Spatial–temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran

Amiri, Reza; Weng, Qihao; Alimohammadi, Abbas; Alavipanah, Seyed Kazem

doi:10.1016/j.rse.2009.07.021

Cited by 400 publications

(192 citation statements)

References 32 publications

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“…This may be because that PCACA model could partially counteract the effects of miscalculated VFC on LE estimations using the constraint of spatial context of them. The results of our study are supported by some previous studies [18,60], which have reported that the VFC-LST methodology could be used in complex urban scenes and produce acceptable results.…”

Section: Discussionsupporting

confidence: 89%

Comparative Assessment of Two Vegetation Fractional Cover Estimating Methods and Their Impacts on Modeling Urban Latent Heat Flux Using Landsat Imagery

Liu

2017

Remote Sensing

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Quantifying vegetation fractional cover (VFC) and assessing its role in heat fluxes modeling using medium resolution remotely sensed data has received less attention than it deserves in heterogeneous urban regions. This study examined two approaches (Normalized Difference Vegetation Index (NDVI)-derived and Multiple Endmember Spectral Mixture Analysis (MESMA)-derived methods) that are commonly used to map VFC based on Landsat imagery, in modeling surface heat fluxes in urban landscape. For this purpose, two different heat flux models, Two-source energy balance (TSEB) model and Pixel Component Arranging and Comparing Algorithm (PCACA) model, were adopted for model evaluation and analysis. A comparative analysis of the NDVI-derived and MESMA-derived VFCs showed that the latter achieved more accurate estimates in complex urban regions. When the two sources of VFCs were used as inputs to both TSEB and PCACA models, MESMA-derived urban VFC produced more accurate urban heat fluxes (Bowen ratio and latent heat flux) relative to NDVI-derived urban VFC. Moreover, our study demonstrated that Landsat imagery-retrieved VFC exhibited greater uncertainty in obtaining urban heat fluxes for the TSEB model than for the PCACA model.

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Section: Discussionsupporting

confidence: 89%

Comparative Assessment of Two Vegetation Fractional Cover Estimating Methods and Their Impacts on Modeling Urban Latent Heat Flux Using Landsat Imagery

Liu

2017

Remote Sensing

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“…The relationship between landscape pattern and UHI becomes globally considerable (Chen, Yao, Sun, & Chen, 2014;Coseo & Larsen, 2014;Du et al, 2016a;Du, Xiong, Wang, & Guo, 2016b;Li, Song, Cao, Meng, & Wu, 2011;Peng, Xie, Liu, & Ma, 2016). A large number of studies considered that the built-up area and bare land accelerate the effect of UHI, whereas green space and water reduce the UHI intensity (Amiri, Weng, Alimohammadi, & Alavipanah, 2009;Song, Du, Feng, & Guo, 2014). Furthermore, LST is controlled by the complex pattern of landscape composition and configuration (Asgarian, Amiri, & Sakieh, 2015;Zhou, Qian, Li, Li, & Han, 2014).…”

Section: Introductionmentioning

confidence: 99%

Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy

Guha

Govil

Dey³

et al. 2018

European Journal of Remote Sensing

381

153

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The present study focuses on determining the relationship of estimated land surface temperature (LST) with normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) for Florence and Naples cities in Italy using Landsat 8 data. The study also classifies different land use/land cover LU-LC) types using NDVI and NDBI threshold values, iterative self-organizing data analysis technique and maximum likelihood classifier, and analyses the relationship built by LST with the built-up area and bare land. Urban thermal field variance index was applied to determine the thermal and ecological comfort level of the city. Several urban heat islands (UHIs) were extracted as the most heated zones within the city boundaries due to increasing anthropogenic activities. The difference between the mean LST of UHI and non-UHI is 3.15°C and 3.31°C, respectively, for Florence and Naples. LST build a strong correlation with NDVI (negative) and NDBI (positive) for both the cities as a whole, especially for the non-UHIs. But, the strength of correlation becomes much weaker within the UHIs. Moreover, most of the UHIs (85.21% in Naples and 76.62% in Florence) are developed within the built-up area or bare land and are demarcated as an ecologically stressed zone. ARTICLE HISTORY

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“…As a result, NDVI is mentioned in much of the remote sensing analysis between vegetation information and land surface temperature [16,17]. In addition, scholars have widely investigated the empirical relationships between LST and various biophysical or socio-economic measurements [6,18].…”

Section: Introductionmentioning

confidence: 99%

Spatial Modeling of Urban Vegetation and Land Surface Temperature: A Case Study of Beijing

Huang

2015

Sustainability

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Abstract:The coupling relationship between urban vegetation and land surface temperature (LST) has been heatedly debated in a variety of environmental studies. This paper studies the urban vegetation information and LST by utilizing a series of remote sensing imagery covering the period from 1990 to 2007. Their coupling relationship is analyzed, in order to provide the basis for ecological planning and environment protection. The results show that the normalized difference vegetation index (NDVI), urban vegetation abundance (UVA) and urban forest abundance (UFA) are negatively correlated with LST, which means that both urban vegetation and urban forest are capable in decreasing LST. The apparent influence of urban vegetation and urban forest on LST varies with the spatial resolution of the imagery, and peaks at the resolutions ranging from 90 m to 120 m.

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Spatial–temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran

Cited by 400 publications

References 32 publications

Comparative Assessment of Two Vegetation Fractional Cover Estimating Methods and Their Impacts on Modeling Urban Latent Heat Flux Using Landsat Imagery

Comparative Assessment of Two Vegetation Fractional Cover Estimating Methods and Their Impacts on Modeling Urban Latent Heat Flux Using Landsat Imagery

Analytical study of land surface temperature with NDVI and NDBI using Landsat 8 OLI and TIRS data in Florence and Naples city, Italy

Spatial Modeling of Urban Vegetation and Land Surface Temperature: A Case Study of Beijing

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