Iryna Dronova, Assistant Professor in Landscape Architecture & Environmental Planning
Yang Ju, PhD student, LAEP, Assistant Professor Iryna Dronova, LAEP, Qin Ma, PhD candidate at University California, Merced and Xiang Zhang, research associate at Nanjing University
Landscape Architecture and Environmental Planning at the University of California, Berkeley
University of California, Merced
Ph.D student Yang Ju, Assistant Professor Iryna Dronova, and their co-authors, Qin Ma, PhD candidate at University California, Merced, and Xiang Zhang, research associate at Nanjing University, recently published a paper using satellite-sensed night-time light images and machine learning to detect fine-scale urbanization trends over time. The paper, titled ‘Analysis of Urbanization Dynamics in Mainland China Using Pixel-based Nighttime Light Trajectories from 1992 to 2013’, was published in the International Journal of Remote Sensing’s special issue, Remote Sensing of Night-time Light, featuring the application and methodological innovations of night-time light remote sensing.
Understanding how urbanization progresses over both space and time has several important implications in planning and management. Night-time light sensing is suitable for determing that data reflects three major aspects of urbanization: urban land-cover expansion, population growth, and economic activity intensification. This paper developed a framework to use night-time light time-series trajectories to identify major typologies of urbanization trends at 1 km by 1 km resolution. With several machine learning techniques, the authors found five major trends – stable, high and low level steady growth, acceleration, and fluctuation, in mainland China between 1992 and 2013.
The paper focuses on one trend, acceleration, as it is a potential indicator for aggressive urbanization. The authors identified several spatial clusters of cities with high concentration of the acceleration trend. One cluster is in the Yangtze River Delta whose growth is facilitated by rural industries. The other is in the Inner Mongolian region where urbanization is stimulated by its rich natural resources and land-centered development policy.
Ju and Dronova concluded with observations about the promising future of using night-time light time-series to monitor urbanization, as this approach provides a low-cost, timely, and large-scale understanding about how urbanization happens over space and time. Ju is looking to apply this approach to other topics, such as understanding urban exposure to climate change, and assessing environmental disaster impact and recovery.
Read the journal article here.