NAOC Open IR
Land-Cover Classification of Coastal Wetlands Using the RF Algorithm for Worldview-2 and Landsat 8 Images
Wang, Xiaoxue1; Gao, Xiangwei1; Zhang, Yuanzhi2,3; Fei, Xianyun1; Chen, Zhou1; Wang, Jian1; Zhang, Yayi1; Lu, Xia1; Zhao, Huimin1
2019-08-01
Source PublicationREMOTE SENSING
Volume11Issue:16Pages:22
AbstractWetlands are one of the world's most important ecosystems, playing an important role in regulating climate and protecting the environment. However, human activities have changed the land cover of wetlands, leading to direct destruction of the environment. If wetlands are to be protected, their land cover must be classified and changes to it monitored using remote sensing technology. The random forest (RF) machine learning algorithm, which offers clear advantages (e.g., processing feature data without feature selection and preferable classification result) for high spatial image classification, has been used in many study areas. In this research, to verify the effectiveness of this algorithm for remote sensing image classification of coastal wetlands, two types of spatial resolution images of the Linhong Estuary wetland in Lianyungang-Worldview-2 and Landsat-8 images-were used for land cover classification using the RF method. To demonstrate the preferable classification accuracy of the RF algorithm, the support vector machine (SVM) and k-nearest neighbor (k-NN) methods were also used to classify the same area of land cover for comparison with the results of RF classification. The study results showed that (1) the overall accuracy of the RF method reached 91.86%, higher than the SVM and k-NN methods by 4.68% and 4.72%, respectively, for Worldview-2 images; (2) at the same time, the classification accuracies of RF, SVM, and k-NN were 86.61%, 79.96%, and 77.23%, respectively, for Landsat-8 images; (3) for some land cover types having only a small number of samples, the RF algorithm also achieved better classification results using Worldview-2 and Landsat-8 images, and (4) the addition texture features could improve the classification accuracy of the RF method when using Worldview-2 images. Research indicated that high-resolution remote sensing images are more suitable for small-scale land cover classification image and that the RF algorithm can provide better classification accuracy and is more suitable for coastal wetland classification than the SVM and k-NN algorithms are.
Keywordcoastal wetland classification RF algorithm
Funding OrganizationNational Key Research and Development Program of China ; National Key Research and Development Program of China ; Natural Science Foundation of China (NSFC) ; Natural Science Foundation of China (NSFC) ; Lianyungang Land and Resources Project ; Lianyungang Land and Resources Project ; Lianyungang Science and Technology Bureau Project ; Lianyungang Science and Technology Bureau Project ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) ; Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (TAPP) ; Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (TAPP) ; Jiangsu Postgraduate Innovation Program ; Jiangsu Postgraduate Innovation Program ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; Natural Science Foundation of China (NSFC) ; Natural Science Foundation of China (NSFC) ; Lianyungang Land and Resources Project ; Lianyungang Land and Resources Project ; Lianyungang Science and Technology Bureau Project ; Lianyungang Science and Technology Bureau Project ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) ; Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (TAPP) ; Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (TAPP) ; Jiangsu Postgraduate Innovation Program ; Jiangsu Postgraduate Innovation Program ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; Natural Science Foundation of China (NSFC) ; Natural Science Foundation of China (NSFC) ; Lianyungang Land and Resources Project ; Lianyungang Land and Resources Project ; Lianyungang Science and Technology Bureau Project ; Lianyungang Science and Technology Bureau Project ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) ; Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (TAPP) ; Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (TAPP) ; Jiangsu Postgraduate Innovation Program ; Jiangsu Postgraduate Innovation Program ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; Natural Science Foundation of China (NSFC) ; Natural Science Foundation of China (NSFC) ; Lianyungang Land and Resources Project ; Lianyungang Land and Resources Project ; Lianyungang Science and Technology Bureau Project ; Lianyungang Science and Technology Bureau Project ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) ; Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (TAPP) ; Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (TAPP) ; Jiangsu Postgraduate Innovation Program ; Jiangsu Postgraduate Innovation Program
DOI10.3390/rs11161927
WOS KeywordVEGETATION CLASSIFICATION ; REMOTE ; WATER ; TM ; MULTIRESOLUTION ; ACCURACY ; FEATURES
Language英语
Funding ProjectNational Key Research and Development Program of China[2016YFB0501501] ; Natural Science Foundation of China (NSFC)[31270745] ; Natural Science Foundation of China (NSFC)[41506106] ; Lianyungang Land and Resources Project[LYGCHKY201701] ; Lianyungang Science and Technology Bureau Project[SH1629] ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) ; Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (TAPP) ; Jiangsu Postgraduate Innovation Program[5508201601] ; Jiangsu Postgraduate Innovation Program[SY201808X]
Funding OrganizationNational Key Research and Development Program of China ; National Key Research and Development Program of China ; Natural Science Foundation of China (NSFC) ; Natural Science Foundation of China (NSFC) ; Lianyungang Land and Resources Project ; Lianyungang Land and Resources Project ; Lianyungang Science and Technology Bureau Project ; Lianyungang Science and Technology Bureau Project ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) ; Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (TAPP) ; Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (TAPP) ; Jiangsu Postgraduate Innovation Program ; Jiangsu Postgraduate Innovation Program ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; Natural Science Foundation of China (NSFC) ; Natural Science Foundation of China (NSFC) ; Lianyungang Land and Resources Project ; Lianyungang Land and Resources Project ; Lianyungang Science and Technology Bureau Project ; Lianyungang Science and Technology Bureau Project ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) ; Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (TAPP) ; Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (TAPP) ; Jiangsu Postgraduate Innovation Program ; Jiangsu Postgraduate Innovation Program ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; Natural Science Foundation of China (NSFC) ; Natural Science Foundation of China (NSFC) ; Lianyungang Land and Resources Project ; Lianyungang Land and Resources Project ; Lianyungang Science and Technology Bureau Project ; Lianyungang Science and Technology Bureau Project ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) ; Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (TAPP) ; Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (TAPP) ; Jiangsu Postgraduate Innovation Program ; Jiangsu Postgraduate Innovation Program ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; Natural Science Foundation of China (NSFC) ; Natural Science Foundation of China (NSFC) ; Lianyungang Land and Resources Project ; Lianyungang Land and Resources Project ; Lianyungang Science and Technology Bureau Project ; Lianyungang Science and Technology Bureau Project ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) ; Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (TAPP) ; Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (TAPP) ; Jiangsu Postgraduate Innovation Program ; Jiangsu Postgraduate Innovation Program
WOS Research AreaRemote Sensing
WOS SubjectRemote Sensing
WOS IDWOS:000484387600090
PublisherMDPI
Citation statistics
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/27648
Collection中国科学院国家天文台
Corresponding AuthorGao, Xiangwei
Affiliation1.Jiangsu Ocean Univ, Sch Geomat & Marine Informat, Lianyungang 222002, Peoples R China
2.Univ Chinese Acad Sci, Sch Astron & Space Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Natl Astron Observ, Key Lab Lunar Sci & Deep Space Explorat, Beijing 100101, Peoples R China
Recommended Citation
GB/T 7714
Wang, Xiaoxue,Gao, Xiangwei,Zhang, Yuanzhi,et al. Land-Cover Classification of Coastal Wetlands Using the RF Algorithm for Worldview-2 and Landsat 8 Images[J]. REMOTE SENSING,2019,11(16):22.
APA Wang, Xiaoxue.,Gao, Xiangwei.,Zhang, Yuanzhi.,Fei, Xianyun.,Chen, Zhou.,...&Zhao, Huimin.(2019).Land-Cover Classification of Coastal Wetlands Using the RF Algorithm for Worldview-2 and Landsat 8 Images.REMOTE SENSING,11(16),22.
MLA Wang, Xiaoxue,et al."Land-Cover Classification of Coastal Wetlands Using the RF Algorithm for Worldview-2 and Landsat 8 Images".REMOTE SENSING 11.16(2019):22.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Wang, Xiaoxue]'s Articles
[Gao, Xiangwei]'s Articles
[Zhang, Yuanzhi]'s Articles
Baidu academic
Similar articles in Baidu academic
[Wang, Xiaoxue]'s Articles
[Gao, Xiangwei]'s Articles
[Zhang, Yuanzhi]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wang, Xiaoxue]'s Articles
[Gao, Xiangwei]'s Articles
[Zhang, Yuanzhi]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.