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城市破碎绿化覆盖与热环境的关系——以德国波鸿市为例 被引量:4

Relationship between Urban Fragmented Green Area and Thermal Environment: A Case Study of Bochum, Germany
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摘要 在全球变暖和城市热岛(Urban Heat Island,UHI)现象加剧的趋势下,越来越多的城市居民将暴露于极端的高温天气中。城市绿地通过蒸腾作用和改变地表反射率降低了城市温度,提供了调节温度的生态系统服务功能。文章基于Landsat 5 TM卫星图像分析了德国波鸿市1989—2009年城市建设区域的破碎绿化覆盖率变化和地表温度(Land Surface Temperature,LST)变化,结果表明,在这20年间,波鸿市整体破碎绿化覆盖率有增加的趋势,并且缓解了城市热岛现象;通过地理加权回归(Geographically weighted regression,GWR)分析得知,绿化覆盖率与地表温度的变化具有显著的空间相关性;绿化覆盖率每上升10%,城市平均地表温度可降低0.98℃~1.29℃。借鉴波鸿城市发展经验,工业用地景观转型与提高破碎绿化覆盖率是有效缓解城市热岛现象的途径。 Under the trend of global warming and the intensification of the Urban Heat Island(UHI)effect,more and more urban residents will be exposed to extreme high temperatures.Urban green space reduces urban temperature through transpiration and changes in surface reflectivity,providing the ecosystem services that regulates the temperature.Based on the Landsat 5 TM satellite image,this study analyzes the changes in the fragmented green coverage and the Land Surface Temperature(LST)in the urban constructed area of Bochum,Germany,during the period of 1989-2009.The results show that:The overall fragmented green coverage ratio of Bochum has increased,which has mitigated the urban heat island effect;Geographically weighted regression(GWR)analysis shows there is a significant spatial correlation between the green coverage ratio and the change of surface temperature;and For every 10%increase in green coverage,the average urban surface temperature can be reduced by 0.98-1.29℃.Drawing on the development experience of Bochum,the transformation of industrial land landscape and the improvement of fragmented green coverage are the ways to effectively reduce the urban temperature.
作者 程安祺 Luis Inostroza Cheng Anqi;Luis Inostroza(College of Architecture and Urban Planning,Tongji University,Shanghai 200092,China;Geography Institution,Ruhr University of Bochum,Bochum 44780,Germany)
出处 《中国城市林业》 2019年第6期18-24,共7页 Journal of Chinese Urban Forestry
关键词 城市热岛(UHI) 地表温度(LST) 生态系统服务 线性回归 地理加权回归 urban heat island(UHI) land surface temperature(LST) ecosystem services linear regression geographically weighted regression(GWR)
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