Analyzing the Spatial Heterogeneity of the Built Environment and Its Impact on the Urban Thermal Environment—Case Study of Downtown Shanghai

Han, Jie‐jie; Zhao, Xi; Zhang, Hao; Liu, Yu

doi:10.3390/su132011302

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…Studies have shown that the heterogeneity of urban and suburban surfaces, caused by the substitution of natural surfaces by large impervious surfaces during urbanization, is the fundamental reason for the formation of SUHI [ 22 , 23 , 24 ]. Urban land cover types [ 25 , 26 , 27 , 28 ] and urban forms [ 29 , 30 ] have direct driving effects on SUHI formation and development, while regional climate [ 31 , 32 ], human activities [ 29 , 33 ], and the built-up area [ 34 ] also affect the intensity and distribution of SUHI. However, these studies do not consider surface temperature directionality when calculating SUHI intensity by LST, thus measuring the urban thermal environment, which affects the accuracy of the studies to some extent.…”

Section: Introductionmentioning

confidence: 99%

A Quantitative Study of a Directional Heat Island in Hefei, China Based on Multi-Source Data

Shi

Jiang

et al. 2023

Sensors

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Surface urban heat islands (SUHIs) are essential for evaluating urban thermal environments. However, current quantitative studies of SUHIs ignore the thermal radiation directionality (TRD), which directly affects study precision; furthermore, they fail to assess the effects of TRD characteristics at different land-use intensities, on the quantitative studies of SUHIs. To bridge this research gap, this study eliminates the interference of atmospheric attenuation and daily temperature variation factors, in quantifying the TRD based on land surface temperature (LST), from MODIS data and station air temperature data for Hefei (China) from 2010–2020. The influence of TRD on SUHI intensity quantification was evaluated by comparing the TRD under different land-use intensities in Hefei. The results show that: (1) daytime and nighttime directionality can reach up to 4.7 K and 2.6 K, and occur in areas with the highest and medium urban land-use intensity, respectively. (2) There are two significant TRD hotspots for daytime urban surfaces, where the sensor zenith angle is approximately the same as the forenoon solar zenith angle, and where the sensor zenith angle is near its nadir in the afternoon. (3) The TRD can contribute up to 2.0 K to the results of assessing the SUHI intensity based on satellite data, which is approximately 31–44% of the total SUHI in Hefei.

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Section: Introductionmentioning

confidence: 99%

A Quantitative Study of a Directional Heat Island in Hefei, China Based on Multi-Source Data

Shi

Jiang

et al. 2023

Sensors

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“…At a community level, the availability of green spaces impacts the environmental quality, helps with stress restoration [4,5], enhances the feeling of social safety [6], increases social interaction, and promotes property values [7]. Urban green space has been recognized as a key variable for assessing the livability of cities [8], whereas its spatial data at the finegeographical scale is difficult to generate [9]. One way of obtaining the spatial information of UCGS is to extract it from remote sensing images [10].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Automatic Identification of Urban Community Green Space Based on Semantic Segmentation

Chen

Shao

Zhu

et al. 2022

Land

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At the neighborhood scale, recognizing urban community green space (UCGS) is important for residential living condition assessment and urban planning. However, current studies have embodied two key issues. Firstly, existing studies have focused on large geographic scales, mixing urban and rural areas, neglecting the accuracy of green space contours at fine geographic scales. Secondly, the green spaces covered by shadows often suffer misclassification. To address these issues, we created a neighborhood-scale urban community green space (UCGS) dataset and proposed a segmentation decoder for HRNet backbone with two auxiliary decoders. Our proposed model adds two additional branches to the low-resolution representations to improve their discriminative ability, thus enhancing the overall performance when the high- and low-resolution representations are fused. To evaluate the performance of the model, we tested it on a dataset that includes satellite images of Shanghai, China. The model outperformed the other nine models in UCGS extraction, with a precision of 83.01, recall of 85.69, IoU of 72.91, F1-score of 84.33, and OA of 89.31. Our model also improved the integrity of the identification of shaded green spaces over HRNetV2. The proposed method could offer a useful tool for efficient UCGS detection and mapping in urban planning.

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A Quantitative Analysis of the Complex Response Relationship between Urban Green Infrastructure (UGI) Structure/Spatial Pattern and Urban Thermal Environment in Shanghai

Guan,

Zhang

2024

Sustainability

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The urban heat island (UHI) effect has evolved into one of the key environmental problems affecting the urban ecological environment and sustainable development. Based on 52 Urban Thermal Heat spots (UTHSs) with significant differences between land use structure and urban green infrastructure (UGI) spatial layout within the influence range of UHI in Shanghai, Landsat-8/9 satellite images were used to construct a high-dimensional dataset reflecting the impact of built environment components on urban thermal environment. Descriptive statistical analysis was used to analyze the spatial difference qualitatively. Using the stepwise regression (SWR) model and partial least square regression (PLSR) model, the complex response relationship between UGI’s structure/spatial pattern differentiation and urban thermal environment in three spatial stratification ranges of UTHSs was quantitatively analyzed. Overall, the statistical explanatory power of the PLSR model is much better than the stepwise regression model. The PLSR model points out that moderately increasing the average building height, class area (CA), percentage of landscape (PLAND), landscape shape index (LSI), and largest patch index (LPI) play a positive role in inhibiting the growth of land surface temperature (LST), and the cooling effect of index weights decreases in order. However, the interaction effects of the box-cox transformed indices with underlines, e.g., CA × Cohesion × AI × LPI and PLAND × CA × Cohesion × AI × LPI, exert relatively small weight on the cooling effect. According to the results, suggestions such as optimization of the UGI structure and urban construction layout were proposed, which can effectively mitigate the UHI effect.

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Analyzing the Spatial Heterogeneity of the Built Environment and Its Impact on the Urban Thermal Environment—Case Study of Downtown Shanghai

Cited by 3 publications

References 27 publications

A Quantitative Study of a Directional Heat Island in Hefei, China Based on Multi-Source Data

A Quantitative Study of a Directional Heat Island in Hefei, China Based on Multi-Source Data

Enhanced Automatic Identification of Urban Community Green Space Based on Semantic Segmentation

A Quantitative Analysis of the Complex Response Relationship between Urban Green Infrastructure (UGI) Structure/Spatial Pattern and Urban Thermal Environment in Shanghai

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