Accurate Characterization of Land Cover in Urban Environments: Determining the Importance of Including Obscured Impervious Surfaces in Urban Heat Island Models

Coseo, Paul; Larsen, Larissa

doi:10.3390/atmos10060347

Cited by 18 publications

(9 citation statements)

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“…With rapid development and the intensification of urbanization, the scale of cities has expanded rapidly and urban buildings tend to be denser, which brings more pressure on the population, transportation, and industrial production, and causes severe damage to the ecological environment of the entire city. Urban Heat Islands (UHIs) are attracting more and more attention, especially in summer, as scorching hot temperatures have severely affected people's lives and work [1][2][3]. To date, researchers all over the world have carried out much research on UHIs.…”

Section: Introductionmentioning

confidence: 99%

Correlation Analysis between UBD and LST in Hefei, China, Using Luojia1-01 Night-Time Light Imagery

et al. 2019

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The urban heat island (UHI) is one of the essential phenomena of the modern urban climate. In recent years, urbanization in China has gradually accelerated, and the heat island effect has also intensified as the urban impervious surface area and the number of buildings is increasing. Urban building density (UBD) is one of the main factors affecting UHI, but there is little discussion on the relationship between the two. This paper takes Hefei as the research area, combines UBD data estimated by Luojia1-01 night-time light (NTL) imagery as the research object with land surface temperature (LST) data obtained from Landsat8 images, and carries out spatial correlation analysis on 0.5 × 0.5 km to 2 × 2 km resolution for them, so as to explore the relationship between UBD and UHI. The results show the following: (1) Luojia1-01 data have a good ability to estimate UBD and have fewer errors when compared with the actual UBD data; (2) At the four spatial scales, UBD and LST present a significant positive correlation that increases with the enlargement of the spatial scale; and (3) Moreover, the fitting effect of the Geographically Weighted Regression (GWR) model is better than that of the ordinary least squares (OLS) regression model. Appl. Sci. 2019, 9, 5224 2 of 20 absorbing heat faster and the specific heat capacity being smaller gives rise to limited heat release. In this way, the average LST in the area gradually increases with the spatial distribution of the UHI area to become more continuous and concentrated, forming a high-temperature area centered on artificial buildings in the city [1,9,10]. However, our comprehensive literature research found that past studies focused on the analysis of seasonal, daily, and annual variations of the UHI, which revealed the temporal and spatial variation of the UHI, the distribution characteristics of the horizontal and vertical directions, etc., while there has been less quantitative analysis of the single factors of UHIs, especially discussion of the ubiquitous relationship between impervious surfaces and UHI in the city.As an integral part of the urban impervious surface percentage, the urban building density (UBD) (percentage of built area) can directly reflect the degree of intensification and land-use efficiency in cities to some extent [11]. It is also an essential comprehensive social indicator that can function as an urban planning layout measure [12,13], resource utilization efficiency measure [14,15], and assessment of the urban ecological environment and livability [16][17][18][19]. Some researchers have pointed out that UBD is a crucial indicator to reduce the UHI effect [20] and proved a positive correlation between UBD and LST or UHI [21][22][23]. Additionally, researchers have tried to use a variety of urban design factors to study the relationships of UHIs, including UBD information. For example, Yang studied the summer heat island intensity of three high-rise residential districts in Shanghai, based on the three indices of building layout, density, and greeni...

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Section: Introductionmentioning

confidence: 99%

Correlation Analysis between UBD and LST in Hefei, China, Using Luojia1-01 Night-Time Light Imagery

et al. 2019

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“…GIS-based methodology to evaluate green roofs and green urban areas 9 [4][5][6][7][8][9][10][11][12] Outdoor thermal comfort: UHI mitigation strategies and thermal comfort indexes 20 [2,4,[13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] Energy savings and Indoor thermal comfort from Green roofs 5 [1,[31][32][33][34] 2.…”

Section: No Of Papers Sourcementioning

confidence: 99%

“…As already mentioned, green areas improves the aesthetic value of urban context reducing the UHI effect and achieving comfortable microclimate and climate conditions [13][14][15][16][17][18]. The urbanization plays a key role in UHI expansion, an analysis of land cover types is critical for the outdoor spaces assessment in order to improve the thermal comfort, liveability and air quality of an urban environment [19]. Outdoor thermal comfort varies according to morphological context [16,17], and any increase in the environmental air temperature is influenced to a great extent by the thermal characteristics of buildings and urban surfaces [20].…”

Section: Outdoor Thermal Comfort: Uhi Mitigation Strategies and Thermmentioning

confidence: 99%

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The Effects of Green Roofs on Outdoor Thermal Comfort, Urban Heat Island Mitigation and Energy Savings

Mutani

Todeschi

2020

Atmosphere

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There is growing attention to the use of greenery in urban areas, in various forms and functions, as an instrument to reduce the impact of human activities on the urban environment. The aim of this study has been to investigate the use of green roofs as a strategy to reduce the urban heat island effect and to improve the thermal comfort of indoor and outdoor environments. The effects of the built-up environment, the presence of vegetation and green roofs, and the urban morphology of the city of Turin (Italy) have been assessed considering the land surface temperature distribution. This analysis has considered all the information recorded by the local weather stations and satellite images, and compares it with the geometrical and typological characteristics of the city in order to find correlations that confirm that greenery and vegetation improve the livability of an urban context. The results demonstrate that the land-surface temperature, and therefore the air temperature, tend to decrease as the green areas increase. This trend depends on the type of urban context. Based on the results of a green-roofs investigation of Turin, the existing and potential green roofs are respectively almost 300 (257,380 m2) and 15,450 (6,787,929 m2). Based on potential assessment, a strategy of priority was established according to the characteristics of building, to the presence of empty spaces, and to the identification of critical areas, in which the thermal comfort conditions are poor with low vegetation. This approach can be useful to help stakeholders, urban planners, and policy makers to effectively mitigate the urban heat island (UHI), improve the livability of the city, reduce greenhouse gas (GHG) emissions and gain thermal comfort conditions, and to identify policies and incentives to promote green roofs.

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“…Therefore, later on, Chatterjee et al [18] updated and improved the existing models and realized the simulation of the indoor-outdoor heat transfer and exchange process of urban buildings with the dynamic man-made heat emission taken into consideration; the constructed energy balance equation and the estimated temperature field parameters were more accurate and complete; then, with a medium-sized city as an example, the city's thermal environment evolution in the process of urbanization was quantitatively analyzed. Since there are often contradictions between the time and space resolution of high-resolution remote sensing images [19][20][21][22], in response to this problem, Malings et al [23] proposed an urban-space downscaling thermal environment analysis method, and achieved LST retrieval and space downscaling based on the radiation transfer equation method and the Distrad algorithm; and they used actual examples to analyze the change trend of LST in the city under the influence of temporal and spatial changes of underlying surface in the past 5 years. Sabatino et al [24] analyzed the spatial distribution characteristics of abnormal urban thermal blocks based on the results of LST retrieval; by comparing the boundary of builtup areas and the boundary of thermal environment areas, it was found that the two were basically consistent; after that, they employed MLR to conduct correlation analysis on the LST of areas with different land utilization patterns, and finally proposed a quantitative method for mitigating the thermal abnormalities of high-temperature blocks.…”

Section: Introductionmentioning

confidence: 99%

Influencing Factors and Quality Evaluation of Urban Thermal Environment Based on Artificial Neural Network

Zhao¹,

Li²,

Chai³

2021

IJHT

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To alleviate the urban heat island effect that is getting increasingly serious these days, the research on the monitoring, simulation, and regulation of the thermal environment of cities has become a necessity. Aiming at figuring out the correlations between influencing factors and giving accurate quality evaluation of urban thermal environment, this study extracted 10 influencing factors of urban thermal environment and gave their influence, and then performed the Land Surface Temperature (LST) retrieval of a target city. After that, this paper constructed a Multiple Linear Regression (MLR) model and explored the law of the numerical changes of the influencing factors of urban thermal environment. At last, this paper also built a BP neural network to predict the quality evaluation of urban thermal environment and used experimental results to prove the effectiveness of the proposed algorithm and model.

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Accurate Characterization of Land Cover in Urban Environments: Determining the Importance of Including Obscured Impervious Surfaces in Urban Heat Island Models

Cited by 18 publications

References 58 publications

Correlation Analysis between UBD and LST in Hefei, China, Using Luojia1-01 Night-Time Light Imagery

Correlation Analysis between UBD and LST in Hefei, China, Using Luojia1-01 Night-Time Light Imagery

The Effects of Green Roofs on Outdoor Thermal Comfort, Urban Heat Island Mitigation and Energy Savings

Influencing Factors and Quality Evaluation of Urban Thermal Environment Based on Artificial Neural Network

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