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Improving Urban Population Distribution Models with Very-High Resolution Satellite Information

Author

Listed:
  • Taïs Grippa

    (Department of Geoscience, Environment & Society, Université Libre De Bruxelles (ULB), 1050 Bruxelles, Belgium)

  • Catherine Linard

    (Department of Geography, University of Namur, 5000 Namur, Belgium)

  • Moritz Lennert

    (Department of Geoscience, Environment & Society, Université Libre De Bruxelles (ULB), 1050 Bruxelles, Belgium)

  • Stefanos Georganos

    (Department of Geoscience, Environment & Society, Université Libre De Bruxelles (ULB), 1050 Bruxelles, Belgium)

  • Nicholus Mboga

    (Department of Geoscience, Environment & Society, Université Libre De Bruxelles (ULB), 1050 Bruxelles, Belgium)

  • Sabine Vanhuysse

    (Department of Geoscience, Environment & Society, Université Libre De Bruxelles (ULB), 1050 Bruxelles, Belgium)

  • Assane Gadiaga

    (Department of Geography, University of Namur, 5000 Namur, Belgium)

  • Eléonore Wolff

    (Department of Geoscience, Environment & Society, Université Libre De Bruxelles (ULB), 1050 Bruxelles, Belgium)

Abstract

Built-up layers derived from medium resolution (MR) satellite information have proven their contribution to dasymetric mapping, but suffer from important limitations when working at the intra-urban level, mainly due to their difficulty in capturing the whole range of variation in terms of built-up densities. In this regard, very-high resolution (VHR) remote sensing is known for its ability to better capture small variations in built-up densities and to derive detailed urban land use, which plead in favor of its use when mapping urban populations. In this paper, we compare the added value of various combinations of VHR data sets, compared to a MR one. A top-down dasymetric mapping strategy is applied to reallocate population counts from administrative units into a regular 100 × 100 m grid, according to different weighting layers. These weighting layers are created from MR and/or VHR input data, using simple built-up proportion or reallocation “weights”, obtained from a set of multiple ancillary data used to train a Random Forest regression model. The results reveal that (1) a built-up mask derived from VHR can improve the accuracy of the reallocation by roughly 13%, compared to MR; (2) using VHR land-use information alone results in lower accuracy than using a MR built-up mask; and (3) there is a clear complementarity between VHR land cover and land use.

Suggested Citation

  • Taïs Grippa & Catherine Linard & Moritz Lennert & Stefanos Georganos & Nicholus Mboga & Sabine Vanhuysse & Assane Gadiaga & Eléonore Wolff, 2019. "Improving Urban Population Distribution Models with Very-High Resolution Satellite Information," Data, MDPI, vol. 4(1), pages 1-17, January.
    • Handle: RePEc:gam:jdataj:v:4:y:2019:i:1:p:13-:d:198355
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    References listed on IDEAS

    as
    1. Fennis J. Reed & Andrea E. Gaughan & Forrest R. Stevens & Greg Yetman & Alessandro Sorichetta & Andrew J. Tatem, 2018. "Gridded Population Maps Informed by Different Built Settlement Products," Data, MDPI, vol. 3(3), pages 1-11, September.
    2. Filipe Batista e Silva & Javier Gallego & Carlo Lavalle, 2013. "A high-resolution population grid map for Europe," Journal of Maps, Taylor & Francis Journals, vol. 9(1), pages 16-28, March.
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