Robust drivers of urban land surface temperature dynamics across diverse landscape characters: An augmented systematic literature review

Abstract

To effectively develop strategies that address the escalating surface temperatures of cities in diverse landscape characters, various and sometimes contradicting drivers are presented in the literature. A synthesis of findings and observations in this aspect is lacking. Therefore, the main tenet of our study was to identify robust landscape metrics (LMs) that drive the dynamics of urban land surface temperature (ULST) and analyse the extent to which landscape character influences their impact. We adopted a systematic literature review protocol, augmented with different geospatial datasets (at a global scale) and applied mixed approaches for our review and analyses. A total of 101 relevant articles were identified, although skewed towards Asia; various methods were utilised in analysing the LMs – ULST relationship; about 432 unique LMs were revealed with only 11 % of these confirmed to be robust. Landscape character elements are found to exert a slight to moderate significant influence on the LMs − ULST relationship reported in the literature. This further strengthened our proposition of the need to consider landscape character elements in understanding the dynamics of ULST in different environments. To this end, we developed an interactive scheme to synthesize our findings which reveal robust LMs in diverse landscape characters. Our FAIRly-open study serves as a call to the scientific community and urban stakeholders to engage and interact with our findings as this may help rethink (current) ULST mitigation strategies. Also, combining our scheme with expert and local spatial knowledge of stakeholders can offer a practical foundation for addressing ULSTs across diverse landscapes.

Keywords

• Land surface temperature
• Landscape character
• Landscape metrics
• Unobtrusive methods
• Systematic literature review
• Machine learning