运用遥感数据挖掘解析城市地表温度的空间变异规律

以上海主城区为研究靶区,基于遥感影像（Landsat ETM+）数据,运用单窗算法反演地表温度,并运用地统计学方法分析其空间变异规律。结果表明,地表温度空间差异显著,东西分布不对称,浦东的地表温度明显低于浦西,低温区出现在浦东的南部,与南汇区的交界处；高温区出现在浦西,包括中心城区的黄浦江沿岸、宝山区南部和普陀区北部,其它地区介于高温区和低温区之间。从地表温度的空间变异规律来看,在较小的空间尺度下,随机因素引起的空间异质性占总空间异质性的比重较大,空间自相关性的比重较小；随着空间尺度的增大,随机因素引起的空间异质性所占比重减小,空间自相关性的比重增大。当粒度为180 m和540 m时,地表温度表现为较强的空间自相关性,而当粒度增大到1 080 m时,地表温度具有明显的空间自相关性。

Applying Geo-data Mining to Analysis Spatial Variance Characters of Urban Land Surface Temperature

In this paper,the main city of Shanghai is chosen as the study target,based on Landsat ETM+ images.Using Mono-window Algorithm to calculate land surface temperature,spatial variance of land surface temperature is analyzed by applying the Geostatistical method.The results show that the spatial difference of land surface temperature is significant and asymmetric distribution of east-west direction.The land surface temperature of Pudong area is obvious lower than Puxi area.The lowest temperature zone is located at the junction of the southern part of Pudong District and Nanhui District,but the highest temperature zone is distributed in Puxi area,including city centre along the Huangpu River,the southern of Baoshan District and northern of Putuo District.The land surface temperature is between highest and lowest temperature in other districts.From the view of the spatial variance of land surface temperature,at the lesser spatial scales,spatial heterogeneity caused by random factors is larger proportion of the total,and spatial autocorrelation is unconspicuous.With the scale augmenting,the spatial heterogeneity caused by random factors share decrease and spatial autocorrelation increase.When the grain size is 180m and 540m,the land surface temperature has the moderate degree of the spatial autocorrelation,and when the grain size increases to 1080m,the spatial autocorrelation of land surface temperature is obvious.