夜光遥感大数据视角下的中国城市化时空特征

社会经济的高速发展直接驱动了中国过去几十年的持续快速城市化进程。城市化是一个典型的复杂地理现象,伴随着高密度人口聚集、土地利用改变、基础设施建设和生态环境变化等系列人和自然交互过程的发生。深入理解城市发展的时空演化规律对研究、规划、管理和相关政策制定在内的诸多领域都有十分重要的意义。近些年来,由于快速发展的夜光遥感大数据具有空间清晰的与城市化有关的社会经济活动强度的感知信息,其为探索城市和城市化问题提供了新的研究途径。虽然有许多成果对利用夜光遥感数据进行城市化的研究进行了探讨,但大部分集中在城市化面积、人口规模和其他社会经济变量的定量相关和数值统计分析上,仍然缺乏对中国过去几十年来的城市化时空特征的综合多角度分析和理解。本研究利用1992-2013年的夜光遥感时间序列大数据,从夜光照亮面积、亮度变化时间转折点、不同亮度区的空间结构转换和亮度信号的空间扩散速度4个方面进行了定量化的信息提取与分析。研究结果从夜光辐射遥感大数据的视角综合揭示了中国在过去研究期间包括城市空间扩展、城市化发展的时间分布、城市空间结构演化和城市化活动空间扩散速度在内的定量时空特征。本研究的结果可以为深入理解中国城市化的时空模式与演化特征提供新的参考。

Spatiotemporal Characteristics of Urbanization in China from the Perspective of Remotely Sensed Big Data of Nighttime Light

The rapid growth of nation's economy has driven the unprecedented pace of urbanization in China over the past several decades. Urbanization process is a complicated geographical phenomenon involving human-nature interactions, such as population aggregation, land use change, infrastructure construction and eco-environmental changes. Hence, an understanding of the spatiotemporal dynamics of urban development is increasingly important for a variety of issues including research, planning, management and policy decision making. Owing to a spatially and temporally explicit manner of sensed information with respect to the magnitude of socio-economic activity related to urban development, the recent emergence of satellite-derived nighttime light data provides new means for investigating urban patterns and urbanization processes. In the present study, four kinds of quantitative information, including the spatial lighting area, temporal turning point, the spatial transformation of different types of lit areas and the velocity of spatial disperse of nighttime lightings signals, have been obtained and quantitatively analyzed based on time series of big data of annual composite products of nighttime light radiances during the period 1992-2013 from the Defense Meteorological Satellite Program (DMSP). Analysis results reveal the spatiotemporal patterns of China's urbanization over the past 22 years from the perspective of remotely sensed big data of artificial nighttime lighting signals in context of the spatial expansion, the distribution of urbanization onset time, the evolution of spatial structure and the urbanization velocity. This study can provide new insights into the understating of the fundamental spatiotemporal features of the rapid urbanization process in the present-day China using the remotely sensed big data of observed anthropogenic nighttime lighting signals.