MONITORING THE SPATIO-TEMPORAL TRAJECTORY OF URBAN AREA HOTSPOTS IN WUHAN, CHINA USING TIME-SERIES NIGHTTIME LIGHT IMAGES
Urban area hotspots can be considered as an ideal representation of spatial heterogeneity of human activities within a city, which is susceptible to regional urban expansion pattern pattern. However, in previous studies most researchers focused on extracting urban extent, leaving the interior variation of nighttime radiance intensity poorly explored. With the help of multi-source data sets such as DMSP/OLS (NTL), LST and NDVI, we proposed an applicable framework to identify and monitor the spatiotemporal trajectory of polycentric urban area hotspots. Firstly, the original NTL dataset were calibrated to reduce inconsistency and discontinuity. And we integrated NTL, LST as well as NDVI and established an urban index TVANUI capturing the approximate urban extents. Secondly, multi-resolution segmentation algorithm, neighborhood statistics analysis and a local-optimized threshold method were employed to get more precise urban extent with an overall accuracy above 85% and a Kappa above 0.70. Thirdly, the urban extents were utilized as masks to get corresponding radiance intensity from calibrated NTL. Finally, we established the Gaussian volume model for each cluster and the resulting parameters were used to quantitatively depict hotspot features (i.e., intensity, morphology and centroid dynamics). All the identified urban hotspot showed our framework could successfully capture polycentric urban hotspots, whose fitting coefficients were over 0.7. The spatiotemporal trajectory of hotspot powerfully revealed the impact of the regional urban growth pattern and planning strategies on human activities in the city of Wuhan. This study provides important insights for further studies on the relationship between the regional urbanization and human activities.