An integrated approach to modeling urban growth using modified built-up area extraction technique

Abstract

Prediction of urban growth is often crucial in urban planning decisions. In this paper, we integrated the whole process of urban growth prediction by SLEUTH simulation for Dhaka Metropolitan Development Plan area of Bangladesh for the year of 2035. SLEUTH requires a rigorous preparation of five data inputs, i.e., slope, exclusion area, urban extent, road network and hillshade. This paper improvised the preparation of urban extent input using both Landsat-8 and Landsat-5 imagery. To increase the accuracy of urban area extraction from Landsat-8 images, we integrated normalized difference vegetation index and modified normalized difference water index with normalized difference built-up index. In the case of normalized difference built-up index, we used the principal component image of band 6 and 7 of Landsat-8 to include the effects of both bands. This technique to extract the built-up area increased the overall accuracy by 17.28% point. SLEUTH model ran through three calibration phases—coarse, fine and final—and an additional calibration was run to generate the forecasting coefficients. After the calibration phase, the best fit coefficient values were determined to run the prediction mode. The predicted outputs were derived as percentiles of development probability, from which a probability of above 90% was selected in this study. The prediction reveals that the urban extent of the study area is likely to increase by 158.66% from 2015 to 2035, and the designated conservation areas will significantly decrease during the same time period. This paper will provide researchers with an accurate and structured methodology to predict urban growth.