Monitoring ghost cities at prefecture level from multi-source remote sensing data

Abstract

Monitoring urban spatial information is important to hold the process of urbanization for keeping balance between the human activity and the environment. To promote the application extent of the remote sensing technology in the topic of ghost cities, an effective method was proposed to monitor and evaluate “ghost city” phenomenon in the prefecture level city of China by taking advantage of multi-source remote sensing datasets, namely the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) nighttime light data and other auxiliary data such as Landsat images and the Land-cover/use datasets. Based on several indexes related urban expansion and landscape pattern, experiments were conducted by using the proposed approach in Weihai, as classified by statistics and Landsat images. Compared with the Optimized-Sample-Selection (OSS) method, the proposed method achieved better performance with respect to relative less errors and better visual display of the spatial dynamics of urban expansion in Weihai during the year of 2000–2010, so as to reveal the specific characteristics of urban expansion patterns in those periods.