论文

Remote Sensing Analysis of Urban Thermal Environment Changes Under Background of Enhanced Greenhouse Effect——An Example of Dongguan, Guangdong

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  • 1. Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, Guangdong 510640;
    2. Guangzhou Meteorological Satellite Station, Guangzhou, Guangdong 510640

Received date: 2008-03-13

  Revised date: 2008-05-13

  Online published: 2008-11-20

Abstract

As a typical rapid urbanization city, the thermal environment in Dongguan, Guangdong has changed much in the past 28 years because of the economy development, the population growth, the underlying surface change, and the greenhouse effect enhancement. Taking Dongguan as an example, the paper retrieves the land surface temperature using Mono-window Algorithm proposed by Qin Zhihao from Landsat/TM data, meteorological measurement data and basic geographical data. Based on the Vegetation-Impervious surface-Soil model which was proposed by Ridd in 1995 according to the basic elements of underlying surfaces in a city, it defines the typical underlying surfaces as water, high vegetation region and dense building region,and gets the distribution information of them from the threshold division of normalized difference vegetation index. It takes an overlay analysis between the typical underlying surface information and land surface temperature data. The result shows that this method can effectively get the temperature differences of typical underlying surfaces. From 1988 to 2005, the land surface temperature of dense building region is higher than that of vegetation and water in Dongguan. The distribution of high land surface temperature region is consistent with the distribution of dense building region, and the low is consistent with the vegetation and water. Under the background of global warming caused by enhanced greenhouse effect, the temperature difference of dense building region and water, as the same as the temperature difference of dense building region and high vegetation region, are apparently increased. The former is bigger than the latter.

Cite this article

DENG Yu-jiao, KUANG Yao-qiu, HUANG Ning-sheng, HUANG jiang . Remote Sensing Analysis of Urban Thermal Environment Changes Under Background of Enhanced Greenhouse Effect——An Example of Dongguan, Guangdong[J]. SCIENTIA GEOGRAPHICA SINICA, 2008 , 28(6) : 814 -819 . DOI: 10.13249/j.cnki.sgs.2008.06.814

References

[1] 李国琛.全球大气变暖成因分析[J].自然灾害学报,2005,14(5):38~42.
[2] 徐世晓,赵新全,孙 平,等.温室效应与全球气候变暖[J].青海师范大学学报,2001,(4):43~52.
[3] 卢爱刚,庞德谦,何元庆,等.全球升温对中国区域温度纬向梯度的影响[J].地理科学,2006,26(3):345~350.
[4] 赵云升,杜 嘉,宋开山,等.基于卫星遥感的夏季长春市城区热场分析[J].地理科学,2006,26(1):70~74.
[5] 岳文泽,徐丽华.城市土地利用类型及格局的热环境效应研究——以上海市中心城区为例[J].地理科学,2007,27(2):243~248.
[6] Rao P K. Remote sensing of urban"heat islands"from an environmental satellite[J].Bulletin of American Meteorological Society,1972,53:647-648.
[7] Carlson T N,Augustine J A,Boland F E. Potential application of satellite temperature measurements in the analysis of land use over urban areas[J]. Bulletin of the American Meteorological Society,1977,58:1301-1303.
[8] 周淑贞,吴 林.上海下垫面温度与城市热岛——气象卫星在城市气候研究中的应用之一[J].环境科学学报,1987,7(3):261~268.
[9] Caselles V,Lopez Gareia M J,Melia J, et al. Analysis of the heat island effect of the city of Valencia,Spain,through air temperature transects and NOAA satellite data[J]. Theory and Application of Climatology,1991,43:195-203.
[10] Nichol J E. A GIS-based approach to microclimate monitoring in Singapore's high-rise housing estates[J]. Photogrammetric Engineering and Remote Sensing,1994,60(10),1225-1232.
[11] 陈云浩,李晓兵,史培军,等.上海城市热环境的空间格局分析[J].地理科学,2002,22(3):317~322.
[12] 刘 宇,匡耀求,吴志峰,等.不同土地利用类型对城市地表温度的影响——以广东东莞为例[J].地理科学,2006,26(5):597~602.
[13] 匡耀求,黄宁生,胡振宇.环境污染对东莞市地域经济发展的影响[J].地理科学,2004,24(4):419~425.
[14] 于兴修,杨桂山,王 瑶.土地利用/覆被变化的环境效应研究进展与动向[J].地理科学,2004,24(5):627~634.
[15] 孙凤华,任国玉,赵春雨,等.中国东北地区及不同典型下垫面的气温异常变化分析[J].地理科学,2005,25(2):167~172.
[16] 黄妙芬,刑旭峰,王培娟.利用Landsat/TM热红外通道反演地表温度的三种方法比较[J].干旱区地理,2006,29(1): 132~137.
[17] 张金区. 珠江三角洲地区地表热环境的遥感探测及时空演化研究.中国科学院广州地球化学研究所,2006.
[18] 覃志豪,Zhang Minghua, Karniel Amon等.用陆地卫星TM 6数据演算地表温度的单窗算法[J].地理学报,2001,56(4):456~466.
[19] Qin Z,Karnieli A,Berliner P. A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region[J]. International Journal of Remote Sensing,2001,(18):583-594.
[20] 覃志豪,Li Wenjuan,Zhang Minghua,等.单窗算法的大气参数估计方法[J].国土资源遥感,2003,(2):38~43.
[21] Zhang J Q,Wang Y P,Li Y. AC++ program for retrieving land surface temperature from the data of Landsat TM/ETM+ Band 6[J]. Computers and Geosciences,2006,(32):1796-1805.
[22] 岳文泽.基于遥感影像的城市景观格局及其热环境效应研究[M].上海:华东师范大学,2005.
[23] 何云玲,张一平,杨小波.中国内陆热带地区近40年气候变化特征[J].地理科学,2007,27(4):499~505.
[24] 孙凤华,袁 健,关 颖.东北地区最高、最低温度非对称变化的季节演变特征[J].地理科学,2008,28(4):532~536.
[25] 李国栋,王乃昂,张俊华,等.兰州市城区夏季热场分布与热岛效应研究[J].地理科学,2008,28(5):709~716.
[26] Ridd M K. Exploring a V-I-S model for urban ecosystem analysis through remote sensing:Comparative anatomy for cities[J]. International Journal of Remote Sensing,1995,16(21):2165-2185.
[27] 陈云浩,李 京,李晓兵.城市空间热环境遥感分析——格局、过程、模拟与影响[M].北京:科学出版社,2004.
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