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厦门市绿色植被降温服务功能核算及其时空动态特征
引用本文:赵伟,文凤平,张林波,高艳妮,何珏霖. 厦门市绿色植被降温服务功能核算及其时空动态特征[J]. 环境科学研究, 2019, 32(1): 85-94. DOI: 10.13198/j.issn.1001-6929.2018.06.20
作者姓名:赵伟  文凤平  张林波  高艳妮  何珏霖
作者单位:中国科学院·水利部成都山地灾害与环境研究所,成都 四川 610041;中国科学院·水利部成都山地灾害与环境研究所,成都 四川 610041;中国科学院大学,北京 10049;中国环境科学研究院,北京,100012;成都理工大学地球科学学院,成都 四川,610059
基金项目:国家生态文明试验区(福建)项目;中国科学院水利部成都山地灾害与环境研究所青年百人团队计划项目(No.SDSQB-2015-02);中国科学院青年创新促进会项目(No.2016333)
摘    要:为了准确核算厦门市绿色植被降温服务功能,收集厦门市2010年和2015年18个气象站点数据,采用30 m空间分辨率Landsat卫星数据和250 m空间分辨率、16 d合成的MODIS植被指数产品,在已有基于能量平衡估算模型的基础上,通过考虑植被覆盖及降温服务时长,构建了绿色植被降温服务功能核算的改进模型,并对厦门市2010—2015年绿色植被降温服务功能时空动态特征进行分析.结果表明:①改进模型能够较为准确且合理地描述绿色植被降温服务功能的时空变化特征.②厦门市北部山区由于高植被覆盖度降温服务功能高于南部城市建成区,而城市建成区中的城市绿地也具有明显的降温作用.③2010—2015年各区降温服务功能实物量整体呈增加趋势.其中,同安区降温服务功能实物量变化量最多,为166.12×106 kW·h;湖里区变化量最少,为9.72×106 kW·h;其余各区变化量都在40×106~75×106 kW·h范围内.④森林在降温服务中贡献最大,达60%以上.相比2010年,2015年降温服务功能实物量除了灌木林地变化率为-4.29%外,其余绿色植被类型均呈增长趋势,如森林和农田的增长率为11.97%、14.23%,草地和城市绿地的增长率为87.45%、92.11%.研究显示,厦门市2010—2015年的绿色植被降温服务功能总体呈明显增强趋势,其中城市绿地的降温服务功能增强尤为明显. 

关 键 词:厦门市  降温服务功能  绿色植被  Landsat  MODIS
收稿时间:2018-03-02
修稿时间:2018-06-06

Quantifying the Cooling Effects of the Green Vegetation in Xiamen City and Its Dynamics
ZHAO Wei,WEN Fengping,ZHANG Linbo,GAO Yanni and HE Juelin. Quantifying the Cooling Effects of the Green Vegetation in Xiamen City and Its Dynamics[J]. Research of Environmental Sciences, 2019, 32(1): 85-94. DOI: 10.13198/j.issn.1001-6929.2018.06.20
Authors:ZHAO Wei  WEN Fengping  ZHANG Linbo  GAO Yanni  HE Juelin
Affiliation:1.Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China2.University of Chinese Academy of Sciences, Beijing 100049, China3.Chinese Research Academy of Environmental Sciences, Beijing 100012, China4.College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China
Abstract:The vegetation cooling service is an important component in ecosystem services, playing a key role in regulating climate, especially for urban areas. To accurately evaluate the effects of green vegetation cooling service, an accounting model of cooling service was proposed with the use of meteorological data and multi-source remote sensing data based on previous assessing method. The method was successfully applied to assess the cooling services of the green vegetation surfaces in 2010 and 2015 in Xiamen City, China. The spatio-temporal analysis indicates that the region with more vegetation coverage in the north had significant cooling effects than the built-up area in the south, and the green space in the built-up area also had greater cooling effects. From 2010 to 2015, the physical volume of ecosystem cooling effects in all districts had a large increase. Tong''an District had the maximum change of 166.12×106 kW·h, and Huli District had the minimum change of 9.72×106 kW·h. The changes in other districts were between 40×106 and 75×106 kW·h. Forest contributed the most to the total cooling service, more than 60%. The growth rates for cooling service''s physical volume of shrubbery, forest, farmland, grassland, and urban green space were -4.29%, 11.97%, 14.23%, 87.45% and 92.11% respectively. These results showed that the cooling service of the green vegetation increased in Xiamen city from 2010 to 2015, especially for the cooling service of urban green spaces.
Keywords:Xiamen City  cooling service  green vegetation  Landsat  MODIS
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