基于多源遥感数据的城市热环境响应与归因分析——以深圳市为例

The study of urban thermal environment dynamics and attribution analysis based on multiple remote sensing dataset:in the case of Shenzhen

城市热环境是城市局部气候与环境的综合表现,它与土地利用格局密切相关,但是相应的机制研究还不充分。以深圳市为例,首先基于2018年LANDSAT 8遥感影像,采用支持向量机方法和线性光谱混合模型提取土地利用与覆盖度信息,分析了城市土地覆盖对地表温度以及热量收支状况的影响。基于2003-2018的MODIS地表温度数据,进一步研究了深圳市城市热岛现象的时空变化,从地表能量的角度分析城市热岛变化背后的形成机制。结果表明,深圳市地表温度从西北到东南逐渐降低,城市不透水面温度显著高于植被覆盖区域,城市热岛效应明显。不透水面和城市植被共同影响深圳市的地表温度与热量收支状况,不透水地表与感热具有较好的相关性,城市植被与潜热具有较好的相关性。长时间序列分析表明深圳的城市热岛现象在夏季较高而冬季较低,月均热岛强度为2.14℃;对于年际变化,深圳在2003-2018表现出显著的下降趋势。归因分析显示感热通量的影响在深圳起主导作用,这一模式在全年和季节上都较为明显。结果表明深圳市经过高速扩张阶段,目前发展方向是提高建成区的利用效率,该现象强调了热传输在加强城市热岛效应过程中对近地面湍流的干扰作用。本研究可以为缓解热岛效应与景观格局优化研究提供借鉴。

Urban thermal environment is a comprehensive expression of local climate and environment.While it is closely related to land use patterns,its underlying mechanisms are largely unexplored.Urban heat island affects not only regional climate,vegetation growth and air quality,but also human health.Therefore,the urban thermal environment has been regarded as a critical variable.It is important to characterize the spatial and temporal patterns of urban thermal environments and quantify the response of the associated influencing factors.This study uses the city of Shenzhen as an example where rapid urbanization has occurred.Based on LANDSAT 8 remote sensing images collected in 2018,we first extract land cover types and coverage information using support vector machine and multiple linear spectral analysis models.The accuracies of the estimated land covers are acceptable,which provides confidence for further analyses.The impacts of urban land cover on land surface temperature and heat energy component are then investigated.Using Moderate Resolution Imaging Spectroradiometer(MODIS)land surface temperature dataset obtained from 2003 to 2018,this study further explores the spatiotemporal variations of urban heat island in Shenzhen,and analyzes its potential formation mechanisms from the perspective of surface energy balance.Our results show that the land surface temperature gradually decreases from northwest to Southeast of Shenzhen.This is largely attributed to the spatial distribution of impervious surfaces and the urban vegetation.Specifically,the land surface temperature of impervious surfaces is substantially higher than that of vegetation-covered area,implying that the urban heat island effect is obvious in Shenzhen.Impervious surface and urban vegetation jointly affect the surface temperature and heat budget through,for example,latent heat flux and sensible heat flux.Impervious surface is more correlated with sensible heat flux while urban vegetation is more correlated with latent heat flux.Obvious differences occur in sensible heat and latent heat among land cover types,which provides implications for the formation and elimination of urban heat island.Time series analysis regarding the period of 2003-2018 illustrates that the urban heat island effect in Shenzhen is stronger during summer season whereas relatively weaker during winter season.The urban heat island effect is remarkably exhibited using the time series dataset,with an average monthly intensity of 2.14℃.Regarding the internal variation,the downward trend in urban heat island intensity is shown from 2003 to 2018.This negative trend is significantly present during all seasons except spring,with a change rate of approximately 0.1℃/annual.Attribution analysis based on MODIS dataset and FLDAS flux dataset reveals that sensible heat flux plays a more important role in the urban heat island of Shenzhen in comparison to latent heat flux.This is consistent in the both annual and seasonal scales,and the relative contribution of latent heat flux and sensible heat flux is 42%and 58%,respectively.Results indicate that after a rapid urban expansion period in Shenzhen,the current development focus is to improve the utilization efficiency of built-up areas.This emphasizes the interference of heat transfer with the near-surface turbulence that strengthens the urban heat island effect.Our study provides reference values for mitigating the urban heat island effect and optimizing landscape patterns.