Identification of surface thermal environment differentiation and driving factors in urban functional zones based on multisource data: a case study of Lanzhou, China

• Published in: Frontiers in environmental science Vol. 12
• Main Authors: Wang, Yixuan, Yang, Shuwen
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 27.09.2024
• Subjects: driving factors; Lanzhou city; remote sensing; surface thermal environment; urban functional zones
• ISSN: 2296-665X; 2296-665X
• DOI: 10.3389/fenvs.2024.1466542

The urban functional zone, serving as a bridge to understanding the complex interactions between human spatial activities and surface thermal environmental changes, explores the driving force information of its internal temperature changes, which is crucial for improving the urban thermal environment. However, the impacts of the current urban functional zones on the thermal environment, based on the delineation of human activities, have yet to be sufficiently investigated. To address the issue, we constructed a two-factor weighted dominant function vector model of “population heat—land use scale” to identify urban functional zones. This model is based on multisource data and considers the perspective of urban functional supply and demand matching. We then analyzed the spatial differentiation and driving factors of the relationship between urban functional zones and the surface thermal environment using the random forest algorithm, bivariate spatial autocorrelation, geographical detectors, and geographically weighted regression models. The results showed that there are significant differences in the Land Surface Temperature among different urban functional zones in the central urban area of Lanzhou. Among these, the life service zone has the greatest impact on the surface thermal environment, followed by the industrial zone and catering service zone, while the green space zone has the least impact. The surface thermal environment exhibits high-high clusters in localized spatial clustering patterns with life service, industrial, catering service, and residential zones. In contrast, it tends to exhibit low-high clusters with green spaces. Significant spatial clustering and dependence exist between various functional zones and the surface thermal environment. The land cover types characterized by the Normalized Difference Bare Land and Building Index, the vegetation coverage represented by the Fraction of Vegetation Cover, and the density of industrial activities indicated by the Industrial POI Kernel Density Index are the main drivers of the surface thermal environment in the various functional zones of the central urban area of Lanzhou, and all exhibit significant spatial heterogeneity.