Change detection analysis of urban areas in Baghdad City using remote sensing and GIS

Abstract

Urban expansion has been identified as a causative factor for numerous environmental issues in urban areas, such as diminished air quality, elevated local temperatures, and poor water quality. This research aims to detect change in urban areas in Baghdad over a period of approximately 30 years, spanning from 1993 to 2023. The Landsat (TM5, ETM+7, and OLI8) images were used in this research. After performing image pre-processing, a supervised classification was used by the Support Vector Machine (SVM) to classify the images into five land cover classes, namely urban areas, water bodies, vegetation, soil, and wetlands. The classifications' accuracy assessment for the years 1993, 2003, 2013, and 2023 were 89%, 91%, 92%, and 90%, respectively. The results indicate that there has been a notable increase in urban areas over the specified time intervals. Specifically, the data shows that the urban areas experienced growth of 1.34%, 1.62%, 1.98%, and 4.54% during the time periods of 1993–2003, 2003–2013, 2013–2023, and 1993–2023, respectively.