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A Remote Sensing Based Calibration Framework for the MOLAND Urban Growth Model of Dublin

A Remote Sensing Based Calibration Framework for the MOLAND Urban Growth Model of Dublin

Tim Van de Voorde, Johannes van der Kwast, Frank Canters, Guy Engelen, Marc Binard, Yves Cornet, Inge Uljee
Copyright: © 2012 |Volume: 3 |Issue: 2 |Pages: 21
ISSN: 1947-3192|EISSN: 1947-3206|EISBN13: 9781466610668|DOI: 10.4018/jaeis.2012070101
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MLA

Van de Voorde, Tim, et al. "A Remote Sensing Based Calibration Framework for the MOLAND Urban Growth Model of Dublin." IJAEIS vol.3, no.2 2012: pp.1-21. http://doi.org/10.4018/jaeis.2012070101

APA

Van de Voorde, T., van der Kwast, J., Canters, F., Engelen, G., Binard, M., Cornet, Y., & Uljee, I. (2012). A Remote Sensing Based Calibration Framework for the MOLAND Urban Growth Model of Dublin. International Journal of Agricultural and Environmental Information Systems (IJAEIS), 3(2), 1-21. http://doi.org/10.4018/jaeis.2012070101

Chicago

Van de Voorde, Tim, et al. "A Remote Sensing Based Calibration Framework for the MOLAND Urban Growth Model of Dublin," International Journal of Agricultural and Environmental Information Systems (IJAEIS) 3, no.2: 1-21. http://doi.org/10.4018/jaeis.2012070101

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Abstract

Land-use change models are useful tools for assessing and comparing the environmental impact of alternative policy scenarios. Their increasing popularity as spatial planning instruments also poses new scientific challenges, such as correctly calibrating the model. The challenge in model calibration is twofold: obtaining a reliable and consistent time series of land-use information and finding suitable measures to compare model output to reality. Both of these issues are addressed in this paper. The authors propose a model calibration framework that is supported by information on urban form and function derived from medium-resolution remote sensing data through newly developed spatial metrics. The remote sensing derived maps are compared to model output of the same date for two model scenarios using well-known spatial metrics. Results demonstrate a good resemblance between the simulation output and the remote sensing derived maps.

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