Modelling the population density of China at the pixel level based on DMSP/OLS non-radiance-calibrated night-time light images

The spatial distribution of population density is crucial for analysing the relationships among economic growth, environmental protection and resource use. In this study we simulated China's population density in 1998 at 1 km × 1 km resolution by integrating DMSP/OLS non-radiance-calibrated night-time images, SPOT/VGT 10-day maximum NDVI composite, population census data and vector county boundaries. Population density, both inside and outside of light patches, was estimated for four types of counties, which were classified according to their light characteristics. The model for estimating population density inside the light patches was developed based on a significant correlation between light intensity and population, while the model for estimating population density outside of light patches was constructed by combining Coulomb's law with electric field superposition principle. Our method was simpler and less expensive than existing methods for spatializing population density. The results were consistent with other estimates but exhibited more spatial heterogeneity and richer information.