In this study, classification results were derived from remote sensing data and the Support Vector Machine (SVM) algorithm used in this process, which classifies Landsat land-cover images. The accuracy of image classifications was evaluated by calculation of the Kappa coefficient. The area of study is Karaj, the capital of Alborz province, in north-central Iran. It is situated in the foothills of the Alborz Mountains and occupies a fertile agricultural plain. Landsat data used in the classification of land cover were collected from USGS websites, and multi-temporal images from the data were geometrically corrected. After this process, we calculated 11 metrics at the landscape and class-level scales: five metrics of class level and six metrics of landscape. The results showed that the landscape patterns in Karaj were changed due to the process of urbanization over an 11-year period. At the class level, for all classifications, the AI metric increased and the PD and NP metrics decreased. At the landscape level, the PD, ED, NP, and SHDI metrics decreased, and LPI and AI increased. These results provide insights about urban development policies and about whether the expansion of urban areas is beneficial for environmental sustainability in Iran and elsewhere in the world.

DOI: https://doi.org/10.3846/enviro.2020.649