Author/Authors :
Pijanowski، B C نويسنده Department of Forestry & Natural Resources, Purdue University, West Lafayettee, USA , , Tayyebi، A نويسنده Department of Surveying & Geomatics Eng., College of Eng.,University of Tehran, Iran , , Delavar، M R نويسنده Department of Surveying & Geomatics Eng., College of Eng.,University of Tehran, Iran , , Yazdanpanah، M J نويسنده School of Electrical and Computer Eng., College of Eng., University of Tehran, Iran ,
Abstract :
Urban Expansion Model (UEM) was adapted to simulate urbanization which implements
Geospatial Information Systems (GIS), Artificial Neural Networks (ANNs) and Remote Sensing (RS).
Two satellite imageries with specific time interval, socio-economic and environmental variables have
been employed in order to simulate urban expansion. Socio-economic and environmental variables
were used as inputs while construction and non- construction areas were used as outputs to train
the neural network. Calibration of proposed model was performed with area under the ROC Curve
(AROC) and Kappa Statistic (KS) which are non-shape performance metric. A real-life case study of
Tehran Metropolitan Area (TMA) is presented to demonstrate the process. This paper presents a
version of the UEM which parameterized for TMA and explores how factors such as road, building
area, service centre, green space, elevation, aspect and slope can influence urbanization. Having
urban expansion model with specific time interval and assuming the existence of the same rate of
urbanization, new construction areas of region can be predicted. The overall accuracy of the model
to predict new construction areas was 80% and 78% with AROC and KS, respectively.
