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Comparisons of two global built area land cover datasets in methods to disaggregate human population in eleven countries from the global South

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International Journal of Digital Earth 2020年第1期13卷 78-100页

作者： Forrest R.Stevens Andrea E.Gaughan Jeremiah JNieves Adam King Alessandro Sorichetta Catherine Linard Andrew JTatem Department of Geography and Geosciences University of LouisvilleLouisvilleKYUSA WorldPop Department of Geography and EnvironmentUniversity of SouthamptonSouthamptonUK Spatial Epidemiology Lab(SpELL) UniversitéLibre de BruxellesBrusselsBelgium Department of Geography Universitéde NamurNamurBelgium Flowminder Foundation StockholmSweden

mapping built land cover at unprecedented detail has been facilitated by increasing availability of global high-resolution imagery and image processing *** advances in urban feature extraction and built-area detection can refine the mapping of human population densities,especially in lower income countries where rapid urbanization and changing population is accompanied by frequently out-of-date or inaccurate census ***,in these contexts it is unclear how best to use built-area data to disaggregate areal,count-based census *** we tested two methods using remotely sensed,built-area land cover data to disaggregate population *** included simple,areal weighting and more complex statistical models with other ancillary *** were assessed across eleven countries,representing different world regions varying in population densities,types of built infrastructure,and environmental *** found that for seven of 11 countries a Random Forest-based,machine learning approach outperforms simple,binary dasymetric disaggregation into remotely-sensed built *** these more complex models there was little evidence to support using any single built land cover input over the rest,and in most cases using more than one built-area data product resulted in higher predictive *** discuss these results and implications for future population modeling approaches.

关键词： Land cover built areas remote sensing settlement mapping population modeling

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