Recognizing surface urban heat 'island' effect and its urbanization association in terms of intensity, footprint, and capacity: A case study with multi-dimensional analysis in Northern China

Current research on the surface urban heat island (SUHI) effect rarely address the discussion of what exactly is the 'island' of urban surface thermal environment. Given this, this study attempted to 'islanding' the spatial morphology of the SUHI effect for 13 different case cities in Northern China. A Gaussian surface model was applied to depict the remote sensed thermal characteristics of the case cities along the urban-rural gradient. Multi-dimensional indicators including SUHI intensity, footprint, and capacity were derived to quantify the seasonal SUHI characteristics, representing the maximum intensity, impact range, and accumulated volume of SUHI effect, respectively. Based on that, the seasonal SUHI characteristics of the case cities thus were analyzed during a long-term period from 2000 to 2015. Thereafter the variations of the SUHI effect and its possible as-sociations with several representative urbanization factors were examined with the panel, cross-sectional, and time-series regression analyses separately. In this study, multi-indicator SUHI analysis revealed the generally prevalent and increasing seasonal SUHI effects in most cases, while significant urban cold island phenomena were also documented in part during the cold season. Urbanization processes such as booming population, land transformation, and economic development performed as the key contributors to the thermal variations in most cases. However, the case cities do not only suffer from their own unique thermal risks but are also subjected to varying influencers. In summary, this study highlights the multi-dimensional and heterogeneous characteristics of the SUHI effect and its urbanization association. That is, multi-indexing analysis of surface urban heat 'island' provides more robust and comprehensive characterization of urban land surface thermal environment, and multi-dimensional regression analysis contributes to reexamining the complicated nexus between the SUHI effect and urbanization process in the context of significant spatiotemporal heterogeneity. We believe that our work can provide relevant scholars with meaningful inspiration for future exploration of the SUHI effect.