Comparing the Evolution of Land Surface Temperature and Driving Factors between Three Different Urban Agglomerations in China

Lizhi Pan, Chaobin Yang (), Jing Han, Fengqin Yan, Anhua Ju and Tong Kui
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Lizhi Pan: School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255049, China
Chaobin Yang: School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255049, China
Jing Han: School of Space Science and Physics, Shandong University, Weihai 264209, China
Fengqin Yan: State Key Laboratory of Resource and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Anhua Ju: College of Earth Science, Chengdu University of Technology, Chengdu 610059, China
Tong Kui: School of Civil Engineering and Geomatics, Shandong University of Technology, Zibo 255049, China

Sustainability, 2024, vol. 16, issue 2, 1-17

Abstract: Increases in land surface temperature (LST) and the urban heat island effect have become major challenges in the process of urban development. However, few studies have examined variations in LST between different urban agglomerations (UAs). Based on MODIS LST data, we quantitatively analyzed the spatial and temporal evolution patterns of LST in three different UAs in China from 2000 to 2020—Beijing–Tianjin–Hebei (BTH) at the national level, the Shandong Peninsula (SP) at the regional level, and Central Shanxi (CS) at the city level—by employing urban agglomeration built-up area intensity (UABI), linear regression analyses, and geodetic detector models. The results showed the following: (1) The spatial and temporal evolution pattern of the LST in BTH was the most regularized; the spatial pattern of the LST in SP gradually evolved from “two points” to “a single branch”; and the LST of CS was easily influenced by the neighboring big cities. (2) The best-fitting coefficients for BTH, SP, and CS were R 2 BTH = 0.58, R 2 SP = 0.66, and R 2 CS = 0.58, respectively; every 10% increase in UABI warmed the LSTs in BTH, SP, and CS by 1.47 °C, 1.27 °C, and 1.83 °C, respectively. (3) The ranking of single-factor influence was DEM (digital elevation model) > UABI > NDVI > T 2m (air temperature at 2 m) > POP (population). The UABI interacting with DEM had the strongest warming effect on LST, with the maximum value q(UABI ∩ DEM) BTH = 0.951. All factor interactions showed an enhancement of the LST in CS, but factors interacting with POP showed a weaker effect in BTH and SP, for which q(NDVI ∩ POP) BTH = 0.265 and q(T 2m ∩ POP) SP = 0.261. As the development of UAs gradually matures, the interaction with POP might have a cooling effect on the environment to a certain degree.

Keywords: land surface temperature; urban agglomerations; spatial and temporal evolution; urban remote sensing; geodetic detector models (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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