Seasonal contrast of the dominant factors for spatial distribution of land surface temperature in urban areas

Peng, Jian; Jia, Jinglei; Liu, Yanxu; Li, Huilei; Wu, J.

doi:10.1016/j.rse.2018.06.010

Cited by 391 publications

(169 citation statements)

References 60 publications

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“…As for the driving forces on LST, similarly to other research [66], in summer months, NDVI-LST showed a negative correlation, while NDBI had a quite strongly positive correlation with LST. However, due to the seasonal fluctuations of vegetation, the relationship of NDVI-LST changed dramatically and showed weaker correlations in winter, as reported in other research [67,68]. Different LULC types had different specific heat capacities, and vegetation was transferred to built-up land, which increased LST [69].…”

Section: Discussionmentioning

confidence: 55%

Measuring the Urban Land Surface Temperature Variations Under Zhengzhou City Expansion Using Landsat-Like Data

Yang

Zhao

et al. 2020

Remote Sensing

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Satellite-based remote sensing technologies are utilized extensively to investigate urban thermal environment under rapid urban expansion. Current Moderate Resolution Imaging Spectroradiometer (MODIS) data are, however, unable to adequately represent the spatially detailed information because of its relatively coarser spatial resolution, while Landsat data cannot explore the temporally continued analysis due to the lower temporal resolution. Combining MODIS and Landsat data, “Landsat-like” data were generated by using the Flexible Spatiotemporal Data Fusion method (FSDAF) to measure land surface temperature (LST) variations, and Landsat-like data including Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built Index (NDBI) were generated to analyze LST dynamic driving forces. Results show that (1) the estimated “Landsat-like” data are capable of measuring the LST variations; (2) with the urban expansion from 2013 to 2016, LST increases ranging from 1.80 °C to 3.92 °C were detected in areas where the impervious surface area (ISA) increased, while LST decreases ranging from −3.52 °C to −0.70 °C were detected in areas where ISA decreased; (3) LST has a significant negative correlation with the NDVI and a strong positive correlation with NDBI in summer. Our findings can provide information useful for mitigating undesirable thermal conditions and for long-term urban thermal environmental management.

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Section: Discussionmentioning

confidence: 55%

Measuring the Urban Land Surface Temperature Variations Under Zhengzhou City Expansion Using Landsat-Like Data

Yang

Zhao

et al. 2020

Remote Sensing

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“…Remote sensing has been widely used for investigating urban thermal environment and the associated drivers during the urbanization process, as it can quickly and frequently monitor large area surface change with lower cost, compared to filed survey [13][14][15]. Many studies have revealed the characteristics and spatial-temporal dynamics of the thermal environment in specific (big) cities, and the related driving factors [16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Monitoring the Detailed Dynamics of Regional Thermal Environment in a Developing Urban Agglomeration

Liu

Hui

Gao

et al. 2020

Sensors

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Many studies have revealed the characteristics and spatial-temporal dynamics of the thermal environment in specific cities or urban agglomerations (UA), as well as the associated determining factors. However, few studies focus on the changing relationships (the difference, distance, interaction, etc.) among inner cities’ heat islands in a UA, which represent not only the detailed dynamics of regional thermal environment (RTE), but also the changing competition and cooperation among cities in a developing UA. In this study, we used Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) products to map and analyze the detailed dynamics of the Beijing-Tianjin-Hebei (BTH) UA thermal environment. From 2001 to 2015, the mean surface urban heat island intensity (SUHII) of the BTH increased significantly, and the surface urban heat islands (SUHIs) in the southern BTH have rapidly increased, expanded and connected, eventually forming a large heat islands agglomeration. According to correlation analysis, urban sprawl probably led to the expansion and enhance of SUHIs in the south plain, while the forest has significantly alleviated urban heat island effect in northern mountains. The results expose the detailed evolution process of BTH thermal environment, and the changing relationships among the inner cities. In a developing UA, mitigation solutions (e.g., ecological corridors or controlling energy consumption) are in demand to stop the formation of a great heat region.

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“…27 Multiple statistical methods were integrated to show the seasonal contrast of some LULC indices for LST distribution in Shenzhen, China. 28 A seasonal variation in LST and selected LULC indices was investigated in Jaipur, India. 29 A trend and seasonal decomposition model for LST change over Beijing, China, was investigated.…”

Section: Introductionmentioning

confidence: 99%

Analytical study of seasonal variability in land surface temperature with normalized difference vegetation index, normalized difference water index, normalized difference built-up index, and normalized multiband drought index

Guha

Govil

Diwan

2019

J. Appl. Rem. Sens.

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Remote sensing technique often analyzes the thermal characteristics of any area. Our study focuses on estimating land surface temperature (LST) of Raipur City, emphasizing the urban heat island (UHI) and non-UHI inside the city boundary and the relationships of LST with four spectral indices (normalized difference vegetation index, normalized difference water index, normalized difference built-up index, and normalized multiband drought index). Mono-window algorithm is used as LST retrieval method on Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) data, which needs spectral radiance and emissivity of TIRS bands. The entire study is performed on 11 multidate Landsat 8 OLI and TIRS images taken from four different seasons; premonsoon, monsoon, postmonsoon, and winter, in a single-year time period. The Landsat 8 data derived LST is validated significantly with Moderate Resolution Imaging Spectroradiometer (MOD11A1) data. The results show that the UHI zones are mainly developed along the northern and southern portions of the city. The common area of UHI for four different seasons is developed mainly in the northwestern parts of the city, and the value of LST in the common UHI area varies from 26.45°C to 36.51°C. Moreover, the strongest regression between LST and these spectral indices is observed in monsoon and postmonsoon seasons, whereas winter and premonsoon seasons revealed comparatively weak regression. The results also indicate that landscape heterogeneity reduces the reliability of the regression between LST with these spectral indices.

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Seasonal contrast of the dominant factors for spatial distribution of land surface temperature in urban areas

Cited by 391 publications

References 60 publications

Measuring the Urban Land Surface Temperature Variations Under Zhengzhou City Expansion Using Landsat-Like Data

Measuring the Urban Land Surface Temperature Variations Under Zhengzhou City Expansion Using Landsat-Like Data

Monitoring the Detailed Dynamics of Regional Thermal Environment in a Developing Urban Agglomeration

Analytical study of seasonal variability in land surface temperature with normalized difference vegetation index, normalized difference water index, normalized difference built-up index, and normalized multiband drought index

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