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Predicting future urban impervious surface distribution using cellular automata and regression analysis

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Abstract

Urban impervious surfaces are considered as key indicator of urbanization intensity and environmental quality. Due to their significant impact on surface runoff, flood frequency, and water quality, impervious surfaces have been identified as an important indicator for examining the hydrological impact of urbanization. The amount and distribution of impervious surfaces have been estimated using remote sensing and geographic information system (GIS) techniques. Little research, however, has been conducted to predict future impervious surface distributions. To address this problem, we developed an integrated residential/commercial growth and impervious surface distribution model to predict urban impervious surface distribution. Taking Milwaukee River Basin, Wisconsin as a case study, we simulated future residential and commercial developments using a CA model. Further, we developed a linear regression model to predict impervious surface distributions in residential and commercial land uses. Analysis of results suggests that the proposed model performs significantly better than the traditional approaches.

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Acknowledgements

This work was supported in part under a grant from the U.S. Geological Survey, U.S. Department of Interior, federal grant number G11AP20115, Project 2013WI314B and with Research Committee Award from the University of Wisconsin-Milwaukee. We would also like to thank the suggestions from reviewers for improving the early version of the manuscript.

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  1. Department of Geography, University of Wisconsin-Milwaukee, PO Box 413, Milwaukee, WI, 53201-0413, USA

    Wenliang Li, Changshan Wu & Woonsup Choi

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  1. Wenliang Li
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  2. Changshan Wu
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  3. Woonsup Choi
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Correspondence to Wenliang Li.

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Communicated by: H. A. Babaie

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Li, W., Wu, C. & Choi, W. Predicting future urban impervious surface distribution using cellular automata and regression analysis. Earth Sci Inform 11, 19–29 (2018). https://doi.org/10.1007/s12145-017-0312-8

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  • DOI: https://doi.org/10.1007/s12145-017-0312-8

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