粤港澳大湾区城市热岛空间格局及影响因子多元建模
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摘要
探究影响城市热岛空间格局的因子,及科学分析各因子的作用机制,对揭示城市热岛效应的机理有着重要意义。本文以粤港澳大湾区为研究区,综合利用数据空间化表达、空间叠置、地理探测器等方法,对影响城市热岛空间格局的因子开展研究,并构建地表温度与影响因子间的多元关系模型。结果表明,粤港澳大湾区的城市热岛强度等级呈现中间高四周低的空间分布格局,并在珠江入海口两岸形成半环状城市热岛带。本文选取的5种影响因子对城市热岛的空间格局皆具有较高的解释力,平均解释力排序为:单元人口密度(0.668)>建设用地面积占比(0.577)>单元路网密度(0.573)>植被面积占比(0.538)>水体面积占比(0.428)。所构建的多元关系模型,能较准确地反映城市热岛区域地表温度的分布状况,所拟合的地表温度结果与实际地表温度平均值的误差为0.34℃。
To explore the influence of various factors on the spatial differentiation of urban heat island intensity, and to reveal the impact mechanism of the factors, spatial expression, spatial overlay, and geographical detector methods were used in this study. The impact of five influencing factors on the spatial differentiation of urban heat island intensity in the study area was examined,and a multivariate relationship model was constructed. The results show that the intensity of urban heat island in the Guangdong-Hong Kong-Macao Greater Bay area is high in the central part and low in the surroundings, which has formed a semicircular urban heat island belt on both sides of the estuary of the Pearl River. According to the results of the geographical detector analysis, the five selected factors have a high explanatory power on the spatial differentiation of the urban heat island intensity at the 1 km×1 km grid scale, in the order of population density(0.668) > proportion of construction land area(0.577) > length of roads(0.573) > proportion of vegetation cover(0.538) > proportion of surface water area(0.428). The constructed multivariate relationship model can accurately reflect the distribution of land surface temperature in urban heat island area, and the error between the modeling result and the observed average land surface temperature is 0.34℃.
To explore the influence of various factors on the spatial differentiation of urban heat island intensity, and to reveal the impact mechanism of the factors, spatial expression, spatial overlay, and geographical detector methods were used in this study. The impact of five influencing factors on the spatial differentiation of urban heat island intensity in the study area was examined,and a multivariate relationship model was constructed. The results show that the intensity of urban heat island in the Guangdong-Hong Kong-Macao Greater Bay area is high in the central part and low in the surroundings, which has formed a semicircular urban heat island belt on both sides of the estuary of the Pearl River. According to the results of the geographical detector analysis, the five selected factors have a high explanatory power on the spatial differentiation of the urban heat island intensity at the 1 km×1 km grid scale, in the order of population density(0.668) > proportion of construction land area(0.577) > length of roads(0.573) > proportion of vegetation cover(0.538) > proportion of surface water area(0.428). The constructed multivariate relationship model can accurately reflect the distribution of land surface temperature in urban heat island area, and the error between the modeling result and the observed average land surface temperature is 0.34℃.
引文
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[23]Khomarudin M R,Strunz G,Ludwig R,et al.Hazard analysis and estimation of people exposure as contribution to tsunami risk assessment in the West Coast of Sumatra,the South Coast of Java and Bali[J].Zeitschrift Für Geomorphologie,2010,37(3):337-356.
[24]廖顺宝,孙九林.基于GIS的青藏高原人口统计数据空间化[J].地理学报,2003,58(1):25-33.[Liao S B,Sun J L.GIS based spatialization of population census data in Qinghai-Tibet Plateau[J].Acta Geographica Sinica,2003,58(1):25-33.]
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[26]周玉科,高锡章,倪希亮.利用夜间灯光数据分析我国社会经济发展的区域不均衡特征[J].遥感技术与应用,2017,32(6):1107-1113.[Zhou Y K,Gao X Z,Ni X L.Analyzing regional inequality of socioeconomic development in China with nighttime light[J].Remote Sensing Technology and Application,2017,32(6):1107-1113.]
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[28]李翔,陈振杰,吴洁璇,等.基于夜间灯光数据和空间回归模型的城市常住人口格网化方法研究[J].地球信息科学学报,2017,19(10):1298-1305.[Li X,Chen Z J,Wu J X,et al.Gridding methods of city permanent population based on night light data and spatial regression models[J].Journal of Geo-information Science,2017,19(10):1298-1305.]
[29]杨智威,陈颖彪,千庆兰,等.人口空间化下的公共医疗服务水平匹配度评价:以广州市为例[J].地理与地理信息科学,2019,35(2):74-82.[Yang Z W,Chen Y B,Qian Q L,et al.Evaluation of the matching degree of the public medical service level based on population spatialization:A case study of Guangzhou[J].Geography and Geo-information Science,2019,35(2):74-82.]
[30]Wang J F,Li X H,Christakos G,et al.Geographical detectorsbased health risk assessment and its application in the neural tube defects study of the Heshun region,China[J].International Journal of Geographical Information Science,2010,24(1):107-127.
[31]王劲峰,徐成东.地理探测器:原理与展望[J].地理学报,2017,72(1):116-134.[Wang J F,Xu C D.Geodetector:Principle and prospective[J].Acta Geographica Sinica,2017,72(1):116-134.]
[32]方叶兵,王礼茂,牟初夫,等.中国石油终端利用碳排放空间分异及影响因素[J].资源科学,2017,39(12):2233-2246.[Fang YB,Wang L M,Mou C F,et al.Determinants of spatial disparities of petroleum terminal utilization carbon emissions in China[J].Resources Science,2017,39(12):2233-2246.]
[33]杨丰硕,杨晓梅,王志华,等.江西省典型县域经济差异影响因子地理探测研究[J].地球信息科学学报,2018,20(1):79-88.[Yang F S,Yang X M,Wang Z H,et al.Geographic detection of impact factors of economic differences among typical counties in Jiangxi Province[J].Journal of Geo-information Science,2018,20(1):79-88.]
[34]郑子豪,陈颖彪,吴志峰,等.单元路网长度的DMSP/OLS夜间灯光数据去饱和方法[J].遥感学报,2018,22(1):161-173.[Zheng Z H,Chen Y B,Wu Z F,et al.Method to reduce saturation of DMSP/OLS nighttime light data based on UNL[J].Journal of Remote Sensing,2018,22(1):161-173.]
[2]Voogt J A,Oke T R.Thermal remote sensing of urban climates[J].Remote Sensing of Environment,2003,86(3):370-384.
[3]Oke T R.The energetic basis of the urban heat island[J].Quarterly Journal of the Royal Meteorological Society,1982,108(455):1-24.
[4]Grimm N B,Faeth S H,Golubiewski N E,et al.Global change and the ecology of cities[J].Science,2008,319(5864):756-760.
[5]Kanda M.Progress in urban meteorology:A review[J].Journal of the Meteorological Society of Japan(Series II).2007,85:363-383.
[6]Unger J.Intra-urban relationship between surface geometry and urban heat island:Review and new approach[J].Climate Research,2004,27(3):253-264.
[7]Li D,Sun T,Liu M F,et al.Contrasting responses of urban and rural surface energy budgets to heat waves explain synergies between urban heat islands and heat waves[J].Environmental Research Letters,2015,DOI:10.1088/1748-9326/10/5/054009.
[8]Bornstein R,Lin Q.Urban heat islands and summertime convective thunderstorms in Atlanta:Three case studies[J].Atmospheric Environment,2000,34(3):507-516.
[9]曹峥,廉丽姝,顾宗伟,等.WRF土地利用/覆被数据优选及其在城市热岛模拟中的应用[J].资源科学,2015,37(9):1785-1796.[Cao Z,Lian L S,Gu Z W,et al.Selection of WRF land use/cover data and usage n urban heat island simulations[J].Resources Science,2015,37(9):1785-1796.]
[10]Bonafoni S,Baldinelli G,Verducci P.Sustainable strategies for smart cities:Analysis of the town development effect on surface urban heat island through remote sensing methodologies[J].Sustainable Cities&Society,2017,29:211-218.
[11]Guattari C,Evangelisti L,Balaras C A.On the assessment of urban heat island phenomenon and its effects on building energy performance:A case study of Rome(Italy)[J].Energy&Buildings,2017,158:605-615.
[12]贺丽琴,杨鹏,景欣,等.基于MODIS影像及不透水面积的珠江三角洲热岛效应时空分析[J].国土资源遥感,2017,29(4):140-146.[He L Q,Yang P,Jing X,et al.Analysis of temporal-spatial variation of heat island effect in Pearl River Delta using MODISimages and impermeable surface area[J].Remote Sensing for Land&Resources,2017,29(4):140-146.]
[13]张硕,刘勇洪,黄宏涛.珠三角城市群热岛时空分布及定量评估研究[J].生态环境学报,2017,26(7):1157-1166.[Zhang S,Liu Y H,Huang H T.Research on quantitative evaluations and spatial and temporal distribution of heat islands for the Pearl River Delta agglomeration[J].Ecology and Environmental Sciences,2017,26(7):1157-1166.]
[14]牟雪洁,赵昕奕.珠三角地区地表温度与土地利用类型关系[J].地理研究,2012,31(9):1589-1597.[Mou X J,Zhao X Y.Study on the relationship between surface temperature and land use in Pearl River Delta[J].Geographical Research,2012,31(9):1589-1597.]
[15]Wan Z.New refinements and validation of the collection-6 MO-DIS land-surface temperature/emissivity product[J].Remote Sensing of Environment,2014,140:36-45.
[16]Van Hove L W A,Jacobs C M J,Heusinkveld B G,et al.Temporal and spatial variability of urban heat island and thermal comfort within the Rotterdam agglomeration[J].Building and Environment,2015,83:91-103.
[17]杨智威,陈颖彪,吴志峰,等.粤港澳大湾区建设用地扩张与城市热岛扩张耦合态势研究[J].地球信息科学学报,2018,20(11):1592-1603.[Yang Z W,Chen Y B,Wu Z F,et al.The coupling between construction land expansion and urban heat island expansion in Guangdong-Hong Kong-Macao Greater Bay[J].Journal of Geo-information Science,2018,20(11):1592-1603.]
[18]李丽光,王宏博,贾庆宇,等.辽宁省城市热岛强度特征及等级划分[J].应用生态学报,2012,23(5):1345-1350.[Li L G,Wang H B,Jia Q Y,et al.Urban heat island intensity and its grading in Liaoning Province of Northeast China[J].Chinese Journal of Applied Ecology,2012,23(5):1345-1350.]
[19]张勇,余涛,顾行发,等.CBERS-02 IRMSS热红外数据地表温度反演及其在城市热岛效应定量化分析中的应用[J].遥感学报,2006,10(5):789-797.[Zhang Y,Yu T,Gu X F,et al.Land surface temperature retrieval from CBERS-02 IRMSS thermal infrared data and its applications in quantitative analysis of urban heat island effect[J].Journal of Remote Sensing,2006,10(5):789-797.]
[20]于琛,胡德勇,曹诗颂,等.2005-2016年北京中心城区热岛时空格局及影响因子多元建模[J].地球信息科学学报,2017,19(11):1485-1494.[Yu C,Hu D Y,Cao S S,et al.Spatio-temporal pattern of heat island and multivariate modeling of impact factors of Beijing downtown from 2005 to 2016[J].Journal of Geo-information Science,2017,19(11):1485-1494.]
[21]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].地球信息科学学报,2009,11(2):145-150.[Chen SL,Wang T X.Comparison analyses of equal interval method and mean-standard deviation method used to delimitate urban heat island[J].Journal of Geo-information Science,2009,11(2):145-150.]
[22]黄安,许月卿,孙丕苓,等.基于多源数据人口分布空间化研究:以河北省张家口市为例[J].资源科学,2017,39(11):2186-2196.[Huang A,Xu Y Q,Sun P L,et al.Spatial distribution of population specialization based on multi-source data:A case study of Zhangjiakou City[J].Resources Science,2017,39(11):2186-2196.]
[23]Khomarudin M R,Strunz G,Ludwig R,et al.Hazard analysis and estimation of people exposure as contribution to tsunami risk assessment in the West Coast of Sumatra,the South Coast of Java and Bali[J].Zeitschrift Für Geomorphologie,2010,37(3):337-356.
[24]廖顺宝,孙九林.基于GIS的青藏高原人口统计数据空间化[J].地理学报,2003,58(1):25-33.[Liao S B,Sun J L.GIS based spatialization of population census data in Qinghai-Tibet Plateau[J].Acta Geographica Sinica,2003,58(1):25-33.]
[25]高义,王辉,王培涛,等.基于人口普查与多源夜间灯光数据的海岸带人口空间化分析[J].资源科学,2013,35(12):2517-2523.[Gao Y,Wang H,Wang P T,et al.Population spatial processing for Chinese coastal zones based on census and multiple night light data[J].Resources Science,2013,35(12):2517-2523.]
[26]周玉科,高锡章,倪希亮.利用夜间灯光数据分析我国社会经济发展的区域不均衡特征[J].遥感技术与应用,2017,32(6):1107-1113.[Zhou Y K,Gao X Z,Ni X L.Analyzing regional inequality of socioeconomic development in China with nighttime light[J].Remote Sensing Technology and Application,2017,32(6):1107-1113.]
[27]赵金彩,钟章奇,卢鹤立,等.基于夜间灯光的城市居民直接碳排放及影响因素:以中原经济区为例[J].自然资源学报,2017,32(12):2100-2114.[Zhao J C,Zhong Z Q,Lu H L,et al.Urban residential CO2emissions and its determinants:A case study of Central Plains Economic Region[J].Journal of Natural Resources,2017,32(12):2100-2114.]
[28]李翔,陈振杰,吴洁璇,等.基于夜间灯光数据和空间回归模型的城市常住人口格网化方法研究[J].地球信息科学学报,2017,19(10):1298-1305.[Li X,Chen Z J,Wu J X,et al.Gridding methods of city permanent population based on night light data and spatial regression models[J].Journal of Geo-information Science,2017,19(10):1298-1305.]
[29]杨智威,陈颖彪,千庆兰,等.人口空间化下的公共医疗服务水平匹配度评价:以广州市为例[J].地理与地理信息科学,2019,35(2):74-82.[Yang Z W,Chen Y B,Qian Q L,et al.Evaluation of the matching degree of the public medical service level based on population spatialization:A case study of Guangzhou[J].Geography and Geo-information Science,2019,35(2):74-82.]
[30]Wang J F,Li X H,Christakos G,et al.Geographical detectorsbased health risk assessment and its application in the neural tube defects study of the Heshun region,China[J].International Journal of Geographical Information Science,2010,24(1):107-127.
[31]王劲峰,徐成东.地理探测器:原理与展望[J].地理学报,2017,72(1):116-134.[Wang J F,Xu C D.Geodetector:Principle and prospective[J].Acta Geographica Sinica,2017,72(1):116-134.]
[32]方叶兵,王礼茂,牟初夫,等.中国石油终端利用碳排放空间分异及影响因素[J].资源科学,2017,39(12):2233-2246.[Fang YB,Wang L M,Mou C F,et al.Determinants of spatial disparities of petroleum terminal utilization carbon emissions in China[J].Resources Science,2017,39(12):2233-2246.]
[33]杨丰硕,杨晓梅,王志华,等.江西省典型县域经济差异影响因子地理探测研究[J].地球信息科学学报,2018,20(1):79-88.[Yang F S,Yang X M,Wang Z H,et al.Geographic detection of impact factors of economic differences among typical counties in Jiangxi Province[J].Journal of Geo-information Science,2018,20(1):79-88.]
[34]郑子豪,陈颖彪,吴志峰,等.单元路网长度的DMSP/OLS夜间灯光数据去饱和方法[J].遥感学报,2018,22(1):161-173.[Zheng Z H,Chen Y B,Wu Z F,et al.Method to reduce saturation of DMSP/OLS nighttime light data based on UNL[J].Journal of Remote Sensing,2018,22(1):161-173.]
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