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。



Abstract: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 and further 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.





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