摘要
高温热浪风险评估对于预防和应对高温热浪灾害具有重要意义。综合运用多源遥感数据提取北京市高温热浪期间的气温与人口空间分布,结合其它来源空间数据计算高温热浪危险性、暴露性和脆弱性因子,通过主客观赋权法构建高温热浪风险评估指标体系,生成精细的高温热浪风险空间分布图,对北京市进行高温热浪风险评估。结果表明,北京市西部和北部山区风险等级比较低,主城区的高温热浪风险很高,尤其是二环与四环之间的区域。在乡镇尺度上,朝外、香河园等街道具有非常高的高温热浪风险。本研究能够为北京市高温热浪防治提供数据支撑,并为其它区域开展高温热浪风险遥感综合评估提供参考。
This paper assessed the spatial distribution of heat risk in Beijing based on multi-source spatial data,including EOS/MODIS remote sensing data,NPP/VIRRS remote sensing data,meteorological data,population census data and Statistical yearbook.This period experienced a severe extreme heat event with daily maximum temperatures all exceeding 35 degrees Celsius.The gridded population density was mapped based on NPP/VIIRS nighttime light data and other auxiliary data by random forest algorithm.The remotely sensed gridded population density was integrated with the Normalized Difference Vegetation Index(NDVI),Modified Normalized Difference Water Index(MNDWI)and Normalized Difference Building Index(NDBI)to calculate the exposure index of heat wave by subjective and objective weighting methods.The gridded daily maximum air temperature was mapped from MODIS land surface temperature and other and other auxiliary data using machine learning technology.The average daily maximum air temperature and the number of high temperature days were derived from the remotely sensed air temperature to calculate the hazard index of heat weave.Six indicators that derived from the population census and statistical yearbook were used to calculate the vulnerability index.Then the high-resolution heat risk of Beijing was mapped by combining the exposure index,hazard index and vulnerability index.According to the developed heat risk map,the spatial pattern of the heat risk in Beijing was analyzed.The heat risk map can effectively reflect the spatial difference of heat risk in Beijing.The western and northern mountainous areas of Beijing showed low risk,whereas the south-east region experienced obviously high risk.The main district of Beijing showed the most obvious heat risk and the high risk spread outward in a radial pattern.The urban areas of Huairou,Miyun and Pinggu districts also reflected high level of risk,but the high risk areas were much smaller than that in the main urban area.The extreme heat risk was mostly located between the second and fourth rings,and the relatively low risk levels were mostly located in the second ring,especially in the Dongcheng District.Among 16 districts,Fengtai,Xicheng,Chaoyang and Haidian districts had obviously high proportions of higher risks than other districts,which can mainly be attributed to the urban heat island effect and high population density of these regions.At the township scale,Chaowai,Xiangheyuan,Xiluoyuan,Dongtiejiangying,Balizhuang in Chaoyang District,Zuojiazhuang and Fangzhuang experienced the highest heat risk.This study proposes a comprehensive study of heat risk assessment based on remote sensing,which provides valuable data for heat wave adaptation in Beijing,and also serves as a reference for heat risk assessment in other regions.
作者
何苗
徐永明
莫亚萍
祝善友
He Miao;Xu Yongming;Mo Yaping;Zhu Shanyou(School of Remote Sensing&Geomatics Engineering,Nanjing University of Information Science&Technology,Nanjing 210044,Jiangsu,China;Jiangsu Suli Environmental Technology Co.Ltd.,Nanjing 210036,Jiangsu,China)
出处
《地理科学》
CSSCI
CSCD
北大核心
2023年第7期1270-1280,共11页
Scientia Geographica Sinica
基金
国家自然科学基金项目(42271351,42171101)
江苏省研究生科研创新计划项目(KYCX22_1175)资助。
关键词
高温热浪
热浪风险评估
多源遥感
气温估算
人口空间化
heat wave
heat wave risk assessment
multimated resource remote sensing
temperature estimation
population spatialization
作者简介
何苗(1992—),女,江苏徐州人,硕士,工程师,主要从事热红外遥感和环境遥感。E-mail:hemiaok@126.com;通信作者:徐永明。E-mail:xym30@263.net。
