Effects of building density on land surface temperature in China: Spatial patterns and determinants

Cities are growing higher and denser, and understanding and constructing the compact city form is of great importance to optimize sustainable urbanization. The two-dimensional (2D) urban compact form has been widely studied by previous researchers, while the driving mechanism of three-dimensional (3D) compact morphology, which reflects the reality of the urban environment has seldom been developed. In this study, land surface temperature (LST) was retrieved by using the mono-window algorithm method based on Landsat 8 images of Xiamen in South China, which were acquired respectively on 14 April, 15 August, 2 October, and 21 December in 2017, and 11 March in 2018. We then aimed to explore the driving mechanism of the 3D compact form on the urban heat environment (UHE) based on our developed 3D Compactness Index (VCI) and remote sensing, as well as Geo-Detector techniques. The results show that the 3D compact form can positively effect UHE better than individual urban form construction elements, as can the combination of the 2D compact form with building height. Individually, building density had a greater effect on UHE than that of building height. At the same time, an integration of building density and height showed an enhanced inter-effect on UHE. Moreover, we explore the temporal and spatial UHE heterogeneity with regards to 3D compact form across different seasons. We also investigate the UHE impacts discrepancy caused by different 3D compactness categories. This shows that increasing the 3D compactness of an urban community from 0.016 to 0.323 would increase the heat accumulation, which was, in terms of satellite derived LST, by 1.35 °C, suggesting that higher compact forms strengthen UHE. This study highlights the challenge of the urban 3D compact form in respect of its UHE impact. The related evaluation in this study would help shed light on urban form optimization.

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Quantifying how urban landscape heterogeneity affects land surface temperature: A comparison of discrete and continuous approaches

10.21203/rs.3.rs-311911/v1 ◽

2021 ◽

Author(s):

ehsan Rahimi ◽

Shahindokht Barghjelveh ◽

Pinliang Dong

Keyword(s):

Surface Temperature ◽

Spatial Patterns ◽

Land Surface Temperature ◽

Landscape Metrics ◽

Land Surface ◽

Urban Landscape ◽

Landscape Heterogeneity ◽

Alternative Measures ◽

Heterogeneity Effects ◽

Patch Density

Abstract The present study examines the efficiency of discrete and continuous approaches to measuring urban heterogeneity effects on land surface temperature (LST). In the discrete approach, landscape metrics have been widely applied to quantifying the relationship between land surface temperature and urban spatial patterns and have received acceptable verification from landscape ecologists but some studies have shown their inaccurate results. The objective of the study is to compare landscape metrics and alternative approaches to measuring urban heterogeneity effects on LST. We compared landscape metrics results with nine texture-based measures, and two local spatial autocorrelation indices (local Moran’s I and Gi statistics) applied to NDVI and BAI indices as a proxy of the spatial patterns of Tehran vegetation and built-up classes. The statistical results showed that urban landscape heterogeneity had significant impacts on the LST variations, and there was a compatibility between landscape metrics and alternative measures results. Overall results showed that the less-fragmented, the more complex, larger, and the higher number of patches, the lower LST. The most significant relationship was between patch density (PD) and LST (r= -0.71). Higher values of PD have mostly been interpreted to show higher fragmentation, but other landscape metrics and alternative measures declined this conclusion. Our study demonstrated that PD was not a reliable metric and presented no information about the spatial distribution of landscape elements. This study confirms alternative measures for overcoming landscape metrics shortcomings in estimating the effects of landscape heterogeneity on LST variations and gives land managers and urban planners new insights into the urban design.

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Investigating Seasonal Effects of Dominant Driving Factors on Urban Land Surface Temperature in a Snow-Climate City in China

Remote Sensing ◽

10.3390/rs12183006 ◽

2020 ◽

Vol 12 (18) ◽

pp. 3006

Author(s):

Chaobin Yang ◽

Fengqin Yan ◽

Xuelei Lei ◽

Xiuli Ding ◽

Yue Zheng ◽

...

Keyword(s):

Surface Temperature ◽

Snow Cover ◽

Spatial Patterns ◽

Land Surface Temperature ◽

Land Surface ◽

Thermal Environment ◽

Driving Factors ◽

Seasonal Effects ◽

Landsat 8 ◽

Crop Species

Land surface temperature (LST) is a crucial parameter in surface urban heat island (SUHI) studies. A better understanding of the driving mechanisms, influencing variations in LST dynamics, is required for the sustainable development of a city. This study used Changchun, a city in northeast China, as an example, to investigate the seasonal effects of different dominant driving factors on the spatial patterns of LST. Twelve Landsat 8 images were used to retrieve monthly LST, to characterize the urban thermal environment, and spectral mixture analysis was employed to estimate the effect of the driving factors, and correlation and linear regression analyses were used to explore their relationships. Results indicate that, (1) the spatial pattern of LST has dramatic monthly and seasonal changes. August has the highest mean LST of 38.11 °C, whereas December has the lowest (−19.12 °C). The ranking of SUHI intensity is as follows: summer (4.89 °C) > winter with snow cover (1.94 °C) > spring (1.16 °C) > autumn (0.89 °C) > winter without snow cover (−1.24 °C). (2) The effects of driving factors also have seasonal variations. The proportion of impervious surface area (ISA) in summer (49.01%) is slightly lower than those in spring (56.64%) and autumn (50.85%). Almost half of the area is covered with snow (43.48%) in winter. (3) The dominant factors are quite different for different seasons. LST possesses a positive relationship with ISA for all seasons and has the highest Pearson coefficient for summer (r = 0.89). For winter, the effect of vegetation on LST is not obvious, and snow becomes the dominant driving factor. Despite its small area proportion, water has the strongest cooling effect from spring to autumn, and has a warming effect in winter. (4) Human activities, such as agricultural burning, harvest, and different choices of crop species, could also affect the spatial patterns of LST.

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Complexity of relationship between 2D/3D urban morphology and land surface temperature: A multi-scale perspective

10.21203/rs.3.rs-382333/v1 ◽

2021 ◽

Author(s):

Yu Liu ◽

Zhipeng Wang ◽

Xuan Liu ◽

Baolei Zhang

Keyword(s):

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Urban Morphology ◽

Grid Size ◽

Area Index ◽

Maximum Information ◽

Building Density ◽

Multi Scale ◽

The Relationship

Abstract Urban morphology is a crucial contributor to urban heat island (UHI) effects. However, few studies have explored the complex effect of 2D/3D urban morphology on UHI from a multi-scale perspective. In this study, We chose the central area of Jinan city, which was commonly known as the “furnace”, as the case study area. novel 2D/3D urban morphology indexes-building coverage ratio (BCR)(for assessing the 2D building density), building volume density (BVD)( for assessing the 3D building density), and the frontal area index (FAI)(for assessing 3D ventilation conditions) were calculated and derived to investigated complexity of relationship between 2D/3D urban morphology and land surface temperature(LST) at different scales using the maximum information coefficient (MIC) and geographically weighted regression (GWR). The results indicated that (1) These newly 2D/3D urban morphology indexes as essential factors that are responsible for LST variation, BCR is the most important urban morphology index affecting the LST, followed by BVD and FAI. Importantly, the relationship between the BCR, BVD, and FAI and the LST was an inverse U-shaped curve. (2) The relationship between 2D/3D urban morphology and LST variation showed a significant scale effect. With increased grid size, the correlation between the BCR, BVD, and FAI and the LST strengthened, “inflection point” of inverse U-shaped curve was significantly declined, and their explanation rate to LST first increased and then decreased, with a maximum value at the 700-m scale. Additionally, the FAI exerted a stronger negative effect, while the BCR and BVD generally had stronger positive effects on LST as the grid size increasing. This study extends our scientific understanding of the complexity effect of urban morphology on LST and is of great practical significance for urban thermal environment regulation at multi-scale.

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