城市热环境风险预测及时空格局分析  

Prediction and analysis of urban thermal environment risk and its spatiotemporal pattern

作　　者：乔治[1] 孙宗耀 孙希华 徐新良[3] 杨俊[4] QIAO Zhi;SUN Zongyao;SUN Xihua;XU Xinliang;YANG Jun(School of Environmental Science and Engineering,Tianjin University,Tianjin 300072,China;College of Geography and Environment,Shandong Normal University,Ji′nan 250014,China;Institute of Geographical Sciences and Natural Resources Research,Chinese Academy of Sciences,State Key Laboratory of Resources and EnvironmentalInformation Systems,Beijing 100101,China;Human Settlements Research Center,Liaoning Normal University,Dalian 116029,China)

机构地区：[1]天津大学环境科学与工程学院,天津300072 [2]山东师范大学地理与环境学院,济南250014 [3]中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室,北京100101 [4]辽宁师范大学人居环境研究中心,大连116029

出　　处：《生态学报》2019年第2期649-659,共11页Acta Ecologica Sinica

基　　金：国家自然科学基金项目(41501472;41771178);中国科学院重点部署项目(KJZD-EW-TZ-G10)

摘　　要：人类活动改变土地利用/覆被所诱发的城市热环境风险成为影响城市化进程和城市生态环境可持续发展的重大阻碍。但是,当前热环境风险识别、评估和预控技术和方法缺失使得城市热环境安全防范和调控措施相对滞后。构建城市热环境风险预测模型:(1)将不同时期的地表温度进行正规化分级;(2)构建基于MARKOV-CA的城市热环境时空过程预测模型并验证其精度;(3)建立城市热环境风险评判规则并分析城市热环境风险时空格局特征。通过2005—2015年夏季MODIS地表温度产品及1∶10万土地利用现状遥感监测数据预测2015—2020年北京市城市热环境风险时空格局并分析其特征。北京市城市热环境风险呈增加趋势,其中极高风险区面积比例从9.66%上升到12.08%,极高风险等级区域主要分布于东西城区、朝阳区、丰台区、石景山区、海淀区东部、大兴区西北部,并逐渐向东西方向延伸,斑块数量增加,聚合程度也有所提高。城市热环境风险预测模型可对通过城市空间规划调控和防范城市热岛效应提供理论和技术支撑。Urban thermal environment risk resulting from land use and land cover change(LUCC) has become the most significant barrier to urbanization processes and sustainable development of an urban ecological environment. However,there is currently limited information on risk identification,evaluation,and pre-controlling methods affecting the safety precaution and controlling measures of urban thermal environments. The present study established an Urban Thermal Environment Risk Model(UTERM) as follows:(1) normalized and classified the urban land surface temperature(LST) during three different periods;(2) established a spatio-temporal process prediction model for the urban thermal environment based on MARKOV-CA and further verified its simulation accuracy;and(3) set urban thermal environment risk rules and analyzed its spatio-temporal patterns. This study forecasted spatio-temporal patterns of the urban thermal environment of Beijing andfurther analyzed its characteristics during 2015- 2020 through MODIS surface temperature products in summer and land use datasets at a 1∶100000 scale between 2005 and 2015. The results showed that the urban thermal environment risk might be increasing in the Beijing metropolitan area. The proportion of an extreme-high risk area will reach up to 12.08% from 9.66%in 2020,which will mainly be distributed in Dongcheng,Xicheng,Chaoyang,Fengtai,Shijingshan,eastern Haidian,and northeast Daxing districts,and will expand along the east-west paths. There is a significant increasing trend for the number of patches and aggregation indexes of extreme-high risk patches,becoming more regular for their landscape shape. The method for quantitatively evaluating spatio-temporal patterns of urban thermal environment risk effectively provides theoretical and technical support for planning urban ecological spaces and preventing urban heat islands.

关 键 词：热环境风险 时空格局 地表温度 土地利用 MARKOV-CA 模型 北京市 

分 类 号：X16[环境科学与工程—环境科学]