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.

中文翻译：

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使用改进的建筑面积提取技术对城市发展进行建模的综合方法

对城市增长的预测通常在城市规划决策中至关重要。在本文中，我们通过SLEUTH模拟对孟加拉国达卡都市开发计划区2035年的城市增长预测的整个过程进行了整合。SLEUTH需要严格准备五个数据输入，即坡度，排除区域，城市范围，公路网和山体阴影。本文使用Landsat-8和Landsat-5影像即席准备了城市范围输入。为了提高从Landsat-8影像提取市区的准确性，我们将归一化差异植被指数和修正的归一化差异水指数与归一化差异累积指数相结合。在归一化差异累积指数的情况下，我们使用Landsat-8波段6和7的主成分图像来包括两个波段的影响。这种提取堆积区域的技术使整体精度提高了17.28％。SLEUTH模型经历了三个校准阶段（粗略，精细和最终），并进行了额外的校准以生成预测系数。在校准阶段之后，确定最佳拟合系数值以运行预测模式。预测的输出以发展概率的百分数表示，在此研究中，从中选择了90％以上的概率。该预测表明，从2015年到2035年，研究区域的城市范围很可能会增加158.66％，同时指定的保护区也会显着减少。本文将为研究人员提供一种准确，结构化的方法来预测城市增长。