Using landsat images for urban change detection, a case study in Algiers town

In this paper three images of Landsat Thematic Mapper and Enhanced Thematic Mapper plus TM/ETM+ data of 1987, 2001 and 2010 being used to analyze the urban changes in Algiers areas. The paper followed the expansion of urban tissue using the novel method of difference soil adjusted vegetation index DSAVI, difference normalized difference bult up index DNDBI and also the post classification of multi-spectral and multi-temporal L5 and L7 Landsat satellite. The TM reflectance's images have been corrected and transformed to ETM+ reflectance's images using a regression method. The Maximum Likelihood and neural network algorithm are used to classify the reflectance images into the thematic urban, vegetation and bare soil map. The Thresholds changes are calculated for both DSAVI and DNDBI based on the means and standards deviation images. The proposed method is based on the study which mentioned that the DSAVI, DNDBI and the post classification is a good tools to study the urban change which was faster growing with an annually rate of 0.5% from studied area. Our proposed method is applied to Algiers town.