Decision Tree Land Use/ Land Cover Change Detection of Khoram Abad City Using Landsat Imagery and Ancillary SRTM Data
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
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 word: Land Cover, Change Detection, Decision Tree, Multi-spectral Images, Khoram Abad
