Abstract
Urban area hotspots are considered to be an ideal proxy for spatial heterogeneity of human activity, which is vulnerable to urban expansion. Nighttime light (NTL) images have been extensively employed in monitoring current urbanization dynamics. However, the existing studies related to NTL images mainly concern detection of urban areas, leaving inner spatial differences in urban NTL luminosity poorly explored. In this study, we propose an innovative approach to explore the spatiotemporal trajectory of urban area hotspots using monthly Visible Infrared Imaging Radiometer Suite (VIIRS) NTL images. Firstly, multi-temporal VIIRS NTL intensity was decomposed by time-series analysis to obtain annual stable components after data preprocessing. Secondly, the support vector machine (SVM) regression model was utilized to identify urban area hotspots. In order to ensure the model accuracy, the grid search and cross-validation method was integrated to achieve the optimized model parameters. Finally, we analyzed the spatiotemporal migration trajectory of urban area hotspots by the center of gravity method (i.e., shift distance and angle of urban area hotspot centroid). The results indicate that our method successfully captured urban area hotspots with a regression coefficient over 0.8. Meanwhile, the findings give an intuitive understanding of coupling interaction between urban area hotspots and socioeconomic indicators. This study provides important insights for further decision-making regarding sustainable urban planning.
Highlights
Urbanization is a global concern associated with massive population shift, urban expansion, and industrial structure adjustment [1,2,3]
We proposed an applicable framework to monitor the spatiotemporal variation of urban area hotspots using Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light (NTL) images
Urban area hotspots were identified by the optimal support vector machine (SVM) regression model combined with the grid search and k-fold cross-validation (K-CV) methods
Summary
Urbanization is a global concern associated with massive population shift, urban expansion, and industrial structure adjustment [1,2,3]. As a developing country, China has experienced rapid urbanization since the reform and opening-up policy was implemented [4,5]. The rapid urban expansion led to intensive population increases and regional development differences. With advances in remote sensing and Geographic Information Systems (GIS) technology, satellite imagery provides convenient and spatially explicit ways of mapping urban expansion. Compared with other remote sensing products, the artificial nighttime light (NTL) image, as a recorder of anthropogenic luminosity [6,7,8], provides a more specific perspective of urban dynamics at different scales (i.e., local, regional, and global)
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