西安市景观格局与城市热岛效应的耦合关系

Coupling relationship of landscape pattern and urban heat island effect in Xi'an,China

基于西安市2000、2006和2015年3期遥感数据,进行景观分类和温度反演,建立两者的回归模型,探讨景观格局对城市热岛效应的影响及预测作用.结果表明:不同尺度上的景观格局指数与温度具有不同的相关性,在景观尺度上,景观形状指数(LSI)、景观分割指数(DIVISION)和Shannon多样性指数(SHDI)与温度显著相关;在类型尺度上,CA_1、PD_1、LSI_1、AI_1、LPI_2、AI_2、CA_3、DIVISION_3、LPI_3、AI_4(其中,1代表建设用地、2代表耕地、3代表林地、4代表草地;CA指斑块类型面积,PD指斑块密度,LSI指景观形状指数,AI指聚集指数,DIVISION指景观分割指数,LPI指最大斑块指数)与温度显著相关.景观格局是影响热岛效应的主要因子,对城市热岛效应响应明显.部分景观指数可以表征地表温度,合理的景观配置对缓解城市热岛效应具有长远意义.通过不同尺度上建立的多元线性回归模型预测,2030年的热岛效应同比2015年有所回落,但减幅不明显,依旧强于2006年的热岛状况.热岛效应先由主城区蔓延,以点向面扩展,随后主城区呈现减弱趋势,而周围区(县)热岛出现明显增幅.

To examine the influence of landscape pattern on the urban heat island effect for Xi＇ an, China, landscape classification and temperature inversion were performed using 2000, 2006, and 2015 remote sensing datasets, and the regression model was established. The results showed that correlations between landscape indices and temperature differed according to spatial scale. On the landscape scale, the landscape shape index （LSI）, landscape division index （DIVISION）, and Shannon＇s diversity index （SHDI） were significantly correlated with temperature. On the class scale, CA1, PD1, LSI1, Al1, LPI2, AI2, CA3, DIVISION3, LPI3, AI4 （where ＂1＂ indicated built-up land, ＂2＂ indicated cropland, ＂3＂ indicated forestland, and ＂4＂ indicated grassland ; where CA referred to class area, PD referred to patch density, LSI referred to landscape shape index, AI referred to aggregation index, DIVISION referred to landscape division index, and LPI referred to largest patch index） were significantly correlated with temperature. The landscape pattern was the main factor affecting the urban heat island, and there was a strong response for this effect. Lots of the landscape indices could represent surface temperature, and modifying the configuration of landscapes could have significant long-term effect on reducing the urban heat island effect. The prediction of multiple linear regression models established on different scales showed that the heat island effect was lower in 2030 than in 2015, but the decrease was not significant, and the effect was still stronger than that in 2006. The heat island effect first spread outward from the main city by point-to-area expansion, then it was weakened in the main city and increased in the surrounding area （counties）.