scholarly journals Quantitative Analysis of a Spatial Distribution and Driving Factors of the Urban Heat Island Effect: A Case Study of Fuzhou Central Area, China

Author(s):  
Meizi You ◽  
Riwen Lai ◽  
Jiayuan Lin ◽  
Zhesheng Zhu

Land surface temperature (LST) is a joint product of physical geography and socio-economics. It is important to clarify the spatial heterogeneity and binding factors of the LST for mitigating the surface heat island effect (SUHI). In this study, the spatial pattern of UHI in Fuzhou central area, China, was elucidated by Moran’s I and hot-spot analysis. In addition, the study divided the drivers into two categories, including physical geographic factors (soil wetness, soil brightness, normalized difference vegetation index (NDVI) and modified normalized difference water index (MNDWI), water density, and vegetation density) and socio-economic factors (normalized difference built-up index (NDBI), population density, road density, nighttime light, park density). The influence analysis of single factor on LST and the factor interaction analysis were conducted via Geodetector software. The results indicated that the LST presented a gradient layer structure with high temperature in the southeast and low temperature in the northwest, which had a significant spatial association with industry zones. Especially, LST was spatially repulsive to urban green space and water body. Furthermore, the four factors with the greatest influence (q-Value) on LST were soil moisture (influence = 0.792) > NDBI (influence = 0.732) > MNDWI (influence = 0.618) > NDVI (influence = 0.604). The superposition explanation degree (influence (Xi ∩ Xj)) is stronger than the independent explanation degree (influence (Xi)). The highest and the lowest interaction existed in ”soil wetness ∩ MNDWI” (influence = 0.864) and “nighttime light ∩ population density” (influence = 0.273), respectively. The spatial distribution of SUHI and its driving mechanism were also demonstrated, providing theoretical guidance for urban planners to build thermal environment friendly cities.

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5032 ◽  
Author(s):  
Qiang Zhou ◽  
Yuanmao Zheng ◽  
Jinyuan Shao ◽  
Yinglun Lin ◽  
Haowei Wang

Previously published studies on population distribution were based on the provincial level, while the number of urban-level studies is more limited. In addition, the rough spatial resolution of traditional nighttime light (NTL) data has limited their fine application in current small-scale population distribution research. For the purpose of studying the spatial distribution of populations at the urban scale, we proposed a new index (i.e., the road network adjusted human settlement index, RNAHSI) by integrating Luojia 1-01 (LJ 1-01) NTL data, the enhanced vegetation index (EVI), and road network density (RND) data based on population density relationships to depict the spatial distribution of urban human settlements. The RNAHSI updated the high-resolution NTL data and combined the RND data on the basis of human settlement index (HSI) data to refine the spatial pattern of urban population distribution. The results indicated that the mean relative error (MRE) between the population estimation data based on the RNAHSI and the demographic data was 34.80%, which was lower than that in the HSI and WorldPop dataset. This index is suitable primarily for the study of urban population distribution, as the RNAHSI can clearly highlight human activities in areas with dense urban road networks and can refine the spatial heterogeneity of impervious areas. In addition, we also drew a population density map of the city of Shenzhen with a 100 m spatial resolution for 2018 based on the RNAHSI, which has great reference significance for urban management and urban resource allocation.


2013 ◽  
Vol 361-363 ◽  
pp. 499-503
Author(s):  
Xue Song Li ◽  
Xin Yu Tao ◽  
Su Li Zhang

The urban heat island phenomenon is one of the important problems faced by the metropolitan environment in hot-summer and cold-winter areas. In order to conduct truthful and in-depth analysis on this phenomenon, this study covers temperature data acquisition and a series of data collation and analysis conducted with the mobile testing method, more actually reflects the heat island spatial-temporal changes from the central area of Wuhan in Summer to the urban fringe, analyzes the urban morphology, urban underlying surface characteristics, and impacts of human factors and microclimate, and explores the feasible quantification basis for urban design and planning.


2019 ◽  
Vol 11 (13) ◽  
pp. 1553 ◽  
Author(s):  
Fei Li ◽  
Weiwei Sun ◽  
Gang Yang ◽  
Qihao Weng

Rapid urbanization has resulted in a serious urban heat island effect in the Hangzhou Metropolitan Area of China during the past decades, negatively impacting the area’s sustainable development. Using Landsat images from 2000 to 2015, this paper analysed the spatial-temporal patterns in a surface urban heat island (SUHI) and investigated its relationship with urbanization. The derived land surface temperature (LST) and surface urban heat island intensity (SUHII) were used to quantify the SUHI effect. Spatial analysis was employed to illustrate the spatial distribution and evolution of a SUHI. The geographically weighted regression (GWR) model was implemented to identify statistically significant factors that influenced the change of SUHII. The results show that hot and very hot spot areas increased from 387 km2 in 2000 to 615 km2 in 2015, and the spatial distribution changed from a monocentric to a polycentric pattern. The results also indicate that high-LST clusters moved towards the east, which was consistent with urban expansion throughout the study period. These changes mirrored the intensive development of three satellite towns. The statistical analysis suggests that both population density (e.g., changes in population density, CPOPD) and green space (e.g., changes in green space fraction, CGSF) strongly affected the changes in SUHII at different stages of the urbanization process. Increasing in population density has a lastingly effect on elevating the SUHII, whereas increasing green space has a constantly significant effect in mitigating the SUHII. These findings suggest that urban planners and policymakers should protect the cultivated lands in suburbs and exurbs, and make efforts to improve the utilization efficiency of construction land by encouraging the migrating population to live within the existing built-up regions.


2020 ◽  
Vol 12 (1) ◽  
pp. 442 ◽  
Author(s):  
Guoen Wei ◽  
Pingjun Sun ◽  
Zhenke Zhang ◽  
Xiao Ouyang

In order to analyze the coordination relationship between investment potential and economic development and its driving mechanisms, this study integrated the entropy weight method, coupling coordination degree model, exploratory spatial data analysis, geographic detector, and geographically weighted regression model. The developed approach was applied using data from 51 African countries from 2008 to 2016. The results showed that: (1) While the level of economic development in the African continent has increased steadily, the overall investment potential needs to be improved. The mean economic development index rose from 0.116 to 0.151, but the economic gap among countries was still highly evident. (2) Uncoordinated development and barely coordinated development level were the dominant types of relationship between investment potential and economic development in African countries. The spatial distribution showed significant agglomeration characteristics; the sub-hot spot and sub-cold point regions maintained strong dependence with their hot spot and cold point counterparts. The hot spot areas gradually formed an agglomeration in Southern Africa and highly fragmented distribution in other areas. The cold spot areas formed a spatial distribution pattern of “one core and one belt” with some countries in Western Africa forming the core, while some Central and East African countries constituting the belt. (3) The coordination relationship between investment potential and economic development was influenced mainly by factors including economic base, residents’ living standard, industrial construction level, information support level, and business friendliness. Using geographically weighted regression coefficient distribution of indicators, the driving mechanisms of spatial distribution could be divided into five types: economic base driven, industry-driven, information application-driven, business convenience-driven, and consumer market-driven.


Author(s):  
M. H. Huang ◽  
J. J. Chen

Abstract. China has experienced rapid urbanization and rapid development of economy in the past decades, resulting in severe damage to the urban ecological environment, causing changes in the urban thermal environment and triggering the urban heat island effect. Moreover, the heat island effect has become a hot topic for scholars. The urban heat island effect refers to the phenomenon that the urban surface temperature is significantly higher than that of surrounding suburbs due to the interaction of man-made and natural. The city is considered to be the largest man-made ecosystem. Its heat island effect will not only change the growth habit of urban vegetation, but also affect the outer environment of urban buildings, it further influences human life and has a great negative impact on human health. Therefore, the study of the spatial-temporal variation characteristics of urban heat island effect and its influencing factors can provide data support for the environmental quality control and urban planning of local government departments. Based on the surface temperature remote sensing product data, we studied the spatial distribution characteristics of urban heat island effect in Wuhan from 2001 to 2013, by calculating the temperature difference between the highest and lowest temperatures and the average interval method for heat island classification. We conducted a trend analysis of vegetation cover from 2001 to 2013 initially explore the effects of vegetation cover n heat island effect. The results showed that: (1) From 2001 to 2013, the intensity of heat island in Wuhan was strong in the city center, weaker surrounding city center and the weakest in the suburbs; From 2001 to 2011, the intensity of heat island in Wuhan city was significantly weaken, among which Huangpi, Xinzhou, Jiangxia, Hannan and Caidian district were weaken, and the urban heat island effect of the city center was enhanced; From 2011 to 2013, the intensity of heat island in Wuhan city presented an increasing trend, among which Huangpi district, Xinzhou district and Caidian district were the most obvious, and the urban heat island effect was slightly weaken. (2) Between 2001 and 2013, the vegetation cover in Huangpi district and Xinzhou district increased significantly, and the vegetation cover in the downtown, Jiangxia district and Dongxihu district decreased significantly, corresponding to the urban heat island effect of Wuhan increased volatility. Our results showed that the spatial distribution of urban heat island effect in Wuhan city fluctuated with time during the study period, and the vegetation cover had a significant influence on it.


2017 ◽  
Vol 39 ◽  
pp. 146
Author(s):  
Isabela Fernanda Moraes de Paula ◽  
Cássia Castro Martins Ferreira

A presença da cobertura vegetal nas cidades tem sido considerada por diversos pesquisadores uma variável importante, devido aos diversos benefícios que proporcionam ao homem e ao equilíbrio ambiental. Nesse contexto este artigo objetiva contribuir para o conhecimento do verde urbano da área central do município de Juiz de Fora, calculando índices de cobertura vegetal e aplicando a metodologia proposta por Jim (1989), na análise da forma e espacialização da cobertura vegetal. Nesse sentido, os resultados alcançados demonstram que grande parte das regiões da área central da cidade de Juiz de Fora encontram-se abaixo do desejável em cobertura vegetal, necessitando de investimentos, principalmente, nos espaços de integração urbana, cujo percentual de áreas cobertas por vegetação em relação à totalidade abrange apenas 2%. Destaca-se que quanto maior a densidade demográfica, menor foi o percentual de cobertura vegetal, pode-se afirmar que a cobertura vegetal da área central da cidade de Juiz de Fora é fragmentada, descontínua e apresenta muitos “espaços vazios”. No mapeamento realizado foi encontrado 15,401% de áreas cobertas por vegetação arbórea, cerca de 1,694% de vegetação arbustiva e 8,59% de vegetação rasteira. As maiores extensões de manchas verdes encontram-se dispersas no meio, espalhadas por toda a área e desconectas uma com as outras. Logo, sua mensuração, classificação e distribuição espacial são de suma importância, pois tornam-se base essenciais para melhorias e planejamentos, no contexto das áreas urbanas.ABSTRACTThe presence of vegetation cover in the cities has been considered by many researchers an important variable, due to the many benefits they provide to humans and the environmental balance. In this context, this article aims to contribute to the knowledge of green urban central area of the city of Juiz de Fora, calculating vegetation cover ratios and applying the methodology proposed by Jim (1989), in the analysis of the shape and spatial distribution of vegetation cover. In this sense, the results achieved show that most regions of the central area of the city of Juiz de Fora are less than desirable in vegetation cover, requiring investments, mainly in the areas of urban integration, whose percentage of areas covered by vegetation in respect of all covers only 2%. It is noteworthy that the higher the population density, the lower the percentage of vegetation cover, it can be said that the vegetation cover in the central area of the city of Juiz de Fora is fragmented, discontinuous and presents many "empty spaces". In the mapping carried out was found 15.401% of areas covered by woody vegetation, about 1.694% of shrub and 8.59% of undergrowth. The largest expanses of green spots are scattered in between, scattered throughout the area and disconnect with each other. Therefore, its measurement, classification and spatial distribution are of paramount importance as it become essential basis for improvements and planning in the context of urban areas.


2013 ◽  
Vol 409-410 ◽  
pp. 612-616
Author(s):  
Xue Song Li ◽  
Hong Yi Li ◽  
Su Li Zhang

The urban heat island phenomenon is one of the important problems faced by the metropolitan environment in hot-summer and cold-winter areas. In order to conduct truthful and in-depth analysis on this phenomenon, this study covers temperature data acquisition and a series of data collation and analysis conducted with the mobile testing method, more actually reflects the heat island spatial-temporal changes from the central area of Wuhan in Winter to the urban fringe, analyzes the urban morphology, urban underlying surface characteristics, and impacts of human factors and microclimate, and explores the feasible quantification basis for urban design and planning.


Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 770
Author(s):  
Fan Liu ◽  
Xiaoding Liu ◽  
Tao Xu ◽  
Guang Yang ◽  
Yaolong Zhao

Understanding the driving factors and assessing the risk of rainstorm waterlogging are crucial in the sustainable development of urban agglomerations. Few studies have focused on rainstorm waterlogging at the scale of urban agglomeration areas. We used the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China as a case study. Kernel density estimation (KDE) and spatial autocorrelation analysis were applied to study the spatial distribution characteristics of rainstorm waterlogging spots during 2013–2017. A geographical detector (GD) and geographically weighted regression (GWR) were used to discuss the driving mechanism of rainstorm waterlogging by considering eight driving factors: impervious surface ratio (ISR), mean shape index of impervious surface (Shape_MN), aggregation index of impervious surface (AI), fractional vegetation cover (FVC), elevation, slope, river density, and river distance. The risk of rainstorm waterlogging was assessed using GWR based on principal component analysis (PCA). The results show that the spatial distribution of rainstorm waterlogging in the GBA has the characteristics of multicenter clustering. Land cover characteristic factors are the most important factors influencing rainstorm waterlogging in the GBA and most of the cities within the GBA. The rainstorm waterlogging density increases when ISR, Shape_MN, and AI increase, while it decreases when FVC, elevation, slope, and river distance increase. There is no obvious change rule between rainstorm waterlogging and river density. All of the driving factors enhance the impacts on rainstorm waterlogging through their interactions. The relationships between rainstorm waterlogging and the driving factors have obvious spatial differences because of the differences in the dominant factors affecting rainstorm waterlogging in different spatial positions. Furthermore, the result of the risk assessment of rainstorm waterlogging indicates that the southwest area of Guangzhou and the central area of Shenzhen have the highest risks of rainstorm waterlogging in GBA. These results may provide references for rainstorm waterlogging mitigation through urban renewal planning in urban agglomeration areas.


2021 ◽  
Author(s):  
Han Gao ◽  
Jiahong Liu ◽  
Chao Mei ◽  
Wang Hao ◽  
Weiwei Shao ◽  
...  

Abstract Urban water dissipation is increasing gradually as urbanization progresses. Urban water dissipation mainly includes the dissipation of water in buildings and natural water evapotranspiration. Previous studies have mainly focused on calculating natural evapotranspiration in urban areas and have overlooked the dissipation of water in buildings under the influence of strong human-related water use activities. In this paper, the concept of building water dissipation (BWD) was proposed to describe the phenomenon that water dissipation occurs inside buildings. Moreover, a BWD calculation model was established and applied to calculate global building water dissipation. To reveal the specific water dissipation inside buildings, it is necessary to obtain the urban building floor area first. This paper proposed a new method to calculate the urban building floor area based on global nighttime light data obtained from NPP-VIIRS. Taking the floor area results into the BWD calculation model, the global building water dissipation in urban areas was found to be 127 billion m3 in 2015. The vast building water dissipation that occurs in urban areas mostly results from rapidly developing economies and intense human activities. The results provide a basic understanding of the nexus between water resources and the energy-heat island effect in urban areas.


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