Spatial structure and temporal variability of a surface urban heat island in cold continental climate

Esau, Igor; Miles, Victoria; Varentsov, Mikhail; Konstantinov, Pavel; Melnikov, V. P.

doi:10.1007/s00704-018-02754-z

Cited by 18 publications

(5 citation statements)

References 80 publications

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“…Since then, UHIs have been examined across years, seasons and days, with most of the literature concluding it reaches the peak in summer [ 22 ]. This phenomenon worsens every year and is more pronounced at night [ 23 , 24 ]. As for the formation mechanism, more attention are paid to land use/land cover (LULC), landscape index, and energy consumption [ 25 , 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

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

You

Lai

Lin

et al. 2021

IJERPH

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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.

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Section: Introductionmentioning

confidence: 99%

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

You

Lai

Lin

et al. 2021

IJERPH

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“…These +0.5 to +4.3 • C annual mean LST anomalies should be added on the top of already amplified climate warming in the circum-Arctic region. In fact, the urban climate measured by the LST corresponds to the expected regional temperature change by 2040-2080 and beyond (Esau et al, 2020). Esau et al (2020) and Miles and Esau (2020) updated.…”

Section: Discussionmentioning

confidence: 95%

“…In fact, the urban climate measured by the LST corresponds to the expected regional temperature change by 2040-2080 and beyond (Esau et al, 2020). Esau et al (2020) and Miles and Esau (2020) updated. NA stays not "Not Applied" specifying absence of data.…”

Section: Discussionmentioning

confidence: 95%

Urban heat islands in the Arctic cities: an updated compilation of in situ and remote-sensing estimations

et al. 2021

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Abstract. Persistent warm urban temperature anomalies – urban heat islands (UHIs) – significantly enhance already amplified climate warming in the Arctic. Vulnerability of urban infrastructure in the Arctic cities urges a region-wide study of the UHI intensity and its attribution to UHI drivers. This study presents an overview of the surface and atmospheric UHIs in all circum-Arctic settlements (118 in total) with the population larger than 3000 inhabitants. The surface UHI (SUHI) is obtained from the land surface temperature (LST) data products of the Moderate Resolution Imaging Spectroradiometer (MODIS) archive over 2000–2016. The atmospheric UHI is obtained from screen-level temperature provided by the Urban Heat Island Arctic Research Campaign (UHIARC) observational network over 2015–2018. Several other UHI studies are included for comparisons. The analysis reveals strong and persistent UHI during both summer and winter seasons. The annual mean surface UHI magnitudes vary from −0.6 ∘C (Hammerfest) to 4.3 ∘C (Murmansk). Thus, the observed UHI is likely an important climatic factor that must be included in future adjustment of urban construction, safety, and environmental quality codes.

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“…Other non-spatial variables that may affect energy consumption include building design, heating efficiency, HVAC efficiency, and residents' behavior. Urban planning and design factors are associated with microclimate variables, including daylight exposure, sunlight direction (affecting the heating), wind current (affecting wind protection as well as optimal ventilation), and local temperature (heat island effect) Ayanlade, 2017;Barile, Leoni, Pantoli, & Stornelli, 2018;Chen et al, 2019;Esau, Miles, Varentsov, Konstantinov, & Melnikov, 2019). These microclimate variables, in turn, affect the energy consumption of residential units.…”

Section: Discussionmentioning

confidence: 99%

Computational modeling of land surface temperature using remote sensing data to investigate the spatial arrangement of buildings and energy consumption relationship

Faroughi

Karimimoshaver

Aram

et al. 2020

Engineering Applications of Computational Fluid Mechanics

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The effect of urban form on energy consumption has been the subject of various studies around the world. Having examined the effect of buildings on energy consumption, these studies indicate that the physical form of a city has a notable impact on the amount of energy consumed in its spaces. The present study identified the variables that affected energy consumption in residential buildings and analyzed their effects on energy consumption in four neighborhoods in Tehran: Apadana, Bimeh, Ekbatan-phase I, and Ekbatan-phase II. After extracting the variables, their effects are estimated with statistical methods, and the results are compared with the land surface temperature (LST) remote sensing data derived from Landsat 8 satellite images taken in the winter of 2019. The results showed that physical variables, such as the size of buildings, population density, vegetation cover, texture concentration, and surface color, have the greatest impacts on energy usage. For the Apadana neighborhood, the factors with the most potent effect on energy consumption were found to be the size of buildings and the population density. However, for other neighborhoods, in addition to these two factors, a third factor was also recognized to have a significant effect on energy consumption. This third factor for the Bimeh, Ekbatan-I, and Ekbatan-II neighborhoods was the type of buildings, texture concentration, and orientation of buildings, respectively.

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Spatial structure and temporal variability of a surface urban heat island in cold continental climate

Cited by 18 publications

References 80 publications

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

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

Urban heat islands in the Arctic cities: an updated compilation of in situ and remote-sensing estimations

Computational modeling of land surface temperature using remote sensing data to investigate the spatial arrangement of buildings and energy consumption relationship

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