Journal of University of Chinese Academy of Sciences >
Application of high spatial resolution remote sensing images in urban LUCC
Received date: 2015-02-27
Revised date: 2015-12-03
Online published: 2016-05-15
Urbanization causes land cover changes which lead to deeper regional social, economic, and environmental changes and have important influences on environment and climate on the global scale. The coarse or moderate resolution remote sensing images may have much mixed pixels which limit their applications in the study of urban remote sensing. High spatial resolution remote sensing images with the advantages of high spatial resolution,high definition, and rich information, greatly improve the application of remote sensing in the research area of land use and land change. Accurate urban land use classification is the basis of the study of urban LUCC, and the high spatial resolution remote sensing images become one of the most important data sources because of their inherent advantages. We report the development and features of high resolution remote sensing images, the methods of remote sensing images classification, and their applications in extracting urban information. Much attention is paid to the representative classification methods in urban land use and to the ways of extracting information of buildings, roads, and vegetation in urban environment. The paper indicates that, compared to the traditional classification methods based on the level of pixels, the object-oriented image classification method which makes full use of high spatial resolution remote sensing images is the most widely used method. The discussion focuses on the current status and perspectives of the object-oriented method. With the help of high spatial resolution remote sensing images, accurate urban thematic information can be extracted effectively and it plays a significant role in the study of urban LUCC.
YANG Chaobin , ZHANG Shuwen , BU Kun , YU Lingxue , YAN Fengqin , CHANG Liping , YANG Jiuchun . Application of high spatial resolution remote sensing images in urban LUCC[J]. Journal of University of Chinese Academy of Sciences, 2016 , 33(3) : 289 -297 . DOI: 10.7523/j.issn.2095-6134.2016.03.001
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