Urban Sprawl Evaluation Using Landscape Metrics and Black-and-White Hypothesis (Case Study: Urmia City)

Abstract

The increasing population growth has caused a phenomenon called urban sprawl in most cities in the world. Urban sprawl has imposed adverse economic and environmental impacts on cities. Therefore, study, measurement and recognition of this phenomenon are essential. The purpose of this study is the identification and measurement of urban sprawl intensity in the Urmia city (capital of West Azerbaijan Province of Iran). It causes urban planners and managers to better plan for the future growth of the Urmia and achieve sustainable urban development with greater awareness and understanding. The present research is a theoretical–applied research purpose and is descriptive–analytic in terms of research nature. Several methods have been used for measuring urban sprawl. Nowadays, landscape metrics and spatial data are widely used worldwide because they detected urban sprawl with great detail and accuracy. Thus, in the present research, in order to measure urban sprawl in Urmia city, the black-and-white hypothesis and satellite imagery and integration of these data with landscape metrics have been used for the first time in Iran. In this regard, satellite images of 1991, 2001, 2011 and 2016 were collected with the help of Landsat satellite. These images were classified using an artificial neural network in two classes: built and non-built. Then the detection of changes was done using ENVI 5.3 software and also using landscape metrics (fractal dimension index, largest patch index, contiguity index, number of patches index and shape index) by Fragstats 4.2 software in order to measure urban sprawl in Urmia city. The results of this study indicate that urban sprawl in Urmia during the period of 1991–2011 had a decreasing rate and from then until now has been increasing and it will be continued.