State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, No. 19, Xinjiekouwai Street, Beijing 100875, China.
State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, No. 19, Xinjiekouwai Street, Beijing 100875, China.
Sci Total Environ. 2019 Mar 1;654:430-440. doi: 10.1016/j.scitotenv.2018.11.108. Epub 2018 Nov 10.
The increase in impervious surfaces due to the urbanization has caused many adverse effects on urban ecological systems, including the urban heat environmental risk. Revealing the relationship between landscape composition and pattern and land surface temperature (LST) gives insight into how to effectively mitigate the urban heat island (UHI) effect. It is also essential to simulate and optimize the distribution of impervious surfaces in urban planning. In this study, the multi-scale relationship between impervious surface and LST in Beijing was analyzed. Different distributions of land cover types and the corresponding LSTs were simulated under two development scenarios. Various geospatial approaches, including geographic information system (GIS), remote sensing, and the Conversion of Land Use and its Effects at Small regional extent (CLUE-S), were used to facilitate the analysis. The results showed that (1) impervious surfaces increased from 36.76% to 44.95% of the total area between 2005 and 2015 and the mean LST of impervious surfaces was approximately 2 °C higher than that of the areas with vegetation cover; (2) impervious surfaces had a positive logarithmic correlation with LST, while the vegetation coverage had a negative linear correlation with LST; (3) as the grid size increased, the correlation coefficients between the impervious surface density and mean LST increased at different magnitudes, and the correlation coefficients stabilized after the scale of 900 × 900 m; (4) large and contiguous patches of impervious surfaces aggravated the UHI effect when the total percentage of impervious surface remained the same; and (5) to achieve an improved and healthier urban living environment, populations controls should be considered to decrease future impervious surface demands by 7.69%-which corresponds to an average LST decrease of 1.1 °C. Landscape distribution and configuration should also be better integrated into landscape and urban planning.
由于城市化导致的不透水面的增加对城市生态系统造成了许多不利影响,包括城市热环境风险。揭示景观组成和格局与地表温度(LST)之间的关系,可以深入了解如何有效缓解城市热岛(UHI)效应。模拟和优化城市规划中的不透水面分布也是至关重要的。在本研究中,分析了北京不透水面与 LST 的多尺度关系。在两种发展情景下,模拟了不同的土地覆盖类型分布及其对应的 LST。利用地理信息系统(GIS)、遥感和 Conversion of Land Use and its Effects at Small regional extent(CLUE-S)等各种地理空间方法来促进分析。结果表明:(1)2005 年至 2015 年间,不透水面面积从总面积的 36.76%增加到 44.95%,不透水面的平均 LST 比植被覆盖区域高约 2°C;(2)不透水面与 LST 呈正对数相关,而植被覆盖率与 LST 呈负线性相关;(3)随着网格尺寸的增加,不透水面密度与平均 LST 之间的相关系数以不同的幅度增加,并且在 900×900m 的尺度之后,相关系数趋于稳定;(4)当不透水面总面积保持不变时,大面积连续的不透水面斑块加剧了 UHI 效应;(5)为了实现更好和更健康的城市生活环境,应考虑人口控制,以减少未来 7.69%的不透水面需求,这对应于平均 LST 降低 1.1°C。景观分布和配置也应更好地纳入景观和城市规划。