Understanding Land Surface Temperature Differences of Local Climate Zones Based on Airborne Remote Sensing Data

Bartesaghi-Koc, Carlos; Osmond, Paul; Peters, Alan; Irger, Matthias

doi:10.1109/jstars.2018.2815004

Cited by 55 publications

(22 citation statements)

References 31 publications

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“…The order of sUHI intensity of built types showed that the sUHI intensity was closely related to the height of buildings. In general, the lower the building height, the stronger the sUHI intensity, which is consistent with previous studies [54][55][56]. For the same height level, the sUHI intensity in open zones was significantly lower than those in compact zones.…”

Section: Relationship Between Lcz Population Density and Suhisupporting

confidence: 91%

Spatiotemporal Characteristics of the Surface Urban Heat Island and Its Driving Factors Based on Local Climate Zones and Population in Beijing, China

Zhang

Liu

et al. 2021

Atmosphere

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The increasing degree of urbanization has continuously aggravated the surface urban heat island (sUHI) effect in China. To investigate the correlation between spatiotemporal changes of sUHI and urbanization in Beijing, land surface temperature in summer from 2000 to 2017 and the distribution of local climate zones (LCZs) in 2003, 2005, 2010, and 2017 was retrieved using remote sensing data and used to analyze the sUHI area and intensity change. The statistical method GeoDetector was utilized to investigate the explanatory ability of LCZs and population as the driving factors. The year of 2006 was identified as the main turning year for sUHI evolution. The variation the sUHI from 2000 showed first an increasing trend, and then a decreasing one. The sUHI pattern changed before and after 2009. Before 2009, the sUHI mainly increased in the suburbs, and then, the enhancement area moved to the central area. The sUHI intensity change under different LCZ conversion conditions showed that the LCZ conversion influences the sUHI intensity significantly. Based on population distribution data, we found that the relationship between population density and sUHI gets weaker with increasing population density. The result of GeoDetector indicated that the LCZ is the main factor influencing the sUHI, but population density is an important auxiliary factor. This research reveals the sUHI variation pattern in Beijing from 2000 and could help city managers plan thermally comfortable urban environments with a better understanding of the effect of urban spatial form and population density on sUHIs.

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Section: Relationship Between Lcz Population Density and Suhisupporting

confidence: 91%

Spatiotemporal Characteristics of the Surface Urban Heat Island and Its Driving Factors Based on Local Climate Zones and Population in Beijing, China

Zhang

Liu

et al. 2021

Atmosphere

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“…The results of the LCZ analysis can be compared with the results of Koc et al (2018). The LCZ8 class was among the warmest in both studies during the day.…”

Section: Discussionmentioning

confidence: 99%

“…Very different results appear during the night when in Olomouc, the water cools relatively well and the industrial zones fail to cool down. Koc et al (2018) reports that water remains the warmest surface and the industrial zones are among the coldest. It would be very interesting to research the connection in thermal behaviour of LCZs in various regions more in depth, especially on cases such as Sydney and Olomouc.…”

Section: Discussionmentioning

confidence: 99%

“…Most importantly, TIR remote sensing is used for detection, evaluation and monitoring of urban heat island (UHI) effect including assessment of surface urban heat island. (e.g., Weng 2009) Many studies utilize satellite imagery (Weng 2009, Majkowska, Kolendowicz and Pó 2017, Xiao et al 2018, airborne imagery (Koc et al 2018) and even temperature modelling (Feranec et al 2016b).…”

Section: Leading Applications In Thermal Infrared Remote Sensingmentioning

confidence: 99%

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Thermal data analysis for urban climate research: A case study of Olomouc, Czechia

Pour¹,

Voženílek²

2020

Geographia Cassoviensis

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Due to the recent advancements in thermal camera technology, it is easier than ever to acquire information about surface temperature in very-high or extreme spatial resolution from manned or unmanned airborne vehicles. The main objective of the paper is to investigate the possibilities of extreme resolution thermal data for urban climate research. The main dataset used for the work is thermal mosaic acquired by an aeroplane above the city of Olomouc in the morning and afternoon on 10 th July 2016. The spatial resolution of the dataset fluctuates between 90 and 100 cm per pixel edge. Auxiliary data used for the analysis included open data from European programmes Urban Atlas and CORINE Land Cover. For further analysis, building height information, material type and Local Climate Zones classification were used. The results show a possible comparison in various neighbourhoods and material types while maintaining reference units-Urban Atlas/CORINE Land Cover classes or Local Climate Zones. Verticality analysis shows a significant difference between ground and rooftop levels that need to be taken into account when investigating heat for quality of life. All the analyses confirmed knowledge about the difference between natural and artificial surfaces that most artificial surfaces tend to heat up much more during daytime and fail to cool down completely during the night-time. Moreover, this research lays the foundation to future urban climate research based on extreme resolution thermal images over multiple areas or during long-lasting campaigns.

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“…The direct way to determine LCZ classes is to match the calculated values of LCZ parameters with their reference ranges. This method has been most widely used to distinguish LCZs based on the LCZ parameters provided by Stewart & Oke (2012) or custom parameters (Agathangelidis et al, 2019;Bartesaghi Koc et al, 2018, 2017Cai et al, 2019;Emmanuel & Loconsole, 2015;Jin et al, 2020;Leconte et al, 2017Leconte et al, , 2015Mandelmilch et al, 2020;Mitraka et al, 2015;Nassar et al, 2016;Ndetto & Matzarakis, 2015;Perera & Emmanuel, 2018;Shi et al, 2018;Thomas et al, 2014;Villadiego & Velay-Dabat, 2014;Wang et al, 2018b;Zheng et al, 2018). In addition, some studies have used other methods to determine LCZ types, such as the score assignment method (Lelovics et al, 2014;Unger et al, 2014), the decision-making algorithm (Chen et al, 2020b;Quan, 2019;Zhao et al, 2019a), the multi-dimensional linear interpolation method (Quan et al, 2017), the Naive Bayes algorithm (Hammerberg et al, 2018), the random forest algorithm (Hu et al, 2019), and the k-means method (Hidalgo et al, 2019;Kwok et al, 2019;Zhan et al, 2018).…”

Section: Classification Rulesmentioning

confidence: 99%

Mapping Local Climate Zones: A Bibliometric Meta-Analysis and Systematic Review

Sida¹,

Fan²,

Zhan³

et al. 2021

Preprint

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Understanding Land Surface Temperature Differences of Local Climate Zones Based on Airborne Remote Sensing Data

Cited by 55 publications

References 31 publications

Spatiotemporal Characteristics of the Surface Urban Heat Island and Its Driving Factors Based on Local Climate Zones and Population in Beijing, China

Spatiotemporal Characteristics of the Surface Urban Heat Island and Its Driving Factors Based on Local Climate Zones and Population in Beijing, China

Thermal data analysis for urban climate research: A case study of Olomouc, Czechia

Mapping Local Climate Zones: A Bibliometric Meta-Analysis and Systematic Review

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