城市热场景观格局与热岛效应驱动力分析
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摘要
城市化是一种人口向城市聚集的过程，伴随着自然地表向非渗水表面的转变，会在不同尺度上影响环境和生物化学过程。在快速的城市化进程中，由于缺乏合理的规划，对土地利用、能源消费结构的剧烈改变会破坏城市生态系统的平衡性，引发一系列的城市环境问题。城市热岛效应是一个能够反映城市下垫面与局部大气环境的城市典型特征，同时也在一定程度上影响全球气候变化。因此，对城市热岛效应的格局现状以及成因研究有助于理解城市热环境的变化趋势，对缓解热岛现象以及进行风险规避具有重要的意义。
本文选取广州市市域作为研究对象，在3S技术的支持下，对1989年与2009年的城市热环境强度、分布格局、时空演化进行分析，探索城市化不同阶段下的热环境差异及演化过程；采用网格定量分析法以及通径分析法比对自然、社会经济等影响因子对广州市高温环境的贡献率，确定广州市热岛效应的直接和间接的驱动因子，为城市发展规划、工业结构优化、城市绿化建设提出合理建议。主要研究结果如下：
1.广州市城市热岛效应显著，从1989年到2009年地表温度呈现上升趋势，热岛强度愈加剧烈，但热场分布格局在不同的城市化发展阶段下具有不同的特点。1989年，广州市热岛呈多中心分布，集中在增城、番禺等县镇，且热岛强度高于市中心区。这与广州市上世纪80年代村镇工业园的建设有关，大量高耗能工业厂房的兴起大大增加了废热的排放，成为该时期城镇热岛区形成的一个重要因素。2009年，随着城市建成面积迅速扩大，高温区有所扩散；随着经济的发展，空港、海港吞吐量增大，该区域也成为极端的热岛中心；由于对周边高耗能、高污染、低效益企业进行整改，提倡清洁高效生产，村镇工业热岛中心现象有所缓解。可见社会经济发展政策这一因素对城市热场变化起到极大的决定作用。
2.广州市热场景观格局整体趋向多样化、破碎化，原有的大面积连续的自然中温地表逐渐被高温区域所切割，中温区的优势度降低。在20年的演变过程中，不同温度强度等级的分布范围不仅在数量上出现较大的改变，其位置格局也出现变化，1989年到2009年标准Kappa系数仅为0.0844。由于城市发展，建成区面积增加，人类活动频繁，中温区逐渐向次高温区和高温区转移；与此同时，山林植被的恢复、城市绿地建设带来的降温作用使部分常温区向低温区转变。采用马尔科夫模型预测结果显示，在相同的发展模式和速率下，2029年广州市城市热场向极端温度发展，热岛效应将会更加显著，特高温面积将达到2009年的2.80倍，高温面积达到3.26倍。
3.植被能够通过蒸腾效应带走地表热量，起到较好的降温作用。在广州市不同发展阶段，整体植被覆盖率和植被空间格局的差异造成了其降温效应能力的不同。1989年，城市正处于建设初期，植被覆盖稀疏，植被覆盖度与地表温度相关性较小，植被降温效应不明显。2009年，植被生长状况与地表温度呈显著的负相关，在12000m*12000m网格的尺度下相关性最强，达到0.71。
4.城市热岛效应是多种自然、社会经济因素综合影响的结果。采用通径模型分析归一化植被指数、人口密度、人均国内生产总值、人均工业总产值、人均社会消费品总额等因子对地表温度的影响，这些因子能够较好地解释广州城市热场变异的71.2%。其中植被覆盖度和工业总产值是广州市热场景观格局形成的直接作用因子，其直接效应分别达到0.78和0.20。作为商业化城市，社会消费品总额，即商业发达程度对广州市的热岛有极大的间接影响作用。但在工业区，工业热源排放对地表升温的影响高于下垫面的辐射增温作用。明确不同区域、不同发展阶段、不同城市类型中热岛成因的差异，有助于合理规划城市功能区，是有效规避城市热岛风险的关键。
Urbanization, an artificial process that turning the natural land cover into impervioussurface area, is tremendously affecting environment and bio-geochemical cycles atlocal, regional, and global scales. During the process of rapid urbanization, due to theabsence of rationalized planning, the dramatic change of land use and energyconsumption structure will break the balance of urban ecosystem, triggering a seriesof urban environmental problems. As a typical characteristic of urbanization, urbanheat island effect can reflect city underlying surface and local atmosphericenvironment, and affect the process of global climate change to some extent. Analysison the pattern and driving force of urban heat island effect will help to understand thechanging trend of urban thermal environment under urbanization and of greatsignificance in mitigating heat island and risk aversion.
The study area focus on Guangzhou City, combining the of3S technology, the urbanthermal environmental patterns in1989and2009are studied to explore the differentthermal environments in different stages of urbanization; grid quantitative analysis method and path analysis method are used to compare the contribution rate ofdifferent influential factors on urban heat island, and so to determine the driving forceof Guangzhou urban heat island effect; heat island prediction model is established toprovide scientific advices for urban development planning, industrial structureoptimizing and, urban greenery construction. The main study results are as following:
1. Guangzhou urban heat island effect was significant, the changing trend of landsurface temperature and heat island intensity was rising, but the distribution pattern ofthermal field had different characteristics in different development stages ofurbanization. In1989, the Guangzhou urban heat island was multi-center distributed,concentrating in Zengcheng and Panyu district towns. And its heat island intensitywas higher than the downtown area. This phenomenon was related to the constructionof Town Industrial Park in1980s, the rise of a large number of highenergy-consuming industrial plants had greatly increased the emissions of waste heat,and contributed to the urban heat island formation in this period. In2009, with therapid expansion of urban built-up area, the high temperature zone had spread; Theairport and harbor areas also became extreme heat island zone because of theeconomic development; The town industrial heat island phenomenon was eased due tothe changing from high energy consumption industry to clean and efficient production.Socio-economic factors played a significant role in the changing pattern of urbanthermal environment.
2. The landscape patterns of Guangzhou’s thermal fields tended to be more diversifiedand in fragmentation. The large area of continuous natural surface which was inmiddle temperature zone had cutting up by high temperature surface, which loweredthe dominance of middle temperature zone. During these20years, differenttemperature intensity levels had changed not only by quantity but also by location.The standard Kappa coefficient was only0.844. Due to the rapid urbanization, thebuilt-up area increased and human activities became more frequently, which graduallyled to the transformation from middle temperature zone to sub-high temperature zoneand high temperature zone. At the same time, the restoration of the forest and construction of urban greenery had change the middle temperature zone to lowtemperature zone because of the cooling effect. The result of Markov Predict Modelshowed that, under the same develop pattern, Guangzhou urban heat island effect willbe more significant in2029, with extreme high temperature zone2.80times as in2009and high temperature zone3.26times as in2009.
3. Vegetation can take away surface heat flux through evaporation effect. Underdifferent developing stage of Guangzhou, the different overall vegetation coverageand different greenery patterns had different cooling effects. In1989, the city wasunder the early stage of construction and lack of vegetation coverage. The correlationship between vegetation and LST was too low to indicate the vegetation cooling effect.In2009, the NDVI and LST showed a significant negative correlation, the mostrelevant scale was in12000m*12000m grids with a correlation confidence of0.71.The vegetation overage should be e improved and the urban greenery pattern shouldbe optimized in order to achieve the purpose of mitigation the Guangzhou heat islandeffect.
4. The urban heat island effect is affected by natural and socioeconomic factors. Usingpath analysis model to estimate the UHI driving factors, such like NDVI, populationdensity, GDP(per capita), GIOV(per capita) and SRG(per capita). The result explained71.2%of the formation and transformation of Guangzhou’s heat island effect. TheNDVI and GIOV was the direct effected factor with the determination coefficient of0.78and0.20. As a commercial city, the SRG, which indicated the businessdevelopment level, had a tremendous indirect influence to Guangzhou’s urban heatisland. However, in industrial areas, the waste heat emission from factoriescontributed more to the LST rising than the underlying surface radiation warmingeffect. Determine different driving factors of urban heat island in different regions,different development stages and different types of city will help to city planning andaverse the risk of urban heat island.
本文选取广州市市域作为研究对象，在3S技术的支持下，对1989年与2009年的城市热环境强度、分布格局、时空演化进行分析，探索城市化不同阶段下的热环境差异及演化过程；采用网格定量分析法以及通径分析法比对自然、社会经济等影响因子对广州市高温环境的贡献率，确定广州市热岛效应的直接和间接的驱动因子，为城市发展规划、工业结构优化、城市绿化建设提出合理建议。主要研究结果如下：
1.广州市城市热岛效应显著，从1989年到2009年地表温度呈现上升趋势，热岛强度愈加剧烈，但热场分布格局在不同的城市化发展阶段下具有不同的特点。1989年，广州市热岛呈多中心分布，集中在增城、番禺等县镇，且热岛强度高于市中心区。这与广州市上世纪80年代村镇工业园的建设有关，大量高耗能工业厂房的兴起大大增加了废热的排放，成为该时期城镇热岛区形成的一个重要因素。2009年，随着城市建成面积迅速扩大，高温区有所扩散；随着经济的发展，空港、海港吞吐量增大，该区域也成为极端的热岛中心；由于对周边高耗能、高污染、低效益企业进行整改，提倡清洁高效生产，村镇工业热岛中心现象有所缓解。可见社会经济发展政策这一因素对城市热场变化起到极大的决定作用。
2.广州市热场景观格局整体趋向多样化、破碎化，原有的大面积连续的自然中温地表逐渐被高温区域所切割，中温区的优势度降低。在20年的演变过程中，不同温度强度等级的分布范围不仅在数量上出现较大的改变，其位置格局也出现变化，1989年到2009年标准Kappa系数仅为0.0844。由于城市发展，建成区面积增加，人类活动频繁，中温区逐渐向次高温区和高温区转移；与此同时，山林植被的恢复、城市绿地建设带来的降温作用使部分常温区向低温区转变。采用马尔科夫模型预测结果显示，在相同的发展模式和速率下，2029年广州市城市热场向极端温度发展，热岛效应将会更加显著，特高温面积将达到2009年的2.80倍，高温面积达到3.26倍。
3.植被能够通过蒸腾效应带走地表热量，起到较好的降温作用。在广州市不同发展阶段，整体植被覆盖率和植被空间格局的差异造成了其降温效应能力的不同。1989年，城市正处于建设初期，植被覆盖稀疏，植被覆盖度与地表温度相关性较小，植被降温效应不明显。2009年，植被生长状况与地表温度呈显著的负相关，在12000m*12000m网格的尺度下相关性最强，达到0.71。
4.城市热岛效应是多种自然、社会经济因素综合影响的结果。采用通径模型分析归一化植被指数、人口密度、人均国内生产总值、人均工业总产值、人均社会消费品总额等因子对地表温度的影响，这些因子能够较好地解释广州城市热场变异的71.2%。其中植被覆盖度和工业总产值是广州市热场景观格局形成的直接作用因子，其直接效应分别达到0.78和0.20。作为商业化城市，社会消费品总额，即商业发达程度对广州市的热岛有极大的间接影响作用。但在工业区，工业热源排放对地表升温的影响高于下垫面的辐射增温作用。明确不同区域、不同发展阶段、不同城市类型中热岛成因的差异，有助于合理规划城市功能区，是有效规避城市热岛风险的关键。
Urbanization, an artificial process that turning the natural land cover into impervioussurface area, is tremendously affecting environment and bio-geochemical cycles atlocal, regional, and global scales. During the process of rapid urbanization, due to theabsence of rationalized planning, the dramatic change of land use and energyconsumption structure will break the balance of urban ecosystem, triggering a seriesof urban environmental problems. As a typical characteristic of urbanization, urbanheat island effect can reflect city underlying surface and local atmosphericenvironment, and affect the process of global climate change to some extent. Analysison the pattern and driving force of urban heat island effect will help to understand thechanging trend of urban thermal environment under urbanization and of greatsignificance in mitigating heat island and risk aversion.
The study area focus on Guangzhou City, combining the of3S technology, the urbanthermal environmental patterns in1989and2009are studied to explore the differentthermal environments in different stages of urbanization; grid quantitative analysis method and path analysis method are used to compare the contribution rate ofdifferent influential factors on urban heat island, and so to determine the driving forceof Guangzhou urban heat island effect; heat island prediction model is established toprovide scientific advices for urban development planning, industrial structureoptimizing and, urban greenery construction. The main study results are as following:
1. Guangzhou urban heat island effect was significant, the changing trend of landsurface temperature and heat island intensity was rising, but the distribution pattern ofthermal field had different characteristics in different development stages ofurbanization. In1989, the Guangzhou urban heat island was multi-center distributed,concentrating in Zengcheng and Panyu district towns. And its heat island intensitywas higher than the downtown area. This phenomenon was related to the constructionof Town Industrial Park in1980s, the rise of a large number of highenergy-consuming industrial plants had greatly increased the emissions of waste heat,and contributed to the urban heat island formation in this period. In2009, with therapid expansion of urban built-up area, the high temperature zone had spread; Theairport and harbor areas also became extreme heat island zone because of theeconomic development; The town industrial heat island phenomenon was eased due tothe changing from high energy consumption industry to clean and efficient production.Socio-economic factors played a significant role in the changing pattern of urbanthermal environment.
2. The landscape patterns of Guangzhou’s thermal fields tended to be more diversifiedand in fragmentation. The large area of continuous natural surface which was inmiddle temperature zone had cutting up by high temperature surface, which loweredthe dominance of middle temperature zone. During these20years, differenttemperature intensity levels had changed not only by quantity but also by location.The standard Kappa coefficient was only0.844. Due to the rapid urbanization, thebuilt-up area increased and human activities became more frequently, which graduallyled to the transformation from middle temperature zone to sub-high temperature zoneand high temperature zone. At the same time, the restoration of the forest and construction of urban greenery had change the middle temperature zone to lowtemperature zone because of the cooling effect. The result of Markov Predict Modelshowed that, under the same develop pattern, Guangzhou urban heat island effect willbe more significant in2029, with extreme high temperature zone2.80times as in2009and high temperature zone3.26times as in2009.
3. Vegetation can take away surface heat flux through evaporation effect. Underdifferent developing stage of Guangzhou, the different overall vegetation coverageand different greenery patterns had different cooling effects. In1989, the city wasunder the early stage of construction and lack of vegetation coverage. The correlationship between vegetation and LST was too low to indicate the vegetation cooling effect.In2009, the NDVI and LST showed a significant negative correlation, the mostrelevant scale was in12000m*12000m grids with a correlation confidence of0.71.The vegetation overage should be e improved and the urban greenery pattern shouldbe optimized in order to achieve the purpose of mitigation the Guangzhou heat islandeffect.
4. The urban heat island effect is affected by natural and socioeconomic factors. Usingpath analysis model to estimate the UHI driving factors, such like NDVI, populationdensity, GDP(per capita), GIOV(per capita) and SRG(per capita). The result explained71.2%of the formation and transformation of Guangzhou’s heat island effect. TheNDVI and GIOV was the direct effected factor with the determination coefficient of0.78and0.20. As a commercial city, the SRG, which indicated the businessdevelopment level, had a tremendous indirect influence to Guangzhou’s urban heatisland. However, in industrial areas, the waste heat emission from factoriescontributed more to the LST rising than the underlying surface radiation warmingeffect. Determine different driving factors of urban heat island in different regions,different development stages and different types of city will help to city planning andaverse the risk of urban heat island.
引文
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伊武军.城市环境问题与生态调控.安全与环境工程.2005,12(1):1-4
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Cohen J. A coefficient agreement for nominal data. Educational and Psychological Measurement.1960,2(1):37-46
Streutker D R. Satellite-measured growth of the urban heat island of Houston, Texas. Remote Sensing ofEnvironment.2003,83:282-289
Fumiaki F. Urban warming in Japanese cities and its relation to climate change monitoring. RoyalMeteorological Society.2011,31:162-173
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Hung T, Uchihama D, Ochi S, et al. Assessment with satellite data of the urban heat island effects in Asian megacities. International Journal of Applied Earth Observation and Geoinformation.2006,8:34-48
Imhoff M L, Zhang P, Wolfe R E, Bounoua L. Remote sensing of the urban heat island effect across biomes in thecontinental USA. Remote Sensing of Environment.2010,114:504-513
Jane Jacobs. The death and life of great American cities. New York: Random House.1961
Jiménez-Mu oz J C, Sobfino J A. A generalized single channel method for retrieving land Surface temperaturefrom remote sensing data. Journal of Geophysical Research.2003,108(D22):4688
Jin M S, Kessomkiat W, Pereira G. Satellite-observed urbanization characters in Shanghai, China: Aerosols, Urbanheat island effect, and Land-atmosphere interactions. Remote Sensing.2011,3:83-99
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Kolokotsa D, Psomas A, Katapidakis E. Urban heat island in southern Europe: The case study of Hania, Crete.Solar Energy.2009,83:1871-1883
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Myrup L O. A numerical model of the urban heat island. Journal of Applied Meteorology.1969,8:908-918
Naesset E. Use of the weighted Kappa coefficient in classification error assessment of thematic maps. InternationalJournal of Geographical Information Systems.1996,10(5):591-604
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Oke T R. Boundary layer climates. USA: Methuen,1987
Pontius R G. Quantification error versus location error in comparison of categorical maps. PhotogrammetricEngineering&Remote Sensing.2000,66(8):1011-1016
Qin Z H, Karnieli A. A mono-window algorithm for retrieving land surface temperature from Landsat TM dataand its application to the Israel-Egypt border region. International Journal of Remote Sensing.2001,18(22):3719－3746.
Rao P K. Remote sensing of urban heat islands from an environmental satellite. Bulletin of the AmericamMeteorological Society.1972,53:647-648
Ridd M K, Liu J. A comparison of four algorithms for change detection in an urban environment. Remote Sensingof Environment.1998,63(2):95-100
Rizwan A M, Dennis Y C, Liu C. A review on the generation, determination and mitigation of urban heat island.Journal of Environmental Sciences.2007,20:120-128
Robert L B, Dale J P. Coefficient kappa: some uses, misuses, alternatives. Educational and PsychologicalMeasurement.1981,41:687-699
Sobrino J A, Oltra-Cattio, Soria G, et al. Impact of spatial resolution and satellite overpass time on evaluation ofthe surface urban heat island effects. Remote Sensing of Environment.2011,doi:10.1016/j.rse.2011.04.042
Stathopoulou M, Cartalis C. Daytime urban heat islands from Landsat ETM+and Corine land cover data: Anapplication to major cities in Greece. Solar Energy.2007,81:358-368
Van de Griend, A A, Owe, M. On the relationship between thermal emissivity and the normalized differencevegetation index for natural surfaces. International Journal of Remote Sensing.2003,(14):1119-1131
Velazquez-Lozada A, Gonzalez J E, Winter A. Urban heat island effect analysis for San Juan, Puerto Rico.Atmospheric Environment.2006,40:1731-1741
William D H, Carl W R. Correct formulation of the kappa coefficient of agreement[J]. Photogram MetricEngineering and Remote Sensing.1987,53(4):421-422
Zaksek K, Ostir K. Downscaling land surface temperature for urban heat island diurnal cycle analysis. RemoteSensing of Environment.2011,117:114-124
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徐争艳,孙武.百年来广州热岛强度变化的研究.中国集体经济.2009,1:93-94
伊武军.城市环境问题与生态调控.安全与环境工程.2005,12(1):1-4
岳文泽,徐建华,徐丽华.基于遥感影像的城市土地利用生态环境效应研究——以城市热环境和植被指数为例.生态学报.2006,26(5):1450-1460
张佳华等,城市热环境遥感.北京:气象出版社.2010. ISBN978-7-5029-5108-5
赵小艳,杨沈斌,申双和,等.基于遥感的南京市城市热岛效应时空演变分析.安徽农业科学.2009,37(2):10776-10778
中华人民共和国国务院.国家中长期科学和技术发展规划纲要（2006-2020）.人民日报,2006-2-10(1)
朱焱,朱莲芳,徐永明,等.基于Landsat卫星资料的苏州城市热岛效应遥感分析.高原气象.2010,29(1):244-250.
Aikawa, N, Hiraki T, Eiho J. Vertical atmospheric structure estimated by heat island intensity and temporalvariations of methane concentrations in ambient air in an urban area in Japan. Atmospheric Environment.2006,40:4308-4315
Buyantuyev A. Wu J. Urban heat island and landscape heterogeneity: linking spatiotemporal variations in surfacetemperatures to land-cover and socioeconomic patterns. Landscape Ecology.2010,25:17–33
Cai G, Du M, Xue Y. Monitoring of urban heat island effect in Beijing combining ASTER and TM data.International Jnournal of Remote Sensing.2011,32(5):1213-1232
Changnon S A, Kunkel K E, Reinke B C. Impacts and responses to the1995heat wave: A call to action. Bulletinof the American Meteorological Society.1996,77:1497-1505
Chen X, Zhao H, Li P, et al. Remote sensing image-based analysis of the relationship between urban heat islandand land use/cover changes. Remote Sensing of Environment.2006,104:133-146
Chen Z, Gong C, Wu J, et al. The influence of socioeconomic and topographic factors on nocturnal urban heatislands: a case study in Shenzhen, China. International Journal of Remote Sensing.2012,33(12):3834-3839
Cohen J. A coefficient agreement for nominal data. Educational and Psychological Measurement.1960,2(1):37-46
Streutker D R. Satellite-measured growth of the urban heat island of Houston, Texas. Remote Sensing ofEnvironment.2003,83:282-289
Fumiaki F. Urban warming in Japanese cities and its relation to climate change monitoring. RoyalMeteorological Society.2011,31:162-173
Grimm N B, Faeth S H, Golubiewski N E, et al. Global change and the ecology of cities. Science.2008,319:756-760.
Howard L. The climate of London, deduced from Meteorological observations, made at different places in theneighbourhood of the metropolis.2vol., London,1818-20
Hung T, Uchihama D, Ochi S, et al. Assessment with satellite data of the urban heat island effects in Asian megacities. International Journal of Applied Earth Observation and Geoinformation.2006,8:34-48
Imhoff M L, Zhang P, Wolfe R E, Bounoua L. Remote sensing of the urban heat island effect across biomes in thecontinental USA. Remote Sensing of Environment.2010,114:504-513
Jane Jacobs. The death and life of great American cities. New York: Random House.1961
Jiménez-Mu oz J C, Sobfino J A. A generalized single channel method for retrieving land Surface temperaturefrom remote sensing data. Journal of Geophysical Research.2003,108(D22):4688
Jin M S, Kessomkiat W, Pereira G. Satellite-observed urbanization characters in Shanghai, China: Aerosols, Urbanheat island effect, and Land-atmosphere interactions. Remote Sensing.2011,3:83-99
Kato S, Yamaguchi Y. Analysis of urban heat-island effect using ASTER and ETM+Data: Separation ofanthropogenic heat discharge and natural heat radiation from sensible heat flux. Remote Sensing ofEnvironment.2005,99:44-54
Kolokotroni M, Giridharan R. Urban heat island intensity in London: An investigation of the impact of physicalcharacteristics on changes in outdoor air temperature during summer. Solar Energy.2008,82:986-998
Kolokotsa D, Psomas A, Katapidakis E. Urban heat island in southern Europe: The case study of Hania, Crete.Solar Energy.2009,83:1871-1883
Konopacki S, Akbari H. Energy savings for heat island reduction strategies in Chicago and Houston(includingupdates for Baton Rouge, Sacramento and Salt Lake City). Draft Final Report.2002, LBNL-49638,University of California, Berkeley
Lin L, Zhang Y Z. Urban heat island analysis using the landsat TM data and ASTER data: A case study in HongKong. Remote sensing.2011,3:1535-1552
Mather A S. Land Use. London: Longman,1986
Magarigal K, Marks B J. Fragstats—Spatial pattern analysis program for quantifying landscape structure. USA:Oregon State University,1994
Murphy D J, Hall M H, Hall C A S, et al. The relationship between land cover and the urban heat island innortheastern Puerto Rico. Royal Meteorological Society.2011,31:1222-1239
Myrup L O. A numerical model of the urban heat island. Journal of Applied Meteorology.1969,8:908-918
Naesset E. Use of the weighted Kappa coefficient in classification error assessment of thematic maps. InternationalJournal of Geographical Information Systems.1996,10(5):591-604
Oke T R. The energetic basis of the urban heat island. Symons Memorial Lecture. The University of BritishColumbia, Vancouver, America, May1980
Oke T R. Boundary layer climates. USA: Methuen,1987
Pontius R G. Quantification error versus location error in comparison of categorical maps. PhotogrammetricEngineering&Remote Sensing.2000,66(8):1011-1016
Qin Z H, Karnieli A. A mono-window algorithm for retrieving land surface temperature from Landsat TM dataand its application to the Israel-Egypt border region. International Journal of Remote Sensing.2001,18(22):3719－3746.
Rao P K. Remote sensing of urban heat islands from an environmental satellite. Bulletin of the AmericamMeteorological Society.1972,53:647-648
Ridd M K, Liu J. A comparison of four algorithms for change detection in an urban environment. Remote Sensingof Environment.1998,63(2):95-100
Rizwan A M, Dennis Y C, Liu C. A review on the generation, determination and mitigation of urban heat island.Journal of Environmental Sciences.2007,20:120-128
Robert L B, Dale J P. Coefficient kappa: some uses, misuses, alternatives. Educational and PsychologicalMeasurement.1981,41:687-699
Sobrino J A, Oltra-Cattio, Soria G, et al. Impact of spatial resolution and satellite overpass time on evaluation ofthe surface urban heat island effects. Remote Sensing of Environment.2011,doi:10.1016/j.rse.2011.04.042
Stathopoulou M, Cartalis C. Daytime urban heat islands from Landsat ETM+and Corine land cover data: Anapplication to major cities in Greece. Solar Energy.2007,81:358-368
Van de Griend, A A, Owe, M. On the relationship between thermal emissivity and the normalized differencevegetation index for natural surfaces. International Journal of Remote Sensing.2003,(14):1119-1131
Velazquez-Lozada A, Gonzalez J E, Winter A. Urban heat island effect analysis for San Juan, Puerto Rico.Atmospheric Environment.2006,40:1731-1741
William D H, Carl W R. Correct formulation of the kappa coefficient of agreement[J]. Photogram MetricEngineering and Remote Sensing.1987,53(4):421-422
Zaksek K, Ostir K. Downscaling land surface temperature for urban heat island diurnal cycle analysis. RemoteSensing of Environment.2011,117:114-124
Zhang J, Wang Y, Li Y. A C++program for retrieving land surface temperature from the data of LandsatTM/ETM+band6. Computers&Geosciences.2006,32:1796-1805