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LAND USE MAPPING USING ETM+ DATA (CASE STUDY:CHAMESTAN AREA, IRAN)

By Seyed Zeynalabedin Hossein, Sayed Jamaleddin Khajeddin, Hossein Azarnivad and Seyed Ali Khalilpour

Abstract

Land use maps are useful tools for agricultural and natural resources studies as a base data. Due to dynamism of natural resources, updating these maps is essential. Employing traditional methods through aerial photos interpretation to produce such maps are costly and time consuming. Satellite data is suitable for such purpose, as a consequence of its fast repeatability, wide and unique view and availability of data from most part of electromagnetic spectrum. The present study is conducted to investigate the capability of ETM + data on land use mapping of Chamestan region, Mazandaran, Iran. The studied area was 67000 ha. Image of 18th July, 2000 were registered to 1:25000 digital topographic maps. Images were enhanced using contrast enhancement, False Color Composite (FCC), Principal Component Analysis (PCA), Tasseled cap transformation and vegetation indices. The Optimized Index Factor (OIF) and correlation technique were employed to determine the best band sets for FCC and consequently for classification analysis. Unsupervised (clustering) and supervised (maximum likelihood, minimum distance and parallelepiped classifiers) classification methods were used. Finally Hierarchical method was applied to increase maps accuracies. The results showed that contrast enhancement, FCC, PCA and Tasseled cap have effective role in features enhancement. Using the best bands set (156H) caused to highest accuracy in classification. In supervise classification, overall accuracy and Kappa coefficient for maximum likelihood classifier were estimate 85,83 % and 62,81 % respectively, for minimum distance method 73,77 % and 47,12 % and for parallelepiped 34,27 % and 19,03%. The highest overall accuracy and Kappa coefficient related Hierrarchical method is 94 % and 84.89%

Topics: Land use, Mapping, Image enhancement, Best band set, Classification, Vegetation indices. ABSTRACT
Year: 2011
OAI identifier: oai:CiteSeerX.psu:10.1.1.183.7116
Provided by: CiteSeerX
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