The Spatial Patterns of Land Surface Temperature and Its Impact Factors: Spatial Non-Stationarity and Scale Effects Based on a Geographically-Weighted Regression Model
The Spatial Patterns of Land Surface Temperature and Its Impact Factors: Spatial Non-Stationarity and Scale Effects Based on a Geographically-Weighted Regression Model
复制标题
地表温度的空间格局及其影响因素：基于地理加权回归模型的空间非平稳性和尺度效应
DOI：
10.3390/su10072242
复制
发表时间：
2018-06
期刊：
影响因子：
3.9
通讯作者：
Juntao Tan
中科院分区：
文献类型：
--
作者：
Hongbo Zhao;Zhibin Ren;Juntao Tan
了解地表温度的空间分布及其影响因素对于缓解城市热岛效应至关重要。然而，很少有研究在多尺度上定量考察LST与其影响因素之间关系的空间非平稳性和空间尺度效应。本研究的主要目的如下：(1)利用热点分析方法估计城市热岛强度的空间分布；(2)探讨不同分辨率(30-1200m)下城市热岛强度与相关影响因子之间关系的空间非平稳性和尺度效应，并寻找合适的尺度来揭示平原城市的这种关系。基于Landsat 80LI/TIRS遥感影像的地表温度，利用地理加权回归(GWR)模型研究了郑州市地表温度与植被覆盖度(FVC)、不透水面(IS)、人口密度(PD)、化石燃料CO2排放数据(FFCOE)、Shannon多样性指数(SHDI)和周长分维(PAFRAC)6个驱动指标之间的尺度效应。研究结果表明，郑州市LST的空间分布格局在研究区域的中心呈现出明显的热点区域，部分区域向西部和南部工业区延伸，表明郑州市城市热岛强度具有明显的空间集聚特征。此外，与普通最小二乘法(OLS)模型相比，GWR模型通过考虑不同变量的空间变化关系，特别是在细空间尺度(30-480m)上，具有更好的空间非平稳性和分析LST与其影响因子之间的关系的能力。然而，随着空间尺度(720-1200m)的增大，GWR模式的强度变得相对较弱。这说明GWR模型在分析平原城市480m以下的城市热岛问题及相关影响因素时是值得推荐的。当空间尺度大于720m时，由于OLS和GWR模型的无差异性表现，它们都适合描述平原城市城市热岛效应与其影响因素之间的正确关系。这些研究结果可以为城市规划者和研究人员选择合适的模型和空间尺度以缓解城市热环境影响提供有价值的信息。
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 CO 2 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.
登录
查看更多内容
DOI：
10.1016/j.jag.2014.03.019
发表时间：
2014-10
期刊：
Int. J. Appl. Earth Obs. Geoinformation
影响因子：
--
作者：
Hao Wu;L. Ye;W. Shi;K. Clarke
通讯作者：
Hao Wu;L. Ye;W. Shi;K. Clarke
影响因子：
3.4
作者：
M. Wulder;B. Boots
通讯作者：
M. Wulder;B. Boots
影响因子：
4.9
作者：
Danijel Ivajnšič;M. Kaligarič;Igor Žiberna
通讯作者：
Danijel Ivajnšič;M. Kaligarič;Igor Žiberna
影响因子：
13.5
作者：
ARTIS, DA;CARNAHAN, WH
通讯作者：
CARNAHAN, WH
影响因子：
4.6
作者：
Woolway RI;Merchant CJ
通讯作者：
Merchant CJ