摘要
体感温度指数综合考虑了温度和湿度对人体的影响,可有效表征人体舒适度,对于研究城市热岛效应对人体健康的影响具有重要意义。基于2017年7月10日的Landsat 8卫星影像运用单通道算法反演北京市的地表温度,结合NDVI、MNDWI、海拔及水汽含量等环境因子,构建随机森林模型估算近地表气温;基于同一天的MODIS水汽产品提取大气可降水量,运用逐步回归方法建立近地表水汽含量与大气可降水量、地表温度及海拔之间的关系来估算近地表露点温度,在此基础上计算Humidex指数来表征北京市体感温度的空间分布。结果表明,模型估算的体感温度平均绝对误差(MAE)为1.40℃,均方根误差(RMSE)为1.71℃。北京市7月10日的体感温度范围为24~42℃,具有东南高西北低的分布特征。主城区和近郊区的体感温度显著高于远郊区,并向四周呈递减趋势;受空气湿度的影响,平谷南部、密云河谷一带、延怀盆地以及房山东南部等部分远郊地区的体感温度也比较高。就城市内部的体感温度而言,不同功能区的分布使得体感温度在相邻区域上存在空间差异性,东城区和西城区体感温度最高,平均值达到了39.79℃,丰台区、海淀区、石景山区和朝阳区的平均体感温度也达到了34.29℃。体感温度与气温在空间分布上具有一致性,但整体上高于气温,在主城区和各区中心区域,体感温度比气温高5℃以上。该研究尝试通过遥感手段反演北京地区体感温度指数,获取了北京市体感温度的详细空间分布信息,为城市人居环境和城市热岛效应研究提供了科学参考。
Apparent temperature can reflects the influence of temperature and humidity on human body, which can be used to characterize the thermal discomfort, and it is important to be used for understanding how the urban heat island effect affects human health risk. Based on land surface temperature （LST） retrieved from the Landsat 8/TIRS data by using single channel algorithm, and other environment variables such as normalized difference vegetation index （NDVI）, imperious surface cover （ISC） and modified normalized difference water index （MNDWI） calculated from Landsat8/OLI data, these were combined altitude, and precipitable water vapor data as the input variables of a random forest regression model to retrieve near surface air temperature. Precipitable water vapor were derived from MODIS water vapor product at adjoining times in the same day. A stepwise regression was developed to estimate the dew point temperature, using water vapor, LST and altitude as independent variables. On this basis, Humidex was calculated to represent the spatial distribution of apparent temperature across Beijing was mapped. The results indicated that the mean absolute error （MAE） and root mean square error （RMSE） of the retrieving apparent temperature were 1.40 ℃ and 1.71 ℃ respectively. The apparent temperature in Beijing on July 10th of 2017 was ranged from 24 ℃ to 42 ℃. In general, northern part of Beijing showed lower apparent temperature than the southern part, and the western part showed lower apparent temperature than the east. Apparent temperature across the metropolitan and suburb was also higher than subrural area outside the city, and had formed a decreasing trend. Due to the effect of humidity, subrural area such as southern Pinggu, Miyun River Valley, Yanhuai Basin, and Southeastern Fangshan were also exhibited a high apparent temperature. There was also a significant spatial variability in apparent temperature across the metropolitan area due to the distribution of urban functional district, as a result, Dongcheng district and Xicheng district were observed the highest average apparent temperature of 39.79 ℃, and several suburb districts such as Fengtai, Haidian Shijingshan and Chaoyang district were also observed a higher apparent temperature with average value of 34.29 ℃. A comparison indicated that spatial distributions of apparent temperature and air temperature were similar across the study area, while apparent temperature was generally higher than air temperature. Several urban areas such as the metropolitan area and the central regions in each district had observed an apparent temperature more than 6 ℃ higher than air temperature. In conclusion, this paper attempts to estimate apparent temperature based on remote sensing datasets and to map the detailed spatial distribution of apparent temperature across Beijing, which can be used as a reference for city planning and urban climatic studies in the future.
作者
李宁
徐永明
何苗
吴笑涵
LI Ning;XU Yongming;HE Miao(WU XiaohanSchool of Remote Sensing ＆ Geomatics Engineering,Nanjing University of Information Science ＆ Technology,Nanjing 210044,China)
出处
《生态环境学报》
CSCD
北大核心
2018年第6期1113-1121,共9页
Ecology and Environmental Sciences
基金
国家自然科学基金项目(41201369)
教育部人文社会科学研究项目(17YJCZH205)
关键词
北京
体感温度
遥感
随机森林
Beijing
apparent temperature
remote sensing
random forest
