Local climate zones mapping using object-based image analysis and validation of its effectiveness through urban surface temperature analysis in China

Abstract

The concept of local climate zones (LCZ) received wide acceptance and it is now a global standard for urban structure classification. At present, remote sensing-based LCZ classification studies focus on the pixel level, and object-level-based investigations are scant. In the present study, an object-based remote sensing image analysis was utilized for LCZ mapping of three cities in the Yangtze River Delta including Shanghai, Nanjing, and Hangzhou. We also analyzed the spatial and temporal distributions of LCZ and established relationships between these zones and the surface temperatures in these megacities. According to the spatial and temporal patterns based on the spatial aggregation index, the LCZ in the three cities are mostly aggregated, and these are characterized by intense aggregation around low-rise buildings and weak aggregation near high-rise buildings. Analysis of seasonal characteristics of the surface temperatures of the LCZ types reveals that in the study area, the heat island effect is substantially higher during the summer than in the winter. Based on results from the single-factor and multiple comparison analyses, significant differences were confirmed between the surface temperatures of various object-based LCZs. Hence, we concluded that object-based LCZ classification is suitable for characterizing the urban heat island effect. These results also validate the applicability of the object-based image analysis (OBIA) in LCZ remote sensing mapping. These findings will advance the development and application of OBIA in LCZ mapping.