The Effect of Rapid Population Growth on Urban Expansion and Destruction of Green Space in Tehran from 1972 to 2017

Abstract

Time series analysis of satellite data is a very powerful tool for change detection applications especially for monitoring urban expansion. Urban expansion results in an accelerating proportion of the population living in cities and caused considerable effects on green space structures and areas. Many methods and models have been tested to determine urban expansion. In this paper, after computing the Tehran area and its green space coverage, the relationship between the population with urban expansion and green space coverage is estimated by regression analysis. As a consequence of Tehran texture and dataset for processing, raster to vector conversion was the most effective method for computing urban area, and also green space coverage was determined with normalized difference vegetation index. Machine learning algorithms such as support vector regression (SVR) and random forest (nonparametric method) were utilized to estimate green space coverage and urban area, respectively. Although RF model improves the accuracy of estimated population (R2 = 0.97, RMSE = 38.18, and MAE = 83.44), the use of SVR model considerably improves the estimation of green space coverage (R2 = 0.93, RMSE = 47.12, and MAE = 100.18) as well.