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REMOTE SENSING FOR LAND & RESOURCES    2000, Vol. 12 Issue (2) : 13-17     DOI: 10.6046/gtzyyg.2000.02.04
Technology Application |
APPLICATION OF TM DATA TO LAND USE CHANGE MONITORING
Mo Yuanfu, Zhou Lixin
Institute of Karst Geology, CAGS, Guilin 541004
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Abstract  

Land use change monitoring is a long-term task, its purpose is to find out about the land use change periodically or non-periodically and provide information for planning and administering. Application of TMdata to land use change monitoring was introduced in this paper. Post-classification comparison method was taken. The measure to reduce enlarged change range is to improve the precision of classification and calibration of multi-temporal Landsat data. In this way, the classified result for mono-temporal TMd ata can compared with others for any another mono-temporal TMdata, then the land use change information can be obtained.

Keywords  Vegetation index      Vegetation coverage      Rocky desertification      Information extraction      Remote sensing     
Issue Date: 02 August 2011
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LI Li
TONG Li-Qiang
LI Xiao-Hui
ZHANG Ying-wen
WANG Liang
YANG Sheng-fa
Cite this article:   
LI Li,TONG Li-Qiang,LI Xiao-Hui, et al. APPLICATION OF TM DATA TO LAND USE CHANGE MONITORING[J]. REMOTE SENSING FOR LAND & RESOURCES, 2000, 12(2): 13-17.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2000.02.04     OR     https://www.gtzyyg.com/EN/Y2000/V12/I2/13

1 黎夏.利用主成分分析改善土地利用变化的遥感监测精度.遥感学报,1997 (4)
2 胡永德,王杰生等.用于土地利用分类的计算机复合分层分类方法.环境遥感,1989 4(4)
3 潘贤章,曾志远.长江三峡地区资源遥感处理中的几个技术难题.环境遥感,1994 9(3)

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