Finer Resolution Land-Cover Mapping Using Multiple Classifiers and Multisource Remotely Sensed Data in the Heihe River Basin
文献类型：期刊论文
| 作者 | Zhong, Bo1; Yang, Aixia1; Nie, Aihua1; Yao, Yanjuan1; Zhang, Hang1; Wu, Shanlong1; Liu, Qinhuo1 |
| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
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| 出版日期 | 2015 |
| 卷号 | 8期号:10(SI)页码:1007-1022 |
| 关键词 | Crop classification HJ-1/CCD land cover multiple classifiers multiple scales multisource remotely sensed data phenology river basin time-series analysis |
| 通讯作者 | Zhong, B (reprint author), Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China. |
| 英文摘要 | Land-cover datasets are crucial for research on eco-hydrological processes and earth system modeling. Many land-cover datasets have been derived from remote-sensing data. However, their spatial resolutions are usually low and their classification accuracy is not high enough, which are not well suited to the needs of land surface modeling. Consequently, a comprehensive method for monthly land-cover classification in the Heihe river basin (HRB) with high spatial resolution is developed. Moreover, the major crops in the HRB are also distinguished. The proposed method integrates multiple classifiers and multisource data. Three types of data including MODIS, HJ-1/CCD, and Landsat/TM and Google Earth images are used. Compared to single classifier, multiple classifiers including thresholding, support vector machine (SVM), object-based method, and time-series analysis are integrated to improve the accuracy of classification. All the data and classifiers are organized using a decision tree. Monthly land-cover maps of the HRB in 2013 with 30-m spatial resolution are made. A comprehensive validation shows great improvement in the accuracy. First, a visual comparison of the land-cover maps using the proposed method and standard SVM method shows the classification differences and the advantages of the proposed method. The confusion matrix is used to evaluate the classification accuracy, showing an overall classification accuracy of over 90% in the HRB, which is quite higher than previous approaches. Furthermore, a ground campaign was performed to evaluate the accuracy of crop classification and an overall accuracy of 84.09% for the crop classification was achieved. |
| 研究领域[WOS] | Engineering, Electrical & Electronic ; Geography, Physical ; Remote Sensing ; Imaging Science & Photographic Technology |
| 收录类别 | SCI ; EI |
| 语种 | 英语 |
| WOS记录号 | WOS:000368904000034 |
| 源URL | [http://ir.ceode.ac.cn/handle/183411/38088]  |
| 专题 | 遥感与数字地球研究所_SCI/EI期刊论文_期刊论文 |
| 作者单位 | 1.[Zhong, Bo 2.Liu, Qinhuo] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China 3.[Zhong, Bo 4.Liu, Qinhuo] Joint Ctr Global Change Studies, Beijing 100875, Peoples R China 5.[Yang, Aixia 6.Nie, Aihua 7.Zhang, Hang 8.Wu, Shanlong] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China 9.[Yang, Aixia] Univ Chinese Acad Sci, Beijing 100049, Peoples R China 10.[Yao, Yanjuan] Minist Environm Protect MEP China, SEC, Beijing 100094, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhong, Bo,Yang, Aixia,Nie, Aihua,et al. Finer Resolution Land-Cover Mapping Using Multiple Classifiers and Multisource Remotely Sensed Data in the Heihe River Basin[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2015,8(10(SI)):1007-1022. |
| APA | Zhong, Bo.,Yang, Aixia.,Nie, Aihua.,Yao, Yanjuan.,Zhang, Hang.,...&Liu, Qinhuo.(2015).Finer Resolution Land-Cover Mapping Using Multiple Classifiers and Multisource Remotely Sensed Data in the Heihe River Basin.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,8(10(SI)),1007-1022. |
| MLA | Zhong, Bo,et al."Finer Resolution Land-Cover Mapping Using Multiple Classifiers and Multisource Remotely Sensed Data in the Heihe River Basin".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 8.10(SI)(2015):1007-1022. |
入库方式： OAI收割
来源：遥感与数字地球研究所
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