基于用户兴趣点数据与Landsat遥感影像的城市热场空间格局研究

发布时间：2018-06-27 04:28

本文选题：城市热岛 + 地表温度　；参考：《生态学报》2017年16期

【摘要】：地图用户兴趣点(POI)数据能够反映微观尺度上城市系统中的人类活动。利用2015年夏季Landsat 8遥感影像提取了南京市地表温度和主要土地覆盖类型,利用空间与非空间多元回归模型在2、5、10 km 3个尺度上研究了地表温度与同期POI密度及植被和水体盖度的相关性,并利用方差分解技术定量区分人类活动因子(POI密度)及生态基础设施(植被和水体盖度)对城市热场的相对重要性。结果表明,在3个观测尺度上,POI密度与地表温度均存在极显著的正相关(P0.001),且相关性随观测尺度的增大而升高。植被和水体均具有显著的降温效应,水体盖度与地表温度的相关性仅在2 km尺度上显著,在5 km和10km尺度上其降温效应不再显著。方差分解结果表明,人类活动因子和生态基础设施对地表温度的独立解释率为1.6%—15%,而二者共同解释率达到了40%—70%。研究表明POI作为城市功能节点可以综合反映城市中人类活动的热源强度,在城市热场空间格局研究中是一种可与遥感数据互补的有用数据源。
[Abstract]:Map user points of interest (POI) data can reflect human activities in urban systems on a micro scale. The surface temperature and main land cover types of Nanjing were extracted from Landsat 8 remote sensing image in the summer of 2015. The correlation of surface temperature with POI density, vegetation and water coverage was studied by using spatial and non-spatial multivariate regression models at 3 scales of 10 km. The relative importance of human activity factor (POI density) and ecological infrastructure (vegetation and water coverage) to urban thermal field is quantitatively distinguished by variance decomposition technique. The results show that there is a significant positive correlation between POI density and surface temperature (P0.001) at three observational scales, and the correlation increases with the increase of observational scale. Both vegetation and water have significant cooling effects, the correlation between water coverage and surface temperature is only significant on the scale of 2 km, and the cooling effect on the scale of 5 km and 10km is no longer significant. The results of variance decomposition show that the independent interpretation rate of human activity factors and ecological infrastructure to the surface temperature is 1.6-15, and the co-interpretation rate of the two is 40% -70%. It is shown that POI, as a functional node in cities, can comprehensively reflect the intensity of human activities in cities, and it is a useful data source complementary to remote sensing data in the study of urban thermal field spatial pattern.
【作者单位】：南京大学生命科学学院;江苏省城市规划设计研究院;南京农业大学园艺学院;
【基金】：国家自然科学基金项目(41271197,31200530)
【分类号】：P237;X16

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