scholarly journals Issues with using Landsat based Surface Temperature for Investigating Linkage between Local Climate Zones and Surface Urban Heat Island in Different Seasons: A Case Study of Manhattan, N.Y.

2019 ◽  
Author(s):  
Parth Bansal
Keyword(s):  
Surface Temperature ◽  
Urban Heat Island ◽  
Summer Season ◽  
Heat Island ◽  
Climate Zones ◽  
Local Climate ◽  
Local Climate Zones ◽  
Different Seasons ◽  
Urban Heat

This study was conceptualized to investigate differences in surface temperature profile of Local Climate Zones (LCZ) classes in different seasonal conditions. Manhattan was selected as case study due to its dense, but heterogeneous built-up profile and presence of green area which formed the baseline for temperature comparison. However, this study failed to find significant results, in terms of the distinct Urban Heat Island (UHI) feature often reported in literature. Instead, this study suggests that in the case of Manhattan UHI is predominantly within ± 0.5 C° except during summer season. In summer season, where more difference in built and green LCZ is observed, the noise in data, defined by standard deviation of surface temperature in the class, is also higher. Thus, our study concludes that Landsat based surface temperature should be used with extreme caution to investigate UHI since most imagery is taken during day time.

scholarly journals LINKING THE LOCAL CLIMATE ZONES AND LAND SURFACE TEMPERATURE TO INVESTIGATE THE SURFACE URBAN HEAT ISLAND, A CASE STUDY OF SAN ANTONIO, TEXAS, U.S.

2018 ◽  
Vol IV-3 ◽  
pp. 277-283 ◽  
Author(s):  
Chunhong Zhao
Keyword(s):  
Surface Temperature ◽  
Urban Heat Island ◽  
Land Surface Temperature ◽  
Land Surface ◽  
San Antonio ◽  
Heat Island ◽  
Climate Zones ◽  
Local Climate ◽  
Local Climate Zones ◽  
Urban Heat

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.

Impact of urban heat island on energy demand in buildings: Local climate zones in Nanjing

2020 ◽  
Vol 260 ◽  
pp. 114279 ◽  
Author(s):  
Xiaoshan Yang ◽  
Lilliana L.H. Peng ◽  
Zhidian Jiang ◽  
Yuan Chen ◽  
Lingye Yao ◽  
...  
Keyword(s):  
Urban Heat Island ◽  
Energy Demand ◽  
Heat Island ◽  
Climate Zones ◽  
Local Climate ◽  
Local Climate Zones ◽  
Urban Heat

Analysis of the urban heat island under different synoptic patterns using local climate zones

2020 ◽  
Vol 185 ◽  
pp. 107268 ◽  
Author(s):  
Max Anjos ◽  
Admir Créso Targino ◽  
Patricia Krecl ◽  
Gabriel Yoshikazu Oukawa ◽  
Rodrigo Favaro Braga
Keyword(s):  
Urban Heat Island ◽  
Heat Island ◽  
Climate Zones ◽  
Local Climate ◽  
Synoptic Patterns ◽  
Local Climate Zones ◽  
Urban Heat

scholarly journals Spatiotemporal Changes in the Urban Heat Island Intensity of Distinct Local Climate Zones: Case Study of Zhongshan District, Dalian, China

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Jun Han ◽  
Jiatong Liu ◽  
Liang Liu ◽  
Yuanzhi Ye
Keyword(s):  
Urban Heat Island ◽  
High Density ◽  
Urban Heat Island Intensity ◽  
Low Density ◽  
Heat Island ◽  
Climate Zones ◽  
Local Climate ◽  
Island Effect ◽  
Local Climate Zones ◽  
Urban Heat

Intensified due to rapid urbanization and global warming-induced high temperature extremes, the urban heat island effect has become a major environmental concern for urban residents. Scientific methods used to calculate the urban heat island intensity (UHII) and its alleviation have become urgent requirements for urban development. This study is carried out in Zhongshan District, Dalian City, which has a total area of 43.85 km2 and a 27.5 km-long coastline. The mono-window algorithm was used to retrieve the land surface temperatures (LSTs), employing Landsat remote sensing images, meteorological data, and building data from 2003, 2008, 2013, and 2019. In addition, the district was divided into local climate zones (LCZs) based on the estimated intensities and spatiotemporal variations of the heat island effect. The results show that, from 2003 to 2019, LCZs A and D shrank by 3.225 km2 and 0.395 km2, respectively, whereas LCZs B, C, and 1–6 expanded by 0.932 km2, 0.632 km2, and 2.056 km2, respectively. During this period, the maximum and minimum LSTs in Zhongshan increased by 1.365°C and 1.104°C, respectively. The LST and UHII levels of all LCZs peaked in 2019. The average LSTs of LCZs A–C increased by 1.610°C, 0.880°C, and 3.830°C, respectively, and those of LCZs 1–6 increased by 2°C–4°C. The UHIIs of LCZs A, C, and D increased by 0.730, 2.950, and 0.344, respectively, and those of LCZs 1–6 increased from 1.370–2.977 to 3.744–5.379. Overall, the regions with high LSTs are spatiotemporally correlated with high building densities. In this study, the land cover was then classified into four types (LCZs A–D) using visual interpretation and object-oriented classification, including forested land, low vegetation, bare ground, and water. Besides, the buildings were categorized as LCZs 1–6, which, respectively, represented low-density low-rises buildings, low-density high-rises buildings, low-density super high-rises buildings, high-density low-rises buildings, high-density high-rises buildings, and high-density super high-rises buildings.

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