摘要
以城市化程度较深的北京市海淀区为研究区,基于2005年、2010年和2015年的遥感影像数据,利用基于影像(IB)的算法反演城市地表温度空间分布。将数据同化算法EnKF-3DVar与CA/Markov模型集成,将海淀区的多年平均臭氧浓度空间分布数据同化进行城市地表温度的模拟预测。结果表明海淀区的城市地表温度10年间呈现先下降后上升的趋势,但总体呈现下降趋势,其中2015年的平均温度为31.139 3℃。引入EnKF-3DVar的预测模型能够显著提升模型的模拟精度,所预测的2015年的数据结果 Kappa系数达到0.821 6。在有城市公园绿地的模式下,地表温度高值区呈现缓解趋势,在无城市绿地公园的模式下,城市地表温度高值区呈现出明显的扩张趋势,最高温度达到了56.142 3℃,城市生态绿地对于城市地表温度的空间分布影响巨大,合理布局城市绿地公园意义重大。
Based on the remote sensing image data of 2005, 2010 and 2015, the spatial distribution of urban land surface temperature was studied by using the IB algorithm in the study area of Haidian District, Beijing. The data assimilation algorithm EnKF-3DVar and CA/Markov model integration were used to simulate the urban surface temperature in Haidian District by assimilating the spatial distribution data of the annual mean ozone concentration. The results showed that the urban surface temperature in Haidian District showed a downward trend in the past 10 years, and then showed a rising trend. But its overall showed a downward trend. The average temperature in 2015 was 31. 139 3℃. The prediction model of EnKF - 3DVar can significantly improve the simulation precision of the model, and the Kappa coefficient of the predicted data in 2015 was 0. 821 6. Under the model of urban park green space, the urban surface temperature showed a decreasing trend. In the absence of urban green space park, urban surface temperature had a clear trend of expansion. The maximum temperature reached 56. 142 3℃ , and the urban ecological green space had a great influence on the spatial distribution of urban surface temperature. Rational layout of urban green space was of great significance. The urban green space had a very large effect on the land surface temperature. In the process of urban green space construction, the construction of the green space network should be strengthened, and in the area of high land surface temperature in Haidian District, a large green plate should be built. The research result can provide technical support for the current and future urban green space planning and regional surface temperature mitigation.
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2017年第9期166-172,共7页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家林业局基础性
支撑性和应急性重点项目(CAFYBB2017ZA007-3)
"十二五"国家科技支撑计划项目(2012BAD16B00)
关键词
地表温度
遥感反演
数据同化
海淀区
land surface temperature
remote sensing inversion
data assimilation
Haidian District
作者简介
张耘（1964-），女，副教授，主要从事数学模型分析、数学应用研究，E-mail：jjtzhangyun@buu．edu．cn
