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《Arid Land Geography》 2005-01
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Study on the Methods of Deriving the Information of Land Cover in the Arid Areas by Using TM Data

SHA Zhan-jiang1, , MA Hai-zhou1 , LI Ling-qin2, FAN Qi-shun2, HUANG Hua-bing1 2 YANG Hai-zhen2 CAO Guang-chao1, 2 (1 Institute of Qinghai Salt Lake, Chinese Academy of Sciences, Xining 810008, China; 2 Department of Geographic and Resources Environment Sciences, Qinghai Normal University, Xining 810008, China)  
By taking the Xiangride Oasis as the study area, in this paper, the spatial information and the thematicinformation of land use and land cover in the study area are enhanced by using the ETM remote sensing data afterinosculating the spatial resolving power and analyzing the principal components. The optimal color images arecomposed by using some processed band data, the training samples are selected from the background images. Byusing the maximum likelihood supervision classification (MLC), multi-spatial-scale hierarchical clusteringalgorithm (SSHC) and geographic-knowledge-based fuzzy clustering (KFC), the training samples areseparately initialized to obtain the eigenvalues of all kinds information so as to develop the eigenvalue modedatabase. All pixels were classified by using the mode base to get the initial classified results, the finally classifiedresults and classification precision are derived by post-processing including the clumping, riddling, eliminationand recoding for the initial classified results. The conclusions are as follows: the classified accuracy of SSHC andKFC is 3% higher than that of MLC, and the classified accuracy of SSHC is slightly higher than that of KFC. Inthree methods for deriving the information of land use and land cover, SSHC is more ideal to class farmlands,gravel lands, flood lands and deserts, KFC is more ideal to class farmlands, sand lands, flood lands and deserts,and MLC is more ideal to class farmlands, flood lands and deserts. The accuracy of all three methods forclassifying farmlands, flood lands and deserts is higher than 80%, but it is lower than 80% in classifying sandlands and gravel lands.
【Fund】: 中国科学院知识创新重要方向项目(KZCX2-SW-118);; 国家重点科技攻关项目(97-924-02-02A)
【CateGory Index】: P271
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