The influence of urban spatial pattern on land surface temperature for different functional zones

Li, Tong; Cao, Jianfei; Xu, Mingxue; Wu, Quanyuan; Yao, Lei

doi:10.1007/s11355-020-00417-8

Cited by 34 publications

(19 citation statements)

References 63 publications

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“…Most research to date has demonstrated that the horizontal and vertical differences in three-dimensional cities have an impact on LST. Many researchers have often linked building height to the urban shadow; the higher the building, the lower the temperature in this area due to the effect of shadows [45,46]. However, there was also a significant positive correlation between building density and LST during the hot season, and a negative correlation during the cool season.…”

Section: Discussionmentioning

confidence: 99%

Exploring the Impacts and Temporal Variations of Different Building Roof Types on Surface Urban Heat Island

et al. 2021

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This study examined the impact of different types of building roofs on urban heat islands. This was carried out using building roof data from remotely sensed Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) imagery. The roofs captured included white surface, blue steel, dark metal, other dark material, and residential roofs; these roofs were compared alongside three natural land covers (i.e., forest trees, grassland, and water). We also collected ancillary data including building height, building density, and distance to the city center. The impacts of various building roofs on land surface temperature (LST) were examined by analyzing their correlation and temporal variations. First, we examined the LST characteristics of five building roof types and three natural land covers using boxplots and variance analysis with post hoc tests. Then, multivariate regression analysis was used to explore the impact of building roofs on LST. There were three key findings in the results. First, the mean LSTs for five different building roofs statistically differed from each other; these differences were more significant during the hot season than the cool season. Second, the impact of the five types of roofs on LSTs varied considerably from each other. Lastly, the contribution of the five roof types to LST variance was more substantial during the cool season. These findings unveil specific urban heat retention drivers, in which different types of building roofs are one such driver. The outcomes from this research may help policymakers develop more effective strategies to address the surface urban heat island phenomenon and its related health concerns.

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Section: Discussionmentioning

confidence: 99%

Exploring the Impacts and Temporal Variations of Different Building Roof Types on Surface Urban Heat Island

et al. 2021

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“…The focus of this study was set not only on LST of land cover patches of a given class but also type, defined by the aggregation level of patches of a given class, which in most cases could be associated with different functional imprints within the study area, and allowing for bottom-up considerations regarding the relationship between urban form patterns and LST depending on predominant land use. Literature lists several other studies that have attempted to explain LST means within different functional units of cities by coupling with urban form configuration metrics, such as for example Beijing city transects aggregated into specific functional zones (Li et al 2020b), different types of parks (Li et al 2020a), and regulatory plan management units (Yin et al 2018), allowing for a top-down analysis of the relationships. We propose that the bottom-up approach adopted here can provide complementary insights into spatial arrangement of urban land cover under various uses for effective excess urban heat alleviation and microclimate management by exploring urban form detail that can be missed when descriptors of heterogeneous urban form patterns and thermal responses are averaged over larger parcels of land.…”

Section: Methods In Data Preparation and Analysismentioning

confidence: 99%

The importance of spatial configuration of neighbouring land cover for explanation of surface temperature of individual patches in urban landscapes

2021

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Context Relationships between spatial configuration of urban form and land surface temperature (LST) in the excess heat mitigation context are studied over larger tracts of land not allowing for micro-scale recommendations to urban design. Objectives To identify spatial configuration descriptors (SCDs) of urban form and the size of zone of influence conducive to the formation of the coldest and hottest land cover (LC) patches of different types (buildings, grass, paved and trees) from 2 m resolution LC and 2 and 100 m resolution LST maps at two time-steps in the summer. Methods Random Forest regression models were deployed to explain the LST of individual LC patches of different types based on SCDs of core LC patches and patches in their neighbourhoods. ANOVA was used to determine significantly different values of the most important SCDs associated with the coldest and hottest LC patches, and analysis of quartiles informed specification of their ranges. Results Urban form in the immediate neighbourhood to the core LC patches had a strong influence on their LST. Low elevation, high proximity to water, and high aggregation of trees, being important to the formation of the coldest patches of all types. High resolution of LST contributed to a higher accuracy of results. Elevation and proximity to water gained in importance as summer progressed. Conclusions Spatial configuration of urban form in the nearest proximity to individual LC patches and the use of fine resolution LST data are essential for issuing heat mitigation recommendations to urban planners relevant to micro-scales.

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“…COZ, PSZ, GOZ, and HRZ have similar LSTs and key influence factors for all the seasons. This can be explained by the similar intensity of production activities, landscape composition and building forms in these UFZs (Li et al 2020, Wu et al 2020a. For REZ, the main difference with other UFZs lies in the factor sensitivity related to urban green space, i.e., DIVISION_f/l (+).…”

Section: Factors Responsible For Seasonal Lstsmentioning

confidence: 99%

“…Four radiometrically calibrated Landsat8 imagery of the study area were collected from a geospatial data cloud platform provided by Chinese Academy of Sciences (www.gscloud.cn) to retrieve LST, acquiring in spring (May 15, 2014), summer (August 19, 2014, autumn (October 6, 2014), and winter (December 25, 2014), respectively. By following the LST retrieval procedure presented by Li et al (2020), the radiation equation algorithm is used to estimate LST from Landsat8 data for the study area, and the results were shows in Fig. 3.…”

Section: Land Surface Temperature Retrievalmentioning

confidence: 99%

Detecting Urban Landscape Factors Controlling Seasonal Land Surface Temperatures: From the Perspective of Urban Function Zones

Yao

2021

Preprint

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Understanding of the impact on the thermal effect by urbanization is of great significance for urban thermal regulation, it is essential to determine the relationship between the urban heat island (UHI) effect and the complexities of urban function and landscape structure. For this purpose, we conducted a case research in the metropolitan region of Beijing, China, and >5000 urban blocks assigned with different urban function zones (UFZs) were identified as the basic spatial analysis units. Seasonal land surface temperature (LST) retrieved from remote sensing data were used to represent the UHI characteristics of the study area, and surface biophysical parameters, building forms, and landscape pattern metrics were selected as the urban landscape factors. Then, the effects of urban function and landscape structure on the UHI effect were examined by spatial regression models. The results indicated that: (1) Significant spatio-temporal heterogeneity of LST were found in the study area, and there was obvious temperature gradient with “working-living-resting” UFZs; (2) All the types of urban landscape factors showed significant contribution to seasonal LST, and sorted by surface biophysical factors > building forms > landscape factors. However, their contributions varied in different seasons; (3) The major contribute factors showed a certain difference due to the variation of urban function and landscape complexity. This study expands understanding on the complex relationship among urban landscape, function, and thermal environment, which could benefit urban landscape planning for UHI alleviation.

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The influence of urban spatial pattern on land surface temperature for different functional zones

Cited by 34 publications

References 63 publications

Exploring the Impacts and Temporal Variations of Different Building Roof Types on Surface Urban Heat Island

Exploring the Impacts and Temporal Variations of Different Building Roof Types on Surface Urban Heat Island

The importance of spatial configuration of neighbouring land cover for explanation of surface temperature of individual patches in urban landscapes

Detecting Urban Landscape Factors Controlling Seasonal Land Surface Temperatures: From the Perspective of Urban Function Zones

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