Identification of Key Drivers of Land Surface Temperature Within the Local Climate Zone Framework

Yuan Feng, Guangzhao Wu, Shidong Ge, Fei Feng and Pin Li ()
Additional contact information
Yuan Feng: State Key Laboratory of Efficient Production of Forest Resources, The Key Laboratory for Silviculture and Conservation of Ministry of Education, Key Laboratory for Silviculture and Forest Ecosystem of State Forestry and Grassland Administration, Beijing Forestry University, Beijing 100083, China
Guangzhao Wu: State Key Laboratory of Efficient Production of Forest Resources, The Key Laboratory for Silviculture and Conservation of Ministry of Education, Key Laboratory for Silviculture and Forest Ecosystem of State Forestry and Grassland Administration, Beijing Forestry University, Beijing 100083, China
Shidong Ge: International Union Laboratory of Landscape Architecture, College of Landscape Architecture and Art, Henan Agricultural University, Zhengzhou 450003, China
Fei Feng: State Key Laboratory of Efficient Production of Forest Resources, The Key Laboratory for Silviculture and Conservation of Ministry of Education, Key Laboratory for Silviculture and Forest Ecosystem of State Forestry and Grassland Administration, Beijing Forestry University, Beijing 100083, China
Pin Li: State Key Laboratory of Efficient Production of Forest Resources, The Key Laboratory for Silviculture and Conservation of Ministry of Education, Key Laboratory for Silviculture and Forest Ecosystem of State Forestry and Grassland Administration, Beijing Forestry University, Beijing 100083, China

Land, 2025, vol. 14, issue 4, 1-25

Abstract: The surface urban heat island (SUHI) effect, driven by human activities and land cover changes, leads to elevated temperatures in urban areas, posing challenges to sustainability, public health, and environmental quality. While SUHI drivers at large scales are well-studied, finer-scale thermal variations remain underexplored. This study employed the Local Climate Zones (LCZs) framework to analyze land surface temperature (LST) dynamics in Zhengzhou, China. Using 2022 mean LST data derived from a single-channel algorithm, combined with field surveys and remote sensing techniques, we examined 30 potential driving factors spanning natural and anthropogenic conditions. Results show that built-type LCZs had higher average LSTs (31.10 °C) compared with non-built LCZs (28.91 °C), with non-built LCZs showing greater variability (10.48 °C vs. 6.76 °C). Among five major driving factor categories, landscape pattern indices dominated built-type LCZs, accounting for 44.5% of LST variation, while Tasseled Cap Transformation indices, particularly brightness, drove 42.8% of the variation in non-built-type LCZs. Partial dependence analysis revealed that wetness and landscape fragmentation reduce LST in built-type LCZs, whereas GDP, imperviousness, and landscape cohesion increase it. In non-built LCZs, population density, connectivity, and brightness raise LST, while wetness and atmospheric dryness provide cooling effects. These findings highlight the need for LCZ-specific SUHI mitigation strategies. Built-type LCZs require urban form optimization, enhanced landscape connectivity, and expanded green infrastructure to reduce heat accumulation. Non-built LCZs benefit from maintaining soil moisture, addressing atmospheric dryness, and optimizing vegetation configurations. This study provides actionable insights for sustainable thermal environment management and urban resilience.

Keywords: urban heat island; local climate zones; land surface temperature; landscape pattern indices; driving factors (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-445X/14/4/771/pdf (application/pdf)
https://www.mdpi.com/2073-445X/14/4/771/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:14:y:2025:i:4:p:771-:d:1627804

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().