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REMOTE SENSING FOR LAND & RESOURCES    2012, Vol. 24 Issue (2) : 132-137     DOI: 10.6046/gtzyyg.2012.02.24
Technology Application |
A Study of the Landscape Pattern Change of Longyan City Based on Multi-temporal Remote Sensing Images
CHEN Xue-ling1,2, CHEN Shao-jie2, DU Pei-jun1, XIA Jun-shi1
1. Key Laboratory for Land Environment and Disaster Monitoring of State Bureau of Surveying and Mapping, China University of Mining and Technology, Xuzhou 221116, China;
2. School of Resource Engineering, Longyan University, Longyan 364000, China
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Abstract  Landsat TM /ETM+ images of Longyan city were employed for landscape pattern classification by traditional MLC (maximum likelihood classification), DTC(decision tree classification) and SVM (support vector machine) techniques. A comparison between the three classification methods shows that the SVM classification method has outstandingly higher classification accuracy than the other methods. In combination with the theory of landscape ecology, therefore, the authors used the classification results of SVM to analyze the landscape pattern dynamic change of Xinluo district, Longyan city, from 1992 to 2008. The experimental results indicate that, from 1992 to 2008, the agricultural land of the main city proper of Xinluo district was reduced greatly due to its transformation into the built-up land, but the forest coverage remained good. Meanwhile, urban landscape component changed from the process of diffusion growth into agglutinate clustering process and the urban pattern was converted from unstable into stable. Overall, the urban landscape showed the tendency of less fragmentation, lower diversity and higher concentration, and the built-up land had become the main landscape type of Longyan city.
Keywords mathematical morphology      multi-scale segmentation      panchromatic image      image processing      GIS     
:  TP 79  
  S 29  
Issue Date: 03 June 2012
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YU Xue-qin
ZUO Xiao-qing
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YU Xue-qin,ZUO Xiao-qing,HUANG Liang. A Study of the Landscape Pattern Change of Longyan City Based on Multi-temporal Remote Sensing Images[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(2): 132-137.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2012.02.24     OR     https://www.gtzyyg.com/EN/Y2012/V24/I2/132
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