DocumentCode
2878000
Title
Dynamic Study on Landuse Based on Rs Images in the Western Region of Jinan
Author
Dong Fang ; Xie Fu-ju
Author_Institution
Coll. of Resources & Environ, Univ. of Jinan, Jinan, China
fYear
2012
fDate
1-3 June 2012
Firstpage
1
Lastpage
4
Abstract
Three-phase RS images, including the TM data of 1987 and 1996, the CBERS data of 2010, were selected to classify the study area by using supervised classification of the maximum likelihood method. The study area was divided into four classes including agricultural land, construction land, water and unused land. Then the post-classification comparison method was used to monitor the change of land use dynamically in order to extract the quantitative and spatial change information from 1987 to 2010. The results show that: with the development of economic and the increase of population in twenty-three years, the construction of the new West Railway Station of Jinan made all kinds of land use change more obviously. Firstly, the area of agricultural land and the unused land decreased gradually which transformed into construction land. Secondly, the proportion of urban construction land has been increasing during twenty-three years.
Keywords
geophysical image processing; image classification; maximum likelihood estimation; terrain mapping; AD 1987 to 2010; CBERS data; China; TM data; West Railway Station; agricultural land; land use change monitoring; maximum likelihood method; post-classification comparison method; quantitative information; spatial change information; supervised classification method; three-phase remote sensing images; unused land; urban construction land; water land; western Jinan region; Agriculture; Earth; Economics; Educational institutions; Monitoring; Remote sensing; Satellites;
fLanguage
English
Publisher
ieee
Conference_Titel
Remote Sensing, Environment and Transportation Engineering (RSETE), 2012 2nd International Conference on
Conference_Location
Nanjing
Print_ISBN
978-1-4673-0872-4
Type
conf
DOI
10.1109/RSETE.2012.6260546
Filename
6260546
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