Research on the Contribution of Urban Land Surface Moisture to the Alleviation Effect of Urban Land Surface Heat Based on Landsat 8 Data

Yu, Zhitong; Chen, Longqian; Wang, Yuchen; Chen, Longgao; Yao, Fei; Wu, Peiyao; Wang, Bingyi; Li, Yuanyuan; Zhou, Tianjian; Zhang, Ting

doi:10.3390/rs70810737

Cited by 37 publications

(17 citation statements)

References 46 publications

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“…FVC (Fractional Vegetation Coverage) is also a key parameter in thermal remote-sensing analysis from which surface emissivity can be estimated, and it is an indicator of the LST, with which it has a negative linear relationship. [5] The correlation between LST and NDVI, Greenness, Brightness and Wetness components of TCT also indicates that the negative correlation of LST and NDVI, Greenness suggests that the green land can weaken the effect on urban heat island, while the positive correlation between LST and Brightness means that the built-up land can strengthen the effect of urban heat island in our case study. [6]…”

Section: Introductionmentioning

confidence: 59%

Determining the Impacts of Land Cover/Use Categories on Land Surface Temperature Using Landsat8-Oli

Balçık

Ergene

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Due to unplanned and uncontrolled expansion of urban areas, rural land cover types have been replaced with artificial materials. As a result of these replacements, a wide range of negative environmental impacts seriously impacting human health, natural areas, ecosystems, climate, energy efficiency, and quality of living in town center. In this study, the impact of land surface temperature with respect to land cover and land use categories is investigated and evaluated for Istanbul, Turkey. Land surface temperature data was extracted from 21 October 2014 dated Landsat 8 OLI data using mono-window algorithm. In order to extract land use/cover information from remotely sensed data wetness, greenness and brightness components were derived using Tasseled Cap Transformation. The statistical relationship between land surface temperature and Tasseled Cap Transformation components in Istanbul was analyzed using the regression methods. Correlation between Land Surface Temperature and Meteorological Stations Temperature calculated %74.49.

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Section: Introductionmentioning

confidence: 59%

Determining the Impacts of Land Cover/Use Categories on Land Surface Temperature Using Landsat8-Oli

Balçık

Ergene

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Most previous studies of mitigating the UHI effect have focused on factors including vegetation indices [19,71,72], the "cool island effect" of urban parks (which can be affected by urban parks' size, land surface coverage types and shape, etc.) [73][74][75], and land surface moisture [76]. By approaching the challenge from a new perspective, we explored the magnitude and spatial characteristics of the urban ET alleviation effect on the UHI.…”

Section: Discussionmentioning

confidence: 99%

Simulating the Impact of Urban Surface Evapotranspiration on the Urban Heat Island Effect Using the Modified RS-PM Model: A Case Study of Xuzhou, China

et al. 2020

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As an important energy absorption process in the Earth's surface energy balance, evapotranspiration (ET) from vegetation and bare soil plays an important role in regulating the environmental temperatures. However, little research has been done to explore the cooling effect of ET on the urban heat island (UHI) due to the lack of appropriate remote-sensing-based estimation models for complex urban surface. Here, we apply the modified remote sensing Penman-Monteith (RS-PM) model (also known as the urban RS-PM model), which has provided a new regional ET estimation method with the better accuracy for the urban complex underlying surface. Focusing on the city of Xuzhou in China, ET and land surface temperature (LST) were inversed by using 10 Landsat 8 images during 2014-2018. The impact of ET on LST was then analyzed and quantified through statistical and spatial analyses. The results indicate that: (1) The alleviating effect of ET on the UHI was stronger during the warmest months of the year (May-October) but not during the colder months (November-March); (2) ET had the most significant alleviating effect on the UHI effect in those regions with the highest ET intensities; and (3) in regions with high ET intensities and their surrounding areas (within a radius of 150 m), variation in ET was a key factor for UHI regulation; a 10 W· m −2 increase in ET equated to 0.56 K decrease in LST. These findings provide a new perspective for the improvement of urban thermal comfort, which can be applied to urban management, planning, and natural design.As the UHI effect has a negative environmental impact in urban areas, several studies have explored the factors that mediate the UHI effect with a view to mitigating its effects [9][10][11]. One common conclusion of this previous research is that vegetation and water have cooling and humidifying functions, which are the main factors acting to alleviate the UHI effect [12][13][14]. In general, water bodies in cities are often isolated and tend to be located in urban parks. Therefore, vegetation arguably plays a more important role in alleviating the thermal environment and in improving proximate human thermal comfort [15]. Urban vegetation has various types and functions, including vegetation in recreational parks and gardens, allotments, and street vegetation. One approach to alleviate the heat island effect is to use vegetation as shading on construction land to prevent direct solar heating [3,14,16]. It has been proven, for example, that the area and density of vegetation [3,10,14] can be used as indices for the degree of UHI mitigation, as can other indices such as the normalized difference vegetation index (NDVI) [17][18][19], which have also shown significant negative correlations with the UHI intensity. In addition, a series of remote-sensing-based studies have demonstrated that vegetation characteristics including size [14,20], shape [21], species [22], and spatial patterning [23,24] have significant impacts on the urban cooling effect.Another important mechanism through whic...

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“…The methods for the retrieval of LST from satellite TIR data can be broadly classified into three categories: single-channel methods, multi-channel methods (split-window algorithm, SWA), and multi-angle methods [28][29][30]. In comparison with other methods, the mono-window algorithm is simpler and only three parameters (the average atmospheric operative temperature, land surface emissivity and atmospheric transmittance) are needed; therefore, the mono-window algorithm was used to calculate the LST for the Landsat TM images.…”

Section: Derivation Of Lst and Improvement Of The Parametersmentioning

confidence: 99%

Spatio-temporal Features of Urban Heat Island and its Relationship with Land Use/Cover in Mountainous City: A Case Study in Chongqing

Liu¹,

Li²

2018

Preprint

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The urban heat island (UHI) becomes more and more serious with the acceleration of urbanization. Many researchers have shown interest in studying the UHI by using remote sensing data. But these studies rarely examine the mountainous cities. The studies on UHI in mountainous cities often used empirical parameters to estimate the land surface temperature (LST), and lacked satellite-ground synchronous experiment to test the accuracy. This paper revised the parameters in mono-window algorithm used to retrieve the LST according to the characteristics of mountainous cities. This study examined the spatial and temporal patterns of the UHI intensity in Chongqing, a typical mountainous city, and its relationship with land cover from 2007 to 2011 based on the Landsat TM data and the improved method. The accuracy of the LST derivation increased by about 1°C compared to the traditional method. The high LST areas increased and extended from the downtown to suburban area each year, but the rate of change decreased. The UHI is dramatically impacted by the rivers. There is a good relationship between the urban sprawl and the UHI. The LST was reduced by about 1°C within a 300m distance from large urban fringe green spaces. The urban landscape parks had a strong effect relieving the UHI at a 100m distance. The LST was reduced by about 0.5°C. The study greatly improves the accuracy of LST derivation, and provides a reliable parameters for the UHI researched in mountainous city.

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Research on the Contribution of Urban Land Surface Moisture to the Alleviation Effect of Urban Land Surface Heat Based on Landsat 8 Data

Cited by 37 publications

References 46 publications

Determining the Impacts of Land Cover/Use Categories on Land Surface Temperature Using Landsat8-Oli

Determining the Impacts of Land Cover/Use Categories on Land Surface Temperature Using Landsat8-Oli

Simulating the Impact of Urban Surface Evapotranspiration on the Urban Heat Island Effect Using the Modified RS-PM Model: A Case Study of Xuzhou, China

Spatio-temporal Features of Urban Heat Island and its Relationship with Land Use/Cover in Mountainous City: A Case Study in Chongqing

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