Relationship of land surface and air temperatures and its implications for quantifying urban heat island indicators—An application for the city of Leipzig (Germany)

Schwarz, Nina; Schlink, Uwe; Franck, Ulrich; Großmann, Katrin

doi:10.1016/j.ecolind.2012.01.001

Cited by 304 publications

(170 citation statements)

References 24 publications

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“…First, the anthropogenic heat from buildings and transportation heat as heat from land use also have an effect on LST (Kantzioura et al, 2012), but were not included as we only considered the land cover. Second, simultaneous and adjacent weather conditions could be a factor affecting LST (Schwarz et al, 2012), but has not been included in our analysis.…”

Section: Limitationsmentioning

confidence: 99%

How many metrics are required to identify the effects of the landscape pattern on land surface temperature?

Chen

Yao

Sun

et al. 2014

Ecological Indicators

171

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a b s t r a c tUrban heat island (UHI) is a global issue as a result of urbanization. Land surface temperature (LST) is closely related to the thermal environment and energy budget of the earth surface, and is an important parameter in identifying UHI effects. Previous studies have proved the effects of landscape pattern on LST by using landscape metrics. However, the metrics used were inconsistent in number and type. Further, fewer studies tried to select representative metrics from the numerous metrics for LST indication. In this study we tried to explore the effects of landscape pattern on LST in Beijing by using the representative class level metrics selected through cluster analysis, factor analysis and regression. The results showed a composition metric such as PLAND (e.g. percentage of impervious surface in a landscape) alone explained about 56% of the landscape mean LST, whereas adding a configuration metric such as LSI (landscape shape index) or Gyrate MN (mean gyration index) explained approximate another 6-12%. Adding more other configuration metrics does not improve the regression model performance more than 1%. The regression results also revealed that without targeted dependent variables, the factor analysis is of no use for choosing landscape metrics. These indicate that landscape composition and configuration both have effects on landscape mean LST, while composition is much more important than configuration, and that a combination of one composition metric with no more than four configuration metrics of impervious surface is sufficient for LST prediction. These results can help landscape ecologists in using landscape metrics and further help landscape planners to balance land cover in urban planning.

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Section: Limitationsmentioning

confidence: 99%

How many metrics are required to identify the effects of the landscape pattern on land surface temperature?

Chen

Yao

Sun

et al. 2014

Ecological Indicators

171

View full text Add to dashboard Cite

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“…They suggested that the fraction of active vegetation is a better predictor of LST than NDVI. Schwarz et al (2012) calculated the mean LST values for selected land classes, and their results showed that the highest mean difference between evening and morning LST occurred in industrial areas (industrial units, roads, railways, and associated lands).…”

Section: Introductionmentioning

confidence: 99%

The urban heat island in the city of Poznań as derived from Landsat 5 TM

Majkowska

Kolendowicz

Półrolniczak

et al. 2016

Theor Appl Climatol

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To study urban heat island (UHI), Landsat 5 TM data and in situ measurements of air temperature from nine points in Poznań (Poland) for the period June 2008-May 2013 were used. Based on data from measurement points located in different types of land use, the surface urban heat island (SUHI) maps were created. All available and qualitycontrolled Landsat 5 TM images from 15 unique days were used to obtain the characteristics of land surface temperature (LST) and UHI intensity. In addition, spatial analysis of UHI was conducted on the basis of Corine Land Cover 2006 dataset. In situ measurements at a height of 2 m above ground level show that the UHI is a common occurrence in Poznań with a mean annual intensity of 1.0°C. The UHI intensity is greater during the warm half of the year. Moreover, results based on the remote sensing data and the Corine Land Cover 2006 indicate that the highest value of the mean LST anomalies (3.4°C) is attained by the continuous urban fabric, while the lowest value occurs within the broad-leaved forests (−3.1°C). To re-count from LST to the air temperature at a height of 2 m above ground level (T agl ), linear and non-linear regression models were created. For both models, coefficients of determination equal about 0.80, with slightly higher value for the non-linear approach, which was applied to estimate the T agl spatial variability over the city of Poznań.

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“…The UHI effect can be assessed by measuring surface air temperature (SAT) (Schwarz et al, 2012;Wang et al, 2014) and land surface temperature (LST) (Buyantuyev and Wu, 2012;Cao et al, 2010;Rotem-Mindali et al, 2015). Studies have shown that LST was significantly correlated with SAT in the daytime (Nichol and Wong, 2008;Weng, 2009).…”

Section: Introductionmentioning

confidence: 99%

Effects of green space dynamics on urban heat islands: Mitigation and diversification

2017

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A B S T R A C TUnderstanding how green spaces affect urban temperature is crucial for assessing thermal benefits of landscape planning. This study investigated green space dynamics and land surface temperature (LST) in the Beijing metropolis. Landscape types were classified from QuickBird (2002) ). (2) LST change was not significant in the unchanged (0-0.19°C) and exchanged green space (in the range of −0.02-0.25°C). However, there were minor decreases of LST in areas of green expansion (in the range of −1.11°C to −0.67°C) and major increases in LST in the areas of green losses (1.64-2.21°C). The results indicated that the number of green spaces is not the only criteria that should be assessed for temperature mitigation. Ecosystem services of temperature mitigation are not equal between the loss and expansion of green spaces even within same area. Greater focus on protecting natural forests in cities might provide greater benefits for climate mitigation.

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Relationship of land surface and air temperatures and its implications for quantifying urban heat island indicators—An application for the city of Leipzig (Germany)

Cited by 304 publications

References 24 publications

How many metrics are required to identify the effects of the landscape pattern on land surface temperature?

How many metrics are required to identify the effects of the landscape pattern on land surface temperature?

The urban heat island in the city of Poznań as derived from Landsat 5 TM

Effects of green space dynamics on urban heat islands: Mitigation and diversification

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