Urban morphology and landscape structure effect on land surface temperature: Evidence from Shiraz, a semi-arid city

Azhdari, Abolghasem; Soltani, Alì; Alidadi, Mehdi

doi:10.1016/j.scs.2018.06.034

Cited by 127 publications

(43 citation statements)

References 57 publications

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“…The vegetation-covered LCZs (A and B) in D6 generally include established parks and mature trees that show the coolest LCZs in the district. In accordance with previous studies on LST of land cover types in semi-arid cities (Azhdari et al, 2018; Bokaie et al, 2016), bare soil land cover and paved surfaces (LCZ F and E) in D6 and D22 show high levels of LST. Vegetation cover in semi-arid cities cools the air and surface through evapotranspiration and shading (Wheeler et al, 2019).…”

Section: Discussionsupporting

confidence: 92%

Priority Areas for Developing Green Infrastructure in Semi-arid Cities: A Case Study of Tehran

Jamali

Khaledi

Razavian

2021

Environment and Urbanization ASIA

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Urban green infrastructure (GI) approach supports building resilience, mitigating greenhouse gases emissions and adapting to the impacts of climate change. However, the development and maintenance of GI in semi-arid cities can be hindered by limitations such as available water resources. In this article, we study priority areas for GI development schemes at the neighbourhood scale through a seasonal vulnerability framework with the case study of two urban districts in the semi-arid city of Tehran, Iran. Heat mitigation and stormwater runoff control are considered as the main objectives of GI development. The results show that priority areas have high levels of land surface temperature, impervious surfaces and population density, with a low proportion of vegetation land cover. The necessary GI services vary in different local climate zones (LCZ) during the year. Although heat mitigation is required in both compact and open LCZs, the runoff control service of GI is also needed for neighbourhoods with compact midrise settings. To promote sustainability at the neighbourhood scale, the findings of the study can be used for initiating nature-based solutions and GI development projects.

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Section: Discussionsupporting

confidence: 92%

Priority Areas for Developing Green Infrastructure in Semi-arid Cities: A Case Study of Tehran

Jamali

Khaledi

Razavian

2021

Environment and Urbanization ASIA

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“…The impervious surface is the core area of the urban heat island. In agreement with the large-scale research [16,64,65], among the different types of urban land use, the UHI is mainly concentrated in the urban impervious areas, such as industrial areas, urban squares, roads, building roofs, and other gray infrastructure. The urban cold islands (UCI) are mainly concentrated in urban green space (UGS) and water areas such as urban forests, parks and water systems.…”

Section: Influence Of Coverage Types On Thermal Microclimatesupporting

confidence: 85%

“…Because the air is flowing and transparent, there is no effective recording method to indicate its spatial state. Most scholars study the urban heat island effect by using satellites [11][12][13][14][15][16]. The surface temperature obtained from the image needs to be calibrated.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Cooling and Humidification Effects of Different Coverage Types in Small Green Spaces (SGS) in the Context of Urban Homogenization: A Case of HAU Campus Green Spaces in Summer in Zhengzhou, China

Meng²,

et al. 2020

Atmosphere

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In the context of global warming, more and more cities are experiencing extreme Urban Heat Island (UHI) effects and extreme weather phenomena, but urban green spaces are proven to mitigate UHI. Most of UHI’s research focuses on the large scale and uses remote sensing methods, which do not reflect the dynamic characteristics in detail and do not detect internal influencing factors of the green space cooling effect. Therefore, this study focused on Small Green Spaces (SGS), carrying out the measurement of the meteorological parameters (temperature, relative humidity, wind direction, wind speed, photosynthetic radiation) of the 16 sites in four types of coverage (Impervious surface; Shrub-grass; Tree-grass; Tree-shrub-grass) in a university campus. At the same time, the coverage characteristic parameters, such as Canopy Density (CD), Leaf Area Index (LAI), Photosynthetically Active Radiation (PAR), Mean Leaf Angle (MLA), of each plot were analyzed and compared. The results showed that there were significant differences in temperature among different coverage types in SGS. The biggest difference was concentrated in the noon period when solar radiation is strongest during the day. The difference between the four types of coverage with vegetation at night was small. The maximum air temperature difference among the four types could reach 8.9 ℃ and the maximum relative humidity difference was 28.5%. The cooling effect of the multi-layer vegetation-covered (Tree-shrub-grass) area was the largest compared to the impervious surface, indicating that tree cover was the core factor affecting the temperature. Temperature and relative humidity had a close correlation with surface coverage types and some plant community characteristics (such as CD and LAI). The cooling and humidifying effects of plants were also related to PAR and leaf angle. The results provide suggestions for green space management and landscape design.

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“…To study the multi-scale relationship between urban landscape metrics and LST, the following landscape composition and configuration metrics were selected (Table 2): Normalized Difference Built-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI) were selected to describe the spatial distribution of urban impervious surface and vegetation, respectively; Shannon's diversity index (SHDI) was selected to describe landscape Diversity; Contagion Index (CONTAG) was selected to describe the degree of agglomeration or extension of the landscape; Landscape Division Index (DIVISION) was selected to describe the dispersion of the landscape; Landscape Shape Index (LSI) was selected to describe the spatial form of the landscape [26,47,48]. These landscape metrics are calculated at the landscape level of 1 km window size.…”

Section: High Temperature Center and Landscape Metricsmentioning

confidence: 99%

Multi-Scale Relationship between Land Surface Temperature and Landscape Pattern Based on Wavelet Coherence: The Case of Metropolitan Beijing, China

Tan

Guo

et al. 2019

Remote Sensing

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The relationship between urban landscape pattern and land surface temperature (LST) is one of the core issues in urban thermal environment research. Although previous studies have shown a significant correlation between LST and landscape pattern, most were conducted at a single scale and rarely involve multi-scale effects of the landscape pattern. Wavelet coherence can relate the correlation between LST and landscape pattern to spatial scale and location, which is an effective multi-scale correlation method. In this paper, we applied wavelet coherence and Pearson correlation coefficient to analyze the multi-scale correlations between landscape pattern and LST, and analyzed the spatial pattern of the urban thermal environment during the urbanization of Beijing from 2004 to 2017 by distribution index of high-temperature center (HTC). The results indicated that the HTC of Beijing gradually expands from the main urban zone and urban function extended zone to the new urban development zone and far suburb zone, and develops from monocentric to polycentric spatial pattern. Land cover types, such as impervious surfaces and bare land, have a positive contribution to LST, while water and vegetation play a role in mitigating LST. The wavelet coherence and Pearson correlation coefficients showed that landscape composition and spatial configuration have significant effects on LST, but landscape composition has a greater effect on LST in Beijing metropolitan area. Landscape composition indexes (NDBI and NDVI) showed significant multi-scale characteristics with LST, especially at larger scales, which has a strong correlation on the whole transect. There was no significant correlation between the spatial configuration indexes (CONTAG, DIVISION, and LSI) and LST at smaller scales, only at larger scales near the urban area has a significant correlation. With the increase of the scale, Pearson correlation coefficient calculated by spatial rectangle sampling and wavelet coherence coefficient have the same trend, although it had some fluctuations in several locations. However, the wavelet coherence coefficient diagram was smoother and less affected by position and rectangle size, which more conducive to describe the correlation between landscape pattern index and LST at different scales and locations. In general, wavelet coherence provides a multi-scale method to analyze the relationship between landscape pattern and LST, helping to understand urban planning and land management to mitigate the factors affecting urban thermal environment.

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Urban morphology and landscape structure effect on land surface temperature: Evidence from Shiraz, a semi-arid city

Cited by 127 publications

References 57 publications

Priority Areas for Developing Green Infrastructure in Semi-arid Cities: A Case Study of Tehran

Priority Areas for Developing Green Infrastructure in Semi-arid Cities: A Case Study of Tehran

Analysis of Cooling and Humidification Effects of Different Coverage Types in Small Green Spaces (SGS) in the Context of Urban Homogenization: A Case of HAU Campus Green Spaces in Summer in Zhengzhou, China

Multi-Scale Relationship between Land Surface Temperature and Landscape Pattern Based on Wavelet Coherence: The Case of Metropolitan Beijing, China

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