兰州是河谷型城市的典型代表,利用多期Landsat遥感影像,定量反演兰州中心城区1990—2015年的夏季地表温度,分别采用普通线性回归模型(OLS)和地理加权回归模型(GWR)拟合土地覆被变化比例与地表温度的关系,分析其空间非稳定性.结果表明:空间分布上,兰州市中心城区夏季地表温度高的区域主要集中在南北两山的未利用地,黄河流经的河谷盆地温度较低;城市热岛比例指数也呈现出先下降后增长的特点.土地覆被变化比例对地表温度的变化影响显著,且二者之间存在空间上的非稳定性,地理位置和周边环境是产生空间非稳定性的主要原因.OLS回归模型会高估或低估不同土地覆被类型的增温或降温能力,GWR模型的拟合结果优于OLS模型,能够更直观准确地量化土地覆盖比例与地表温度二者关系的空间非稳定性时空格局.%By using the Landsat remote sensing images, land surface temperature (LST) of Lanzhou City, a valley city in Northwest China, in the summer was retrieved during 1990—2015. Ordinary linear regressions (OLS) models and geographically weighted regressions (GWR) models were used to investigate the relationships between the proportions of land cover change and LST and analyzed the spatial non-stability. The results indicated that the high summer land surface temperature mainly focus on unused land in north and south mountains, the valley basin where the Yellow river runs across has low temperature. Urban-heat-island ratio index decreased firstly and then increased. The proportions of land cover change were significantly correlated to LST, but with spatial non-stability which is mainly due to the different geographical locations and surrounding environments of different areas. OLS model might overestimate or underestimate the adjusting ability of different cover types on temperature, which may decrease or increase LST. The results obtained by GWR models are better than those by OLS models. What's more, GWR models could intuitively and accurately reveal the spatial non-stability of the relationships between the proportions of different land cover and LST.
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