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

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.

scholarly journals Quantifying how urban landscape heterogeneity affects land surface temperature at multiple scales

2021 ◽  
Vol 45 (1) ◽  
Author(s):  
Ehsan Rahimi ◽  
Shahindokht Barghjelveh ◽  
Pinliang Dong
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Landscape Metrics ◽  
Land Surface ◽  
Urban Landscape ◽  
Multiple Scales ◽  
Landscape Heterogeneity ◽  
Spatial Scales ◽  
Edge Density ◽  
Region Scale

Abstract Background 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 texture-based measures as alternative indices in measuring urban heterogeneity effects on LST at multiple scales. Results The statistical results showed that the correlation between urban landscape heterogeneity and LST increased as the spatial extent (scale) of under-study landscapes increased. Overall, landscape metrics showed that the less fragmented, the more complex, larger, and the higher number of patches, the lower LST. The most significant relationship was seen between edge density (ED) and LST (r = − 0.47) at the sub-region scale. Texture measures showed a stronger relationship (R2 = 34.84% on average) with LST than landscape metrics (R2 = 15.33% on average) at all spatial scales, meaning that these measures had a greater ability to describe landscape heterogeneity than the landscape metrics. Conclusion This study suggests 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 urban design.

scholarly journals On the Scale Effect of Relationship Identification between Land Surface Temperature and 3D Landscape Pattern: The Application of Random Forest

2022 ◽  
Vol 14 (2) ◽  
pp. 279
Author(s):  
Qiong Wu ◽  
Zhaoyi Li ◽  
Changbao Yang ◽  
Hongqing Li ◽  
Liwei Gong ◽  
...  
Keyword(s):  
Random Forest ◽  
Linear Regression ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Landscape Metrics ◽  
Landscape Pattern ◽  
Land Surface ◽  
Landscape Patterns ◽  
Impervious Surfaces ◽  
Multi Scale

Urbanization processes greatly change urban landscape patterns and the urban thermal environment. Significant multi-scale correlation exists between the land surface temperature (LST) and landscape pattern. Compared with traditional linear regression methods, the regression model based on random forest has the advantages of higher accuracy and better learning ability, and can remove the linear correlation between regression features. Taking Beijing’s metropolitan area as an example, this paper conducted multi-scale relationship analysis between 3D landscape patterns and LST using Pearson Correlation Coefficient (PCC), Multiple Linear Regression and Random Forest Regression (RFR). The results indicated that LST was relatively high in the central area of Beijing, and decreased from the center to the surrounding areas. The interpretation effect of 3D landscape metrics on LST was more obvious than that of the 2D landscape metrics, and 3D landscape diversity and evenness played more important roles than the other metrics in the change of LST. The multi-scale relationship between LST and the landscape pattern was discovered in the fourth ring road of Beijing, the effect of the extent of change on the landscape pattern is greater than that of the grain size change, and the interpretation effect and correlation of landscape metrics on LST increase with the increase in the rectangle size. Impervious surfaces significantly increased the LST, while the impervious surfaces located at low building areas were more likely to increase LST than those located at tall building areas. It seems that increasing the distance between buildings to improve the rate of energy exchange between urban and rural areas can effectively decrease LST. Vegetation and water can effectively reduce LST, but large, clustered and irregularly shaped patches have a better effect on land surface cooling than small and discrete patches. The Coefficients of Rectangle Variation (CORV) power function fitting results of landscape metrics showed that the optimal rectangle size for studying the relationship between the 3D landscape pattern and LST is about 700 m. Our study is useful for future urban planning and provides references to mitigate the daytime urban heat island (UHI) effect.

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

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.

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

2020 ◽  
Vol 198 ◽  
pp. 103794 ◽  
Author(s):  
Jinchao Song ◽  
Wei Chen ◽  
Jianjun Zhang ◽  
Ke Huang ◽  
Boyan Hou ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Spatial Patterns ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Building Density

scholarly journals The Influence of Green Space Patterns on Land Surface Temperature in Different Seasons: A Case Study of Fuzhou City, China

2021 ◽  
Vol 13 (24) ◽  
pp. 5114
Author(s):  
Liuqing Yang ◽  
Kunyong Yu ◽  
Jingwen Ai ◽  
Yanfen Liu ◽  
Lili Lin ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Spatial Autocorrelation ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Green Space ◽  
Thermal Environment ◽  
Coefficient Of Determination ◽  
Spatial Autoregression ◽  
Heat Islands ◽  
Patch Density

Background: Urban green space (UGS) has been shown to play an important role in mitigating urban heat island (UHI) effects. In the context of accelerating urbanization, a better understanding of the landscape pattern mechanisms affecting the thermal environment is important for the improvement of the urban ecological environment. Methods: In this study, the relationship between land surface temperature (LST) and the spatial patterns of green space was analyzed using a bivariate spatial autocorrelation and spatial autoregression model in three seasons (summer, transition season (spring), and winter) with different grid scales in Fuzhou city. Results: Our results indicated that the LST in Fuzhou City has a significant spatial autocorrelation. The percentage of landscape and patch density area were negatively correlated with surface temperature. The results of our indicators differed according to the season, with population density and distance to the water indicators not being significant in the winter. The coefficient of determination was higher at the 510 m grid scale on this study’s scale. Conclusion: This study extends our understanding on the influence of UHI effects after accounting for different seasonal and spatial scale factors. It also provides a reference for urban planners to mitigate heat islands in the future.

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