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A 30 meter land cover mapping of China with an efficient clustering algorithm CBEST |
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| Authors: | LuanYun Hu YanLei Chen Yue Xu YuanYuan Zhao Le Yu Jie Wang Peng Gong |
| Institution: | 1. Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing, 100084, China 2. Department of Environmental Science, Policy and Management, University of California, Berkeley, CA, 94720-3114, USA 3. College of Global Change and Earth System Science, Beijing Normal University, Beijing, 100875, China 4. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, and Beijing Normal University, Beijing, 100101, China 5. Joint Center for Global Change Studies, Beijing, 100875, China |
| Abstract: | Remote sensing based land cover mapping at large scale is time consuming when using either supervised or unsupervised classification approaches. This article used a fast clustering method—Clustering by Eigen Space Transformation (CBEST) to produce a land cover map for China. Firstly, 508 Landsat TM scenes were collected and processed. Then, TM images were clustered by combining CBEST and K-means in each pre-defined ecological zone (50 in total for China). Finally, the obtained clusters were visually interpreted as land cover types to complete a land cover map. Accuracy evaluation using 2159 test samples indicates an overall accuracy of 71.7% and a Kappa coefficient of 0.64. Comparisons with two global land cover products (i.e., Finer Resolution Observation and Monitoring of Global Land Cover (FROM-GLC) and GlobCover 2009) also indicate that our land cover result using CBEST is superior in both land cover area estimation and visual effect for different land cover types. |
| Keywords: | land cover mapping cluster Landsat TM CBEST |
| 本文献已被 CNKI SpringerLink 等数据库收录！ | |