How Do Two- and Three-Dimensional Urban Structures Impact Seasonal Land Surface Temperatures at Various Spatial Scales? A Case Study for the Northern Part of Brooklyn, New York, USA

Identifying the driving factors of urban land surface temperatures (U-LSTs) is critical in improving urban thermal environments and in supporting the sustainable development of cities. Previous studies have demonstrated that two- and three-dimensional (2D and 3D) urban structure parameters (USPs) largely influence seasonal U-LSTs. However, the effects of 2D and 3D USPs on seasonal U-LSTs at different spatial scales still await a general explanation. In this study, we used very-high-resolution remotely sensed data to investigate how 2D and 3D USPs impact seasonal U-LSTs at different spatial scales (including pixel and city block scales). In addition, the influences of various functional zones on U-LSTs were analyzed. The results show that, (1) generally, the links between USPs and U-LSTs at the city block scale were more obvious than those at the pixel scale, e.g., the Pearson correlation coefficient (r) between U-LST and the mean building height at the city block scale (summer: r = −0.156) was higher than that at the pixel scale (summer: r = −0.081). Tree percentage yielded a considerable cooling effect on summer U-LSTs on both the pixel (r = −0.199) and city block (r = −0.369) scales, and the effect was more obvious in regions with tall trees. (2) The independently total explained variances (R2) of 3D USPs on seasonal U-LSTs were considerably higher than those of 2D USPs in most urban functional zones (UFZs), suggesting the distinctive roles of 3D USPs in U-LST regulation at the local scale. Three-dimensional USPs (R2 value = 0.66) yielded more decisive influences on summer U-LSTs than 2D USPs did (R2 value = 0.48). (3) Manufacturing zones yielded the highest U-LST, followed by residential and commercial zones. Notably, it is found that the explained variances of the total study area for seasonal U-LSTs were significantly lower than those of each UFZ, suggesting the different roles of 2D and 3D USPs played in various UFZs and that it is critical to explain U-LST variations by using UFZs.

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Is everyone hot in the city? Spatial pattern of land surface temperatures, land cover and neighborhood socioeconomic characteristics in Baltimore, MD

Journal of Environmental Management ◽

10.1016/j.jenvman.2011.02.006 ◽

2011 ◽

Vol 92 (7) ◽

pp. 1753-1759 ◽

Cited By ~ 117

Author(s):

Ganlin Huang ◽

Weiqi Zhou ◽

M.L. Cadenasso

Keyword(s):

Land Cover ◽

Spatial Pattern ◽

Land Surface ◽

Socioeconomic Characteristics ◽

Surface Temperatures ◽

Land Surface Temperatures ◽

The City

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ANALYSIS OF THE SPATIOTEMPORALLY VARYING EFFECTS OF URBAN SPATIAL PATTERNS ON LAND SURFACE TEMPERATURES

Journal of Environmental Engineering and Landscape Management ◽

10.3846/jeelm.2018.5378 ◽

2018 ◽

Vol 26 (3) ◽

pp. 216-231 ◽

Cited By ~ 1

Author(s):

Cheng Li ◽

Jie Zhao ◽

Nguyen Xuan Thinh ◽

Wenfu Yang ◽

Zhen Li

Keyword(s):

Spatial Patterns ◽

Land Surface ◽

Spatial Scales ◽

Pearson Correlation ◽

Ordinary Least Squares ◽

Related Factors ◽

Spatial Metrics ◽

Surface Temperatures ◽

Land Surface Temperatures ◽

Gwr Model

Urban heat islands (UHIs) are a worldwide phenomenon that have many ecological and social consequences. It has become increasingly important to examine the relationships between land surface temperatures (LSTs) and all related factors. This study analyses Landsat data, spatial metrics, and a geographically weighted regression (GWR) model for a case study of Hangzhou, China, to explore the correlation between LST and urban spatial patterns. The LST data were retrieved from Landsat images. Spatial metrics were used to quantify the urban spatial patterns. The effects of the urban spatial patterns on LSTs were further investigated using Pearson correlation analysis and a GWR model, both at three spatial scales. The results show that the LST patterns have changed significantly, which can be explained by the concurrent changes in urban spatial patterns. The correlation coefficients between the spatial metrics and LSTs decrease as the spatial scale increases. The GWR model performs better than an ordinary least squares analysis in exploring the relationship of LSTs and urban spatial patterns, which is indicated by the higher adjusted R2 values, lower corrected Akaike information criterion and reduced spatial autocorrelations. The GWR model results indicate that the effects of urban spatial patterns on LSTs are spatiotemporally variable. Moreover, their effects vary spatially with the use of different spatial scales. The findings of this study can aid in sustainable urban planning and the mitigation the UHI effect.

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The surface urban heat island in the city of Brno (Czech Republic) derived from land surface temperatures and selected reasons for its spatial variability

Theoretical and Applied Climatology ◽

10.1007/s00704-012-0717-8 ◽

2012 ◽

Vol 112 (1-2) ◽

pp. 89-98 ◽

Cited By ~ 28

Author(s):

Petr Dobrovolný

Keyword(s):

Czech Republic ◽

Spatial Variability ◽

Urban Heat Island ◽

Land Surface ◽

Heat Island ◽

Surface Temperatures ◽

Land Surface Temperatures ◽

Urban Heat ◽

Surface Urban Heat Island ◽

The City

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Estimation of Groundwater Temperatures in Paris, France

Geofluids ◽

10.1155/2019/5246307 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Hannes Hemmerle ◽

Sina Hale ◽

Ingo Dressel ◽

Susanne A. Benz ◽

Guillaume Attard ◽

...

Keyword(s):

Land Surface ◽

Large Scale ◽

Mean Squared Error ◽

Shallow Groundwater ◽

Estimation Procedure ◽

Temperature Data ◽

Groundwater Temperature ◽

Surface Temperatures ◽

Land Surface Temperatures ◽

The City

Subsurface temperature data is usually only accessible as point information with a very limited number of observations. To spatialize these isolated insights underground, we usually rely on interpolation methods. Unfortunately, these conventional tools are in many cases not suitable to be applied to areas with high local variability, like densely populated areas, and in addition are very vulnerable to uneven distributions of wells. Since thermal conditions of the surface and shallow subsurface are coupled, we can utilize this relationship to estimate shallow groundwater temperatures from satellite-derived land surface temperatures. Here, we propose an estimation approach that provides spatial groundwater temperature data and can be applied to natural, urban, and mixed environments. To achieve this, we combine land surface temperatures with anthropogenic and natural processes, such as downward heat transfer from buildings, insulation through snow coverage, and latent heat flux in the form of evapotranspiration. This is demonstrated for the city of Paris, where measurements from as early as 1977 reveal the existence of a substantial subsurface urban heat island (SUHI) with a maximum groundwater temperature anomaly of around 7 K. It is demonstrated that groundwater temperatures in Paris can be well predicted with a root mean squared error of below 1 K by means of satellite-derived land surface images. This combined approach is shown to improve existing estimation procedures that are focused either on rural or on urban conditions. While they do not detect local hotspots caused by small-scaled heat sources located underground (e.g., sewage systems and tunnels), the findings for the city of Paris for the estimation of large-scale thermal anomalies in the subsurface are promising. Thus, the new estimation procedure may also be suitable for other cities to obtain a more reliable insight into the spatial distribution of urban ground and groundwater temperatures.

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