Analysis method of ghost city phenomenon based on multi-source remote sensing data
Remote sensing technology plays a crucial role in monitoring the urbanization process and understanding the harmonious coexistence of humans and nature. To further promote the in-depth application of remote sensing technology in urban thematic research,this paper proposed an analysis method of ghost city phenomenon based on multi-source remote sensing data. This method integrated features and complemented advantages between night-time light data from the defense meteorological satellite program (DMSP) and data from Landsat remote sensing images. On this basis,the paper established a sample refinement and iterative classification mechanism within the supervised classification framework of support vector machine (SVM). The refined urban built-up area results obtained were then incorporated into the existing ghost city index (GCI),so as to analyze the spatio-temporal distribution characteristics of ghost city phenomena within the study area. To test this method,multiple cities under the jurisdiction of the Guangxi Zhuang Autonomous Region were selected as the study areas to analyze the ghost city phenomenon from 2008 to 2015. The effectiveness of the proposed method was validated in terms of visualization effects and accuracy. Experimental results indicate that the proposed approach can obtain more precise spatio-temporal distribution characteristics of ghost city phenomena compared to traditional SVM-based supervised classification methods.
remote sensing technologyghost city phenomenonnight-time light dataghost city index(GCI)spatio-temporal distribution characteristics
万方数据
维普
