A statistical method to downscale aggregated land use data and scenarios

N. Dendoncker, P. Bogaert, M. Rounsevell

    Research output: Contribution to journalArticlepeer-review


    This paper presents a method to downscale aggregated land use data based on statistical techniques. A purely spatial multinomial logistic regression (MNLR) model is proposed using observed fine resolution land use data. This model provides initial probability maps of land use presence, which are updated using aggregated land use data and an iterative procedure based on Bayes' theorem. The simplicity of the method as well as its low data requirements makes it easily reproducible. An example is shown using the CORINE land cover dataset (1990) to downscale future land use change scenarios (2020) for a small area in Belgium. The results from the MNLR as well as from the iterative procedure gave appropriate representation of land use patterns. The method was also useful in removing potential artificial border effects, which often arise when downscaling from adjacent spatial units. The resulting probability maps could be used for a variety of applications.

    Original languageEnglish
    Pages (from-to)63-82
    Number of pages20
    JournalJournal of Land Use Science
    Issue number2-4
    Publication statusPublished - 2006


    • Bayes' theorem
    • Downscaling
    • Multinomial logistic regression
    • Probability maps


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