Influence of land cover change on spatio-temporal distribution of urban heat island —a case in Wuhan main urban area

Chen, Haoyuan; Deng, Qinli; Zhou, Zeng; Ren, Zhiyuan; Shan, Xiaofang

doi:10.1016/j.scs.2022.103715

Cited by 58 publications

(16 citation statements)

References 42 publications

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“…Proximity to the coast and tree canopy or vegetation have a negative correlation with LST during the day, but this relationship is positive during the night. This result is similar to previous studies for SUHI [12,48,49]. Higher day LST values appear to be contained in coastal urban areas while night values are more spread out in the MSA, as Figure 3 shows.…”

Section: Discussionsupporting

confidence: 91%

Evaluating Land Surface Temperature Trends and Explanatory Variables in the Miami Metropolitan Area from 2002–2021

Shapiro,

Liu

2023

Geomatics

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Physical and climatic variables such as Tree Canopy coverage, Normalized Difference Vegetation Index (NDVI), Distance to Roads, Distance to the Coast, Impervious Surface, and Precipitation can affect land surface temperature (LST). This paper examines the relationships using linear regression models and explores LST trends in the Miami Statistical Area (MSA) between 2002 and 2021. This study evaluates the effect of dry and wet seasons as well as day and night data on LST. A multiscale investigation is used to examine LST trends at the MSA scale, the individual county level, and at the pixel level to provide a detailed local perspective. The multiscale results are needed to understand spatiotemporal LST distributions to plan mitigation measures such as planting trees or greenery to regulate temperature and reduce the impacts of surface urban heat islands. The results indicate that LST values are rising in the MSA with a positive trend throughout the 20-year study period. The rate of change (RoC) for the wet season is smaller than for the dry season. The pixel-level analysis suggests that the RoC is primarily in rural areas and less apparent in urban areas. New development in rural areas may trigger increased RoC. This RoC relates to LST in the MSA and is different from global or regional RoC using air temperature. Results also suggest that climatic explanatory variables have different impacts during the night than they do in the daytime. For instance, the Tree Canopy variable has a positive coefficient, while during the day, the Tree Canopy variable has a negative relationship with LST. The Distance to the Coast variable changes from day to night as well. The increased granularity achieved with the multiscale analysis provides critical information needed to improve the effectiveness of potential mitigation efforts.

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Section: Discussionsupporting

confidence: 91%

Evaluating Land Surface Temperature Trends and Explanatory Variables in the Miami Metropolitan Area from 2002–2021

Shapiro,

Liu

2023

Geomatics

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“…Residents' demand for cold islands in parks and green spaces is mainly manifested in two aspects: physiological and psychological. In terms of physiological health, Chen et al found through pre and post comparative experiments that people who engage in activities such as jogging, walking, and talking in the park experience had significant relief in headaches and stress [10]; Chen et al research showed that living close to urban parks is beneficial for improving residents' physical fitness and self-rated health levels [11]; Yu et al concluded that residents who frequently use parks could effectively reduce their cardiovascular disease risk [12]; Li Jing et al believed that in the post pandemic era, the public's emphasis on health had increased, and urban parks could provide places for residents' health activities [13]. In terms of mental health, Korpela et al selected different scenarios as the experimental subjects.…”

Section: Introductionmentioning

confidence: 99%

Quantitative analysis and optimization of urban park cold island in Wuhan’s main urban area from the perspective of residents’ demand: based on Landsat-8 remote sensing images

Wang,

Wu,

Guo

2024

International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2024)

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Urban heat island effect has become increasingly severe, causing a series of negative impacts on people's physical and mental health. Urban parks have a significant cold island effect, which can effectively alleviate the thermal environment. From the perspectives of the physiological and psychological needs of urban residents, this study establishes four indicator criteria: surface temperature (LST), heat island patch area (S), population density (PD), and accessibility (AI), with aiming for constructing a quantitative evaluation system for cold island demand; Indicators and weights are calculated through research methods such as surface temperature retrieval, mean standard deviation method, kernel density analysis, improved two-step moving search method, subjective and objective weighting method. This study selects 59 urban parks within the main urban area of Wuhan and uses the above evaluation system to calculate the cold island demand index, in order to generate a map of Wuhan park’s cold island demand distribution. The results show the following: (1) the total cold island demand urgent area in Wuhan’s main urban area is 459.4 km2, among which the most urgently needed area (demand level III) is 78.2 km2, accounting for 11.1% of the research area. This demand area has obvious spatial characteristics, particularly scattered multicore and spatial distribution along the main road. (2) The cold island demand in Wuhan, particularly in areas classified under demand level III, can be attributed to three main reasons: lack of walking accessibility, high population density, and lack of ecological coverage. Finally, on the basis of three categories, the most typical and urgent points are selected and then optimization measures are proposed based on the actual nature of land planning and ecological conditions, which can contribute to the alleviation of urban thermal environment in Wuhan.

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“…Others have adopted a reverse-thinking approach to explore the thermal spatial pattern in this area, attempting to alleviate the urban heat island effect by identifying the heat source points [24]. Additionally, some researchers have focused on studying LST variations at the individual city level by using Wuhan City as their research scope [45,46]. However, studies on the seasonal variations and driving factors of the LST in this region are still relatively scarce.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of the Seasonal Driving Factors of the Urban Heat Environment Using Machine Learning: Evidence from the Wuhan Urban Agglomeration, China, 2020

Xu,

Huang,

Zhang

2024

Atmosphere

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With the ongoing advancement of globalization significantly impacting the ecological environment, the continuous rise in the Land Surface Temperature (LST) is increasingly jeopardizing human production and living conditions. This study aims to investigate the seasonal variations in the LST and its driving factors using mathematical models. Taking the Wuhan Urban Agglomeration (WHUA) as a case study, it explores the seasonal characteristics of the LST and employs Principal Component Analysis (PCA) to categorize the driving factors. Additionally, it compares traditional models with machine-learning models to select the optimal model for this investigation. The main conclusions are as follows. (1) The WHUA’s LST exhibits significant differences among seasons and demonstrates distinct spatial-clustering characteristics in different seasons. (2) Compared to traditional geographic spatial models, Extreme Gradient Boosting (XGBoost) shows better explanatory power in investigating the driving effects of the LST. (3) Human Activity (HA) dominates the influence throughout the year and shows a significant positive correlation with the LST; Physical Geography (PG) exhibits a negative correlation with the LST; Climate and Weather (CW) show a similar variation to the PG, peaking in the transition; and the Landscape Pattern (LP) shows a weak positive correlation with the LST, peaking in winter while being relatively inconspicuous in summer and the transition. Finally, through comparative analysis of multiple driving factors and models, this study constructs a framework for exploring the seasonal features and driving factors of the LST, aiming to provide references and guidance for the development of the WHUA and similar regions.

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Influence of land cover change on spatio-temporal distribution of urban heat island —a case in Wuhan main urban area

Cited by 58 publications

References 42 publications

Evaluating Land Surface Temperature Trends and Explanatory Variables in the Miami Metropolitan Area from 2002–2021

Evaluating Land Surface Temperature Trends and Explanatory Variables in the Miami Metropolitan Area from 2002–2021

Quantitative analysis and optimization of urban park cold island in Wuhan’s main urban area from the perspective of residents’ demand: based on Landsat-8 remote sensing images

Comparative Analysis of the Seasonal Driving Factors of the Urban Heat Environment Using Machine Learning: Evidence from the Wuhan Urban Agglomeration, China, 2020

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