Towards an Improved Large-Scale Gridded Population Dataset: A Pan-European Study on the Integration of 3D Settlement Data into Population Modelling

Remote Sensing

Greg Yetman¹  Kytt MacManus¹  Daniela Palacios-Lopez²  Thomas Esch²  Julian Zeidler²  Peter Reinartz²  Mattia Marconcini²  Stefan Dech²  Alessandro Sorichetta³  Andrew J. Tatem³ 

[1] Center of International Earth Science Information Network CIESIN, The Earth Institute, Columbia University, Palisades, NY 10964, USA;German Remote Sensing Data Center (DFD) of the German Aerospace Center (DLR), Oberpfaffenhofen, D-82234 Weßling, Germany;WorldPop, School of Geography and Environmental Science, University of Southampton, Southampton SO17 1BJ, UK;

关键词: large-scale gridded population dataset;    dasymetric modelling;    accuracy assessment;    world settlement Footprint-3D;    random forest classifier;    spatial metrics;   

DOI  :  10.3390/rs14020325

来源: DOAJ

【 摘 要 】

Large-scale gridded population datasets available at the global or continental scale have become an important source of information in applications related to sustainable development. In recent years, the emergence of new population models has leveraged the inclusion of more accurate and spatially detailed proxy layers describing the built-up environment (e.g., built-area and building footprint datasets), enhancing the quality, accuracy and spatial resolution of existing products. However, due to the consistent lack of vertical and functional information on the built-up environment, large-scale gridded population datasets that rely on existing built-up land proxies still report large errors of under- and overestimation, especially in areas with predominantly high-rise buildings or industrial/commercial areas, respectively. This research investigates, for the first time, the potential contributions of the new World Settlement Footprint—3D (WSF3D) dataset in the field of large-scale population modelling. First, we combined a Random Forest classifier with spatial metrics derived from the WSF3D to predict the industrial versus non-industrial use of settlement pixels at the Pan-European scale. We then examined the effects of including volume and settlement use information into frameworks of dasymetric population modelling. We found that the proposed classification method can predict industrial and non-industrial areas with overall accuracies and a kappa-coefficient of ~84% and 0.68, respectively. Additionally, we found that both, integrating volume and settlement use information considerably increased the accuracy of population estimates between 10% and 30% over commonly employed models (e.g., based on a binary settlement mask as input), mainly by eliminating systematic large overestimations in industrial/commercial areas. While the proposed method shows strong promise for overcoming some of the main limitations in large-scale population modelling, future research should focus on improving the quality of the WFS3D dataset and the classification method alike, to avoid the false detection of built-up settlements and to reduce misclassification errors of industrial and high-rise buildings.

【 授权许可】

Unknown