A GIs Based Weight of Evidence for Prediction Urban Growth of Baghdad City by Using Remote Sensing Data

Authors

  • Noor Moutaz Asmael HWY and Transportation Department/ Al-Mustansiriyah University

Keywords:

Weight of Evidence, GIS, Urban Growth

Abstract

The rapid growth of Baghdad city has an adverse effect on the environment; therefore, it is crucial to have a well-concerted plan for urban expansion. This paper presents the problem of urban growth in Baghdad city; hence, it is develop a methodology that combines remote sensing data and GIS with Weight of evidence to estimate the occurrence spatial distribution of urban extent. Accordingly, the required data for the proposed model building were identified by using satellite imagery of Landsat MSS/TM/ETM for years, 1976, 1990 and 2000 respectively. The satellite imagery is utilized for geometric correction, supervised and unsupervised classification, accuracy assessment, derivation of change detection and urban growth modeling. Three factors were considered in model building of urban growth in weight of evidence: environmental, social and economical factors. Geodatabase was digitized in ArcGIS and combined to develop statistical models relating land use to population density, distance from the center of the city, distance from highway, river and slope of study area. The work emphasizes spatial relationships between various geographic, land-use, and demographic variables to predict future urban extent. Based on the urban growth model in GIS, results show: the urban area in Baghdad is increased rapidly; the result of the work shows a rapid growth in built-up land between 1976 and 1990 from 100 km2 to 380 km2 and from 452 km2 in 2000 to 610 km2 in 2015. Finally, the case study demonstrated that GIS based weight of evidence is recognized to be used as a useful tool for prediction of urban growth by considering saving of money, time and effort.

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Published

31-05-2017

How to Cite

A GIs Based Weight of Evidence for Prediction Urban Growth of Baghdad City by Using Remote Sensing Data. (2017). Al-Nahrain Journal for Engineering Sciences, 18(2), 168-178. https://nahje.com/index.php/main/article/view/175

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