Using DMSP/OLS nighttime light data and K–means method to identify urban–rural fringe of megacities

Abstract

Urban–rural fringe, which form a link between urban construction areas and rural hinterland, is the most sensitive area to urbanization. Its accurate identification is of great significance for the further study of urbanization related socio–economic and eco-environmental changes in the perspective of urban–rural contrast. Previous studies of urban–rural fringe identification had problems with narrow scope of application, low efficiency of identification, and the results were greatly influenced by subjective factors. Nighttime light, as an important product of human activities, can reflect the gradient changes of urban–rural landscapes, and can be used to identify urban–rural fringes. Therefore, a K–means–based approach was developed using Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light data. Taking Beijing City as an example, in this study we delineated its urban–rural fringes. Our results indicate that a ring–shaped urban–rural fringe surrounds urban central areas, with an area of 3712 km2, which is mainly located in new urban development zones. Inside the urban–rural fringe, lights fluctuated obviously, and the fluctuation index was up to 76.75. Meanwhile, the combination of nighttime light intensity and light fluctuation had better performance than that when they were considered separately in the identification of urban–rural fringes. Furthermore, the K–means algorithm based on nighttime light found more details related to urban–rural fringes when compared with the traditional mutation detection method. This study provided an approach to identifying urban–rural fringes accurately and objectively, which is conducive to the study of eco–environmental effects in the process of urbanization.