Urban sprawl trend analysis using statistical and remote sensing approach Case Study: Mashhad City

Urban sprawl is a significant challenge in urban areas and considered as the most influential drivers of landuse and land cover change associated with growth of populations and economy. Iran's cities have been facedwith the urban sprawl phenomenon, especially since the 1970s. More recently, scientific studies have beenproved negative impacts of urban sprawl in Iran's cities including the destruction of landscapes and naturalresources around the city and coastal areas. Mashhad is a metropolis which has faced urban sprawl in recentdecades. The present study aims to generate an urban sprawl model using statistical and remote sensingapproach by the integration of geographic information system (GIS) in Mashhad city, northeastern Iran.For this purpose, various temporal LANDSAT satellite datasets were used to map land use/land covercharacteristics and to evaluate built-up growth in Mashhad in 1996, 2006 and 2016. Maximum likelihoodclassification method (MLC) mapped the land cover for the Mashhad using Landsat TM datasets. Theability of MLC in minimizing misclassification errors by allowing variable weight specifications during theclassification process and use of training data made it a suitable method for this study. After MLC proses,Shannon’s Entropy based on the land use classification result is used to measure urban sprawl. Resultsindicated a significant increase of urban built-up area during the last two decades. During the two timeperiods of this study the Shannon entropy increased in all of the two time periods that showing the City ofMashhad continue to have a problem with urban sprawl.

Urban sprawl trend analysis using statistical and remote sensing approach Case Study: Mashhad City Keywords:

urban sprawl , Shannon’s entropy , remote sensing , Landsat TM , Maximum likelihood classification