Sky view factor calculations and its application in urban heat island studies

Dirksen, Marieke; Ronda, R.J.; Theeuwes, Natalie; Pagani, Giuliano Andrea

doi:10.1016/j.uclim.2019.100498

Cited by 122 publications

(47 citation statements)

References 21 publications

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“…A ne level sky view factor (SVF) map is important for us to model the solar radiation uxes, understand the impacts of urban form on the urban microclimate, study the spatial pattern of urban heat islands, and investigate human heat exposure and public health (Gong et al, 2018;Li et al, 2018a;Dirksen et al, 2019;Yuan and Chen, 2011). However, the SVF mapping is usually very time-consuming, which blocks large scale urban environmental analyses.…”

Section: Discussionmentioning

confidence: 99%

“…The value of SVF ranges from 0 to 1, which indicates the sky is totally obstructed and unobstructed, respectively. The SVF has been widely applied in urban microclimate modeling, urban forestry, urban morphology, and urban air pollution studies (Dirksen et al, 2019;Eeftens et al, 2013;Li et al, 2018a;Li and Ratti, 2019;Gong et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Although the shadow casting-based method increases the e ciency signi cantly, it stills takes days or even longer for high performance computers to generate the SVF map for a relatively large geographic area. To solve this problem, cloud computing has been applied for SVF mapping for large geographic areas (Dirksen et al, 2019), but it is still time-consuming and expensive for many users.…”

Section: Introductionmentioning

confidence: 99%

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GPU Accelerated Parallel Computing for Estimating Continuous Sky view Factor Map

Wang¹

2021

Preprint

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The sky view factor (SVF) that represents the fraction of visible sky on a hemisphere or the percentage of radiation reaching the planar ground in the entire hemisphere’s input radiation is an important parameter for urban climate studies. However, the estimation of a continuous SVF map is very time-consuming, which limits the applications of SVF to small geographical areas. This study proposed to use graphics processing unit (GPU) parallel computing to accelerate the computing of SVF in the city of Philadelphia, Pennsylvania, USA. This study implemented and compared both the GPU-accelerated version and regular CPU version of two major methods for estimating continuous SVF maps, ray tracing-based algorithm and shadow casting-based algorithm based on the high-resolution building height model. Results show that the GPU-accelerated algorithms can reduce the time consumption dramatically and estimate the SVF map for the city of Philadelphia in less than 20 minutes on a personal computer with one NVIDIA GPU. The ray tracing-based algorithm has a much more efficiency increase than the shadow casting-based algorithm on GPU. The proposed method makes it possible to generate large-scale continuous SVF maps using regular personal computers with GPU. The proposed GPU-accelerated SVF estimation methods would benefit urban climate studies.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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GPU Accelerated Parallel Computing for Estimating Continuous Sky view Factor Map

Wang¹

2021

Preprint

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“…Moreover, SVF plays an important role in the description of urban radiation properties: the higher its value, the greater the incidence of direct solar radiation (Jiao et al, 2019). The calculation methodology was introduced by Oke [72] and has been widely developed in urban climatology studies [75][76][77]. For a clearer understanding, the SVF calculation of urban geometry is explained by the following Equation [72]:…”

Section: Sky View Factor (Svf)mentioning

confidence: 99%

Thermal Summer Diurnal Hot-Spot Analysis: The Role of Local Urban Features Layers

et al. 2021

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This study was focused on the metropolitan area of Florence in Tuscany (Italy) with the aim of mapping and evaluating thermal summer diurnal hot- and cool-spots in relation to the features of greening, urban surfaces, and city morphology. The work was driven by Landsat 8 land surface temperature (LST) data related to 2015–2019 summer daytime periods. Hot-spot analysis was performed adopting Getis-Ord Gi* spatial statistics applied on mean summer LST datasets to obtain location and boundaries of hot- and cool-spot areas. Each hot- and cool-spot was classified by using three significance threshold levels: 90% (LEVEL-1), 95% (LEVEL-2), and 99% (LEVEL-3). A set of open data urban elements directly or indirectly related to LST at local scale were calculated for each hot- and cool-spot area: (1) Normalized Difference Vegetation Index (NDVI), (2) tree cover (TC), (3) water bodies (WB), (4) impervious areas (IA), (5) mean spatial albedo (ALB), (6) surface areas (SA), (7) Shape index (SI), (8) Sky View Factor (SVF), (9) theoretical solar radiation (RJ), and (10) mean population density (PD). A General Dominance Analysis (GDA) framework was adopted to investigate the relative importance of urban factors affecting thermal hot- and cool-spot areas. The results showed that 11.5% of the studied area is affected by cool-spots and 6.5% by hot-spots. The average LST variation between hot- and cold-spot areas was about 10 °C and it was 15 °C among the extreme hot- and cool-spot levels (LEVEL-3). Hot-spot detection was magnified by the role of vegetation (NDVI and TC) combined with the significant contribution of other urban elements. In particular, TC, NDVI and ALB were identified as the most significant predictors (p-values < 0.001) of the most extreme cool-spot level (LEVEL-3). NDVI, PD, ALB, and SVF were selected as the most significant predictors (p-values < 0.05 for PD and SVF; p-values < 0.001 for NDVI and ALB) of the hot-spot LEVEL-3. In this study, a reproducible methodology was developed applicable to any urban context by using available open data sources.

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“…In addition, the sky view factor (SVF) of urban street space is highly correlated with the urban heat island effect. The smaller the SVF, the greater the probability and intensity of the urban heat island effect [12][13][14]. In terms of 3D urban landscape index, the 3D compactness index, distribution uniformity index, space crowded degree (SCD), and SVF have high correlations with the intensity of urban heat islands [15][16].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Influence of Urban Spatial Morphologies on Thermal Microenvironment

Meng¹,

Yu²,

Yang³

2021

Pol. J. Environ. Stud.

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In the process of urbanization, microclimate issues such as heat islands and haze caused by the unique spatial forms of cities have attracted increasing attention. In this study, we analyze the measured data of the city of Jinan at 10 a.m. in the summer and the simulation results of ENVI-met to explore the urban morphological parameters (UMPs) at different spatial scales. The correlations between the urban thermal environment and various indicators of spatial form were studied. The results show that in the vertical direction, the 3D morphologies of vegetation and buildings have different ranges of influence on air temperature (T a). In the case of commercial land and a building height of lower than 12.5 m, its temperature is mainly affected by the shadow of the building, and the floor area ratio (FAR) and cubic indexes are significantly negatively correlated with temperature. The spatial scale and grid resolution simulated by the model are critical in the study of the relationship between urban morphology and thermal environment. Among the seven UMPs, the FAR and cubic index are most affected by the spatial scale. In comparison, the SVF is less affected by the spatial scale.

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Sky view factor calculations and its application in urban heat island studies

Cited by 122 publications

References 21 publications

GPU Accelerated Parallel Computing for Estimating Continuous Sky view Factor Map

GPU Accelerated Parallel Computing for Estimating Continuous Sky view Factor Map

Thermal Summer Diurnal Hot-Spot Analysis: The Role of Local Urban Features Layers

Analysis of Influence of Urban Spatial Morphologies on Thermal Microenvironment

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