Diurnal Land Surface Temperature Characteristics of Local Climate Zones: A Case Study in Beijing, China

Author(s):  
Jinling Quan
Urban Climate ◽  
2020 ◽  
Vol 34 ◽  
pp. 100700 ◽  
Author(s):  
Jun Yang ◽  
Yixuan Zhan ◽  
Xiangming Xiao ◽  
Jianhong Cecilia Xia ◽  
Wei Sun ◽  
...  

Author(s):  
Chunhong Zhao

The Local Climate Zones (LCZs) concept was initiated in 2012 to improve the documentation of Urban Heat Island (UHI) observations. Despite the indispensable role and initial aim of LCZs concept in metadata reporting for atmospheric UHI research, its role in surface UHI investigation also needs to be emphasized. This study incorporated LCZs concept to study surface UHI effect for San Antonio, Texas. LCZ map was developed by a GIS-based LCZs classification scheme with the aid of airborne Lidar dataset and other freely available GIS data. Then, the summer LST was calculated based Landsat imagery, which was used to analyse the relations between LST and LCZs and the statistical significance of the differences of LST among the typical LCZs, in order to test if LCZs are able to efficiently facilitate SUHI investigation. The linkage of LCZs and land surface temperature (LST) indicated that the LCZs mapping can be used to compare and investigate the SUHI. Most of the pairs of LCZs illustrated significant differences in average LSTs with considerable significance. The intra-urban temperature comparison among different urban classes contributes to investigate the influence of heterogeneous urban morphology on local climate formation.


Urban Climate ◽  
2019 ◽  
Vol 27 ◽  
pp. 259-271 ◽  
Author(s):  
Terence Darlington Mushore ◽  
Timothy Dube ◽  
Moven Manjowe ◽  
Wester Gumindoga ◽  
Abel Chemura ◽  
...  

Author(s):  
T. D. Mushore

<p><strong>Abstract.</strong> This study sought to determine Local Climate Zones (LCZs) in Harare metropolitan City, using Landsat 8 multi-spectral and multi-temporal data. The World Urban Database and Access Portal Tool (WUDAPT) and Support Vector Machine classifiers were applied. Training datasets were extracted from Google Earth as prescribed by the WUDAPT procedure. Before image classification, we tested the separability of the LCZs, using the Transformed Divergence Separability Index (TDSI) based on the digitized training datasets and Landsat 8 data. Derived LCZs were then linked with Landsat 8 derived Land Surface Temperature (LST) for the cool and hot seasons. TDSI values greater 1.9 were obtained indicating that LCZs were highly separable. Comparatively, the WUDAPT method produced more accurate LCZs results (Overall accuracy = 95.69%) than the SVM classifier (Overall accuracy = 89.86%) based on seasonal Landsat 8 data. However, SVM derived accuracies were within the acceptable range of at least 80% (overall accuracy) in literature. Further, LST was observed to be high in LCZs with high built-up density and low vegetation proportion, when compared to other zones. Due to high proportion of vegetation, sparsely built areas were at least 1&amp;thinsp;&amp;deg;C cooler. Although LCZs are usually linked at 2&amp;thinsp;m air temperature, they also strongly explain LST distribution. This work provides insight into the importance of mapping LCZs in third world countries where such information remains scarce.</p>


2020 ◽  
Vol 12 (7) ◽  
pp. 2974 ◽  
Author(s):  
Yaping Chen ◽  
Bohong Zheng ◽  
Yinze Hu

The local climate zone (LCZ) has become a new tool for urban heat island research. Taking Chenzhou as the research object, eight urban spatial form elements and land cover elements are calculated respectively through ArcGIS, Skyhelios and ENVI software. The calculation results are then rasterized and clustered in ArcGIS to obtain the LCZ map at a resolution of 200 m. Afterwards, the land surface temperature (LST) of different local climate zones in the four seasons from 2017 to 2018 is further analyzed using one-way ANOVA F-test and Student’s t-test. The results suggest that: (1) by adding localized LCZ classes and applying the semi-automatic algorithm on the Arc-GIS platform, the final overall accuracy reaches 69.54%, with a kappa value of 0.67, (2) the compact middle-rise buildings (LCZ-2′) and open low-rise buildings (LCZ-6) heavily contribute to the high LST of the downtown area, while the large low-rise buildings (LCZ-8) cause the high LST regions in the eastern part of the town, (3) obvious land surface temperature differences can be detected in four seasons among different LCZ classes, with high LST in summer and autumn. Built-up LCZ classes generally revealed higher LSTs than land cover LCZs in all seasons. The findings of this study provide better understandings of the relationship between LCZ and LST, as well as important insights for urban planners on urban heat mitigation.


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