Spatial pattern of greenspace affects land surface temperature: evidence from the heavily urbanized Beijing metropolitan area, China
Spatial pattern of greenspace affects land surface temperature: evidence from the heavily urbanized Beijing metropolitan area, China
复制标题
绿地空间格局影响地表温度：来自中国高度城市化的北京都市区的证据
DOI：
10.1007/s10980-012-9731-6
复制
发表时间：
2012-07-01
影响因子：
5.2
通讯作者：
Zheng, Hua
中科院分区：
文献类型：
--
作者：
Li, Xiaoma;Zhou, Weiqi;Zheng, Hua
城市热岛是指城市地区的空气/地表温度高于周围农村地区的现象。许多研究表明，增加绿地覆盖率（PLAND）可以显着降低地表温度（LST）。然而，很少有研究探讨绿地配置对地表温度的影响。本文旨在填补这一空白，中国北京作为一个案例研究。PLAND沿着与六个配置指标被用来衡量绿地的组成和配置。的度量计算的基础上，来自SPOT图像的绿地地图，和LST数据从Landsat TM热波段检索。以人口普查小区为分析单元，采用普通最小二乘回归和空间自回归方法，研究了地表温度与绿地空间格局的关系。结果表明，PLAND是LST最重要的预测因子。PLAND增加10%导致LST降低约0.86 °C。绿地配置也显著影响LST。在绿地数量固定的情况下，LST随着斑块密度的增加而显著增加。此外，LST的变化在很大程度上解释了绿地的组成和配置。由组成解释的独特变化相对较小，并且接近于构型的变化。本研究的结果可以扩展我们对地表温度与植被之间关系的理解，并为改善城市绿地规划和管理提供见解。
The urban heat island describes the phenomenon that air/surface temperatures are higher in urban areas compared to their surrounding rural areas. Numerous studies have shown that increased percent cover of greenspace (PLAND) can significantly decrease land surface temperatures (LST). Fewer studies, however, have investigated the effects of configuration of greenspace on LST. This paper aims to fill this gap using Beijing, China as a case study. PLAND along with six configuration metrics were used to measure the composition and configuration of greenspace. The metrics were calculated based on a greenspace map derived from SPOT imagery, and LST data were retrieved from Landsat TM thermal band. Ordinary least squares regression and spatial autoregression were employed to investigate the relationship between LST and spatial pattern of greenspace using the census tract as the analytical unit. The results showed that PLAND was the most important predictor of LST. A 10 % increase in PLAND resulted in approximately a 0.86 °C decrease in LST. Configuration of greenspace also significantly affected LST. Given a fixed amount of greenspace, LST increased significantly with increased patch density. In addition, the variance of LST was largely explained by both composition and configuration of greenspace. The unique variation explained by the composition was relatively small, and was close to that of the configuration. Results from this study can expand our understanding of the relationship between LST and vegetation, and provide insights for improving urban greenspace planning and management.