摘要
对城市热环境与城市形态特征间关系的定量分析,能为城市国土空间规划、城市生态环境的改善提供依据。局地气候区(Local Climate Zones, LCZ)可以很好反映城市局部气候的差异,因此可以基于LCZ构建局部气候与城市形态之间的定量关系来研究城市热环境。利用Landsat-8遥感数据反演地表温度,基于Sentinel-2数据利用深度学习方法进行局地气候区分类,分析各类别LCZ的地表温度与城市地表形态的关系,以此分析武汉市地表热环境特征。结果表明,不同LCZ类别间的地表温度存在较大差异,城市形态对地表温度有较大影响。其中:(1)LCZ10(重工业)温度最高,LCZG(水体)温度最低,且夏季各类别的温差大于冬季;(2)密集型建筑(LCZ1-3)的地表温度比开阔型建筑(LCZ4-6)高,低层建筑比高层建筑的地表温度高;(3)城市形态中对地表温度影响较大的为水域比例(WSF)、建筑高度(BH)和建筑比例(BSF),其中水域比例(WSF)和建筑高度(BH)与地表温度呈明显的负相关,建筑比例(BSF)与地表温度呈正相关。
Quantitative analysis of the relationship between urban thermal environment and urban morphological characteristics can provide a basis for urban territorial space planning and improvement of urban ecological environment.Local Climate Zones(LCZ)can well reflect the difference in local climate in cities.Therefore,the quantitative relationship between local climate and urban morphology can be constructed based on LCZ to study the urban thermal environment.In this paper,the Landsat-8 remote sensing data is used to invert the surface temperature,and the deep learning method is used to classify the local climate zone based on the Sentinel-2 data.The results show large differences in the surface temperature between different LCZ categories,and the urban morphology had a greater impact on the surface temperature.Among them:(1)LCZ10(heavy industry)had the highest temperature,LCZG(water body)had the lowest temperature,and the temperature difference of each category in summer was greater than that in winter;(2)The surface temperature of dense buildings(LCZ1-3)was higher than open buildings(LCZ4-6),and the surface temperature of low-rise buildings was higher than high-rise buildings;(3)In urban morphology,the water proportion(WSF),building height(BH)and building proportion(BSF)had a greater impact on the surface temperature,of which the water proportion(WSF)and building height(BH)were significantly negatively correlated with land surface temperature,and building proportion(BSF)was positively correlated with land surface temperature.
作者
赵恩灵
邓帆
李志远
郑培鑫
冯倩
韩杨
ZHAO En-ling;DENG Fan;LI Zhi-yuan;ZHENG Pei-xin;FENG Qian;HAN Yang(School of Geosciences,Yangtze University,Wuhan 430100,China;Key Laboratory of Urban Land Resources Monitoring and Simulation,Ministry of Natural Resources,Shenzhen 518034,China)
出处
《长江流域资源与环境》
CAS
CSSCI
CSCD
北大核心
2023年第5期1030-1041,共12页
Resources and Environment in the Yangtze Basin
基金
自然资源部城市国土资源监测与仿真重点实验室开放基金资助课题(KF-2020-05-047)
长江大学2021年教学研究项目。
关键词
局地气候区
深度学习
热环境
城市形态
武汉市
local climate zone
deep learning
thermal environment
urban morphological
Wuhan city
