Dasymetric mapping of urban population in China based on radiance corrected DMSP-OLS nighttime light and land cover data

Abstract

High spatial resolution urban population dataset is increasingly required for sustainable urban planning and management. Dasymetric mapping is an effective approach to create such dataset. However, the created gridded total population datasets usually have limitation for urban analysis in developing countries as they usually underestimate urban population because of the strong urban-rural difference. In this study, we aimed to create a dataset of gridded urban population with 1 km resolution in China in year 2000 and 2010. We proposed an index of urban nighttime light (UNTL) by integrating radiance corrected DMSP nighttime light (RcNTL) and urban land, which is then used as weight to disaggregate county-level urban population. The validation using township population in Beijing as references shows reasonable accuracy with a mean relative error of 38% and a R2 of 68%. Using only two widely available datasets (RcNTL and urban land), the proposed method is simple and computing efficient compared with methods using multiple geospatial data (e.g., land use and land cover, distance to city center, slope) and that combined with remote sensing imagery. As the used two auxiliary datasets are accessible globally, the method has great potential to produce similar urban population dataset for other developing countries where fine scale census population datasets are scarce. The produced urban population dataset is valuable for enriching our understanding of the urbanization process and designing sustainable urban planning and management strategies in China.