中小尺度下植被冠层对屋顶表面温度的调控效应分析
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摘要
随着城市化进程的加快,城市热岛问题日益严重,对人类健康和城市可持续发展产生了巨大威胁。植被可有效遮蔽阳光直射,并通过蒸腾作用降低气温,是改善局部热环境的重要途径之一。开展植被对建筑物温度的调控效应的研究,对于理解城市热岛成因、缓解城市热环境恶化等方面都有重要意义。然而,当前研究往往是在遥感影像的基础上进行的,缺乏植被结构信息,同时,受制于有限的空间分辨率,研究大多在城市尺度下开展。在中小尺度上定量地研究植被冠层密度对建筑物温度的影响仍然具有一定挑战性。鉴于此,本文使用激光雷达(Light Detection and Ranging, LiDAR)获取的高分辨率冠层密度数据,在楼间尺度和街区尺度下开展圣罗莎市三维植被结构与单体建筑物表面温度之间定量关系的研究,分析不同尺度下植被冠层的降温特征及其在局部环境中的降温贡献。结果表明:植被对建筑物的降温作用与其周围的冠层密度有密切关系:冠层密度需达到17%才能起到有效的降温作用,其中在中小尺度上冠层密度分别高于30%和40%时,能最大限度发挥植被的温度调控功能;当冠层密度相同时,2个尺度下的温度变化显著不同:随着冠层密度的增加,街区尺度下的屋顶温度比楼间尺度下的屋顶温度平均下降了0.89℃;中小尺度下的屋顶温度变化不仅受到其周围植被结构的影响,还与整体热环境状况有关。本文的研究思路与结果有助于在有限的城区土地资源上合理规划绿地建设,构建可持续的人类宜居环境。
With the acceleration of urbanization, urban heat island(UHI) effect has become an increasingly serious problem, which poses a great threat to public health and urban sustainability. Vegetation can lower the air temperature by reflecting direct sunlight and through the process of evapotranspiration, and hence plays a key role in improving local thermal environments. Investigating the effect of vegetation on regulating building temperature is very useful for understanding the principle of urban heat island and mitigating the deterioration of urban thermal environment. However, most previous studies are based on remote sensing imagery, which lacks three-dimensional information on vegetation structure. Additionally, these studies are mainly carried out at the urban scale due to the limitation of spatial resolution. Therefore, it remains challenging to quantitatively investigate the effects of vegetation canopy structure on building temperature at small and medium scales. In this paper, we quantitatively investigated the relationship between the Li DAR-derived 3D vegetation structure(canopy density, CD) and the rooftop surface temperature(RST) at the city-block(medium) and individual building(small) scale. We improved the Building Thermal Functional Area model(BTFA). Considering the spatial and quantity characteristics of buildings in Santa Rosa, the optimal sizes of the small and medium thermal function areas were estimated. Then the vegetation canopy density around the buildings at two scales were calculated.The cooling capacity of CD was analyzed by nonlinear fitting model and other statistical methods. Moreover, we used spatial autoregression model to analyze the contribution of CD to lower the rooftop temperature under the interaction of various factors. Results show that the cooling effect of vegetation on buildings is closely related to the canopy density around them: the minimum threshold of 17% is required to achieve effective cooling effect,while 30% and 40% are the optimal thresholds at medium and small scales, respectively. Additionally, changes of RST vary at different scales with the same canopy density. The decrease of RST at the medium scale is on average 0.89 ℃ lager than that at the small scale. The findings suggest that the planning of urban green space should be considered comprehensively in different scales. Moreover, the RST changes at small and medium scales are affected by not only the vegetation structure nearby the buildings but also the overall thermal environment. The methods and results of this paper are helpful to better plan green spaces on the limited urban land resources and build a more sustainable human livable environment.
With the acceleration of urbanization, urban heat island(UHI) effect has become an increasingly serious problem, which poses a great threat to public health and urban sustainability. Vegetation can lower the air temperature by reflecting direct sunlight and through the process of evapotranspiration, and hence plays a key role in improving local thermal environments. Investigating the effect of vegetation on regulating building temperature is very useful for understanding the principle of urban heat island and mitigating the deterioration of urban thermal environment. However, most previous studies are based on remote sensing imagery, which lacks three-dimensional information on vegetation structure. Additionally, these studies are mainly carried out at the urban scale due to the limitation of spatial resolution. Therefore, it remains challenging to quantitatively investigate the effects of vegetation canopy structure on building temperature at small and medium scales. In this paper, we quantitatively investigated the relationship between the Li DAR-derived 3D vegetation structure(canopy density, CD) and the rooftop surface temperature(RST) at the city-block(medium) and individual building(small) scale. We improved the Building Thermal Functional Area model(BTFA). Considering the spatial and quantity characteristics of buildings in Santa Rosa, the optimal sizes of the small and medium thermal function areas were estimated. Then the vegetation canopy density around the buildings at two scales were calculated.The cooling capacity of CD was analyzed by nonlinear fitting model and other statistical methods. Moreover, we used spatial autoregression model to analyze the contribution of CD to lower the rooftop temperature under the interaction of various factors. Results show that the cooling effect of vegetation on buildings is closely related to the canopy density around them: the minimum threshold of 17% is required to achieve effective cooling effect,while 30% and 40% are the optimal thresholds at medium and small scales, respectively. Additionally, changes of RST vary at different scales with the same canopy density. The decrease of RST at the medium scale is on average 0.89 ℃ lager than that at the small scale. The findings suggest that the planning of urban green space should be considered comprehensively in different scales. Moreover, the RST changes at small and medium scales are affected by not only the vegetation structure nearby the buildings but also the overall thermal environment. The methods and results of this paper are helpful to better plan green spaces on the limited urban land resources and build a more sustainable human livable environment.
引文
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[6]林波荣.绿化绿化对室外热环境影响的研究[D].北京:清华大学,2004.[Lin B R.Studies of greening's effects on outdoor thermal environment[D].Beijing:Tsinghua University,2004.]
[7]汪丹.高密度社区绿量分布及其热环境影响研究[D].北京:北方工业大学,2017.[Wang D.Studay on green quantity distribution and its thermal environment impact in hight density community[D].Beijing:North China University of Technology,2017.]
[8]李英汉,王俊坚,李贵才,等.居住区植物绿量与其气温调控效应的关系[J].生态学报,2011,31(3):830-838.[Li YH,Wang J J,Li G C,et al.Research of the vegetation’s cooling effect in city's residential quarter[J].Acta Ecologica Sinica,2011,31(3):830-838.]
[9]李丹,岳彩荣.激光雷达在森林参数反演中的应用[J].测绘与空间地理信息,2011,34(6):54-58.[Li D,Yue C R.The application of LiDAR in inversion of the forest parameters[J].Geomatics&Spatial Information Technology,2011,34(6):54-58.]
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[12]Hudak A T,Lefsky M A,Cohen W B,et al.Integration of LiDAR and Landsat ETM+data for estimating and mapping forest canopy height[J].Remote Sensing of Environment,2002,82(2-3):397-416.
[13]Riano D,Valladares F,Condés S,et al.Estimation of leaf area index and covered ground from airborne laser scanner(Lidar)in two contrasting forests[J].Agricultural and Forest Meteorology,2004,124(3-4):269-275.
[14]Korhonen L,Korpela I,Heiskanen J,et al.Airborne discrete-return LIDAR data in the estimation of vertical canopy cover,angular canopy closure and leaf area index[J].Remote Sensing of Environment,2011,115(4):1065-1080.
[15]Chen Z Y,Xu B,Devereux B.Urban landscape pattern analysis based on 3D landscape models[J].Applied Geography,2014,55:82-91.
[16]Morabito M,Crisci A,Georgiadis T,et al.Urban imperviousness effects on summer surface temperatures nearby residential buildings in different urban zones of Parma[J].Remote Sensing,2017,10(1):26-42.
[17]胡德勇,乔琨,王兴玲,等.单窗算法结合Landsat8热红外数据反演地表温度[J].遥感学报,2015,19(6):964-976.[Hu D Y,Qiao K,Wang X L,et al.Land surface temperature retrieval from Landsat 8 thermal infrared data using moto-window algorithm[J].Journal of Remote Sensing,2015,19(6):964-976.]
[18]Qin Z H,Kamieli 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,2001,22(18):3719-3746.
[19]Barsi J A,Barker J L,Schott J R,et al.An atmospheric correction parameter calculator for a single thermal band Earth-sensing instrument[C].IEEE International Geoscience&Remote Sensing Symposium.2003.
[20]Zhao Q S,Myint S W,Wentz E,et al.Rooftop surface temperature analysis in an urban residential environment[J].Remote Sensing,2015,7(9):12135-12159.
[21]芦蕊,马廷.中国市级人口增长的多因素空间建模分析[J].地球信息科学学报,2018,20(7):939-946.[Lu R,Ma T.Spatially modeling of multiple factors for city-level population growth in China[J].Journal of Geo-information Science,2018,20(7):939-946.]
[22]Anselin L,Spatial econometrics:Methods and models[J].Economic Geography,1988,65(2):160-162.
[23]程好好,曾辉,汪自书,等.城市绿地类型及格局特征与地表温度的关系--以深圳特区为例[J].北京大学学报(自然科学版),2009,45(3):495-501.[Chen H H,Zeng H,Wang Z S,et al.Relationships between the types,pattern characteristics of urban green space and land surface temperature:A case study in Shenzhen special economic zone[J].Acta Scientiarum Naturalium Universitatis Pekinensis,2009,45(3):495-501.]
[24]高凯,秦俊,胡永红.城市居住区景观绿化格局改善热环境变化的遥感监测分析[J].生态环境学报,2012,21(3):464-469.[Gao K,Qin J,Hu Y H.Plant landscape patterns improvement of the thermal environment based on remote sensing in the urban residential areas[J].Ecology and Environmental Sciences,2012,21(3):464-469.]
[25]Zhao Q S,Wentz E A,Murray A T.Tree shade coverage optimization in an urban residential environment[J].Building&Environment,2017,115:269-280.
[26]Dimoudi A,Nikolopoulou M.Vegetation in the urban environment:microclimatic analysis and benefits[J].Energy&Buildings,2003,35(1):69-76.
[27]林荣平,祁新华,叶士琳,等.沿海河谷盆地城市热岛时空特征及驱动机制[J].生态学报,2017,37(1):294-304.[Lin R P,Qi X H,Ye S L,et al.Spatial-temporal characteristics of urban heat islands and driving mechanisms in a coastal valley-basin city:A case study of Fuzhou city[J].Acta Ecologica Sinica,2017,37(1):294-304.]
[28]王美雅,徐涵秋.中国大城市的城市组成对城市热岛强度的影响研究[J].地球信息科学学报,2018,20(12):1787-1798.[Wang M Y,Xu H Q.Analyzing the influence of urban forms on surface urban heat islands intensity in Chinese mega cities[J].Journal of Geo-information Science,2018,20(12):1787-1798.]
[2]Chen W,Zhang Y,Pengwang C Y,et al.Evaluation of urbanization dynamics and its impacts on surface heat islands:A case study of Beijing,China[J].Remote Sensing,2017,9(5):453-468.
[3]Myint S W,Brazel A J,Quattrochi D A.The impact of distinct anthropogenic and vegetation features on urban warming[J].Landscape Ecology,2013,28(5):959-978.
[4]Zhou W Q,Qian Y G,Li X M,et al.Relationships between land cover and the surface urban heat island:Seasonal variability and effects of spatial and thematic resolution of land cover data on predicting land surface temperatures[J].Landscape Ecology,2014,29(1):153-167.
[5]马伟,赵珍梅,刘翔,等.植被指数与地表温度定量关系遥感分析--以北京市TM数据为例[J].国土资源遥感,2010,22(4):108-112.[Ma W,Zhao Z M,Liu X,et al.Aquantitative analysis of the relationship between vegetation indices and land surface temperature based on remote sensing:A case study of tm data for beijing[J].Remote Sensing for Land&Resources,2010,22(4):108-112.]
[6]林波荣.绿化绿化对室外热环境影响的研究[D].北京:清华大学,2004.[Lin B R.Studies of greening's effects on outdoor thermal environment[D].Beijing:Tsinghua University,2004.]
[7]汪丹.高密度社区绿量分布及其热环境影响研究[D].北京:北方工业大学,2017.[Wang D.Studay on green quantity distribution and its thermal environment impact in hight density community[D].Beijing:North China University of Technology,2017.]
[8]李英汉,王俊坚,李贵才,等.居住区植物绿量与其气温调控效应的关系[J].生态学报,2011,31(3):830-838.[Li YH,Wang J J,Li G C,et al.Research of the vegetation’s cooling effect in city's residential quarter[J].Acta Ecologica Sinica,2011,31(3):830-838.]
[9]李丹,岳彩荣.激光雷达在森林参数反演中的应用[J].测绘与空间地理信息,2011,34(6):54-58.[Li D,Yue C R.The application of LiDAR in inversion of the forest parameters[J].Geomatics&Spatial Information Technology,2011,34(6):54-58.]
[10]Nelson R,Krabill W,Maclean G.Determining forest canopy characteristics using airborne laser data[J].Remote Sensing of Environment,1984,15(3):201-212.
[11]李丹,庞勇,岳彩荣,等.基于TLS的单木胸径和树高提取研究[J].北京林业大学学报,2012,34(4):79-86.[Li D,Pang Y,Yue C R,et al.Extraction of individual tree DBHand height based on terrestrial laser scanner data[J].Journal of Beijing Forestry University,2012,34(4):79-86.]
[12]Hudak A T,Lefsky M A,Cohen W B,et al.Integration of LiDAR and Landsat ETM+data for estimating and mapping forest canopy height[J].Remote Sensing of Environment,2002,82(2-3):397-416.
[13]Riano D,Valladares F,Condés S,et al.Estimation of leaf area index and covered ground from airborne laser scanner(Lidar)in two contrasting forests[J].Agricultural and Forest Meteorology,2004,124(3-4):269-275.
[14]Korhonen L,Korpela I,Heiskanen J,et al.Airborne discrete-return LIDAR data in the estimation of vertical canopy cover,angular canopy closure and leaf area index[J].Remote Sensing of Environment,2011,115(4):1065-1080.
[15]Chen Z Y,Xu B,Devereux B.Urban landscape pattern analysis based on 3D landscape models[J].Applied Geography,2014,55:82-91.
[16]Morabito M,Crisci A,Georgiadis T,et al.Urban imperviousness effects on summer surface temperatures nearby residential buildings in different urban zones of Parma[J].Remote Sensing,2017,10(1):26-42.
[17]胡德勇,乔琨,王兴玲,等.单窗算法结合Landsat8热红外数据反演地表温度[J].遥感学报,2015,19(6):964-976.[Hu D Y,Qiao K,Wang X L,et al.Land surface temperature retrieval from Landsat 8 thermal infrared data using moto-window algorithm[J].Journal of Remote Sensing,2015,19(6):964-976.]
[18]Qin Z H,Kamieli 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,2001,22(18):3719-3746.
[19]Barsi J A,Barker J L,Schott J R,et al.An atmospheric correction parameter calculator for a single thermal band Earth-sensing instrument[C].IEEE International Geoscience&Remote Sensing Symposium.2003.
[20]Zhao Q S,Myint S W,Wentz E,et al.Rooftop surface temperature analysis in an urban residential environment[J].Remote Sensing,2015,7(9):12135-12159.
[21]芦蕊,马廷.中国市级人口增长的多因素空间建模分析[J].地球信息科学学报,2018,20(7):939-946.[Lu R,Ma T.Spatially modeling of multiple factors for city-level population growth in China[J].Journal of Geo-information Science,2018,20(7):939-946.]
[22]Anselin L,Spatial econometrics:Methods and models[J].Economic Geography,1988,65(2):160-162.
[23]程好好,曾辉,汪自书,等.城市绿地类型及格局特征与地表温度的关系--以深圳特区为例[J].北京大学学报(自然科学版),2009,45(3):495-501.[Chen H H,Zeng H,Wang Z S,et al.Relationships between the types,pattern characteristics of urban green space and land surface temperature:A case study in Shenzhen special economic zone[J].Acta Scientiarum Naturalium Universitatis Pekinensis,2009,45(3):495-501.]
[24]高凯,秦俊,胡永红.城市居住区景观绿化格局改善热环境变化的遥感监测分析[J].生态环境学报,2012,21(3):464-469.[Gao K,Qin J,Hu Y H.Plant landscape patterns improvement of the thermal environment based on remote sensing in the urban residential areas[J].Ecology and Environmental Sciences,2012,21(3):464-469.]
[25]Zhao Q S,Wentz E A,Murray A T.Tree shade coverage optimization in an urban residential environment[J].Building&Environment,2017,115:269-280.
[26]Dimoudi A,Nikolopoulou M.Vegetation in the urban environment:microclimatic analysis and benefits[J].Energy&Buildings,2003,35(1):69-76.
[27]林荣平,祁新华,叶士琳,等.沿海河谷盆地城市热岛时空特征及驱动机制[J].生态学报,2017,37(1):294-304.[Lin R P,Qi X H,Ye S L,et al.Spatial-temporal characteristics of urban heat islands and driving mechanisms in a coastal valley-basin city:A case study of Fuzhou city[J].Acta Ecologica Sinica,2017,37(1):294-304.]
[28]王美雅,徐涵秋.中国大城市的城市组成对城市热岛强度的影响研究[J].地球信息科学学报,2018,20(12):1787-1798.[Wang M Y,Xu H Q.Analyzing the influence of urban forms on surface urban heat islands intensity in Chinese mega cities[J].Journal of Geo-information Science,2018,20(12):1787-1798.]