Quantifying the Spatial Heterogeneity Relations Between Thermal Environment Effects and Influencing Factors Based on Geodetector in Central City of Chengdu
DOI:
https://doi.org/10.54097/hset.v64i.11245Keywords:
Thermal environmental effects; Geodetector; spatial heterogeneity; influence factors; Chengdu city.Abstract
To address the current situation that urban thermal environment research lacked comprehensive assessment and analysis from natural-social multidimensional factors, this study analyzed the evolution characteristics of thermal environment effects in the central urban area of Chengdu from 2004 to 2019 based on multi-source data. A total of seven influencing factors of natural and social factors that were highly associated with the urban thermal environment were selected, and the influencing factors of spatial differentiation of land surface temperature (LST) and their interactions were studied by applying Geodetector. The results showed that: (1) the heat island in the central city of Chengdu from 2004 to 2019 was radiating outward from the center, and the spatial development spine of the thermal environment remained in the "southeast-northwest" direction, with a significant shift of the center of gravity of the thermal environment from 2011 to 2019; (2) the average LST from 2004 to 2019 increased from 36.57℃to 40.64℃, and the total area of heat island area increased from 641 km2 to 958 km2. overall, it showed a shift from sub-medium zone and medium temperature zone to sub-high temperature zone and high temperature zone, and the urban heat island effect tended to enhance; (3) the nighttime light intensity had the largest amount of contribution to the spatial differentiation of LST, and social factors had a more important position relative to natural factors in influencing the urban thermal environment but the amount of contribution from natural factors kept increasing.
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Yu W, Xu J, Xu L. An analysis on eco-environmental effect of urban land use based on remote sensing images: a case study of urban thermal environment and NDVI [J]. Acta Ecologica Sinica, 2006, 26(5): 1450-1460.
Shen Q, Zhang X, Zhou Y. Urban Ecological Crisis: Characteristics, Factors and Analysis [J]. Urban Planning Forum, 2012(6): 23–33.
Kovats RS, Hajat S. Heat Stress and Public Health: A Critical Review [J]. Annu Rev Public Health, 2008, 29: 41–55.
Grimm NB, Faeth SH, Golubiewski NE, Redman CL, Wu J, Bai X, et al. Global Change and the Ecology of Cities [J]. Science, 2008, 319(5864): 756–760.
Bokaie M. Assessment of Urban Heat Island Based on the Relationship between and Land Use/Land Cover in Tehran [J]. Sustainable Cities and Society. 2016, 23: 94–104.
Wang Y, Wu J, Li W, Li Z. Study on land surface temperature vegetation cover relationship in urban region: a case in Shenzhen City [J]. Geographical Research, 2006, 25(3): 369-377+561.
Li Y, Sun Y, Li J, Gao C. Socioeconomic drivers of urban heat island effect: Empirical evidence from major Chinese cities [J]. Sustainable Cities and Society, 2020, 63.
Mitchell BC, Chakraborty J. Urban Heat and Climate Justice: A Landscape of Thermal Inequity in Pinellas County, Florida [J]. Geographical Review, 2014, 104(4): 459–480.
Zhou P, Deng W, Zhang S, Meng B. Evolution Characteristics and Its Driving Force of Territory Space Pattern in the Taihang Mountain, China [J]. Mountain Research, 2020, 38(2): 276–289.
Luo J, Shi P, Zhang X. Population distribution and influencing factors in Lanzhou-Xi’ning urban agglomeration based on township scale [J]. Journal of Arid Land Resources and Environment, 2020, 34(7): 104–111.
Zhou J, Zhang X, Tang W, Ding L, Ma J, Zhang X. Daily 1-km all-weather land surface temperature dataset for Western China (TRIMS LST-TP; 2000-2021) V2. 2019.
Zhang X, Zhou J, Liang S, Wang D. A practical reanalysis data and thermal infrared remote sensing data merging (RTM) method for reconstruction of a 1-km all-weather land surface temperature [J]. Remote Sensing of Environment. 2021, 260.
Zhang X, Zhan W, Liu S, Cao R. A Method Based on Temporal Component Decomposition for Estimating 1-km All-Weather Land Surface Temperature by Merging Satellite Thermal Infrared and Passive Microwave Observations [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2019, 57(7): 4670-4691.
Zhou J, Zhang X, Liu S, Olesen FS, Hu W, Dai F. A Thermal Sampling Depth Correction Method for Land Surface Temperature Estimation from Satellite Passive Microwave Observation Over Barren Land [J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2017, 55(8): 4743–4756.
Yang J, Huang X. The 30 m annual land cover dataset and its dynamics in China from1990 to 2019 [J]. Earth System Science Data, 2021, 13(8): 3907-3925.
Chen S, Wang T. Comparison Analyses of Equal Interval Method and Mean-standard Deviation Method Used to Delimitate Urban Heat Island [J]. Journal of Geo-information Science. 2009, 11(2): 145–150.
Yao X, Wang X, Yao X. Thermal environmental effect of the main urban area of Fuyang City in northern Anhui based on RS technology [J]. Chinese Journal of Ecology, 2021, 40(10): 3290–3303.
Wang J, Li X, Christakos G, Liao Y, Zhang T, Gu X, et al. Geographical Detectors-Based Health Risk Assessment and its Application in the Neural Tube Defects Study of the Heshun Region, China [J]. International Journal of Geographical Information Science, 2010, 24(1): 107-127.
Wang J, Zhang T, Fu B. A measure of spatial stratified heterogeneity [J]. Ecological Indicators, 2016, 67: 250–256.
Wang J, Xu C. Geodetector: Principle and prospective [J]. Acta Geographica Sinica, 2017, 72(01): 116–134.
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