Analyzing the Influencing Factors of Urban Thermal Field Intensity Using Big-Data-Based GIS

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Highlights

  • Big data are used to determine factors influencing urban thermal field.

  • Scale characteristics of human influence on urban thermal field are analyzed.

  • Correlation exists between land-surface and air temperatures and varies with scale.

  • Composition factors and scales affecting air and land-surface temperatures were differentiated.

Abstract

The effects of human activities and land cover changes on urban thermal field patterns are closely related to the land surface temperature (LST) and air temperature. At present, the number of studies on the quantitative relationship between these two indexes and the effect of the observational scale on their influence is insufficient. In this study, spatial analysis methods such as geographic modeling were combined with remote sensing images, meteorological data, and points of insert and used to investigate the composition and scale of the factors influencing the temperature field in Beijing. The results showed that there are differences in the positive and negative correlations between LST and air temperature and various influencing factors. At a spatial resolution of 90 m, LST had a strong linear relationship with the average air temperature. Indicators reflecting elements of human activity, such as buildings, roads, and entertainment, were easily measured by meteorological stations at a small scale, and the natural green space ratio could also be easily captured by satellite thermal sensors at small scales. These results have substantial implications for environmental impact assessments in areas experiencing an increasing urban heat island effect due to rapid urbanization.

Introduction

Since the reform and opening up of China, the country’s economy has developed rapidly, and the urban population has continued to grow concomitant with expanded urban land areas; this has accelerated the process of urbanization (Tang, Shi, & Bi, 2014). Development has changed the thermal properties of the natural underlying surfaces in urban areas, causing them to absorb a large amount of excess heat emitted by human social and economic activities, thereby affecting the urban thermal environment and generating urban heat islands (UHIs) (Debbage & Shepherd, 2015; Wilby, 2007) that have a serious negative impact on the health of residents (Mika et al., 2018; Salata et al., 2017). Land surface temperature (LST) refers to the temperature of Earth's land surface. It is mainly obtained through remote sensing using a certain spatial resolution. Near-surface air temperature (NSAT) refers to the atmospheric temperature 1.5–2 m above the land surface. The land surface temperature (LST) and near-surface air temperature (NSAT) are important parameters for studying the interaction of urban thermal fields. Determining the factors that influence urban thermal fields and relating them to spatial patterns has practical significance for mitigating UHIs and improving the urban environmental.

Urban albedo is compared and analyzed, based on the LST retrieval from satellite data, to clarify the spatial pattern and temporal evolution of the urban thermal field pattern (Bonafoni, Baldinelli, & Verducci, 2017). Furthermore, measures to mitigate UHIs were determined by comparing the daily variation in air temperature and analyzing the thermal field patterns of five European cities (Bokwa et al., 2019). These studies were independent quantitative analyses of urban thermal fields based on LST or air temperature data. Several other studies have analyzed the temperature field directly, while many more have focused on the UHI space. However, from the perspective of urban thermal fields, the number of studies based on the relationship between LST and air temperature is insufficient for determining the factors that influence urban thermal fields. An UHI is analyzed by generating a UHI map, which relies on the relationship between the air temperature and LST, and comparing that relationship to the urban canopy coverage rate (Black, Ahmad, & Stephen, 2019). However, the effect of the observation scale of the influencing factors is not considered.

The urban thermal field pattern is mainly related to the underlying urban surface and human activities (Coseo & Larsen, 2014; Mbuh, Wheeler, & Cook, 2019). The underlying urban surface is mainly composed of impervious surfaces and vegetation. The continuous increase in impervious surface area is the dominant factor in the growth of UHIs, and vegetation can significantly reduce its effect (Cao, Hu, & Meng, 2011; Gusso et al., 2015). Roads and buildings have a certain impact on the urban thermal field pattern, including building density, plot ratio, and road density, resulting in significant differences in the urban thermal field distribution at different scales (Huang, Yun, & Xu, 2017; Oke, 2010; Palme, Inostroza, Villacreses, Lobato-Cordero, & Carrasco, 2017). The artificial heat generated by human activities within the city is also an important factor in the formation of the urban thermal field (Scarano & Mancini, 2017). Point of insert (POI) data are the main indicators of human activities in urban systems, which can record precise geographical locations and quantitative information about human activities (such as the number of visitors) in a specific time period, allowing the type and intensity of human activities to be reflected at a very precise spatial scale (Han et al., 2017). Therefore, POI data have the potential to be very useful for studying urban thermal fields and their driving mechanisms.

With the continuous development of Internet technology, humans have entered the era of the “Information Age”, and massive data with attributes such as social values, information resources, and political spatial structures are generated in real time. The advantages of big data are the precision and high processing efficiency can support research related to measuring specific city functions, such as assisting passengers with public transportation, and balancing the supply and demand of car sharing (Gavalas et al., 2015; Song, Janowicz, & Couclelis, 2017; Willing, Klemmer, Brandt, & Neumann, 2017). POI data are point-space data that cover the location and attributes of various types of infrastructure in the city and reflect the intensity of human activities (Sheha, Sheha, & Petilli, 2012; Yu & Ai, 2015). These data can be used to understand the factors influencing the urban thermal field patterns at different scales through multivariate statistical analysis (Han et al., 2017). POI data are diverse, and different types of POI data have various effects on urban thermal field patterns. It is worthwhile to study the relationship between POI data and UHIs by comparing the attributes of the former and probing the dynamic mechanisms driving the latter.

This study was conducted to investigate (a) the LST and air temperature influencing factors at different observational scales and (b) the spatial-scale characteristics in Beijing by using data analysis software such as ArcGIS, SPSS, and MATLAB to carry out correlation, regression, and density analyses, in order to provide information on the coupled relationship between the urban thermal field pattern and LST and air temperature. This information will allow us to improve our future urban landscape development.

Section snippets

Data source and preprocessing

In this study, the LST data were obtained by retrieving Landsat 8 satellite images by atmospheric correction. Satellite imagery was provided by the United States Geological Survey, and the imaging time was 10:30–11:00 local standard time in July and August of 2015 and 2018. Data were automatically recorded every five minutes. Historical weather records indicate that there was no precipitation on the days before the imaging time, and the selected images were sunny days with low wind speeds. At

Analysis of POI data and LST

In the ENVI remote sensing software, an atmospheric correction method was used to retrieve the LST data from the Landsat image; the POI data point density was calculated via ArcGIS (Fig. 3). From the perspective of the spatial distribution, the POI data is in good agreement with the surface thermal field. The POI data were mainly concentrated in the main urban area of Beijing and gradually decreased outward. The LST of the main urban area was higher than that of the surrounding areas and rural

Relationship between LST and air temperature

Land surface temperature (LST) refers to the surface temperature of the Earth's land. It is mainly obtained through remote sensing with a certain spatial resolution. Near-surface air temperature (NSAT) refers to the atmospheric temperature of 1.5 to 2 m above the surface. The results of this study show that the air temperature of 10:30 has a good correlation with the LST. LST and NSAT are two closely related parameters for studying urban thermal fields. Based on the correlation between air

Conclusions

This study analyzed the influencing factors of thermal fields and their relationship with LST and air temperature based on Beijing urban multi-source big data at multiple research observation scales. The conclusions are as follows:

  • (1)

    There is a clear linear correlation between the LST and POI data type of human activity intensity, and positively correlated with MNDWI, plot ratio, and road density, and negatively correlated with NDVI, green space ratio, and building density, and the correlation

Declaration of Competing Interest

The authors declare that there are no conflicts of interest.

Funding

This work was supported by the National Natural Science Foundation of China (NSFC) [grant number 31600571] and the Top-notch Academic Programs Project of Jiangsu [grant number PPZY2015A063].

Acknowledgments

This study was supported by grants from the National Natural Science Foundation of China (NSFC) project (31600571), the Top-notch Academic Programs Project of Jiangsu (PPZY2015A063). The authors declare that there is no conflict of interests. We sincerely thank professor Yun Yingxia for the invaluable advice provided.

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