A simulation study on the urban population of China based on nighttime light data acquired from DMSP/OLS

Abstract

The urban population (UP) measure is one of the most direct indicators that reflect the urbanization process and the impacts of human activities. The dynamics of UP is of great importance to studying urban economic, social development, and resource utilization. Currently, China lacks long time series UP data with consistent standards and comparability over time. The nighttime light images from the Defense Meteorological Satellite Program's (DMSP) Operational Linescan System (OLS) allow the acquisition of continuous and highly comparable long time series UP information. However, existing studies mainly focus on simulating the total population or population density level based on the nighttime light data. Few studies have focused on simulating the UP in China. Based on three regression models (i.e., linear, power function, and exponential), the present study discusses the relationship between DMSP/OLS nighttime light data and the UP and establishes optimal regression models for simulating the UPs of 339 major cities in China from 1990 to 2010. In addition, the present study evaluated the accuracy of UP and non-agricultural population (NAP) simulations conducted using the same method. The simulation results show that, at the national level, the power function model is the optimal regression model between DMSP/OLS nighttime light data and UP data for 1990-2010. At the provincial scale, the optimal regression model varies among different provinces. The linear regression model is the optimal regression model for more than 60% of the provinces. In addition, the comparison results show that at the national, provincial, and city levels, the fitting results of the UP based on DMSP/OLS nighttime light data are better than those of the NAP. Therefore, DMSP/OLS nighttime light data can be used to effectively retrieve the UP of a large-scale region. In the context of frequent population flows between urban and rural areas in China and difficulty in obtaining accurate UP data, this study provides a timely and effective method for solving this problem.