The first all-season sample set for mapping global land cover with Landsat-8 data

查看全文 作　　者：Congcong [1,2]Li;Peng [1,3]Gong;Jie [4]Wang;Zhiliang [5]Zhu;Gregory S. [2]Biging;Cui [4]Yuan;Tengyun [4]Hu;Haiying [4]Zhang;Qi [1]Wang;Xuecao [1]Li;Xiaoxuan [1]Liu;Yidi [1]Xu;Jing [1]Guo;Caixia [4]Liu;Kwame O. [1]Hackman;Meinan [1]Zhang;Yuqi [6]Cheng;Le [1]Yu;Jun [1]Yang;Huabing [4]Huang;Nicholas [7]Clinton

机构地区：[1]Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China;[2]Department of Environmental Science, Policy and Management, University of California, Berkeley. CA 94720-3J 14, USA;[3]Joint Center for Global Change Studies, Beijing 100875, China;[4]State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;[5]United States Geological Survey, Restan, VA 20192, USA;[6]Ministry of Education Key Laboratory for Geospatial Technology for the Middle and Lower Yellow River Regions, College of Environment and Planning, Henan University Kaifeng 475004, China;[7]Googie, Inc., 1600 Amphitheatre Pkwy., Mountain View, CA 94042,, USA

出　　处：《Science Bulletin》索引2017年第62卷第7期,共8页

基　　金：partially supported by the National High Technology Program(2013AA122804);the Special Fund for Meteorology Scientific Research in the Public Welfare(GYHY201506023)of China;Open Fund of State Key Laboratory of Remote Sensing Science(OFSLRSS201514) 摘　　要：We report the world's first all-season training and validation sample sets for global land cover classification with Landsat-8 data.Prior to this,such samples were only available at a single date primarily from the growing season.It is unknown how much limitation such a single-date sample has to mapping global land cover in other seasons of the year.To answer this question,we selected available Landsat-8 images from four seasons and collected training and validation samples from them.We compared the performances of training samples in different seasons using Random Forest algorithm.We found that the use of training samples from any individual season would result in the best overall classification accuracy when validated by samples in the same season.The global overall accuracy from combined best seasonal results was 67.2% when classifying the 11 Level-1 classes in the Finer Resolution Observation and Monitoring of Global Land Cover(FROM-GLC) classification system.The use of training samples from all seasons(named all-season training sample set hereafter) produced an overall accuracy of 67.0%.We also tested classification within 10° latitude 60° longitude zones using all-season training subsample within each zone and obtained an overall accuracy of 70.2%.This indicates that properly grouped subsamples in space can help improve classification accuracies.All the results in this study seem to suggest that it is possible to use an all-season training sample set to reach global optimality with universal applicability in classifying images acquired at any time of a year for global land cover mapping. 关键词：土地覆盖分类检验样本数据映射训练样本集生长季节精度验证性能比较分类系统

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The first all-season sample set for mapping global land cover with Landsat-8 data

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