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Monitoring Spatiotemporal Changes of Impervious Surfaces in Beijing City Using Random Forest Algorithm and Textural Features
Dong, Xuegang1,2; Meng, Zhiguo1,2,3; Wang, Yongzhi1,4; Zhang, Yuanzhi3; Sun, Haoteng5; Wang, Qingshuai1
2021
Source PublicationREMOTE SENSING
Volume13Issue:1Pages:19
AbstractAs the capital city of China, Beijing has experienced unprecedented economic and population growth and dramatic impervious surface changes during the last few decades. An application of the classification method combining the spectral and textural features based on Random Forest was conducted to monitor the spatial and temporal changes of Beijing's impervious surfaces. This classification strategy achieved excellent performance in the impervious surface extraction in complex urban areas, as the Kappa coefficient reached 0.850. Based on this strategy, the impervious surfaces inside Beijing's sixth ring road in 1997, 2002, 2007, 2013, and 2017 were extracted. As the development of Beijing has a special regional feature, the changes of impervious surfaces within the sixth ring road were assessed. The findings are as follows: (1) the textural features can significantly improve the classification accuracy of land cover in urban areas, especially for the impervious surface with high albedo. (2) Impervious surfaces within the sixth ring road expanded dramatically from 1997 to 2017, had three expanding periods: 1997-2002, 2002-2007, and 2013-2017, and only shrank in 2007-2013. There are different possible major driving factors for each period. (3) The region between the fifth and sixth ring roads in Beijing underwent the most significant changes in the two decades. (4) The inner three regions are relatively highly urbanized areas compared to the outer two regions. Urbanization processes in the interior regions tend to be completed compared to the exterior regions.
Keywordimpervious surface spatiotemporal change textural feature Random Forest Beijing urbanization process
Funding OrganizationNational Key R&D Program of China ; National Key R&D Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key R&D Program of China ; National Key R&D Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key R&D Program of China ; National Key R&D Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key R&D Program of China ; National Key R&D Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China
DOI10.3390/rs13010153
WOS KeywordIMAGE TEXTURE ; SOIL MODEL ; CLASSIFICATION ; LAND ; URBAN ; ANATOMY ; AREAS
Language英语
Funding ProjectNational Key R&D Program of China[2016YFC0600501] ; National Natural Science Foundation of China[42071309]
Funding OrganizationNational Key R&D Program of China ; National Key R&D Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key R&D Program of China ; National Key R&D Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key R&D Program of China ; National Key R&D Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key R&D Program of China ; National Key R&D Program of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China
WOS Research AreaEnvironmental Sciences & Ecology ; Geology ; Remote Sensing
WOS SubjectEnvironmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing
WOS IDWOS:000606061300001
PublisherMDPI
Citation statistics
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/79946
Collection中国科学院国家天文台
Corresponding AuthorWang, Yongzhi
Affiliation1.Jilin Univ, Coll Geoexplorat Sci & Technol, Changchun 130026, Peoples R China
2.Jilin Univ, Inst Natl Dev & Secur Studies, Changchun 130026, Peoples R China
3.Chinese Acad Sci, Natl Astron Observ, Key Lab Lunar & Deep Space Explorat, Beijing 100101, Peoples R China
4.Jilin Univ, Inst Integrated Informat Mineral Resources Predic, Changchun 130061, Peoples R China
5.Jilin Univ, Coll Phys, Changchun 130026, Peoples R China
Recommended Citation
GB/T 7714
Dong, Xuegang,Meng, Zhiguo,Wang, Yongzhi,et al. Monitoring Spatiotemporal Changes of Impervious Surfaces in Beijing City Using Random Forest Algorithm and Textural Features[J]. REMOTE SENSING,2021,13(1):19.
APA Dong, Xuegang,Meng, Zhiguo,Wang, Yongzhi,Zhang, Yuanzhi,Sun, Haoteng,&Wang, Qingshuai.(2021).Monitoring Spatiotemporal Changes of Impervious Surfaces in Beijing City Using Random Forest Algorithm and Textural Features.REMOTE SENSING,13(1),19.
MLA Dong, Xuegang,et al."Monitoring Spatiotemporal Changes of Impervious Surfaces in Beijing City Using Random Forest Algorithm and Textural Features".REMOTE SENSING 13.1(2021):19.
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