Abstract

Understanding the spatial distribution of land surface temperature (LST) and its impact factors is crucial for mitigating urban heat island effect. However, few studies have quantitatively investigated the spatial non-stationarity and spatial scale effects of the relationships between LST and its impact factors at multi-scales. The main purposes of this study are as follows: (1) to estimate the spatial distributions of urban heat island (UHI) intensity by using hot spots analysis and (2) to explore the spatial non-stationarity and scale effects of the relationships between LST and related impact factors at multiple resolutions (30–1200 m) and to find appropriate scales for illuminating the relationships in a plain city. Based on the LST retrieved from Landsat 8 OLI/TIRS images, the Geographically-Weighted Regression (GWR) model is used to explore the scale effects of the relationships in Zhengzhou City between LST and six driving indicators: The Fractional Vegetation Cover (FVC), the Impervious Surface (IS), the Population Density (PD), the Fossil-fuel CO2 Emission data (FFCOE), the Shannon Diversity Index (SHDI) and the Perimeter-area Fractal Dimension (PAFRAC),which indicate the vegetation abundance, built-up, social-ecological variables and the diversity and shape complexity of land cover types. Our findings showed that the spatial patterns of LST show statistically significant hot spot zones in the center of the study area, partly extending to the western and southern industrial areas, indicating that the intensity of the urban heat island is significantly spatial clustering in Zhengzhou City. In addition, compared with the Ordinary Least Squares (OLS) model, the GWR model has a better ability to characterize spatial non-stationarity and analyze the relationships between the LST and its impact factors by considering the space-varying relationships of different variables, especially at the fine spatial scales (30–480 m). However, the strength of GWR model has become relatively weak with the increase of spatial scales (720–1200 m). This reveals that the GWR model is recommended to be applied in the analysis of UHI problems and related impact factors at scales finer than 480 m in the plain city. If the spatial scale is coarser than 720 m, both OLS and GWR models are suitable for illustrating the correct relationships between UHI effect and its influence factors in the plain city due to their undifferentiated performance. These findings can provide valuable information for urban planners and researchers to select appropriate models and spatial scales seeking to mitigate urban thermal environment effect.

Highlights

  • Rapid and unprecedented urbanization has occurred across China over the past several decades—its urbanization rate, which can be defined as the proportion of urban population in the total population, has increased from 17.92% to 56.10% in 1978–2015 (National Bureau of Statistics of China, http://data.stats.gov.cn)

  • We explored the relationship between land surface temperature (LST) and Fractional Vegetation Cover (FVC), Impervious Surface (IS), Population Density (PD), Fossil-fuel CO2 emission (FFCOE), Shannon Diversity Index (SHDI) and Perimeter-area Fractal Dimension (PAFRAC) by using the Geographically-Weighted Regression (GWR) and Ordinary Least Squares (OLS) models in Zhengzhou City

  • It should be pointed out that population density (PD) and Fossil-fuel CO2 emission (FFCOE) variables are positively correlated to the LST and the associations are stronger in the central center of the city, where rapid urbanization and economic development occurred

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Summary

Introduction

Rapid and unprecedented urbanization has occurred across China over the past several decades—its urbanization rate, which can be defined as the proportion of urban population in the total population, has increased from 17.92% to 56.10% in 1978–2015 (National Bureau of Statistics of China, http://data.stats.gov.cn). With the development of remote sensing technology, satellite-based estimation of Land Surface Temperature (LST) and other surface temperature (e.g., lake surface temperatures, etc.) derived from thermal infrared remote sensing imagery [11,12] becomes effective in interpreting UHI effect because it provides a relatively rapid and low-cost data information on landscape scale. Numerous studies have been conducted to deal with the spatial characteristics, changing trends and impact factors of UHI effect based on remote sensing data and geographic information systems (GIS) technology [13,14,15,16,17]. Several works have used other biophysical parameters like, Normalized Difference Water Index (NDWI), the Normalized Difference Bareness Index (NDBaI) and other land-cover types as complimentary metrics to research UHI effect deeply by using remote sensing data [21,22,23]. The Fossil-fuel CO2 Emission and Population Density were incorporated into our impact factors analyses, in addition to other land use/land cover (LULC) variables and landscape pattern metrics

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