Seasonal contrast of the dominant factors for spatial distribution of land surface temperature in urban areas
Seasonal contrast of the dominant factors for spatial distribution of land surface temperature in urban areas
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城市地表温度空间分布主导因素季节对比
DOI：
10.1016/j.rse.2018.06.010
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发表时间：
2018-09-15
影响因子：
13.5
通讯作者：
Wu, Jiansheng
中科院分区：
文献类型：
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作者：
Peng, Jian;Jia, Jinglei;Wu, Jiansheng
城市热岛已成为全球性的城市生态环境问题。地表温度（LST）被广泛用于量化城市热岛。以南方沿海城市深圳为例，综合运用普通最小二乘回归、逐步回归、全子集回归和分层分区分析等方法，从5个维度探讨了不同季节地表温度的空间变化与影响因素的关系。结果表明，影响夏季地表温度空间异质性的主要因子是归一化差异积累指数（53.62%），而在过渡季影响地表温度空间异质性的主要因子是归一化差异植被指数（47.84%）。在冬季，建设用地比例和植被指数（分别为26.84%和25.56%）的影响最大。人工地表和绿色空间对地表温度的空间分异起主导作用。景观格局和多样性在夏季或过渡季节不是主要影响因子。在过渡季节，Shannon多样性指数（SHDI）的独立贡献率达到8.79%，而在冬季，SHDI和景观形状指数的独立贡献率分别为8.52%和3.45%。随着地表温度的降低，景观多样性和景观结构因子的影响有增大的趋势，而人工地表和绿色空间等重要因子的贡献率显著降低。这些关系表明，景观结构和多样性因子对地表温度的影响相对较弱，且容易被景观组分的影响所掩盖，尤其是在地表温度空间变异性不强的情况下。这些研究结果有助于根据当地条件制定城市热岛适应策略。
Urban heat island (UHI) has become an urban eco-environmental problem globally. Land surface temperature (LST) is widely used to quantify UHI. This study used Shenzhen, a southern coastal city in China, as an example to explore the relationship between spatial variation of LST in different seasons and the influencing factors in five dimensions, integrating the methods of ordinary least-squares regression, stepwise regression, all-subsets regression, and hierarchical partitioning analysis. The results showed that the most important factor affecting spatial heterogeneity of LST in summer was the normalized difference build-up index (53.62%, for contributing rate), whereas in the transition season the most important factor was the normalized difference vegetation index (NDVI) (47.84%). In winter the construction land percentage and NDVI (26.84% and 25.56%, respectively) were the most influential. Artificial surface and green space had a dominant effect on LST spatial differentiation. Landscape configuration and diversity were not the dominant influencing factors in summer or in the transition season. Furthermore, the independent contribution rate of the Shannon diversity index (SHDI) reached 8.79% in the transition season, while in winter, the independent contribution rates of SHDI and the landscape shape index were 8.52% and 3.45%, respectively. The influence of landscape diversity and configuration factors tended to increase as LST reduced, while the contribution rate of the important factors such as artificial surface and green space decreased significantly. These relationships indicate that the influence of landscape configuration and diversity factors on LST is relatively weak, and can be easily concealed by the influence of landscape components, especially when the spatial variation of LST is not strong. These findings can help to develop UHI adaptation strategies based on local conditions.