Nanjing,China

出　　处：《Big Earth Data》索引2019年第3卷第3期,共29页

基　　金：Funding support for this work were provided by the following programs:the Strategic Priority Research Program of the Chinese Academy of Sciences[Grant No.XDA20100104];the Basic Resources Investigation of Science and Technology[Grant No.2017FY100900];and the National Earth System Science Data Sharing Infrastructure,National Science&Technology Infrastructure of China[Grant No.2005DKA32300]. 摘　　要：In the past decades,global land cover datasets have been produced but also been criticized for their low accuracies,which have been affecting the applications of these datasets.Producing a new global dataset requires a tremendous amount of efforts;however,it is also possible to improve the accuracy of global land cover mapping by fusing the existing datasets.A decision-fuse method was developed based on fuzzy logic to quantify the consistencies and uncertainties of the existing datasets and then aggregated to provide the most certain estimation.The method was applied to produce a 1-km global land cover map(SYNLCover)by integrating five global land cover datasets and three global datasets of tree cover and croplands.Efforts were carried out to assess the quality:1)inter-comparison of the datasets revealed that the SYNLCover dataset had higher consistency than these input global land cover datasets,suggesting that the data fusion method reduced the disagreement among the input datasets;2)quality assessment using the human-interpreted reference dataset reported the highest accuracy in the fused SYNLCover dataset,which had an overall accuracy of 71.1%,in contrast to the overall accuracy between 48.6%and 68.9%for the other global land cover datasets. 关键词：Global land cover data integration accuracy evaluation

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A global land cover map produced through integrating multi-source datasets

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