An improved nightlight threshold method for revealing the spatiotemporal dynamics and driving forces of urban expansion in China.

Abstract An accurate and efficient extraction of urban extent is important for understanding the dynamics of urban expansion process and for sustainable planning and management of cities. We proposed an improved dynamic nightlight threshold method to model urban extent and to reveal the spatiotemporal dynamics and driving forces of urban expansion. Differing from previous studies, we correct the blooming and over-saturation problems of nighttime light (NTL), and highlight a combination of NTL with urban population data for determining a yearly-continued and city-class-wide threshold for urban mapping. China is selected as a case study area to test the improved method and to gain insights to its urban expansion process. Through the validation, our method has been proven to be more accurate than the traditional NTL threshold method. Accordingly, the yearly-continued NTL data can better describe the changing patterns and driving forces of urban expansion than the yearly-discontinued land use and land cover data do. It is found that the total urban area in China has more than quadrupled from 25.2 in 1992 to 108.2 thousand km2 in 2013. Some significant pulses of urban expansion have been detected in our study, which may be attributed to the policy and socioeconomic impacts. Moreover, the panel regression based on annual NTL data indicates that GDP is a more important driver of urban expansion than urban population.