城市绿量的遥感估算与热岛效应的相关分析——以北京市五环区域为例
收稿日期: 2011-09-23
修回日期: 2012-07-14
网络出版日期: 2012-08-22
基金资助
"十二五"国家科技支撑计划课题"节水灌溉技术应用模式"(2011BAD25B02)。
The Correlationship between Urban Greenness and Heat Island Effect with RS Technology:A Case Study within 5th Ring Road in Beijing
Received date: 2011-09-23
Revised date: 2012-07-14
Online published: 2012-08-22
Supported by
null
本研究以遥感分析北京城市绿地对地表温度的影响,研究包括绿地提取、绿量估算、地表温度反演,地表温度和绿量相关分析。并以高精度Rapid Eye遥感影像,提取了五环内的绿地面积(197.3km2,占城区总面积的29.6%),且估算绿量总值为2450.7km2。同时用2009年7月20日的Landsat5 TM 6波段数据进行地表温度反演,低温区、中温区、次热岛和热岛区域所占的五环内城区面积的比例分别为12.3%,34.7%,40.4%和12.6%。绿量和地表温度呈负相关关系:y=-1278.7x+60650,城市绿地可以使城区平均温度降低2.6℃。
邸苏闯, 吴文勇, 刘洪禄, 杨胜利, 潘兴瑶 . 城市绿量的遥感估算与热岛效应的相关分析——以北京市五环区域为例[J]. 地球信息科学学报, 2012 , 14(4) : 481 -489 . DOI: 10.3724/SP.J.1047.2012.00481
Remote sensing technology was used to estimate the interrelationship between urban green land development and land surface temperature in Beijing. The processes involved green land identification, greenness evaluation, land surface temperature retrieval, and analysis of relationship between land surface temperature and greenness. The decision tree for hierarchical classification was built up to identify the green vegetation area according to reflectance value in different bands for land covers in urban area: water, built-ups, bare soil, high-density vegetation and low density vegetation. NDVI value was used to specify the vegetation area, for high-density vegetation area it was between 0.27 and 1.0, and for low-density area it was between 0.1and 0.26. With the help of Rapid Eye remote sensing image and on site investigation, the green land area was evaluated and it was 197.3 km2 accounting for 29.6% of total urban area.50 investigated quadrates were distributed in 10 parks, such as Summer Palace, Olympic Park, and so on. The correlation coefficient for greenness and NDVI is 0.73 based on Rapid Eye imagines, while it is 0.60 based on Landsat 5 TM imagines. The estimated greenness is 2450.7 km2 within 5th ring road of Beijing. The land surface temperature was retrieved from the Landsat 5 TM 6 remote sensing image, and it was between 31℃ to 46.2℃. The land surface temperature for water body and vegetation area was 4-6℃ lower than nearby built-ups. The proportion for low temperature region, middle temperature region, sub heat island region, and heat island region were 12.3%,34.7%,40.4%, and 12.6%, respectively. It shown a negative correlation between land surface temperature and greenness as y=-1278.7x+60650 (x represented land surface temperature(℃); y represented greenness(m2). The green land declined the land surface temperature by 2.6℃ in Beijing.
Key words： urban heat island; greenness; remote sensing; NDVI; urban greenland
[1] 肖荣波,欧阳志云,李伟峰,等. 城市热岛的生态环境效应[J].生态学报,2005,25(8),2055-2060.
[2] 黄金海. 杭州市热岛效应与植被覆盖关系的研究[M].杭州:浙江大学出版社,2006:2-3.
[3] Howard L. Climate of London deduced from metrological observations[M]. London: Harvey and Dorton Press(3rd edition), 1833,1: 348.
[4] 王情,张广录,王晓磊,等. 基于RS和GIS的城市热岛效应分析——以石家庄市为例[J].世界科技研究与发展,2008,30(3):320-323.
[5] 易佳,田永中,高阳华,等. 基于RS的山地城市热岛效应及其与土地覆被变化关系研究——以重庆市主城区为例[J].云南师范大学学报,2008,28(6):64-69.
[6] 林学椿,于淑秋. 北京地区气温的年代际变化和热岛效应[J].地球物理学报,2005,48(1):39-45.
[7] 于淑秋,卞林根,林学椿. 北京城市热岛尺度变化与城市发展[J].中国科学D辑地球科学,2005,35(增刊I):97-106.
[8] 李延明,张济和,古润泽. 北京城市绿化与热岛效应的关系研究[J].中国园林,2004(1):72-75.
[9] 陈松林,王天星. 等间距法和均值标准差法界定城市热岛的对比研究[J].地球信息科学,2009,11(2):145-150.
[10] 肖胜,廖福霖,倪志荣,等. 应用遥感技术研究厦门市热岛效应与植被覆盖的关系[J].东北林业大学学报, 2002,30(3): 141-143.
[11] Nichol J. Remote sensing of urban heat islands by day and night[J]. Photogrammetric Engineering & Remote Sensing,2005(5):613-621.
[12] Amirl R, Weng Q H, Abbas A, et al. Spatial-temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran[J]. Remote Sensing of Environment,2009,(113):2606-2617.
[13] 宋巍巍. 基于遥感的热岛效应研究——以广水地区、武汉市为例[M].武汉:武汉大学出版社,2005:58-60.
[14] 吴可军,王兴荣,王善型,等. 利用NOAA卫星资料分析气温的城市热岛效应[J].气象学报,1993,51(2):204-208.
[15] Cao L Q, Li P X, Zhang L, et al. Remote sensing imagine-based analysis of the relationship between urban heat island and vegetation fraction. The International Archives of the Photogrammetry, Remote Sensing and spatial Information Sciences, Beijing, 2008(37):1379-1384.
[16] 韩贵锋,梁保平. 地表温度与植被指数相关性的空间尺度特征——以重庆市为例[J].中国园林,2011(1):68-72.
[17] 张兆明,何国金. 北京市TM 图像城市扩张与热环境演变分析[J].地球信息科学,2007,9(5):83-88.
[18] 罗小波,陈丹,刘明皓,等. 基于HJ1B/IRS的重庆市热岛效应监测应用[J].地球信息科学学报,2011,13(6):833-839.
[19] 陈自新.合理增加绿量是提高城市流程四生态效益的重要出路[J].北京园林,2001,55(17):2-4.
[20] 曾丽红,宋开山,张柏,等.应用Landsat数据和SEBAL模型反演区域蒸散发及其参数估算[J]. 遥感技术与应用,2008,23(3):255-263.
[21] 胡鸿瑞,吴泽民,吴文友. 应用遥感技术估测合肥市植被叶面积绿量[J].安徽农业大学学报,2010,37(2):306-311.
[22] 陈自新,苏雪痕,刘少宗,等. 北京城市园林绿化生态效益的研究(2)[J].中国园林,1998,14(56):51-54.
/
| 〈 |
|
〉 |