Middle-East Journal of Scientific Research 13 (8): 1057-1064, 2013 ISSN 1990-9233 © IDOSI Publications, 2013 DOI: 10.5829/idosi.mejsr.2013.13.8.519 Corresponding Author: H. Matinfar, Department of Soil Science, Lorestan University, Lorestan, Iran. 1057 Decision Tree Land Use/ Land Cover Change Detection of Khoram Abad City Using Landsat Imagery and Ancillary SRTM Data Hamid Reza Matinfar and Majid Shadman Roodposhti 1 2 Department of Soil Science, Lorestan University, Lorestan, Iran 1 Department of Geography, Tehran University, Tehran, Iran 2 Abstract: Change detection is a general remote sensing technique that compares imagery collected over the same area at different times and highlights features that have changed. In this paper, land cover of Khoram Abad, a city in Lorestan province of Iran, was examined in a case study via post classification technique and decision tree classifier. The Decision Tree (DT) classifier performs multistage classifications by using a series of binary decisions to place pixels into proper classes. Input data may be used from various sources and data types. Such as, multispectral data, digital elevation model (DEM) and slop to find features with similar spectral reflectance but different in elevation. In order to carry out comprehensive analysis of Khoram Abad land cover changes from years 1992 to 2009, TM data obtained from Landsat Satellite and digital elevation model of shuttle radar topography mission were used. Finally, post classification analysis using DT classifier showed notable improvement in classification accuracy in spite of high correlation of multi-spectral data. Key words: Land Cover Post Classification Decision Tree Multi-spectral Images Change Detection Khoram Abad Iran INTRODUCTION detect land cover changes in Aykitali, Turkey. He found The geospatial phenomena are changing over time among these techniques while PCA and post and the land cover information has to be up-date classification analyses showed better results in change periodically. Up-to-date knowledge of land cover is an detection. important tool for the various planning authorities with Tardi and Contalgon [5] also used three methods responsibilities for the management of territory [1]. including: multi-temporal color composite, subtraction and However, it should be noted that planners and land classification in order to examine physical development of managers require accurate data to address land cover Massachusett's urban area and resulting land cover problems. Although the priority is for land use (economic) changes. Finally, they used post classification analysis in information, land cover information is more easily mapped order to estimate total accuracy. Qiasvand [6], also and can serve as an approximation of land use. concluded similar study via PCA and subtraction During the past decades, not only remote sensing techniques so as to present south Tehran land cover map images have become an important tool for land use and he reported that regression analysis in conjunction classification and mapping [2] but also because of the with PCA showed better results. Jahani [7], utilized advantages of repetitive data acquisition, they have satellite images (Spot) and normalized difference become major data sources from local to global scales for vegetation index in Tehran land cover mapping project. different change detection applications [3]. There were Consequently, on the basis of the earlier studies about several studies conducted to investigate land use land cover change detection, it is obvious that most changes during the time some of which will be referred researchers used subtraction and PCA techniques to to in this section. Sunar [4], used five techniques, detect changes in land cover and, in further step; by including: adding, subtracting, dividing, principle classifying multi-temporal images they showed results in component (PCA) and post classification analysis to quantitative form. that adding and subtracting images were the most simple