李元征
LI Yuanzheng
通讯作者:
收稿日期: 2016-04-1
修回日期: 2016-07-1
网络出版日期: 2016-09-20
版权声明: 2016 地理科学进展 《地理科学进展》杂志 版权所有
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作者简介:
作者简介：李元征(1986-),男,山东临清人,博士研究生,主要从事环境遥感方面的研究,E-mail: rushfuture@sina.com。
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摘要
全球正经历快速、高强度的城市化,导致城市热岛加剧,并对城市、区域乃至全球许多的生态环境要素直接或间接地产生多方面的影响,与人类福祉密切相关。遥感具有宽覆盖、信息量大、重复观测周期短等优点,已成为地表城市热岛(Surface Urban Heat Island, SUHI)监测广泛采用的一种方法。针对前人相关研究对热红外数据源、监测指标及SUHI时空变化规律尚缺乏系统总结且内容需要更新等问题,本文首先分类评述了SUHI遥感监测所采用的热红外遥感数据源。其次将现有的SUHI监测指标分为土地覆盖类型驱动型、地表温度格局驱动型及两者复合驱动型3类来述评,详细介绍了它们的计算方法、应用案例及优缺点;并从日间变化、夜间变化及昼夜对比的变化3个方面述评了SUHI的年内时空变化规律;归纳了其年际变化规律。最后,依据现有研究结论中相互冲突或尚需深化的地方,指出几个潜在的关键问题或研究方向。
关键词：
Abstract
Rapid and high intensity urbanization is currently occurring in the world, resulting in increasingly more serious urban heat island phenomenon. Urban heat islands have direct and indirect impacts on various eco-environment factors of cities, regions, and the world, which are closely related to the human well-being. Remote sensing method has been widely used for Surface Urban Heat Island (SUHI) monitoring for its obvious advantages, such as wide range, huge amount of information, short observation cycle, among others. Considering the issue that existing studies have not systematically summarized the thermal infrared data sources, monitoring indicators, and the spatiotemporal variation patterns of monitoring results, and the related information needs to be updated, this study conducted a review of progress of surface urban heat island monitoring by remote sensing. First, we presented and classified the thermal infrared remote sensing data sources for the SUHI monitoring by remote sensing in previous studies. Second, we divided the monitoring indicators into three types, including land cover types-driven kind, Land Surface Temperature (LST) pattern-driven kind, and complex kind driven by both land cover types and LST pattern. We introduced the main indicator calculation methods and application cases in detail and analyzed their advantages and disadvantages. We then reviewed the intraannual spatiotemporal change regulation of the SUHI from three aspects, including diurnal variation, nocturnal variation, and comparative variations between day and night. We also considered the patterns of interannual changes. Finally, we proposed several key issues and potential research directions based on the research areas in which conflicting conclusions are found or research needs to be deepened in the existing studies.
Keywords：
2014年,全球有54%的人口居住在城市,预计至2050年该比例将上升至66%(UN DESA, 2014)。快速和高强度的城市化进程显著地改变着城市、区域乃至全球的生态环境(Simon et al, 2005)。其中城市地区气候的一个显著特征就是城市热岛效应,即城区气温明显高于周边农村的现象(Howard, 1833; Manley, 1958)。城市热岛直接或间接地影响着局地气候(Kanda, 2007)、能源利用(Unger, 2004)、空气质量(Grimm et al, 2008)、城市水文(Grimm et al, 2008)、城市土壤理化性质(Shi et al, 2012)、生物空间分布和行为活动(Knapp, 2010)以及人类健康和舒适度等(Unger, 2004; Wong et al, 2013)。
城市热岛的研究方法主要包括气象站观测(Memon et al, 2009)、定点现场观测(Huang et al, 2008)、移动样带观测(Huang et al, 2008)、数值模拟(Zehnder, 2002)和遥感监测法(Rao, 1972; Weng, 2009)。遥感具有时间同步性好、覆盖宽、可长时间连续监测等优点。随着卫星数量的增多及地球空间信息技术的发展,热红外遥感已经成为城市气候和环境监测广泛采用的一种方法(Rao, 1972; Weng, 2009)。虽然基于遥感反演的地表温度(Land Surface Temperature, LST)并不等同于地表上面的气温,但两者却是密切相关的(Mostovoy et al, 2006; Schwarz et al, 2011)。尽管如此,为与传统的基于气温分析的城市热岛相区分,通常将基于LST分析的称之为地表城市热岛(Surface Urban Heat Island, SUHI) (Voogt et al, 2003; 胡华浪等, 2005; Yuan et al, 2007; 陈爱莲等, 2012; Zhan et al, 2014)。针对前人相关研究对热红外数据源、监测指标及SUHI时空变化规律尚缺乏系统总结且内容需要更新等问题(胡华浪等, 2005; Weng, 2009; 陈爱莲等, 2012),本文从上述3个方面对国内外SUHI遥感监测进展和不足进行综述,并对今后的研究重点和方向给出建议。
SUHI监测常用的热红外数据源见表1。其中,对地静止卫星获取的数据,包括GOES/GOES、FY-2/SVISSR和MSG/SEVIRI数据(Tomlinson et al, 2011),时间分辨率很高,空间分辨率较低,适合宏观监测单个、区域及全球多个超大型城市的SUHI的时空变化,并在日动态监测上优势明显。但目前应用较少。
表1 SUHI监测常用的热红外数据源
Tab.1 Frequently-used thermal infrared data sources for SUHI monitoring
| 平台/传感器 | 空间分辨率 | 覆盖周期 | 过境时间 | 起始年份 | 可用热红外波段数 |
|---|---|---|---|---|---|
| GOES系列/GOES成像器 | 4 km | ~0 d | 多个,间隔30 min | 1974 | 双 |
| FY-2/SVISSR | 5 km | ~0 d | 多个,2006年后间隔为15 min | 2004 | 双 |
| MSG系列/SEVIRI | 3 km | ~0 d | 多个,间隔15 min | 2005 | 双 |
| NOAA系列/AVHRR | 1.1 km | ≤0.25 d | 具体见官网① | 1979 | 双 |
| Terra/MODIS | 1 km | 0.5 d | ~10:30、~22:30 | 2000 | 双 |
| Aqua/MODIS | 1 km | 0.5 d | ~01:30、~13:30 | 2002 | 双 |
| HJ-1B/IRS | 300 m | 4 d | ~10:00 | 2008 | 单 |
| FY-3/MERSI | 250 m | 5.5 d | ~10:45 | 2008 | 单 |
| Landsat/TM、ETM+、TIRS② | 60~120 m | 16 d | ~10:30 | 1982 | 单或双 |
| Terra/ASTER | 90 m | 15 d | 按要求 | 1999 | 多 |
| CBERS/IRMSS、IRS③ | 80~156 m | 26 d | ~10:30 | 1999 | 单 |
| 机载 | ~1 m | 按要求 | 按要求 | 1985 | 多 |
| 热视频辐射仪 | ~1.8 mrad | 按要求 | 按要求 | 1997 | 多 |
NOAA系列的AVHRR、Terra和Aqua上的MODIS数据空间分辨率有了较大提升,约为1 km;每天过境4次以上,并可获取典型时刻的LST格局;拥有较长的存档数据,适合宏观监测单个、区域及全球多个城市的SUHI的时空变化。其中,AVHRR数据的连续观测时间更长,有30多年的存档数据;而MODIS数据的其他波段数量更多、空间分辨率更高、过境时间较为稳定,迄今也有十几年的连续观测数据。这些数据目前均已得到广泛应用(Peng et al, 2011; Schwarz et al, 2011; Clinton et al, 2013; Haashemi et al, 2016; Ma et al, 2016; Shen et al, 2016),主要用于分析超大及大型城市的热岛问题,而很少应用于中小型城市。
中国自行研制的HJ-1B/IRS和FY-3/MERSI数据既可以在一定程度上克服AVHRR和MODIS数据空间分辨率略低以及Landsat/TM、ETM+、TIRS数据和Terra/ASTER数据返回周期较长的缺点,又可以用于宏观监测SUHI时空变化,还可用来分析其影响要素(Yang et al, 2010; Ye et al, 2011; 杨何群等, 2013; Wu et al, 2014)。
Landsat/TM、ETM+、TIRS数据由于空间分辨率较高、存档时间长、数据可获取性好,在监测单个或局地城市群地表热场时空变化,特别是分析其影响因素方面优势明显,已得到广泛应用(Chen et al, 2006; Zhou W Q et al, 2014; Liu et al, 2015; Pan, 2016; Shen et al, 2016);但由于重返周期较长,较难获取不同年份中相同甚至相近日期的可比性强的影像;此外,该数据过境时期热岛强度较弱(Chudnovsky et al, 2004)。Terra/ASTER数据虽然时空分辨率更高,但发射时间较晚、存档数据较少、数据可获取性差,目前应用相对较少(Weng et al, 2008; Connors et al, 2013; Adams et al, 2014)。此外,中巴合作研制的CBERS/IRMSS、IRS数据虽然在空间分辨率、重返周期及存档数据与Landsat数据相比要较差,但可用作补充数据,目前应用也较少(张勇等, 2006)。
航空遥感获取的热红外数据空间分辨率高(Goldreich, 1985; Sobrino et al, 2013; Liu et al, 2014; Liu et al, 2015),适合分析地表热场与影响因素的相互关系。但其数据昂贵、获取不易,并多为瞬时数据而不重复观测,并很难获得大范围的数据。
热视频辐射仪可以较低成本即时连续地获取热红外信息,但受到角度、距离和地物遮挡的限制,仅能观测面积不大的区域(Voogt et al, 1997; Chudnovsky et al, 2004),目前应用也不多。
现有的热红外遥感数据具有多种时空分辨率,在全球、洲际及区域城市群、单个城市或小区SUHI时空演变规律监测及其影响要素分析中均有一定程度的应用。但仍存在以下2类问题：①虽然存在相关热红外遥感数据,但相关研究较少。如缺乏基于对地静止观测卫星数据对SUHI日动态的监测研究;缺乏基于热视频辐射仪对小区尺度全天时、全天候,特别是在阴天条件下的研究;较为缺乏对国产热红外遥感卫星优势挖掘的研究。②缺乏相关的热红外遥感数据,无法开展研究。如中等空间分辨率的夜间热红外数据较为缺乏,中分的白天热红外遥感数据的过境时间较少,高分热红外数据更为稀有。这些均直接影响到相关研究的开展。
为定量监测地表城市热岛,前人发展了许多指标,按计算过程中的要素类型,大体上可分为土地覆盖类型驱动型、地表温度格局驱动型及两者复合驱动型3种(表2)(Schwarz et al, 2011; Schwarz et al, 2012)。
表2 SUHI的监测指标代表性类型
Tab.2 Typical monitoring indicators of SUHI
| 指标 类型 | 指标 | 量化方法 | 传感器、时间范围、文献 |
|---|---|---|---|
| 土地覆盖类型驱动型 | 地表温差 城市核心区-乡村/K | 城市核心区与乡村LST均值的差值。前者指不透水面比例(ISA)大于75%的城区;后者指距离ISA为25%的等值线的45和50 km或15和20 km之间的ISA小于5%的区域。 | MODIS;白天和夜间,多年;美国大陆上的超大城市;Imhoff等(2010)MODIS;白天和夜间,多年;全球3000多个城市;Zhang等(2010) |
| 地表温差 城市-乡村/K | 行政建成区与乡村LST均值的差值。分别计算城乡地区最高与最低LST的幅度;然后计算城乡幅度的差值。 | Landsat-TM和ETM;白天,1天;南充和广安;Dan等(2010) | |
| 4个及其以上多层建筑结构城市像元与其5 km和10 km缓冲区内去除城区和水体像元的LST均值的差值。 | MODIS;昼夜,1年;全球;Clinton等(2013) | ||
| 对ISA比例大于50%区域进行聚合操作得到的城区与邻近12个一半城区面积缓冲区中最远的3个内去除水体像元后区域LST均值的差值。 | MODIS;昼夜,多年;中国32个大城市;Zhou et al (2015) | ||
| 全区与乡村气象站处LST的差值。 | MODIS;昼夜,多年夏季;伯明翰;Tomlinson等(2012) | ||
| 五环路内城区与指定代表乡村区域LST均值的差值。 | MODIS;昼夜,多年;北京;王建凯等(2007) | ||
| 地表温差 城市-郊区/℃ | 基于城区聚类算法得到的城区与邻近相同面积缓冲区内去除城区及水体像元后区域LST均值的差值。 | MODIS;昼夜,多年;全球;Peng等(2011) | |
| 对ISA比例大于50%区域进行聚合操作得到的城区与邻近相同面积缓冲区内去除水体像元后区域LST均值的差值。 | MODIS;昼夜,多年;中国32个大城市;Zhou D C等(2014) | ||
| 地表温差 城市-农田/K | 城区与农田LST均值的差值。 | MODIS;白天和夜间,同一年中的1月和7月;全球;Jin等(2005) MODIS;白天和夜间,2年;德黑兰;Haashemi等(2016) | |
| 地表温差 城市-林地/K | 城区与林地LST均值的差值。 | MODIS;白天和夜间,同一年中的1月和7月;全球;Jin等(2005) | |
| 地表温差 城市-水体/K | 城区与水体LST均值的差值。 | Landsat-TM;白天,多年中的代表天;珠江三角洲;Chen等(2006) MODIS;白天和夜间,2年;德黑兰;Haashemi等(2016) | |
| 地表温度格局驱动型 | 高斯面积/km2 | 乡村LST背景值去掉以后基于LST场数据拟合的高斯曲面下的面积。 | NOAA-AVHRR;夜间,4月;休斯顿;Streutker(2002) MODIS;白天和夜间,多年;东亚和东南亚的超大城市;Tran等(2006) |
| 高斯幅度/K | 乡村LST背景值去掉以后基于LST场数据拟合的高斯曲面的高度。 | NOAA-AVHRR;夜间,4年;休斯顿;Streutker(2003) MODIS;白天和夜间,多年;东亚和东南亚的超大城市;Tran等(2006) | |
| 高斯幅度经验值/K | 城区与拟合后的乡村LST的最大差值。 | MODIS;白天和夜间,某一年7月及另一年的1月和7月;263个欧洲城市;Schwarz等(2011) | |
| 标准差/K | 研究区内LST的标准差。 | MODIS;白天和夜间,某一年7月及另一年的1月和7月;263个欧洲城市;Schwarz等(2011) | |
| 幅度/K | 基于卷积核技术处理后影像LST场的最大值和最小值的差值。 | MODIS;白天和夜间,1年;印第安纳波利斯;Rajasekar等(2009) | |
| 基于地温数理统计的热岛面积/km2 | LST高于区域LST均值和一倍标准差之和的区域的面积。 | Landsat-ETM+;白天,1天;广东的10个城市;Zhang等(2008) | |
| 地温差值 高温区-低温区/℃ | 高温区(地温大于区域均值和一倍标准差之和的区域)与低温区(地温低于区域均值和一倍标准差之差的区域)LST均值的差值。 | Landsat TM和ETM;白天,3年中的3天;珠江三角洲;张金区(2006) | |
| 热岛变异指数 | 各像元LST与研究区LST均值的差值与研究区LST均值的比值。 | CBERS02-IRMSS;白天,1天;北京、无锡;张勇等(2006) | |
| 基于地温空间统计的热岛面积/km2 | 基于莫兰指数计算的空间相关性及热点分析的统计量来识别高温和低温聚集区。 | Landsat Landsat/TM、ETM+;白天,5年中的5天;杭州;张伟等(2015) | |
| 基于剖线突检测的热岛面积/km2 和强度/℃ | 对多条从市中心发射出的地表温度剖线利用诺夫法检测突变点即热岛边界并结合缓冲区分析法计算热岛强度。 | MODIS;白天和夜间,多年7月;布加勒斯特;Cheval等(2009) | |
| 亮温级别变化指数 | 对两期影像的LST单独进行分级;综合考虑各像元两期级别的变化类型和比例;旨在分析SUHI强度的变化趋势。 | Landsat-TM和ETM;白天,2年中的2天;庐州;Xu等(2011) | |
| 两者复合驱动型 | 热岛比例指数 | 综合考虑了LST高于郊区区域中各等级所占的比例及强度;最后给出一个0~1范围内的数值。 | Landsat-TM和ETM;白天,2年中的2天;厦门;徐涵秋等(2003)。FY-3A/MERSI和MODIS;白天和夜间,2年;北京;Ye等(2011) Landsat-TM和ETM;白天,3年中的3天;兰州;Pan(2016) |
| 加权平均热岛 强度/℃ | 综合考虑城区内各级别LST所占的比例及其均值与郊区LST均值的差值。 | Landsat-TM和ETM;白天,2年中的2天;庐州;Xu等(2011) |
由于LST显著与土地覆盖相关,以往不少研究首先主要基于土地覆盖并同时考虑距离城区的空间距离、地形等确定“城市”、“郊区”或“乡村”的范围,然后计算城市或城市核心区与周边乡村(Dan et al, 2010; Imhoff et al, 2010; Tomlinson et al, 2012; Clinton et al, 2013; Haashemi et al, 2016; 葛荣凤等, 2016)、郊区(Peng et al, 2011; Zhou D C et al, 2014)、农田(Jin et al, 2005; Haashemi et al, 2016)、森林(Jin et al, 2005)、水体(Chen et al, 2006; 朱焱等, 2010; Haashemi et al, 2016)的瞬时或累计地表温差来表征SUHI强度(表2)(Dousset et al, 2003)。值得注意的是,累计地表温差由于考虑了长时间对热岛的暴露情况,会更加科学(Clinton et al, 2013)。
虽然该类方法清晰、直接、有效且通用性强,但仍存在以下3个问题：①对城市、郊区和乡村的定义多样、甚至混乱(Schwarz et al, 2011),并由先验知识决定的。②这类指标有的未定义出城市、郊区和乡村的边界;或不少城郊乡间的边界是脱离具体生态过程来确定的,是唯一不变的。这不同于现实中城区气象要素对于周边地区影响范围的边界。实际上,即使在特定地区的特定外界条件下,对不同的气象要素来说,城区对于周边地区的影响范围也是不同的(Lowry, 1977)。此外,外界条件不同,同一种气象要素的影响范围也会不同,这是由于内在的生态学过程机制不同所导致的。例如白天和夜间的SUHI强度并不相关,其内在的驱动因素是不同的(Peng et al, 2011)。而此类方法中SUHI的影响区域完全基于土地覆盖而定,其默认前提就是土地覆盖是SUHI的唯一决定要素,这显然有失偏颇。③特定的土地覆盖类型,如农田、森林、水体等并不能完全代表乡村的生态要素特点。因为乡村是一个由多种土地覆盖要素整合的功能单元。基于特定土地覆盖类型的指标也不适用于周边缺乏该类土地覆盖类型的城市。
该类SUHI监测指标完全或主要依赖于LST格局,较少或完全不考虑土地覆盖类型(表2)。其中,高斯曲面拟合的方法首先掩去城区、郊区和水体范围内的LST场,得出乡村背景的LST场,并拟合出其空间分布;其次,将该区域初始的LST图层减去拟合出的乡村LST图层;然后对剩余区域的LST场进行高斯拟合,进而得出多种监测指标,如高斯面积(Streutker, 2002, 2003; Tran et al, 2006)、高斯幅度(Streutker, 2003; Tran et al, 2006)、高斯幅度经验值等(Schwarz et al, 2011)。此外,SUHI强度的量化指标还包括基于卷积核技术处理后区域LST的最大值和最小值的差值(Rajasekar et al, 2009)、区域LST的标准差(Schwarz et al, 2011)、LST高于区域LST均值与1倍标准差之和的热岛面积(Zhang et al, 2008)、各像元LST与研究区LST均值的差值与研究区LST均值的比值(张勇等, 2006)、基于莫兰指数计算的空间相关性及热点分析得出的统计量来识别的热岛面积(张伟等, 2015),采用诺夫法检测从市中心发射出的多个地表温度剖线的突变点确定城市热岛边界,并结合缓冲区分析计算的SUHI强度(Cheval et al, 2009)。另外,前人首先基于归一化的LST均值和标准差设定出规则将其划分为不同的冷热强度级别,然后采用亮温级别变化指数(Brightness Tmperature Grade Change Index, TGCI) 来量化SUHI强度(Xu et al, 2011)。
该类方法直接基于热力学生态过程的结果即LST格局来量化SUHI强度,克服了土地覆盖类型驱动型指标只将土地覆盖类型作为唯一驱动要素而忽视生态过程的不足。但该类指数的主要问题首先是LST场通常需要满足一定的空间分布特征。如高斯曲面拟合的方法要求去掉背景温度场后剩余区域的LST场为高斯曲面,但当白天发生城市冷岛时,此条件并不成立。基于标准差概念的方法也需要研究区内的LST是符合正态分布的。但只有在研究区仅包含城区和郊区的情况下才可能会如此;如果研究区内还包含乡村,则因为城区和乡村像元数量较多使得LST经常表现为两头偏的分布类型。其次,不少LST格局驱动型指标的计算结果和研究区边界的设定密切相关,但这存在一定的主观性。再次,完全忽视土地利用与土地覆盖(Land Use and Land Cover, LULC)及地形因素,而仅仅基于地温空间统计获取的高温及低温聚集区有时会偏离用SUHI来反映城乡热格局对比的本意。而上文中提到的基于对剖线突变分析的方法可一定程度克服此问题,并较好确定城市边界(Cheval et al, 2009),但其计算热岛强度时并没确定郊区和乡村的边界,从而导致其结果的生态意义模糊,应在考虑周边地形、水体、卫星城等干扰因素的影响下,进一步加以判别。此外,还应对该方法的普适性加以检验。
该类指标同时关注土地覆盖类型和LST格局要素(表2)。首先采用一定标准和方法将区域各像元的LST初始值(王天星等, 2009; Dan et al, 2010)、归一化的LST值(Xu et al, 2011; 张建明等, 2012)与区域LST均值的比值划分为不同的冷热强度级别(张勇等, 2006; 赵小锋等, 2009)。常见标准化方法包括均值—标准差偏差法(张金区, 2006; Xu et al, 2011)、阈值法(孙飒梅等, 2002; 赵小锋等, 2009)、等距分割的方法(徐涵秋等, 2003; 张建明等, 2012)和空间统计与自然断裂法相结合的方法(Pan, 2016)。然后,结合土地覆盖类型采用热岛比例指数(Urban Heat Island Ratio, URI)(徐涵秋等, 2003; Xu et al, 2011; Pan, 2016)或加权平均热岛强度(Weighted Average Heat Island Intensity, WAI)(Dan et al, 2010; Xu et al, 2011)来量化SUHI强度。该类指数继承了上述2类方法的问题,如边界定义时的主观性、LST分级标准的多样性、研究结果对研究区边界的敏感性等。
上述监测指标均可一定程度上揭示SUHI的时空变化规律,但是不同指标间相关性低、可比性差(Schwarz et al, 2011)。只有建立在同样量化指标的前提下的SUHI时空变化规律对比才科学(Schwarz et al, 2012)。此外,不同时刻SUHI强度及格局差异显著。在同时注意监测指标及监测时间2类问题的基础上,本文对前人工作加以归纳。
4.1.1 日间变化
大量研究表明,除少数沙漠城市外,白天全球绝大多数城市年均SUHI强度都是正数(Tran et al, 2006; Zhang et al, 2010; Peng et al, 2011);发达国家白天的SUHI强度显著地高于发展中国家(Peng et al, 2011);面积越大的城市SUHI越强(Zhang et al, 2010; Meng et al, 2013)。温带地区城市地表热岛强度和面积在夏季最大,春秋次之,冬季最弱并经常出现冷岛效应(Tran et al, 2006; 石涛等, 2013; Quan et al, 2014; Zhou D C et al, 2014; 赵颜创等, 2014);热带地区城市则在整个干季变化不大(Tran et al, 2006)。但基于Aqua/MODIS数据对全球多个城市一年中白天LST超过20℃时段内的SUHI研究表明,白天全球普遍存在地表城市冷岛,其强度最小值多在热岛最强值出现之前的晚春和夏季(Clinton et al, 2013)。基于Terra/MODIS数据对全球1月和7月多个城市的研究表明(Jin et al, 2005),北半球高纬度地区,白天城市比农村更冷。沙漠城市方面,若采用城郊差或城乡差的方法,则白天常年存在冷岛现象(Lazzarini et al, 2013; Haashemiet al, 2016),冬季比夏季的冷岛强度更大(Lazzarini et al, 2013);但若采用城市与农田或水体差值的方法,则白天不存在冷岛现象(Jin et al, 2005; Haashemiet al, 2016),且全球较大的城市和农田的地表温差会出现在沙漠广布的纬度地区(Jin et al, 2005)。
4.1.2 夜间变化
通常夜间四季均存在SUHI现象(Jin et al, 2005; Prado, 2010; Peng et al, 2011; Clinton et al, 2013),全球95%的城市热岛强度为0~2℃(Peng et al, 2011),北半球强于南半球,特别是印度和巴基斯坦的热岛强度很大(Clinton et al, 2013),发达国家与发展中国家的热岛强度比较接近(Peng et al, 2011)。但在夜间SUHI的季节变化研究方面,结论却不一致,甚至用同一个时刻数据对同一个城市或城市群的分析结论也不一致。一些研究表明：冬季有明显的强SUHI,热岛面积也最大,春秋次之,夏季最小(Ye et al, 2011; Lazzarini et al, 2013; Quan et al, 2014; Quan et al, 2016)。还有研究证明,夏季夜晚SUHI最强,冬季最弱(张佳华等, 2005; Prado, 2010; 曾胜兰, 2014);或温带城市SUHI最强的时刻普遍出现在秋季,而在热带地区则具有较大的时空变异性,特别是在印度和撒哈拉以南的非洲地区更是如此(Clinton et al, 2013);或夜间SUHI强度季节变化不明显(王建凯, 2007; Prado, 2010; 石涛等, 2013)。此外,基于Terra的MODIS数据对德黑兰研究发现,若采用城市与乡村差的方法,夜间常年存在热岛,其中5月最强;若采用城市与水体差值的方法,则在3-9月存在冷岛现象(Haashemi et al, 2016)。这些针对夜间SUHI季节变化的研究结论不一致,可能是由于各研究区特点、采用数据源、观测时间及监测指标的不同导致的。同时,这也说明了夜间SUHI内在驱动机制的复杂性。
4.1.3 昼夜变化的对比
SUHI的昼夜强度相关性很低(Peng et al, 2011; Schwarz et al, 2011; Klok et al, 2012)。全球不少城市白天的SUHI强度大于夜间(Tran et al, 2006; Cheval et al, 2009; Zhang et al, 2010; Peng et al, 2011; Schwarz et al, 2011; Haashemi et al, 2016),但仍有一些城市反之(Peng et al, 2011; Sobrino et al, 2013)。基于Aqua/MODIS数据对全球多个城市多年SUHI的监测表明,全球白天的平均SUHI强度显著高于夜间 (Peng et al, 2011);但尚有36%的城市夜间热岛更强,特别是在西亚、南亚和南非地区城市。年均昼夜热岛强度最大的大洲是南美洲和北美洲;最低的为非洲(Peng et al, 2011)。SUHI昼夜强度的对比结果还可能与季节密切相关。如基于Terra/MODIS对安徽典型城市昼夜热岛强度对比的季节变化研究表明,冬季和秋季夜晚的SUHI强于白天,春季和夏季则白天强于夜晚(石涛等, 2013)。此外,有研究采用热视频辐射仪对以色列特拉维夫冬季多种地物的LST开展了昼夜连续对比观测研究,结果表明：研究区内各地物LST的最大差异出现在约12:00-13:00时的正午时间(Chudnovskyet al, 2004)。每天9:00-10:00与17:00-20:00内地物热差异不显著,不利于被遥感识别。最佳识别时间为约5:00时的日出前的早晨,以及约12:00-13:00时的正午时间(Chudnovskyet al, 2004)。还有学者对SUHI昼夜足迹、城乡LST剖线特点、SUHI空间形状及景观格局开展了对比研究。如基于Aqua/MODIS对中国32个大城市多年研究表明,夜间热岛的足迹范围比白天要大(Zhou et al, 2015)。虽然总体上昼夜地表温度由城区到乡村呈现指数衰减,但夜间夏、冬季的趋势差别不大,而白天的则差异很大(Zhou et al, 2015)。基于Terra/MODIS数据对东亚多个温带城市多年冬、夏两季和东南亚多个热带城市多年干季的研究表明,内陆城市SUHI昼夜形状类似,冬季面积会变小;但海滨城市SUHI强度的日变化规律是不同的,空间形状也不一样(Tran et al, 2006)。采用标准差法对美国印第安纳波利斯四季中午的LST进行分级,并分析其景观格局发现：除冬季外,其他季节的中午LST格局相似(Liu et al, 2008);冬季的平均斑块大小、聚合度和连接度均最大。
随着城市扩展,SUHI空间范围变大,一些城市的SUHI格局也发生了轻度或重度的变化。如北京市由摊大饼型变为中心城区加周边卫星城镇分散的模式,并且昼夜地表热岛的重心均往东北方向偏移,南北维度上变化大于东西维度,总体呈现破碎化趋势( Quan et al, 2014; 刘勇洪等, 2014; 葛荣凤等, 2016)。成都市由中心型演变为多中心环形并且城市东北部和西南部高温区的温度梯度出现逆向升降趋势(但尚铭等, 2011; 曾胜兰, 2014; 张好等, 2014)。西安市城区内夏季白天的SUHI强度相对差异缩小,格局由中心城区模式转为建成区模式(杨丽萍等, 2015)。沈阳市和南宁市热岛均有从单中心变为多中心态势(李丽光等, 2013; 林奕桐等, 2014)。福州市总体趋势由西北—东南走向逐渐向北—南方向偏移,城市热岛重心向东南方向偏移(王天星等, 2009)。对兰州市和唐山市的夏季、上海市的秋季、宁波市的冬季及武汉市多年白天的研究表明,城市热岛斑块的数量和密度增加,斑块形状趋于复杂,景观越加分散,连接度降低(白杨等, 2013; 赵颜创等, 2014; 高建成等, 2015; Pan, 2016; Shen et al, 2016)。但宁波市夏季热岛虽然斑块数量也增多,形状也变复杂,但分布却更聚集(赵颜创等, 2014)。
城市各个地块的热岛强度有增有减或变化不大。随着城市扩展及人类活动增强,绝大部分地块的热岛强度虽然也会增加,特别是刚由郊区或乡村转换为城区的地块(Quan et al, 2014; Ramdani et al, 2014; Pan, 2016; 葛荣凤等, 2016);但对于那些已经针对性采取了一系列缓解SUHI措施的地块,SUHI强度或程度反而会降低(徐涵秋等, 2003; 但尚铭等, 2011; Chen et al, 2016; Shen et al, 2016; 葛荣凤等, 2016)。值得一提的是,宁波市夏季的热岛强度在增强,冬季的却在变弱(赵颜创等, 2014)。其他代表性的研究还有对苏州市前后两期不同距离缓冲区内LST的标准差的对比研究(Xu et al, 2010)。该研究结果显示,随着与中心城区距离的加大,标准差逐步变大并在一定距离后保持高值,且数值明显高于城市化之前数值。还有学者关注了塞浦路斯一次热浪事件与同时段多年SUHI强度均值的区别,其中2个行政区明显增强,2个则影响不大(Retalis et al, 2013);但热浪时期乡村昼夜LST也会增加,进而一定程度削弱热岛效应(Wu et al, 2013)。
一些学者采用多年的中分数据来分析SUHI的年际变化。但通常较难获取不同年份同一时期的数据(徐涵秋等, 2003; Ramdani et al, 2014; 刘勇洪等, 2014; Shen et al, 2015; Pan, 2016)。卫星每日的过境时间也会有所变动(de Lucena et al, 2013; 刘勇洪等, 2014),而不同地物的LST与时刻密切相关(Chudnovsky et al, 2004),这些均会影响到SUHI格局和强度的年际对比结果。
虽然国内外已经开展了许多SUHI遥感监测的研究工作,但仍存在一些空白或现有结论相互冲突的领域,有待进一步深化。为更好地理解SUHI,基于上述综述提出几个潜在的关键问题或研究方向。
(1) 现有的热红外遥感数据时间分辨率高,但空间分辨率低。可基于LST与地表生物物理参数或LULC类型的关系实现LST尺度的下推(Zakšek et al, 2012; Weng et al, 2014),为SUHI时空变化的遥感监测提供时空分辨率均高的数据。此外,中分的夜间过境的热红外数据非常缺乏,极大地限制了对夜间SUHI时空变化及影响因素的研究。
(2) 目前,应用最广的星载热红外遥感数据存有一定不足,如时空分辨率较低、难以克服云层干扰、遮蔽的视场导致其难以观测近地面的地温分布、无法精细刻画平面及各立面地物表面温度等。机载的热红外遥感数据虽然空间分辨率高,但成本昂贵,并存在与星载数据类似的问题。据此,为精细分析SUHI的时空演变规律及影响要素,应积极开展基于热视频辐射仪对小区尺度全天时全天候、特别是阴天条件下的SUHI时空变化规律监测及影响要素分析。
(3) 现有的土地覆盖类型驱动的监测指标在使用中存在城郊乡边界定义多样甚至混乱(Schwarz et al, 2011)、脱离生态学过程、特定土地覆盖类型并不能完全代表乡村特点且不适用于周边缺乏该类土地覆盖的城市的问题;地表温度格局驱动的监测指标通常仅适用于研究区内地表温度符合一定空间分布或统计特征的情况,其结果对研究区边界的选择较为敏感,而且单独依靠地温统计特征得出的结果通常生态学意义模糊,甚至背离采用监测指标反映城乡差异的本意;土地覆盖与地表温度符合驱动型指数也同时存在上述2类方法的问题,如边界定义时的主观性、LST分级标准的多样性、研究结果对研究区边界的敏感性等。据此,很有必要发展根据LST空间格局为主,以LULC为辅的指标。这类指标不仅具有生态学意义,而且普适性强、自动化程度高,并能考虑时间累计效应,可定量地确定城郊乡边界进而计算地表热岛面积和强度的监测指标。
(4) 不同监测指标所得结果相关性低、可比性差(Schwarz et al, 2011)。只有建立在同样量化指标前提下的SUHI时空规律对比才具科学性(Schwarz et al, 2012)。与此同时,前人研究主要集中在单个城市与区域城市群尺度,较少关注洲际及全球尺度的研究,导致较难科学、完整地了解SUHI的时空演变规律。为详细理解全球SUHI的时空变化规律,今后应进一步开展基于统一的、科学的指标对多类气候带、多个不同规模城市在多个季节多个时刻或时间段内的监测研究。
(5) 与LST相比,气温对人类热舒适度的影响更为直接。虽然LST数值的高低和气温密切相关,但在城市环境中对两者直接进行转换依然无法获取到满意的结果(Wen et al, 2004; Mostovoy et al, 2006; 张金区, 2006; Schwarz et al, 2012)。未来应结合气象站数据、LST数据、LULC数据、地表生物物理参数、景观格局、地理位置等探索预测气温分布的方法 (Wong et al, 2011; Hengl et al, 2012)。
(6) 目前,相关研究通常仅关注如何消减热岛,但SUHI的影响不一定完全是负面的,应继续研究热岛对全球不同气候带、不同季节的正向、中性或负向的影响,进而判别应在何时何地缓解、无视或增强热岛,达到趋利避害的目的。
The authors have declared that no competing interests exist.
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上海市城市热岛景观格局演变特征研究 [J].
城市热岛空间格局特征及其内在驱动机制的研究可为缓解城市热岛效应、城市规划与产业布局提供科学依据,目前这方面研究还比较少,相关机制并没有得到完全的揭示。该研究以上海市为研究对象,采用1987、1996、2002和2010年4景Landsat TM/ETM+遥感影像,对城市地表温度进行了反演与分级。借助景观格局指数,分析了上海市城市热岛景观格局时空演变特征。结果表明:随着1987-2010年上海市不断城市化,各级热岛景观类型斑块数量持续增加,高等级的城市热岛景观类型面积也持续增加,整个城市热岛景观趋于破碎化。热岛景观总体聚集度下降,连通性下降,但是低等级热岛景观向高等级热岛景观转移的面积逐渐增加,景观类型之间面积差异逐渐减小,景观均匀度和多样性增加。城市化过程中,人口数量的增加和经济的快速发展对城市热岛景观格局的形成和演变具有重要的影响。研究结果揭示了城市热岛格局随城市化进程的时空演变特征,可以为制定有效的热岛效应缓解措施提供参考依据。
Spatial and temporal changes of urban thermal landscape pattern in Shanghai [J].
城市热岛空间格局特征及其内在驱动机制的研究可为缓解城市热岛效应、城市规划与产业布局提供科学依据,目前这方面研究还比较少,相关机制并没有得到完全的揭示。该研究以上海市为研究对象,采用1987、1996、2002和2010年4景Landsat TM/ETM+遥感影像,对城市地表温度进行了反演与分级。借助景观格局指数,分析了上海市城市热岛景观格局时空演变特征。结果表明:随着1987-2010年上海市不断城市化,各级热岛景观类型斑块数量持续增加,高等级的城市热岛景观类型面积也持续增加,整个城市热岛景观趋于破碎化。热岛景观总体聚集度下降,连通性下降,但是低等级热岛景观向高等级热岛景观转移的面积逐渐增加,景观类型之间面积差异逐渐减小,景观均匀度和多样性增加。城市化过程中,人口数量的增加和经济的快速发展对城市热岛景观格局的形成和演变具有重要的影响。研究结果揭示了城市热岛格局随城市化进程的时空演变特征,可以为制定有效的热岛效应缓解措施提供参考依据。
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首先对城市热岛效应的研究历史、大气城市热岛(AUHI)和地表城市热岛(SUHI)等概念、以及数据获取方式等方面进行简要地概述;随之着重综述从景观格局角度对城市热岛效应进行的研究。统计描述、传统景观格局指数分析和模型模拟法是目前该方向研究的主要方法,统计和景观格局指数分析的研究方法相似,主要统计地表参数或地表景观格局指数与地表温度的相关关系,在SUHI的研究中用得较多;AUHI和SUHI的数据源和机理不尽相同,其研究方法也不同;AUHI一般使用固定气象站点的数据和精细的局部移动气象站数据,较难和景观格局指数结合;模型模拟法则既可以使用地表温度也可以使用大气温度,其结果具体可靠,但目前模型模拟中涉及的景观格局参数,尤其是二维或多维的格局参数并不多;最后从数据源和景观格局参数的参与两个角度讨论了该方向研究存在的问题并提出今后研究的重点,包括(1)针对研究目标,选取或生产最合适的高质量数据;(2)深入从景观格局角度模拟城市热岛效应的研究,尤其是二维和三维景观格局的模拟,并发展多维度的景观格局指数;(3)中尺度上充分利用多光谱遥和热红外遥感数据,结合小尺度的测量和模拟,建立基于机理的景观模型或格局指数以评价中尺度的城市热岛效应;(4)多领域数据的融合和多学科方法的交叉研究和应用。
Studies on urban heat island from a landscape pattern view: A review [J].https://doi.org/10.5846/stxb201106280965 URL Magsci [本文引用: 2] 摘要
首先对城市热岛效应的研究历史、大气城市热岛(AUHI)和地表城市热岛(SUHI)等概念、以及数据获取方式等方面进行简要地概述;随之着重综述从景观格局角度对城市热岛效应进行的研究。统计描述、传统景观格局指数分析和模型模拟法是目前该方向研究的主要方法,统计和景观格局指数分析的研究方法相似,主要统计地表参数或地表景观格局指数与地表温度的相关关系,在SUHI的研究中用得较多;AUHI和SUHI的数据源和机理不尽相同,其研究方法也不同;AUHI一般使用固定气象站点的数据和精细的局部移动气象站数据,较难和景观格局指数结合;模型模拟法则既可以使用地表温度也可以使用大气温度,其结果具体可靠,但目前模型模拟中涉及的景观格局参数,尤其是二维或多维的格局参数并不多;最后从数据源和景观格局参数的参与两个角度讨论了该方向研究存在的问题并提出今后研究的重点,包括(1)针对研究目标,选取或生产最合适的高质量数据;(2)深入从景观格局角度模拟城市热岛效应的研究,尤其是二维和三维景观格局的模拟,并发展多维度的景观格局指数;(3)中尺度上充分利用多光谱遥和热红外遥感数据,结合小尺度的测量和模拟,建立基于机理的景观模型或格局指数以评价中尺度的城市热岛效应;(4)多领域数据的融合和多学科方法的交叉研究和应用。
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环形热岛格局演变过程的遥感分析 [J].
<p>城市热岛效应的空间格局在一定程度上可以反映城市规划和管理的成败得失，也是城市大气污染的驱动因素之一。〖JP2〗使用2000、2003、2004、2006、2008和2010年2～3月下午成都的6次NOAA/AVHRR（National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer）卫星遥感数据反演亮温，采用均值标准差法将热岛强度分为7个等级，用面积加权平均法将研究区强度分为高温、中温和低温3类。结果表明：（1）热岛空间格局呈现巨大变异，由前期的中心型演变为中期的环型，后期是热岛环的形态和高温中心的调整阶段；（2）研究区的强度为前期强，2004年迅速减弱，高温类的强度2004年比2003年减弱259℃，2004年之后的平均减弱率仅为024℃/a；（3）城市东北部和西南部高温区的温度梯度出现逆向升降趋势。由于地表温度对城市气温有重要影响，热岛形态的变化是人们从更复杂角度分析大气污染机制、设计热岛数值模型和进行城市规划的客观依据</p>
Analysis about evolution of annular urban heat island based on remote sensing [J].
<p>城市热岛效应的空间格局在一定程度上可以反映城市规划和管理的成败得失，也是城市大气污染的驱动因素之一。〖JP2〗使用2000、2003、2004、2006、2008和2010年2～3月下午成都的6次NOAA/AVHRR（National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer）卫星遥感数据反演亮温，采用均值标准差法将热岛强度分为7个等级，用面积加权平均法将研究区强度分为高温、中温和低温3类。结果表明：（1）热岛空间格局呈现巨大变异，由前期的中心型演变为中期的环型，后期是热岛环的形态和高温中心的调整阶段；（2）研究区的强度为前期强，2004年迅速减弱，高温类的强度2004年比2003年减弱259℃，2004年之后的平均减弱率仅为024℃/a；（3）城市东北部和西南部高温区的温度梯度出现逆向升降趋势。由于地表温度对城市气温有重要影响，热岛形态的变化是人们从更复杂角度分析大气污染机制、设计热岛数值模型和进行城市规划的客观依据</p>
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以唐山市建成区1992、1999、2006和2013年4个时 相的Landsat TM/ETM+遥感影像为主要数据源,在ENVI5.0、Fragstats3.3和ArcGIS10软件支持下,借助景观格局指数,分析了研究区城市热 岛景观的动态变化特征.研究结果表明:唐山市建成区的高强度热岛景观斑块面积持续增加,热岛斑块单体面积正在逐年增大,并自1999年起开始出现向高强度 热岛过渡的趋势,热岛景观斑块形状日趋复杂,其热岛景观的连通性持续下降;2006年以后,唐山市建成区的热岛景观斑块的破碎化程度加剧,与此同时,其景 观斑块出现了蔓延态势,各热岛景观斑块单元间呈分散化;自1999年以来热岛现象持续加剧,出现低等级热岛景观向高等级热岛景观转移的特征,以4和5级热 岛景观增幅最大.研究成果揭示了快速城镇化地区热岛景观的动态变化特征,可为该区下阶段缓解热岛效应提供技术层面的指导.
Spatial and temporal changes of urban thermal landscape pattern in rapid urbanization area: Taking Tangshan urban built-up area as examples [J].https://doi.org/10.13448/j.cnki.jalre.2015.311 URL [本文引用: 1] 摘要
以唐山市建成区1992、1999、2006和2013年4个时 相的Landsat TM/ETM+遥感影像为主要数据源,在ENVI5.0、Fragstats3.3和ArcGIS10软件支持下,借助景观格局指数,分析了研究区城市热 岛景观的动态变化特征.研究结果表明:唐山市建成区的高强度热岛景观斑块面积持续增加,热岛斑块单体面积正在逐年增大,并自1999年起开始出现向高强度 热岛过渡的趋势,热岛景观斑块形状日趋复杂,其热岛景观的连通性持续下降;2006年以后,唐山市建成区的热岛景观斑块的破碎化程度加剧,与此同时,其景 观斑块出现了蔓延态势,各热岛景观斑块单元间呈分散化;自1999年以来热岛现象持续加剧,出现低等级热岛景观向高等级热岛景观转移的特征,以4和5级热 岛景观增幅最大.研究成果揭示了快速城镇化地区热岛景观的动态变化特征,可为该区下阶段缓解热岛效应提供技术层面的指导.
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系统总结了应用遥感技术开展城市热岛研究的数据、方法与成果.对国内外学者有关城市热岛的形态结构、过程变化及成因分析等方面的研究内容进行了较为详细的评述,在此基础上,对未来城市热岛遥感研究的方向进行了展望.
Advances in the application of remotely sensed data to the study of urban heat island [J].https://doi.org/10.3969/j.issn.1001-070X.2005.03.002 URL [本文引用: 2] 摘要
系统总结了应用遥感技术开展城市热岛研究的数据、方法与成果.对国内外学者有关城市热岛的形态结构、过程变化及成因分析等方面的研究内容进行了较为详细的评述,在此基础上,对未来城市热岛遥感研究的方向进行了展望.
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杭州市城市空间扩展及其热环境变化 [J].https://doi.org/10.11873/j.issn.1004-0323.2014.2.0264 URL Magsci 摘要
<p>通过Landsat卫星影像分别获取了杭州市1989、2000和2010年的城市空间扩张、地表温度及作为主要地表参数的建筑用地和植被的信息,用以研究杭州市城市扩展及其城市热环境变化。结果表明:在21 a间,杭州市建成区范围有了大幅扩展,且城市热岛区域的空间变化与建成区的空间扩展变化基本一致。研究还发现杭州市区的特高温区面积比例在逐渐减小,城市热岛比例指数(URI)从0.78降至0.71,表明城市热岛效应有一定缓解。建筑用地比例的减小与建筑用地密度的下降是城市热岛得以缓解的主要原因。定量分析表明建筑用地的升温效应要强于植被的降温效应。总的看来,杭州市的城市热岛效应现象在整个研究时段内虽有一定的改善,但仍一直处于较强烈的状态。</p>
Urban expansion and thermal environment changes in Hangzhou City of East China [J].https://doi.org/10.11873/j.issn.1004-0323.2014.2.0264 URL Magsci 摘要
<p>通过Landsat卫星影像分别获取了杭州市1989、2000和2010年的城市空间扩张、地表温度及作为主要地表参数的建筑用地和植被的信息,用以研究杭州市城市扩展及其城市热环境变化。结果表明:在21 a间,杭州市建成区范围有了大幅扩展,且城市热岛区域的空间变化与建成区的空间扩展变化基本一致。研究还发现杭州市区的特高温区面积比例在逐渐减小,城市热岛比例指数(URI)从0.78降至0.71,表明城市热岛效应有一定缓解。建筑用地比例的减小与建筑用地密度的下降是城市热岛得以缓解的主要原因。定量分析表明建筑用地的升温效应要强于植被的降温效应。总的看来,杭州市的城市热岛效应现象在整个研究时段内虽有一定的改善,但仍一直处于较强烈的状态。</p>
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基于源汇指数的沈阳热岛效应 [J].
<p>基于2001和2010年Landsat 遥感影像,利用GIS技术识别沈阳城市热岛源区和汇区,利用地表温度(LST)、源区和汇区面积比率指数（CI）和热岛强度指数（LI）,评价分析了沈阳土地利用发展布局模式对热岛效应的影响.结果表明: 2001—2010年,沈阳三环内土地利用类型变化较大,导致热岛源、汇区面积变化明显,且主要发生在二环和三环.2001年,一环内热岛源、汇区面积比例分别为94.3%和5.7%,三环内分别为64.0%和36.0%;2010年,其比例在一环内分别为93.4%和6.6%,三环内分别为70.2%和29.8%,说明10年来“摊饼式”土地利用布局决定了沈阳热岛效应的“摊饼式”布局.研究期间,沈阳地表温度从一环至三环均呈递减趋势,热岛效应强度在2001年以单一中心为主,至2010年发展为多中心态势,热岛效应强度等级有所降低.从一环至三环,CI绝对值均呈增加趋势,LI值均小于1,说明期间研究区土地利用布局变化对改善区域热岛效应没有明显作用.</p>
Urban heat island effect based on urban heat island source and sink indices in Shenyang, Northeast China [J].
<p>基于2001和2010年Landsat 遥感影像,利用GIS技术识别沈阳城市热岛源区和汇区,利用地表温度(LST)、源区和汇区面积比率指数（CI）和热岛强度指数（LI）,评价分析了沈阳土地利用发展布局模式对热岛效应的影响.结果表明: 2001—2010年,沈阳三环内土地利用类型变化较大,导致热岛源、汇区面积变化明显,且主要发生在二环和三环.2001年,一环内热岛源、汇区面积比例分别为94.3%和5.7%,三环内分别为64.0%和36.0%;2010年,其比例在一环内分别为93.4%和6.6%,三环内分别为70.2%和29.8%,说明10年来“摊饼式”土地利用布局决定了沈阳热岛效应的“摊饼式”布局.研究期间,沈阳地表温度从一环至三环均呈递减趋势,热岛效应强度在2001年以单一中心为主,至2010年发展为多中心态势,热岛效应强度等级有所降低.从一环至三环,CI绝对值均呈增加趋势,LI值均小于1,说明期间研究区土地利用布局变化对改善区域热岛效应没有明显作用.</p>
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南宁市热岛效应的遥感研究 [J].https://doi.org/10.3969/j.issn.1000-811X.2014.04.035 URL [本文引用: 1] 摘要
利用MODIS数据、TM数据对南宁市的地表温度、植被覆盖度、热场强度、热岛强度分别进行计算，从热岛的时空分布、热岛强度与面积、热岛成因三方面讨论南宁热岛特征及近十年的演变，研究南宁市热岛与高温天数的关系，建立了植被覆盖度与热场强度的联系。结果表明：南宁市从2000年的单一热岛中心逐渐发展成为多热岛中心；热岛面积以年均15.7％的速率增长，与之对应的是南宁市的高温天数平均以每年1.1 d的速率增长。尽管南宁市的植被对热岛的缓解作用强于城市用地及裸土的增温作用，但由于南宁市植被覆盖的面积（26.44％）远小于城市用地的面积（55.33％），城市热岛仍处于发展的状态。
Remote sensing research of heat island effect in Nanning [J].https://doi.org/10.3969/j.issn.1000-811X.2014.04.035 URL [本文引用: 1] 摘要
利用MODIS数据、TM数据对南宁市的地表温度、植被覆盖度、热场强度、热岛强度分别进行计算，从热岛的时空分布、热岛强度与面积、热岛成因三方面讨论南宁热岛特征及近十年的演变，研究南宁市热岛与高温天数的关系，建立了植被覆盖度与热场强度的联系。结果表明：南宁市从2000年的单一热岛中心逐渐发展成为多热岛中心；热岛面积以年均15.7％的速率增长，与之对应的是南宁市的高温天数平均以每年1.1 d的速率增长。尽管南宁市的植被对热岛的缓解作用强于城市用地及裸土的增温作用，但由于南宁市植被覆盖的面积（26.44％）远小于城市用地的面积（55.33％），城市热岛仍处于发展的状态。
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北京城市热岛的定量监测及规划模拟研究 [J].https://doi.org/10.3969/j.issn.1674-5906.2014.07.010 URL [本文引用: 4] 摘要
为定量地评估北京城市热岛现状并预测未来北京城市热岛发展趋势，分别采用气温资料、遥感资料 和城市规划资料进行了研究分析。对北京20个气象台站按照台站距离城市中心的距离划分为远郊、近郊和城市三类，分别计算三种类型站点经过海拔订正后的年平 均气温，利用1971-2012年城市站和远郊站的年平均气温差值估算北京气温热岛的时间变化；利用1987-2012年的NOAA/AVHRR和 Landsat-TM两种不同分辨率的卫星资料，采用定量化的指标--地表热岛强度和热岛比例指数分别估算了不同时期北京地区和城六区热岛强度和范围，并 对北京平原地区的城市热岛状况进行了评估；利用2020年的北京城市规划土地利用资料，结合2008年的城市热岛现状监测结果对2020年的北京热岛状况 进行了模拟分析。研究结果表明，北京城市的气温热岛与遥感监测地表热岛在时间变化趋势上具有一致性，不同分辨率卫星资料监测地表热岛在时空分布上也具有一 致性。其中1971-2012年，以年平均气温计算的北京城市热岛强度增温率为0.33℃·（10 a）-1，近5年（2008-2012）平均热岛为1.12℃。遥感监测结果显示1987-2001年北京地区的热岛持续增强，2001年之后由于北京申 奥的成功进行了大面积的旧城改造和绿化，使得城市热岛强度和范围在2004年和2008年有所降低，2008年之后城市热岛继续向东、南和北方向扩展，并 出现了中心城区热岛与通州、顺义、大兴、昌平热岛连成片的趋势，到2012年城六区热岛面积百分比已从1990年的31%增加到77%。由热岛比例指数确 定的北京各区县热岛强度排名前三分别是城区、海淀和丰台，延庆县最低。对2020年城市规划图热岛模拟结果显示北京热岛已由“摊大饼”演变为“中心＋周边 分散”模式，中心城区热岛强度和范围明显减弱，周边广大远郊区将出现分散17
Quantitative assessment and planning simulation of Beijing urban heat island [J].https://doi.org/10.3969/j.issn.1674-5906.2014.07.010 URL [本文引用: 4] 摘要
为定量地评估北京城市热岛现状并预测未来北京城市热岛发展趋势，分别采用气温资料、遥感资料 和城市规划资料进行了研究分析。对北京20个气象台站按照台站距离城市中心的距离划分为远郊、近郊和城市三类，分别计算三种类型站点经过海拔订正后的年平 均气温，利用1971-2012年城市站和远郊站的年平均气温差值估算北京气温热岛的时间变化；利用1987-2012年的NOAA/AVHRR和 Landsat-TM两种不同分辨率的卫星资料，采用定量化的指标--地表热岛强度和热岛比例指数分别估算了不同时期北京地区和城六区热岛强度和范围，并 对北京平原地区的城市热岛状况进行了评估；利用2020年的北京城市规划土地利用资料，结合2008年的城市热岛现状监测结果对2020年的北京热岛状况 进行了模拟分析。研究结果表明，北京城市的气温热岛与遥感监测地表热岛在时间变化趋势上具有一致性，不同分辨率卫星资料监测地表热岛在时空分布上也具有一 致性。其中1971-2012年，以年平均气温计算的北京城市热岛强度增温率为0.33℃·（10 a）-1，近5年（2008-2012）平均热岛为1.12℃。遥感监测结果显示1987-2001年北京地区的热岛持续增强，2001年之后由于北京申 奥的成功进行了大面积的旧城改造和绿化，使得城市热岛强度和范围在2004年和2008年有所降低，2008年之后城市热岛继续向东、南和北方向扩展，并 出现了中心城区热岛与通州、顺义、大兴、昌平热岛连成片的趋势，到2012年城六区热岛面积百分比已从1990年的31%增加到77%。由热岛比例指数确 定的北京各区县热岛强度排名前三分别是城区、海淀和丰台，延庆县最低。对2020年城市规划图热岛模拟结果显示北京热岛已由“摊大饼”演变为“中心＋周边 分散”模式，中心城区热岛强度和范围明显减弱，周边广大远郊区将出现分散17
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基于MODIS的安徽省代表城市热岛效应时空特征 [J].https://doi.org/10.3969/j.issn.1001-7313.2013.04.011 URL [本文引用: 3] 摘要
利用2001—2010年覆盖安徽省的MODIS数据,选取在气候、地理、城市化等方面具有代表性的合肥、芜湖、阜阳作为研究对象,并结合GIS技术,分析地表温度的日变化及季节变化特征,得到安徽省代表城市热岛效应的时空分布。结果表明:安徽省省会合肥的热岛效应最为显著,安徽省南部代表城市芜湖的热岛效应强于北部代表城市阜阳,同时具有显著的日变化和季节变化特征。近10年来,安徽代表城市热岛面积和热岛强度均呈增加趋势,但合肥热岛强度大于3℃的极端热岛效应有一定缓解。白天大片水体对缓解城市的热岛效应作用明显,而夜晚则不明显,甚至成为地表温度的高值中心。夏季地表温度与归一化植被指数的负相关最显著,即提高城市植被覆盖度对降低地表温度和缓解城市热岛效应有重要影响。
Spatial temporal characteristics of urban heat island in typical cities of Anhui Province based on MODIS [J].https://doi.org/10.3969/j.issn.1001-7313.2013.04.011 URL [本文引用: 3] 摘要
利用2001—2010年覆盖安徽省的MODIS数据,选取在气候、地理、城市化等方面具有代表性的合肥、芜湖、阜阳作为研究对象,并结合GIS技术,分析地表温度的日变化及季节变化特征,得到安徽省代表城市热岛效应的时空分布。结果表明:安徽省省会合肥的热岛效应最为显著,安徽省南部代表城市芜湖的热岛效应强于北部代表城市阜阳,同时具有显著的日变化和季节变化特征。近10年来,安徽代表城市热岛面积和热岛强度均呈增加趋势,但合肥热岛强度大于3℃的极端热岛效应有一定缓解。白天大片水体对缓解城市的热岛效应作用明显,而夜晚则不明显,甚至成为地表温度的高值中心。夏季地表温度与归一化植被指数的负相关最显著,即提高城市植被覆盖度对降低地表温度和缓解城市热岛效应有重要影响。
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遥感监测城市热岛强度及其作为生态监测指标的探讨 [J].https://doi.org/10.3321/j.issn:0438-0479.2002.01.015 URL [本文引用: 1] 摘要
"热岛效应"现象是现代城市气候的主要特征之一.利用TM卫星遥 感数据的分析结果,根据地理相似准则,提出以相对亮温来表示热岛强度,并将此无量纲因子应用于比较不同城市同一季节的不同小区、或同一小区不同时期中的热 岛强度的差异,以此探讨利用遥感技术监测城市热岛强度的可能性.并提出将城市热岛强度作为城市生态环境状况的监测指标之一.
Study on monitoring intensity of urban heat island and taking it as an indicator for urban ecosystem by remote sensing [J].https://doi.org/10.3321/j.issn:0438-0479.2002.01.015 URL [本文引用: 1] 摘要
"热岛效应"现象是现代城市气候的主要特征之一.利用TM卫星遥 感数据的分析结果,根据地理相似准则,提出以相对亮温来表示热岛强度,并将此无量纲因子应用于比较不同城市同一季节的不同小区、或同一小区不同时期中的热 岛强度的差异,以此探讨利用遥感技术监测城市热岛强度的可能性.并提出将城市热岛强度作为城市生态环境状况的监测指标之一.
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基于MODIS地表温度产品的北京城市热岛(冷岛)强度分析 [J].https://doi.org/10.3321/j.issn:1007-4619.2007.03.007 URL Magsci [本文引用: 1] 摘要
城市热岛是影响城市及其周边地区天气气候和空气质量的重要因素。利用2000-2005年MODIS（Moderate Resolution Imaging Spectroradiometer,中分辨率成像光谱仪）分裂窗算法反演得到的1km分辨率地表温度产品分析了北京的城市热岛效应,发现白天城市热岛强度具有明显的季节变化,夏季最大值可以达到10℃以上,冬季变为冷岛,即城市地表温度低于乡村,最低值可以达到-5℃;模拟结果表明冬季城市冷岛的存在主要是城市和乡村地表热特性（热惯量）的差异引起的。夜间热岛强度的季节变化较小,全年稳定在5℃左右。选择北京周边地区比较典型的乡村耕地、山区森林以及永定河流域来研究乡村的选择对热岛强度的影响。发现选择不同的邻近区域作为乡村时,不仅城市热岛（冷岛）强度有较大变化,而且热岛强度的季节变化情况也有较大差异。冬季白天北京城市冷岛增加了近地层的大气稳定度,可能会降低城市空气污染物的扩散能力,加剧了北京冬季的空气污染。
Urban heat (or cool) island over Beijing from MODIS land surface temperature [J].https://doi.org/10.3321/j.issn:1007-4619.2007.03.007 URL Magsci [本文引用: 1] 摘要
城市热岛是影响城市及其周边地区天气气候和空气质量的重要因素。利用2000-2005年MODIS（Moderate Resolution Imaging Spectroradiometer,中分辨率成像光谱仪）分裂窗算法反演得到的1km分辨率地表温度产品分析了北京的城市热岛效应,发现白天城市热岛强度具有明显的季节变化,夏季最大值可以达到10℃以上,冬季变为冷岛,即城市地表温度低于乡村,最低值可以达到-5℃;模拟结果表明冬季城市冷岛的存在主要是城市和乡村地表热特性（热惯量）的差异引起的。夜间热岛强度的季节变化较小,全年稳定在5℃左右。选择北京周边地区比较典型的乡村耕地、山区森林以及永定河流域来研究乡村的选择对热岛强度的影响。发现选择不同的邻近区域作为乡村时,不仅城市热岛（冷岛）强度有较大变化,而且热岛强度的季节变化情况也有较大差异。冬季白天北京城市冷岛增加了近地层的大气稳定度,可能会降低城市空气污染物的扩散能力,加剧了北京冬季的空气污染。
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地表参数反演及城市热岛时空演变分析 [J].
采用TES算法实现了ASTER地表温度的反演,在对发射率估算方法改进基础上,利用单窗算法反演1989年TM地表温度。结合C地形校正,利用线性光谱模型提取植被覆盖度与城市不透水面密度,利用反射率提取NDVI。从多方面对城市热岛时空演变进行综合研究,研究表明,与等间距法相比,均值-标准差法可以较合理地刻画地表热场的分布,一定程度上可以避开不同时相的差异。最后时空对比及空间统计学分析显示,1989~2004年间福州市城市热岛面积、热岛强度都有所增加,城市热岛总体趋势为西北-东南走向,并逐渐向北-南方向偏移,而且城市热岛重心向东南方向偏移。
Estimation of land surface parameters and spatio-temporal characteristics of urban heat island [J].
采用TES算法实现了ASTER地表温度的反演,在对发射率估算方法改进基础上,利用单窗算法反演1989年TM地表温度。结合C地形校正,利用线性光谱模型提取植被覆盖度与城市不透水面密度,利用反射率提取NDVI。从多方面对城市热岛时空演变进行综合研究,研究表明,与等间距法相比,均值-标准差法可以较合理地刻画地表热场的分布,一定程度上可以避开不同时相的差异。最后时空对比及空间统计学分析显示,1989~2004年间福州市城市热岛面积、热岛强度都有所增加,城市热岛总体趋势为西北-东南走向,并逐渐向北-南方向偏移,而且城市热岛重心向东南方向偏移。
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不同时相的遥感热红外图像在研究城市热岛变化中的处理方法 [J].https://doi.org/10.3969/j.issn.1004-0323.2003.03.002 URL Magsci [本文引用: 4] 摘要
<p>卫星图像的热红外波段已被广泛地用来研究城市热岛效应。由于客观条件的限制,在城市热岛变化的比较研究中,很难获得不同年代的同时相图像,特别是在南方多云雨的地区。所以,这给城市热岛的变化研究带来了很大的困难。为此,采用了将不同时相的热红外图像进行正规化、分级,并制成差值影像图的方法,较好地减少了季相差异的影响,使得不同时相的热红外图像得以对比。为了定量地研究城市热岛(UHI)的变化,还创建了城市热岛比例指数(URI)。该指数通过热岛面积和城市建成区面积的比例关系并赋于不同的权重值来定量地评估热岛现象的变化情况。</p>
An image processing technique for the study of urban heat island changes using different seasonal remote sensing data [J].https://doi.org/10.3969/j.issn.1004-0323.2003.03.002 URL Magsci [本文引用: 4] 摘要
<p>卫星图像的热红外波段已被广泛地用来研究城市热岛效应。由于客观条件的限制,在城市热岛变化的比较研究中,很难获得不同年代的同时相图像,特别是在南方多云雨的地区。所以,这给城市热岛的变化研究带来了很大的困难。为此,采用了将不同时相的热红外图像进行正规化、分级,并制成差值影像图的方法,较好地减少了季相差异的影响,使得不同时相的热红外图像得以对比。为了定量地研究城市热岛(UHI)的变化,还创建了城市热岛比例指数(URI)。该指数通过热岛面积和城市建成区面积的比例关系并赋于不同的权重值来定量地评估热岛现象的变化情况。</p>
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FY-3A/MERSI数据在典型大城市热环境监测预报中的应用: 以上海市为例 [J].
以上海市为例,探索中国新型自主的FY-3A/MERSI数据在 典型大城市热环境研究中的应用模式,包括实时监测与同化数值预报两个方面.结果表明:FY-3 A/MERSI反演的250m地表温度及其他衍生指标能够客观呈现城市热场格局及热岛效应,精细揭示出上海市区—近郊—远郊的地温明显分异,热岛由中心城 区呈放射性面状过渡到远郊以乡镇为中心的团块状,其形成与下垫面介质关系密切.同化FY-3A/MERSI数据产品的中尺度数值模式,耦合城市冠层参数化 方案,可实现250m格网0～48 h城市近地层气温空间精细化预报,提取1h间隔的热环境日演变特征将有助于在不同天气形式下进行热环境灾害潜式预报及机理分析.预报结果在时空分辨率方面 明显优于常规数值预报,并避免了卫星云污染的影响,说明FY-3A卫星探测资料能有效改善模式背景场与初始场.
Application of FY-3A/MERSI satellite data for thermal environment monitoring and forecast of typical cities: A case study of Shanghai [J].
以上海市为例,探索中国新型自主的FY-3A/MERSI数据在 典型大城市热环境研究中的应用模式,包括实时监测与同化数值预报两个方面.结果表明:FY-3 A/MERSI反演的250m地表温度及其他衍生指标能够客观呈现城市热场格局及热岛效应,精细揭示出上海市区—近郊—远郊的地温明显分异,热岛由中心城 区呈放射性面状过渡到远郊以乡镇为中心的团块状,其形成与下垫面介质关系密切.同化FY-3A/MERSI数据产品的中尺度数值模式,耦合城市冠层参数化 方案,可实现250m格网0～48 h城市近地层气温空间精细化预报,提取1h间隔的热环境日演变特征将有助于在不同天气形式下进行热环境灾害潜式预报及机理分析.预报结果在时空分辨率方面 明显优于常规数值预报,并避免了卫星云污染的影响,说明FY-3A卫星探测资料能有效改善模式背景场与初始场.
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西安城区地表温度的遥感反演与时空演变分析 [J].https://doi.org/10.13885/j.issn.0455-2059.2015.03.013 URL [本文引用: 1] 摘要
基于2002年6月11日和2009年6月30日两期 Landsat5 TM影像,利用辐射传输方程法反演了西安城区的地表温度,并通过统计分析和剖面线分析,研究了地表温度的空间分布格局及方向特性,进而就其演变特征与原因 进行了探讨.结果表明:西安城区存在两到三个热岛中心地带,热岛效应明显;除2009年WE向热场剖面线自西向东逐渐降低外,其余3条均呈中间高、两端低 的特点;除水体外,地表温度和NDVI通常具有相反的变化趋势;在建成区面积、人口和城市气温综合影响下,热岛范围与城市扩张体现出较强的时空一致性.8 年间,城市内部相对差异缩小,热环境格局由“中心城区分布模式”转变为“建成区分布模式”.
Inversion and temperal-spatial evolution analysis of land surface temperature in urban Xi'an based on remote sensing data [J].https://doi.org/10.13885/j.issn.0455-2059.2015.03.013 URL [本文引用: 1] 摘要
基于2002年6月11日和2009年6月30日两期 Landsat5 TM影像,利用辐射传输方程法反演了西安城区的地表温度,并通过统计分析和剖面线分析,研究了地表温度的空间分布格局及方向特性,进而就其演变特征与原因 进行了探讨.结果表明:西安城区存在两到三个热岛中心地带,热岛效应明显;除2009年WE向热场剖面线自西向东逐渐降低外,其余3条均呈中间高、两端低 的特点;除水体外,地表温度和NDVI通常具有相反的变化趋势;在建成区面积、人口和城市气温综合影响下,热岛范围与城市扩张体现出较强的时空一致性.8 年间,城市内部相对差异缩小,热环境格局由“中心城区分布模式”转变为“建成区分布模式”.
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道路建设对成都市热岛效应的影响 [J].https://doi.org/10.3969/j.issn.1674-5906.2014.10.009 URL [本文引用: 2] 摘要
热岛效应是城市气候最显著的特征之一。土地利用方式及土地覆盖的 改变，如城市化和道路建设是导致热岛现象的重要原因之一。然而目前针对道路属性（道路密度及类型）对城市热岛效应的影响研究还较缺乏。本研究运用2012 年成都市不同时次（冬夏季）的遥感数据及城市道路交通专题图，运用3S技术探讨道路密度对城市热岛效应的影响以及不同类型道路对城市热岛效应的热贡献。研 究表明：（1）成都市热岛效应明显，市区地表平均温度显著高于郊区且热岛强度呈现夏强（3~4℃）冬弱（2.5~3℃）、夜强昼弱的特征。日间城市热岛效 应呈现多热中心的分布模式，但冬夏季热岛中心位置不同。夏季日间热中心位于城市的西南部和中东北部，最高可达32.66℃，而冬季日间城市的西南部地表温 度较高且热中心主要分布于城市边界地区，地表温度超过16℃。无论冬夏，夜间城市热岛效应均呈现环状分布特征，即从城市边缘到中心，地表温度逐渐升高，夏 季城乡地表温差高达4.37℃而冬季达到2.82℃。（2）成都市区道路呈现“圈层型+辐射型”分布模式，道路密度与道路的分布有关，城市南部及西南部的 道路密度高于北部区域。（3）无论冬夏，道路密度与地表温度正相关，但两者相关性呈现昼弱夜强的特征，其中夜间相关系数达到0.5左右。对热效应贡献度指 数、热单元权重指数、区域热单元权重指数3个指标的分析都表明无论冬夏、无论昼夜，市区分布面积最广的三级道路对城市热岛效应的热贡献最大，其热效应贡献 度指数均在95%以上，其次是二级道路，各项热效应贡献度指数为45%~80%。本研究结果将有助于未来城市建设和道路规划，并为缓解城市热岛效应提供理 论支持。
The effect of road construction on urban heat island effect in Chengdu [J].https://doi.org/10.3969/j.issn.1674-5906.2014.10.009 URL [本文引用: 2] 摘要
热岛效应是城市气候最显著的特征之一。土地利用方式及土地覆盖的 改变，如城市化和道路建设是导致热岛现象的重要原因之一。然而目前针对道路属性（道路密度及类型）对城市热岛效应的影响研究还较缺乏。本研究运用2012 年成都市不同时次（冬夏季）的遥感数据及城市道路交通专题图，运用3S技术探讨道路密度对城市热岛效应的影响以及不同类型道路对城市热岛效应的热贡献。研 究表明：（1）成都市热岛效应明显，市区地表平均温度显著高于郊区且热岛强度呈现夏强（3~4℃）冬弱（2.5~3℃）、夜强昼弱的特征。日间城市热岛效 应呈现多热中心的分布模式，但冬夏季热岛中心位置不同。夏季日间热中心位于城市的西南部和中东北部，最高可达32.66℃，而冬季日间城市的西南部地表温 度较高且热中心主要分布于城市边界地区，地表温度超过16℃。无论冬夏，夜间城市热岛效应均呈现环状分布特征，即从城市边缘到中心，地表温度逐渐升高，夏 季城乡地表温差高达4.37℃而冬季达到2.82℃。（2）成都市区道路呈现“圈层型+辐射型”分布模式，道路密度与道路的分布有关，城市南部及西南部的 道路密度高于北部区域。（3）无论冬夏，道路密度与地表温度正相关，但两者相关性呈现昼弱夜强的特征，其中夜间相关系数达到0.5左右。对热效应贡献度指 数、热单元权重指数、区域热单元权重指数3个指标的分析都表明无论冬夏、无论昼夜，市区分布面积最广的三级道路对城市热岛效应的热贡献最大，其热效应贡献 度指数均在95%以上，其次是二级道路，各项热效应贡献度指数为45%~80%。本研究结果将有助于未来城市建设和道路规划，并为缓解城市热岛效应提供理 论支持。
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成都市热岛效应与城市空间发展关系分析 [J].https://doi.org/10.3724/SP.J.1047.2014.00070 URL Magsci [本文引用: 1] 摘要
利用Landsat卫星影像反演成都市中心城区1992、2001和2009年的地表温度，建筑用地和植被等信息，计算其城市热岛比例指数（URI），对成都市中心城区热岛效应与城市空间发展关系进行了分析。结果表明，在1992-2009年期间成都市主城区范围从91.24km<sup>2</sup>扩展到403.8km<sup>2</sup>。成都市建成区的大面积扩展导致了城市热岛空间分布发生迁移，从单中心聚集分布转变为多中心环状分布。回归分析说明，建筑用地和植被都是影响地表温度的重要因素，其中建筑用地与地表温度呈指数型正相关关系，而植被与地表温度呈负相关关系。总的看来，成都市中心城区在这17年间的热岛效应有了明显的缓解，城市热岛比例指数从0.72下降到0.33。城市植被覆盖率的增加和合理的规划对缓解城市热岛效应起到了积极的作用。
Analysis of the relationship between urban heat island effect and urban expansion in Chengdu, China [J].https://doi.org/10.3724/SP.J.1047.2014.00070 URL Magsci [本文引用: 1] 摘要
利用Landsat卫星影像反演成都市中心城区1992、2001和2009年的地表温度，建筑用地和植被等信息，计算其城市热岛比例指数（URI），对成都市中心城区热岛效应与城市空间发展关系进行了分析。结果表明，在1992-2009年期间成都市主城区范围从91.24km<sup>2</sup>扩展到403.8km<sup>2</sup>。成都市建成区的大面积扩展导致了城市热岛空间分布发生迁移，从单中心聚集分布转变为多中心环状分布。回归分析说明，建筑用地和植被都是影响地表温度的重要因素，其中建筑用地与地表温度呈指数型正相关关系，而植被与地表温度呈负相关关系。总的看来，成都市中心城区在这17年间的热岛效应有了明显的缓解，城市热岛比例指数从0.72下降到0.33。城市植被覆盖率的增加和合理的规划对缓解城市热岛效应起到了积极的作用。
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北京城市及周边热岛日变化及季节特征的卫星遥感研究与影响因子分析 [J].
正 以卫星遥感资料、土地利用图、气象资料为主要信息源,基于地理信息系统空间分析技术,对2001年北京城市及周边热岛空间分布的季节规律和日变化及影响因子进行研究.对该区的土地覆盖类型、地形高程、植被绿地状况、地表蒸散量与热岛效应时空分布状况的关系进行分析.得出主要结论为:(1)北京城市中心一年四季均存在明显的热岛效应,以夏季最为明显,城市与地形想对平坦的近郊区的地表温度差异在4-6℃,与地势较高的西北远郊区的地表温度差异在8-10℃;(2)北京地区
The diurnal and seasonal characteristics of urban heat island variation in Beijing City and surrounding areas and impact factors based on remote sensing satellite data [J].
正 以卫星遥感资料、土地利用图、气象资料为主要信息源,基于地理信息系统空间分析技术,对2001年北京城市及周边热岛空间分布的季节规律和日变化及影响因子进行研究.对该区的土地覆盖类型、地形高程、植被绿地状况、地表蒸散量与热岛效应时空分布状况的关系进行分析.得出主要结论为:(1)北京城市中心一年四季均存在明显的热岛效应,以夏季最为明显,城市与地形想对平坦的近郊区的地表温度差异在4-6℃,与地势较高的西北远郊区的地表温度差异在8-10℃;(2)北京地区
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河谷地形下兰州市城市热岛效应的时空演变研究 [J].
<p>基于1999 年和2010 年的Landsat ETM+和TM影像, 以单窗算法反演了兰州市地表温度, 研究兰州市最近10 a 的城市热岛时空分布以及演变特征。研究结果表明:兰州市城市热岛的空间分布与延展与城市建城区的扩展相一致, 热岛范围不断扩大, 次中温和中温区大面积减少, 相应的次高温和高温区大面积增加, 热岛强度明显增强;除了城市下垫面覆盖类型, 黄河低温带亦逐渐成为影响城市热岛分布的重要因子。各土地利用类型的平均温度均有所升高, 建设用地和未利用地温度最高, 对热岛效应贡献最大, 是城市热岛的主要贡献因子, 绿地和水体能够很好的缓解热岛效应。地表温度和信息指数NDVI、MNDWI、NDBI、NDBaI在兰州市河谷空间格局上显著相关, 存在很好的对应关系。</p>
Spatial-temporal evolution of urban heat island effect in basin valley: A case study of Lanzhou City [J].
<p>基于1999 年和2010 年的Landsat ETM+和TM影像, 以单窗算法反演了兰州市地表温度, 研究兰州市最近10 a 的城市热岛时空分布以及演变特征。研究结果表明:兰州市城市热岛的空间分布与延展与城市建城区的扩展相一致, 热岛范围不断扩大, 次中温和中温区大面积减少, 相应的次高温和高温区大面积增加, 热岛强度明显增强;除了城市下垫面覆盖类型, 黄河低温带亦逐渐成为影响城市热岛分布的重要因子。各土地利用类型的平均温度均有所升高, 建设用地和未利用地温度最高, 对热岛效应贡献最大, 是城市热岛的主要贡献因子, 绿地和水体能够很好的缓解热岛效应。地表温度和信息指数NDVI、MNDWI、NDBI、NDBaI在兰州市河谷空间格局上显著相关, 存在很好的对应关系。</p>
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珠江三角洲地区地表热环境的遥感探测及时空演化研究[D] .Thermal environment detection in the Pearl River Delta area by remote sensing and analysis of its spatial and temporal evolutions[D] . |
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基于空间统计特征的城市热环境时空演化 [J].
<div style="line-height: 150%">利用遥感反演和GIS空间分析等工具,提出了一种基于空间统计特征的城市热岛范围界定方法,并应用该方法分析了1984—2010年杭州市城市热环境的时空演化规律.结果表明: 1984—2010年间,杭州市的城市热岛面积增加了8.66倍;杭州城市热岛的空间形态日趋复杂,空间分布由单中心的聚集状态逐渐向多中心的扩散状态发展;杭州城市热环境呈现出由区域低温均衡向区域高温均衡发展的态势.城市热岛的动态变化检测表明,城市扩张是杭州城市热岛发育的主要原因.本文所提方法考虑了城市地表温度的空间相关关系,反映了城市地表温度的全局统计特征,提供的信息更多,也更为客观和准确.通过该方法的推广,有助于解决当前城市热岛研究中研究样本之间缺乏通用性和可比性的问题.</div><div style="line-height: 150%"> </div><div style="line-height: 150%"> </div>
Spatial-temporal evolution of urban thermal environment based on spatial statistical features [J].
<div style="line-height: 150%">利用遥感反演和GIS空间分析等工具,提出了一种基于空间统计特征的城市热岛范围界定方法,并应用该方法分析了1984—2010年杭州市城市热环境的时空演化规律.结果表明: 1984—2010年间,杭州市的城市热岛面积增加了8.66倍;杭州城市热岛的空间形态日趋复杂,空间分布由单中心的聚集状态逐渐向多中心的扩散状态发展;杭州城市热环境呈现出由区域低温均衡向区域高温均衡发展的态势.城市热岛的动态变化检测表明,城市扩张是杭州城市热岛发育的主要原因.本文所提方法考虑了城市地表温度的空间相关关系,反映了城市地表温度的全局统计特征,提供的信息更多,也更为客观和准确.通过该方法的推广,有助于解决当前城市热岛研究中研究样本之间缺乏通用性和可比性的问题.</div><div style="line-height: 150%"> </div><div style="line-height: 150%"> </div>
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| [24] |
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].https://doi.org/10.3321/j.issn:1007-4619.2006.05.028 URL Magsci [本文引用: 3] 摘要
针对CBERS-02IRMSS的热红外通道特性，对Jimenez-Munoz和Sobfino提出的普适性单通道地表温度反演算法进行改进，并利用该传感器热红外遥感数据反演北京地区和苏锡常地区的地表温度；利用2004年8月17日在青海湖的野外实测数据对该算法的地表温度反演结果进行的验证表明，改进的单通道反演算法应用于CBERS-02IRMSS传感器热红外数据的地表温度反演具有很高的反演精度。并在此基础上，运用城市热场变异指数对北京地区和苏锡常地区的城市热岛效应进行分析，运用中国研制的热红外卫星遥感数据给出了城市热岛效应的定量化描述。结果表明，CBERS-02IRMSS热红外遥感数据完全可以满足定量化应用的要求，具有很大的应用潜力。
Land surface temperature retrieval from CBERS-02 IRMSS thermal infrared data and its applications in quantitative analysis of urban heat island effect [J].https://doi.org/10.3321/j.issn:1007-4619.2006.05.028 URL Magsci [本文引用: 3] 摘要
针对CBERS-02IRMSS的热红外通道特性，对Jimenez-Munoz和Sobfino提出的普适性单通道地表温度反演算法进行改进，并利用该传感器热红外遥感数据反演北京地区和苏锡常地区的地表温度；利用2004年8月17日在青海湖的野外实测数据对该算法的地表温度反演结果进行的验证表明，改进的单通道反演算法应用于CBERS-02IRMSS传感器热红外数据的地表温度反演具有很高的反演精度。并在此基础上，运用城市热场变异指数对北京地区和苏锡常地区的城市热岛效应进行分析，运用中国研制的热红外卫星遥感数据给出了城市热岛效应的定量化描述。结果表明，CBERS-02IRMSS热红外遥感数据完全可以满足定量化应用的要求，具有很大的应用潜力。
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| [25] |
热岛效应季节动态随城市化进程演变的遥感监测 [J].https://doi.org/10.3969/j.issn.1674-5906.2009.05.042 URL [本文引用: 2] 摘要
作为城市特有的一种环境问题,热岛效应动态发展变化的规律是城市 热岛研究的基础工作.遥感技术在城市热岛动态变化监测方面的有用性已经得到了证实.然而当前城市热岛遥感研究基本都取少数几景进行对比分析,这使得遥感在 城市热岛时空动态监测方面不能充分发挥作用.采用50景长时间序列Landsat TM和ETM+SLC-on/off影像,采取定性和定量分析相结合的方法,使用热岛强度、热岛范围等指标和热岛显著区的概念对厦门市1987-2008 年20年间热岛季节动态随城市化进程演变的趋势进行分析,结果表明:厦门城市热岛在2003、2004年之后已由春夏秋扩展到冬季,且冬季热岛的高等级斑 块在数量、个体面积和总面积上均有明显增长趋势.引起这种变化的原因还需要进一步研究和分析.
Monitoring the changes of urban heat island seasonal dynamics in the process of urbanization by remote sensing [J].https://doi.org/10.3969/j.issn.1674-5906.2009.05.042 URL [本文引用: 2] 摘要
作为城市特有的一种环境问题,热岛效应动态发展变化的规律是城市 热岛研究的基础工作.遥感技术在城市热岛动态变化监测方面的有用性已经得到了证实.然而当前城市热岛遥感研究基本都取少数几景进行对比分析,这使得遥感在 城市热岛时空动态监测方面不能充分发挥作用.采用50景长时间序列Landsat TM和ETM+SLC-on/off影像,采取定性和定量分析相结合的方法,使用热岛强度、热岛范围等指标和热岛显著区的概念对厦门市1987-2008 年20年间热岛季节动态随城市化进程演变的趋势进行分析,结果表明:厦门城市热岛在2003、2004年之后已由春夏秋扩展到冬季,且冬季热岛的高等级斑 块在数量、个体面积和总面积上均有明显增长趋势.引起这种变化的原因还需要进一步研究和分析.
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| [26] |
宁波城市热岛随城市化演变的多指标综合分析 [J].https://doi.org/10.3969/j.issn.1674-5906.2014.10.010 URL [本文引用: 4] 摘要
以宁波市为研究对象,利用1984─2010年间冬、夏各5景Landsat TM/ETM+遥感影像数据进行地表温度反演,在此基础上使用热岛强度、热岛面积、景观格局指数综合分析了宁波城市化进程中城市热岛在冬、夏两季的演变趋势,得出如下结果,(1)强度方面,冬季平均热岛强度为1.57℃,夏季为8.67℃,夏季明显强于冬季;热岛强度在夏季呈增强趋势,在冬季呈变弱趋势。(2)热岛面积方面,在冬季,平均89%的建成区受热岛效应的影响,而在夏季,该比例为98%。无论冬夏,热岛面积均随城市化的发展持续增加。(3)数量方面,热岛景观在冬季以低等级热岛斑块为主,占热岛面积的96%左右,夏季以中、高等级热岛斑块为主,比例约为热岛面积的92%。无论冬夏高等级热岛斑块个数均随着城市化进程显著增加。(4)形态方面,无论冬夏主要热岛景观类型乃至整个热岛的斑块形状均变得更加复杂。(5)结构方面,热岛景观在冬季总体上逐渐破碎化,各类景观趋向均匀,多样性增加。在夏季则逐渐聚集,逐步向以高等级斑块为主导的格局方向发展,多样性降低。(6)空间分布方面,随着城市化进展,冬夏两季热岛景观斑块都经历了数量增加、等级升高的变化。冬季在北仑的宁波经济技术开发区形成了两个热点区,夏季在三江口周边、甬江口两岸以及经济技术开发区形成了三大高温片区。利用多指标综合分析可以更加全面的反映城市热岛的演变规律,为减缓城市热岛效应提供理论依据。
Multi-index analysis of heat island dynamics with the process of urbanisation in Ningbo City [J].https://doi.org/10.3969/j.issn.1674-5906.2014.10.010 URL [本文引用: 4] 摘要
以宁波市为研究对象,利用1984─2010年间冬、夏各5景Landsat TM/ETM+遥感影像数据进行地表温度反演,在此基础上使用热岛强度、热岛面积、景观格局指数综合分析了宁波城市化进程中城市热岛在冬、夏两季的演变趋势,得出如下结果,(1)强度方面,冬季平均热岛强度为1.57℃,夏季为8.67℃,夏季明显强于冬季;热岛强度在夏季呈增强趋势,在冬季呈变弱趋势。(2)热岛面积方面,在冬季,平均89%的建成区受热岛效应的影响,而在夏季,该比例为98%。无论冬夏,热岛面积均随城市化的发展持续增加。(3)数量方面,热岛景观在冬季以低等级热岛斑块为主,占热岛面积的96%左右,夏季以中、高等级热岛斑块为主,比例约为热岛面积的92%。无论冬夏高等级热岛斑块个数均随着城市化进程显著增加。(4)形态方面,无论冬夏主要热岛景观类型乃至整个热岛的斑块形状均变得更加复杂。(5)结构方面,热岛景观在冬季总体上逐渐破碎化,各类景观趋向均匀,多样性增加。在夏季则逐渐聚集,逐步向以高等级斑块为主导的格局方向发展,多样性降低。(6)空间分布方面,随着城市化进展,冬夏两季热岛景观斑块都经历了数量增加、等级升高的变化。冬季在北仑的宁波经济技术开发区形成了两个热点区,夏季在三江口周边、甬江口两岸以及经济技术开发区形成了三大高温片区。利用多指标综合分析可以更加全面的反映城市热岛的演变规律,为减缓城市热岛效应提供理论依据。
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| [27] |
基于Landsat卫星资料的苏州城市热岛效应遥感分析 [J].
<FONT face=Verdana>为了对苏州市快速城市化进程中的热岛效应进行研究， 采用三个时期的Landsat/TM遥感影像资料来反映地表热辐射信息。基于CART决策树分类得到每个时期的土地覆盖分类图， 然后通过单通道算法反演得到地表温度， 并对2个时期的地表温度进行标准化分级处理， 计算得到采取本文定义, 1986, 1995及2004年的热岛面积指数分别为4.87%, 11.10%和37.87%\.研究了城市化过程中城市热岛强度和空间分布变化， 分析了土地覆盖变化对热岛效应的影响。结果表明， 苏州市存在比较明显的城市热岛效应， 并且在20多年中随着城市化过程热岛范围进一步扩展。</FONT>
Study on the urban heat island of Suzhou City based on landsat remote sensing data [J].
<FONT face=Verdana>为了对苏州市快速城市化进程中的热岛效应进行研究， 采用三个时期的Landsat/TM遥感影像资料来反映地表热辐射信息。基于CART决策树分类得到每个时期的土地覆盖分类图， 然后通过单通道算法反演得到地表温度， 并对2个时期的地表温度进行标准化分级处理， 计算得到采取本文定义, 1986, 1995及2004年的热岛面积指数分别为4.87%, 11.10%和37.87%\.研究了城市化过程中城市热岛强度和空间分布变化， 分析了土地覆盖变化对热岛效应的影响。结果表明， 苏州市存在比较明显的城市热岛效应， 并且在20多年中随着城市化过程热岛范围进一步扩展。</FONT>
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| [28] |
A systematic approach to model the influence of the type and density of vegetation cover on urban heat using remote sensing [J].https://doi.org/10.1016/j.landurbplan.2014.08.008 URL Magsci [本文引用: 1] 摘要
Cities around the world are pursuing increasing green or vegetation cover as a way of managing heat whilst improving beauty, biodiversity and recreational value. However, the pattern of the relationship between vegetation cover and urban temperature can be masked, controlled or exaggerated by vegetation structure, topography and other climate variables. This study examines the relationship between Sydney's urban surface temperature and vegetation cover as defined by two vegetation indices; mixed vegetation cover and tree cover exclusively. The shape of this relationship and relative influence of confounding factors are explored using penalised-likelihood criteria ranked regressions. Overall, increasing tree cover reduces average surface temperatures more dramatically than mixed vegetation cover. This study demonstrates that the extent of influence of greencover on surface temperatures is more accurately defined by identifying and incorporating site specific factors that confound the influence. Best predictor models are significantly improved when the influences of elevation, coastal effects and urban structure are added. Therefore, heat reducing urban greening strategies will be improved if based on local variables and conditions. (C) 2014 Elsevier B.V. All rights reserved.
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| [29] |
Surface heat island in Shanghai and its relationship with urban development from 1989 to 2013 [J].https://doi.org/10.1155/2016/9782686 URL [本文引用: 1] 摘要
The continuous expansion of impervious artificial surfaces in cities has significantly influenced the urban thermal environment. This paper examines the spatiotemporal variation of the diurnal surface urban heat island (SUHI) in Shanghai from 1989 to 2013, a period during which the city has experienced drastic development changes. A remote sensing approach was taken to derive the spatial patterns of Shanghai’s land surface temperature (LST) from Landsat Thematic Mapper (TM) images and Operational Land Imager (OLI) data. The LST pattern was further classified into five LST classes to look at the relative SUHI intensity level across the whole city. Spatial analyses, namely, spatial association and centroid movement analysis, were conducted to reveal the trends of LST changes at both local and holistic scales. To understand the potential drivers for the present spatiotemporal variation of SUHI, different indicators including land use change, population density, night light data, and vegetation were analyzed and compared with LST changes. Based on the quantitative analysis and the socioeconomic context of Shanghai, “heating up” regions were identified, possible reasons for such SUHI variation were summarized, and districts that are most vulnerable to extreme heat conditions were projected. In terms of implication for urban development, planning and design recommendations were suggested to improve the urban thermal environment in Shanghai.
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| [30] |
Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes [J].https://doi.org/10.1016/j.rse.2005.11.016 URL [本文引用: 2] 摘要
Global warming has obtained more and more attention because the global mean surface temperature has increased since the late 19th century. As more than 50% of the human population lives in cities, urbanization has become an important contributor for global warming. Pearl River Delta (PRD) in Guangdong Province, southern China, is one of the regions experiencing rapid urbanization that has resulted in remarkable Urban Heat Island (UHI) effect, which will be sure to influence the regional climate, environment, and socio-economic development. In this study, Landsat TM and ETM+ images from 1990 to 2000 in the PRD were selected to retrieve the brightness temperatures and land use/cover types. A new index, Normalized Difference Bareness Index (NDBaI), was proposed to extract bare land from the satellite images. Additionally, Shenzhen, which has experienced the fastest urbanization in Guangdong Province, was taken as an example to analyze the temperature distribution and changes within a large city as its size expanded in the past decade. Results show that the UHI effect has become more prominent in areas of rapid urbanization in the PRD region. The spatial distribution of heat islands has been changed from a mixed pattern, where bare land, semi-bare land and land under development were warmer than other surface types, to extensive UHI. Our analysis showed that higher temperature in the UHI was located with a scattered pattern, which was related to certain land-cover types. In order to analyze the relationship between UHI and land-cover changes, this study attempted to employ a quantitative approach in exploring the relationship between temperature and several indices, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Bareness Index (NDBaI) and Normalized Difference Build-up Index (NDBI). It was found that correlations between NDVI, NDWI, NDBaI and temperature are negative when NDVI is limited in range, but positive correlation is shown between NDBI and temperature.
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| [31] |
The July urban heat island of Bucharest as derived from modis images [J].https://doi.org/10.1007/s00704-008-0019-3 URL [本文引用: 3] 摘要
The urban heat island (UHI) of the city of Bucharest (Romania) is analyzed in terms of its extension, geometry, and magnitude using the surface thermal data provided by the moderate resolution imaging
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| [32] |
Diurnal thermal behavior of selected urban objects using remote sensing measurements [J].https://doi.org/10.1016/j.enbuild.2004.01.052 URL [本文引用: 5] 摘要
This research analyzes and summarizes some thermal behavior of various urban surfaces in time and space using high-resolution video thermal radiometer situated at a height of 103 m, in the city of Tel-Aviv. The physical properties of the various urban elements, their color, the sky view factor (SVF), street geometry, traffic loads, and anthropogenic activity are important among the factors that determine the radiant surface temperature in the urban environment. During daytime, asphalt paved roads and rooftops were found to be the warmest urban elements in our study area. In contrast, exterior walls and trees hold the highest surface temperatures at night. Open spaced surfaces that are exposed to direct solar radiation during daytime and to heat loss at night were characterized by the highest diurnal temperature range. The radiometric stationary experiment revealed the temperature differences between diverse urban coverage to be at most 10 °C; such maximum temperature differences were measured in the early noon hours. The minimal temperatures were observed just before sunrise, when the temperature contrasts (4–5 °C) were smaller than in the early noon hours. The daytime hours between 9–10 a.m. and 5–8 p.m. turned out to be problematic for remote sensing of the urban environment, because the thermal differences between different objects were found to be insignificant. A remote survey aiming to study the urban environment should be conducted twice: in the early morning hours before sunrise (5 a.m.) and in the early noon hours (12–1 p.m.). The knowledge of thermal behavior of various urban components is an important tool for designers and decision-makers. If utilized properly, it can lead to climatic rehabilitation in urban areas and a reduction of the UHI.
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| [33] |
MODIS detected surface urban heat islands and sinks: Global locations and controls [J].https://doi.org/10.1016/j.rse.2013.03.008 URL Magsci [本文引用: 7] 摘要
Urbanization is a global problem with emergent properties. The difference in temperature between urban and rural surfaces is one such property that affects health, energy consumption budgets, regional planning and climate. We used remotely sensed datasets and gridded population to estimate the magnitude of thermal differentials (urban heat islands and/or sinks), the timing of heat differential events, and the controlling variables. The global scope of the study provides a consistent analytical environment that enables identification of the key factors that contribute to deleterious heat differentials. We propose new indices of thermal differential and use them to show particular prevalence of heat islands and sinks in arid regions. A variable ranking analysis indicates that development intensity, vegetation amount and the size of the urban metropolis are the most important urban variables to predict heat differentials. Population was of lesser importance in this study. Urban structure indices were also ranked lower, though a different measurement scale qualifies this conclusion. The results support the paradigm of compact development and incorporation of vegetation to the urban infrastructure. (C) 2013 Elsevier Inc. All rights reserved.
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| [34] |
Landscape configuration and urban heat island effects: Assessing the relationship between landscape characteristics and land surface temperature in Phoenix, Arizona [J].https://doi.org/10.1007/s10980-012-9833-1 URL Magsci [本文引用: 1] 摘要
Abstract<br/><p class="a-plus-plus">The structure of urban environments is known to alter local climate, in part due to changes in land cover. A growing subset of research focuses specifically on the UHI in terms of land surface temperature by using data from remote sensing platforms. Past research has established a clear relationship between land surface temperature and the proportional area of land covers, but less research has specifically examined the effects of the spatial patterns of these covers. This research considers the rapidly growing City of Phoenix, Arizona in the United States. To better understand how landscape structure affects local climate, we explored the relationship between land surface temperature and spatial pattern for three different land uses: mesic residential, xeric residential, and industrial/commercial. We used high-resolution (2.4 m) land cover data and an ASTER temperature product to examine 90 randomly selected sample sites of 240 square-meters. We (1) quantify several landscape-level and class-level landscape metrics for the sample sites, (2) measure the Pearson correlation coefficients between land surface temperature and each landscape metric, (3) conduct an analysis of variance among the three land uses, and (4) model the determinants of land surface temperature using ordinary least squares linear regression. The Pearson’s correlation coefficients reveal significant relationships between several measures of spatial configuration and LST, but these relationships differ among the land uses. The ANOVA confirmed that mean land surface temperature and spatial patterns differed among the three land uses. Although a relationship was apparent between surface temperatures and spatial pattern, the results of the linear regression indicate that proportional land cover of grass and impervious surfaces alone best explains temperature in mesic residential areas. In contrast, temperatures in industrial/commercial areas are explained by changes in the configuration of grass and impervious surfaces.</p><br/>
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| [35] |
Comparison and analysis of research methods for urban heat island effect based on Landsat TM6 [ |
| [36] |
Urban climate and clues of heat island events in the metropolitan area of Rio de Janeiro [J].https://doi.org/10.1007/s00704-012-0668-0 URL [本文引用: 1] 摘要
This paper aims to map the thermal field in the metropolitan region of Rio de Janeiro (MARJ) considering the atmospheric characteristics and the land use that contribute to understanding the urban heat island. Three thermal maps are defined through the use of Landsat5-TM satellite images for three winter events chosen for the decades of 1980, 1990, and 2000, respectively. The results reveal a concentration of warmer cores in urban central areas as well as some local warmer areas in suburban region. Sites with lower temperatures correspond to vegetated areas which are away from the central part of the MARJ, including points of suburban areas. This work emphasizes the importance of the combined analysis of surface temperature with land use and atmospheric conditions, depicting a distinct pattern of heat islands for tropical climate.
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| [37] |
Satellite multi-sensor data analysis of urban surface temperatures and landcover [J].https://doi.org/10.1016/S0924-2716(03)00016-9 URL [本文引用: 1] 摘要
Multiple satellite sensors are used to analyze physical processes that determine energy fluxes and their interaction at the urban surface. The study is based on summertime microclimate analyses of the Los Angeles and Paris metropolises. The method consists of deriving some parameters governing the surface heat fluxes, constructing statistics of thermal infrared images, and using a GIS to combine them with a landcover classification from SPOT-HRV multispectral images, and with data from intensive in-situ experiments. The average images reveal spatial and temporal variations of land surface temperature (LST), and distinct microclimatic patterns. The combined interpretation of the statistics images and of the landcover classification shows: (i) the effect of surface physical properties, especially in downtown business and industrial districts that display heat-islands larger than 7 C; (ii) the temperating influence of water; (iii) the negative correlation between afternoon land surface temperature and normalized vegetation index, which confirms the cooling effect of urban parks; (iv) the correlation between variations of surface temperature and ozone concentration at diurnal and longer time scales.
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| [38] |
The structure of the ground-level heat island in a central business district [J].https://doi.org/10.1175/1520-0450(1985)0242.0.CO;2 URL [本文引用: 1] 摘要
Ground level temperature variations in Johannesburg were estimated from airborne infrared scanner images. During predawn flights over the city center and vicinity, radiances were observed from a 1-km wide swath under clear skies with a strong nocturnal inversion. The image was rescanned with the aid of a densitometer and the data were clustered to a 20 76 matrix. In order to locate the heat island center, the distance correlation matrix (DISTCORMAT) method was used. The spatial structure of the ground heal island core shows a steep thermal gradient approximately 600-700 m from the city center, which fits the screen-level temperature distribution obtained previously using a meteorological mobile unit. In addition to the fact that the heat island center does not coincide with the highest ground elevation, the findings support the empirical contribution of the centrality factor to the study of heat island magnitude and structure.
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| [39] |
Global change and the ecology of cities [J].https://doi.org/10.1126/science.1150195 URL PMID: 18258902 [本文引用: 2] 摘要
Urban areas are hot spots that drive environmental change at multiple scales. Material demands of production and human consumption alter land use and cover, biodiversity, and hydrosystems locally to regionally, and urban waste discharge affects local to global biogeochemical cycles and climate. For urbanites, however, global environmental changes are swamped by dramatic changes in the local environment. Urban ecology integrates natural and social sciences to study these radically altered local environments and their regional and global effects. Cities themselves present both the problems and solutions to sustainability challenges of an increasingly urbanized world.
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| [40] |
Seasonal variations of the surface urban heat island in a semi-arid city [J].https://doi.org/10.3390/rs8040352 URL [本文引用: 8] 摘要
The process of the surface urban heat island (SUHI) varies with latitude, climate, topography and meteorological conditions. This study investigated the seasonal variability of SUHI in the Tehran metropolitan area, Iran, with respect to selected surface biophysical variables. Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) was retrieved as nighttime LST data, while daytime LST was retrieved from Landsat 8 Thermal Infrared Sensor (TIRS) using the split-window algorithm. Both data covered the time period from September 2013 to September 2015. To assess SUHI intensity, we employed three SUHI indicators, i.e., the LST difference of urban-rural, that of urban-agriculture and that of urban-water. Physical and biophysical surface variables, including land use and land cover (LULC), elevation, impervious surface (IS), fractional vegetation cover (FVC) and albedo, were selected to estimate the relationship between LST seasonal variability and the surface properties. Results show that an inversion of the SUHI phenomenon (i.e., surface urban cool island) existed at daytime with the maximal value of urban-rural LST difference of 4 K in March; whereas the maximal value of SUHI at nighttime yielded 3.9 K in May. When using the indicators of urban-agriculture and urban-water LST differences, the maximal value of SUHI was found to be 8.2 K and 15.5 K, respectively. Both results were observed at daytime, suggesting the role of bare soils in the inversion of the SUHI phenomenon with the urban-rural indicator. Maximal correlation was observed in the relationship between night LST and elevation in spring (coefficient: 0.76), night LST and IS in spring (0.60), night LST and albedo in winter (0.53) and day LST with fractional vegetation cover in summer (0.41). The relationship between all surface properties with LST possessed large seasonal variations, and thus, using these relationships for SUHI modeling may not be effective. The only exception existed in the correlation between elevation and IS, which may be useful to simulate the SUHI at night. This study suggests that in semi-arid cities, such as Tehran, with the urban-rural indicator, a surface urban cool island may be observed in daytime while SUHI at nighttime; with other indicators, SUHI can be observed in both day and night. Thus, SUHI studies require the acquisition of remote sensing image data at both daytime and nighttime and careful selection of SUHI indicators.
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| [41] |
Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images [J].https://doi.org/10.1007/s00704-011-0464-2 URL Magsci [本文引用: 1] 摘要
A computational framework to generate daily temperature maps using time-series of publicly available MODIS MOD11A2 product Land Surface Temperature (LST) images (1 km resolution; 8-day composites) is illustrated using temperature measurements from the national network of meteorological stations (159) in Croatia. The input data set contains 57,282 ground measurements of daily temperature for the year 2008. Temperature was modeled as a function of latitude, longitude, distance from the sea, elevation, time, insolation, and the MODIS LST images. The original rasters were first converted to principal components to reduce noise and filter missing pixels in the LST images. The residual were next analyzed for spatio-temporal auto-correlation; sum-metric separable variograms were fitted to account for zonal and geometric space-time anisotropy. The final predictions were generated for time-slices of a 3D space-time cube, constructed in the R environment for statistical computing. The results show that the space-time regression model can explain a significant part of the variation in station-data (84%). MODIS LST 8-day (cloud-free) images are unbiased estimator of the daily temperature, but with relatively low precision (+/- 4.1A degrees C); however their added value is that they systematically improve detection of local changes in land surface temperature due to local meteorological conditions and/or active heat sources (urban areas, land cover classes). The results of 10-fold cross-validation show that use of spatio-temporal regression-kriging and incorporation of time-series of remote sensing images leads to significantly more accurate maps of temperature than if plain spatial techniques were used. The average (global) accuracy of mapping temperature was +/- 2.4A degrees C. The regression-kriging explained 91% of variability in daily temperatures, compared to 44% for ordinary kriging. Further software advancement-interactive space-time variogram exploration and automated retrieval, resampling and filtering of MODIS images-are anticipated.
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| [42] |
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| [43] |
Scale impacts of land cover and vegetation corridors on urban thermal behavior in Nanjing, China [J].https://doi.org/10.1007/s00704-007-0359-4 URL [本文引用: 2] 摘要
In order to integrate urban microclimatic pattern, heat island intensity, land cover and cooling effect of vegetation corridors in a more comprehensive way, both fixed and mobile observations at two types of scale have been made simultaneously in different weather conditions during hot weather in Nanjing, China. Then the air temperature distribution of Nanjing and its impact factor were detected and quantified, and vegetation cooling models were developed in this paper. There was a significant day-to-day variation of urban heat island (UHI), and the average intensity of UHI during the measurement period was 2.065°C. The strong UHI usually occurred around midnight; however, a peak UHI was frequently observed 2–365h after sunset. During daytime, several isolated hot areas were observed next to cooler ones, indicating a negative UHI. The air temperature distributions of the vegetation corridors at mesoscale in the evening were similar to those at micro-scale at noon. The influence of the Purple Mountain was very obvious with the maximum cooling effect up to 3.065°C/10065m at microscale and 0.465°C/km at mesoscale. This research indicated that the pattern of the UHI in Nanjing is a function of the climatic conditions, the local topography, the effect of the Purple Mountain and the Yangtze River and the urban characteristics such as land uses, vegetation, building density, traffic loads, construction materials and anthropogenic heat sources, which should be taken into account for urban planning and ecologically comfortable residence. The results have further relevance for environmental implications in a subtropical city on the Yangtze River.
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| [44] |
Remote sensing of the urban heat island effect across biomes in the continental USA [J].https://doi.org/10.1109/IGARSS.2010.5653907 URL [本文引用: 1] 摘要
We find that ecological context significantly influences the amplitude of summer daytime UHI (urban–rural temperature difference) the largest (802°C average) observed for cities built in biomes dominated by temperate broadleaf and mixed forest. For all cities combined, ISA is the primary driver for increase in temperature explaining 70% of the total variance in LST. On a yearly average, urban areas are substantially warmer than the non-urban fringe by 2.902°C, except for urban areas in biomes with arid and semiarid climates. The average amplitude of the UHI is remarkably asymmetric with a 4.302°C temperature difference in summer and only 1.302°C in winter. In desert environments, the LST's response to ISA presents an uncharacteristic “U-shaped” horizontal gradient decreasing from the urban core to the outskirts of the city and then increasing again in the suburban to the rural zones. UHI's calculated for these cities point to a possible heat sink effect. These observational results show that the urban heat island amplitude both increases with city size and is seasonally asymmetric for a large number of cities across most biomes. The implications are that for urban areas developed within forested ecosystems the summertime UHI can be quite high relative to the wintertime UHI suggesting that the residential energy consumption required for summer cooling is likely to increase with urban growth within those biomes.
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| [45] |
The footprint of urban areas on global climate as characterized by MODIS [J].https://doi.org/10.1175/JCLI3334.1 URL [本文引用: 6] 摘要
One mechanism for climate change is the collected impact of changes in land cover or land use. Such changes are especially significant in urban areas where much of the world's population lives. Satellite observations provide a basis for characterizing the physical modifications that result from urbanization. In particular, the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on the National Aeronautics and Space Administration (NASA) Terra satellite measures surface spectral albedos, thermal emissivities, and radiative temperatures. A better understanding of these measurements should improve our knowledge of the climate impact of urbanization as well as our ability to specify the parameters needed by climate models to compute the impacts of urbanization. For this purpose, it is useful to contrast urban areas with neighboring nonurban surfaces with regard to their radiative surface temperatures, emissivities, and albedos. Among these properties, surface temperatures have been most extensively studied previously in the context of the urban heat island (UHI). Nevertheless, except for a few detailed studies, the UHI has mostly been characterized in terms of surface air temperatures. To provide a global analysis, the zonal average of these properties are presented here measured over urban areas versus neighboring nonurban areas. Furthermore, individual cities are examined to illustrate the variations of these variables with land cover under different climate conditions [e.g., in Beijing, New York, and Phoenix (a desert city of the United States)]. Satellite-measured skin temperatures are related to the surface air temperatures but do not necessarily have the same seasonal and diurnal variations, since they are more coupled to surface energy exchange processes and less to the overlying atmospheric column. Consequently, the UHI effects from skin temperature are shown to be pronounced at both daytime and nighttime, rather than at night as previously suggested from surface air temperature measurements. In addition, urban areas are characterized by albedos much lower than those of croplands and deciduous forests in summer but similar to those of forests in winter. Thus, urban surfaces can be distinguished from nonurban surfaces through use of a proposed index formed by multiplying skin temperature by albedo.
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| [46] |
Progress in urban meteorology: A review [J].https://doi.org/10.2151/jmsj.85B.363 URL [本文引用: 1] 摘要
This paper reviews the progress made in urban meteorology over the past few decades. The focus is on the impact of urban surfaces on the overlying atmosphere along the conventional meteorological frameworks. Section 1 details the difliculties in generalizing urban surfaces in a meteorological sense because of surface diversity, and considers whether conventional similarity law is applicable. Section 2 describes the characteristics of urban surfaces as the bottom boundary of the atmosphere and includes a discussion of land surface parameters and the resultant surface energy partitioning. Section 3 explains characteristics of the urban atmosphere, including temperature fields, local circulations and rainfall. Section 4 describes recent progress in numerical modeling and promising new technologies, thus revealing a possible future direction for urban meteorological studies.
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| [47] |
The surface heat island of Rotterdam and its relationship with urban surface characteristics [J].https://doi.org/10.1016/j.resconrec.2012.01.009 URL [本文引用: 1] 摘要
Thermal infrared high resolution satellite images from Landsat sensors were used to spatially quantify the surface heat island (SHI) of Rotterdam in the Netherlands. Based on surface temperature maps retrieved on 15 summer days since 1984, the average surface temperature of each district and neighbourhood within the city was compared to the rural surface temperature outside the city, defined as the SHI intensity. The results showed that the daytime SHI intensity of Rotterdam can be as large as 10°C. Differences in the SHI between the neighbourhoods can be explained by urban surface characteristics. A statistical analysis shows that the SHI is largest for neighbourhoods with scarce vegetation that have a high fraction of impervious surface, and a low albedo. Furthermore, NOAA-AVHHR satellite images were used to monitor the heat wave of July 2006 and retrieve the diurnal variation in the SHI of Rotterdam. Average surface temperature differences between the warmest and coolest districts are maximum 12°C during day, and 9°C during night. Districts with a large night-time SHI differ from districts with a large daytime SHI. 08 2012 Elsevier B.V. All rights reserved.
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| [48] |
Changes in the functional composition of a central European urban flora over three centuries [J].https://doi.org/10.1007/978-3-8348-9626-1_5 URL [本文引用: 1] 摘要
Urbanization has shaped European landscapes for many centuries. The first towns already developed around 700 B.C. in the Mediterranean (Antrop 2004). Since these early times, urbanization spread all over Europe which is today one of the most urbanized continents, with 72% of the total population living in urban areas (only Latin and Northern America have higher rates of urban population with 78% and 81% respectively; United Nations 2008). In the 18 th century and especially in the 19 th century, industrialization and trade caused the growth of many European towns (Berry 1990). However, the main phase of urbanization took place in the 20 th century (Berry 1990; United Nations 2006) with its rapid developments in transportation techniques (Berry 1990; Antrop 2004). The increased mobility, together with other factors, such as political frameworks, enabled urban sprawl, which was especially strong in the second half of the 20 th century (Kasanko et al. 2006).
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| [49] |
Temperature-land cover interactions: The inversion of urban heat island phenomenon in desert city areas [J].https://doi.org/10.1016/j.rse.2012.11.007 URL Magsci [本文引用: 3] 摘要
Remote sensing data from MODIS, ASTER and LANDSAT 7 sensors were used to assess land cover-temperature interactions in the Abu Dhabi metropolitan area over a 10-year period between 2000 and 2010 with a multi-sensor approach. Low resolution data from MODIS sensor with high revisiting time have been used to analyze the daily variation of Land Surface Temperature (LST), the derived Surface Urban Heat Island (SUHI), and the Normalized Difference Vegetation Index (NDVI) at city level. Medium resolution data from ASTER and LANDSAT 7 sensors have been used for spot assessment of the above mentioned parameters at district level. With medium resolution satellites, LST and NDVI have been analyzed in correspondence of different level of Impervious Surface Areas (ISAs) over the study period.<br/>With both datasets, the obtained results showed an inversion of the standard SUHI phenomenon during daytime, where the downtown areas appear colder compared to the suburbs. Throughout the study period, the trend has been replicated and seasonality is also observed, where the inversion of SUHI is accentuated mainly in the summer months with a daily difference of 5-6 K compared to 2-3 K during the winter season, while the standard SUHI can be observed during the night with values of downtown 2-3 K higher than the suburbs. Spot analysis of single images confirmed this trend, adding the contribution of ISA to an average increment of 1 K during winter and 2 K during the summer. (C) 2012 Elsevier Inc. All rights reserved.
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| [50] |
Seasonal variations in the relationship between landscape pattern and land surface temperature in Indianapolis, USA [J].https://doi.org/10.1007/s10661-007-9979-5 Magsci [本文引用: 1] 摘要
<a name="Abs1"></a>This paper intended to examine the seasonal variations in the relationship between landscape pattern and land surface temperature based on a case study of Indianapolis, United States. The integration of remote sensing, GIS, and landscape ecology methods was used in this study. Four Terra’s ASTER images were used to derive the landscape patterns and land surface temperatures (LST) in four seasons in the study area. The spatial and ecological characteristics of landscape patterns and LSTs were examined by the use of landscape metrics. The impact of each land use and land cover type on LST was analyzed based on the measurements of landscape metrics. The results show that the landscape and LST patterns in the winter were unique. The rest of three seasons apparently had more agreeable landscape and LST patterns. The spatial configuration of each LST zone conformed to that of each land use and land cover type with more than 50% of overlap in area for all seasons. This paper may provide useful information for urban planers and environmental managers for assessing and monitoring urban thermal environments as result of urbanization.
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| [51] |
Analysis of the urban heat island effect in Shijiazhuang, China using satellite and airborne data [J].https://doi.org/10.3390/rs70404804 URL [本文引用: 2] 摘要
The urban heat island (UHI) effect resulting from rapid urbanization generally has a negative impact on urban residents. Shijiazhuang, the capital of Hebei Province in China, was selected to assess surface thermal patterns and its correlation with Land Cover Types (LCTs). This study was conducted using Landsat TM images on the mesoscale level and airborne hyperspectral thermal images on the microscale level. Land surface temperature (LST) was retrieved from four scenes of Landsat TM data in the summer days to analyze the thermal spatial patterns and intensity of surface UHI (SUHI). Surface thermal characteristics were further examined by relating LST to percentage of imperious surface area (ISA%) and four remote sensing indices (RSIs), the Normalized Difference Vegetation Index (NDVI), Universal Pattern Decomposition method (VIUPD), Normalized Difference Built-up Index (NDBI) and Biophysical Composition Index (BCI). On the other hand, fives scenes of airborne TASI (Thermal Airborne Spectrographic Imager sensor) images were utilized to describe more detailed urban thermal characteristics of the downtown of Shijiazhuang city. Our results show that an obvious surface heat island effect existed in the study area during summer days, with a SUHI intensity of 2-4 degrees C. The analyses reveal that ISA% can provide an additional metric for the study of SUHI, yet its association with LST is not straightforward and this should a focus in future work. It was also found that two physically based indices, VIUPD and BCI, have the potential to account for the variation in urban LST. The results concerning on TASI indicate that diversity of impervious surfaces (rooftops, concrete, and mixed asphalt) contribute most to the SUHI, among all of the land cover features. Moreover, the effect of impervious surfaces on LST is complicated, and the composition and arrangement of land cover features may play an important role in determining the magnitude and intensity of SUHI. Overall, the analysis of urban thermal signatures at two spatial scales complement each other and the use of airborne imagery data with higher spatial resolution is helpful in revealing more details for understanding urban thermal environments.
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| [52] |
Multiscale analysis of urban thermal characteristics: Case study of Shijiazhuang, China [J].https://doi.org/10.1117/1.JRS.8.083649 URL 摘要
The urban heat island (UHI) effect caused by urbanization generally leads to adverse economic and environmental impacts. Thus, a detailed assessment of the thermal properties over individual land cover types at different spatial resolutions is required to better understand the establishment of UHI. Two scale levels are utilized to examine surface thermal characteristics in the case of Shijiazhuang, China. At the regional level, Landsat TM data are used to extract subpixel impervious surface area (ISA) and to inverse land surface temperature (LST) by means of multiple endmember spectral mixture analysis. Urban thermal characteristics are analyzed by relating the LST to normalized difference vegetation index (NDVI) and ISA. On the other hand, thermal airborne spectrographic imager data, with a high spatial resolution, are employed to describe the spatial distribution of urban thermal patterns at the local level. Results indicate that there is an approximate linear relationship among LST, NDVI, and ISA. In addition, the thermal characteristics over each land cover type are consistent at both levels, suggesting UHI is evident at Shijiazhuang and impervious surface is contributing most to this phenomenon. This confirms that different spatial scales are requested in UHI studies.
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| [53] |
Empirical estimation of urban effects on climate: A problem analysis [J].https://doi.org/10.1175/1520-0450(1977)0162.0.CO;2 URL [本文引用: 1] 摘要
Doubt exists among atmospheric scientists about current estimates of local and regional effects of urbanization on climate, but not generally about the existence of these urban effects. This paper presents a framework for discussion of various estimators, uses the framework to make the case for a particular estimator, and then uses the framework to examine possible shortcomings of other estimators which appear in the literature. The measure recommended consists of differences between observations, from urban and pre-urban periods, first stratified by synoptic weather type. The measures whose shortcomings are examined are 1) contemporaneous urban-rural differences, 2) contemporaneous upwind-downwind differences, 3) contemporaneous urban-regional ratios, 4) time trends of differences and ratios and 5) contemporaneous weekday-weekend differences. The paper is designated as a “problem analysis” because its goal is general facilitation of discussion about the problem of empirical estimation of urban effects on climate.
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| [54] |
A hierarchical analysis of the relationship between urban impervious surfaces and land surface temperatures: Spatial scale dependence, temporal variations, and bioclimatic modulation [J].https://doi.org/10.1007/s10980-016-0356-z URL [本文引用: 1] 摘要
Context Understanding how urban impervious surfaces (UIS) affect land surface temperatures (LST) on different scales in space and time is important for urban ecology and sustainability
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| [55] |
On the frequency of snowfall in metropolitan England [J].https://doi.org/10.1002/qj.49708435910 URL [本文引用: 1] 摘要
No abstract is available for this article.
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| [56] |
An investigation of urban heat island intensity (UHII) as an indicator of urban heating [J].https://doi.org/10.1016/j.atmosres.2009.07.006 URL Magsci [本文引用: 1] 摘要
<h2 class="secHeading" id="section_abstract">Abstract</h2><p id="">The objective of this paper is to evaluate the reliability of urban heat island intensity (UHII) as an indicator of urban heating. The diurnal patterns of air and surface-temperature based UHII and variations in urban and rural area heating were analyzed and discussed. The detailed air-temperature based UHII patterns were determined in one urban and four suburban areas of Hong Kong. UHII was determined as spatially-averaged air-temperature difference between an urban/suburban area and its surrounding rural area. Additionally, reported air and surface-temperature based UHII patterns were integrated in the discussion to carry out a comprehensive analysis. The urban and rural area heating variations (i.e., the diurnal variations in net radiation, sensible heat flux, latent heat flux, and heat stored by an area) were examined in the light of UHII patterns to validate UHII as an indicator for urban heating. It is noted that the air-temperature based UHIIs were higher and positive in the night-time but lower and negative during the daytime. On the other hand, most of the surface-temperature based UHIIs, investigated through satellite data were positive during both the daytime and night-time. It is revealed that UHII can well reflect urban heating during night-time and early morning. However, the lower and negative UHII during solar peak time (daytime when solar radiation is the dominant source of heating) has seemingly not been representing urban heating.</p>
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| [57] |
Remote-sensing image-based analysis of the patterns of urban heat islands in rapidly urbanizing Jinan, China [J].https://doi.org/10.1080/01431161.2013.853895 URL [本文引用: 1] 摘要
According to the UN Population Reference Bureau, 1.4 billion more people will have settled in urban areas by 2030. One of the key environmental effects of rapid urbanization is the urban heat island (UHI) effect. Understanding the mechanism of surface UHIs associated with land-use/land-cover (LULC) change patterns is important for improving the ecology and sustainability of cities. In this article, time series Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+) data were used to extract LULC data and land surface temperature (LST) data for the city of Jinan, China, from 1987 to 2011, a period during which the city experienced rapid urbanization. With the aid of a geographical information system (GIS) and remote sensing (RS) approach, the changes in this urban area LULC were explored, and the impact of these changes on the spatiotemporal patterns and underlying driving forces of the surface UHI effect were further quantitatively characterized. The results show that significant changes in land use and land cover occurred over the study period, with loss of farmland, forest, and shrub vegetation to urban use, leading to spatial growth of impervious surfaces. Consequently, the land surface characteristics and spatiotemporal patterns of the UHI have changed drastically. According to the seasonal and inter-annual variations in intensity of UHIs, mean differences in UHI intensity between city centre, peri-urban, and nearby rural areas were stronger during summer and spring and weaker during winter and autumn. Spatially, there were significant LST gradients from the city centre to surrounding rural areas. The city centre exhibited higher LSTs and remarkable variation in LSTs, while the surrounding rural areas exhibited lower LSTs and lower variation in LSTs. Moreover, the analysis of LSTs and indices showed that great differences of temperature even existed in a LULC type except for variations between different LULC types. In addition, a local-level analysis revealed that the intensity of the UHI effect is proportional to the size of the urban area, the population density, and the frequent occurrence of certain activities.
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| [58] |
Statistical estimation of daily maximum and minimum air temperatures from MODIS LST data over the state of Mississippi [J].https://doi.org/10.2747/1548-1603.43.1.78 URL [本文引用: 2] 摘要
value between the air temperature and LST at the involved spatial scales.
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| [59] |
Area delineation and spatial-temporal dynamics of urban heat island in Lanzhou City, China using remote sensing imagery [J]. |
| [60] |
Surface urban heat island across 419 global big cities [J]. |
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Characterizing urban heat island phenomenon of four Texas cities using MODIS LST products[D]. San Antonio, TX: The University of |
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Multi-temporal trajectory of the urban heat island centroid in Beijing, China based on a Gaussian volume model [J].https://doi.org/10.1016/j.rse.2014.03.037 URL Magsci [本文引用: 4] 摘要
The trajectory of the urban heat island (UHI) centroid in three dimensions indicates the overall variation of the intensity and distribution of the UHI. This study applied the Gaussian volume model on the daily MODIS/LST products from 2000 to 2012 to derive the UHI centroid in Beijing on a multi-temporal scale. The trajectories indicated that (1) on a diurnal scale during July-September, the daytime and nighttime centroids of the UHI were primarily located in the Xicheng district near the city center, and the mean intensity was from 2.12 to 2.97 degrees C. The daytime centroid was in the south of the nighttime centroid and demonstrated a higher intensity and a larger core area, where Aqua obtains a higher intensity than Terra. The movement of the UHI centroid was also more significant in the north-south direction than in the east-west direction; (2) on a monthly scale, the daytime centroid moved from the northeast to the southwest by (1.85, 2.91) km from July to September, and the intensity varied from 2.16 degrees C (September) to 3.09 degrees C (August), while the nighttime centroid generally moved anticlockwise from January to December, and the intensity varied from 1.98 degrees C (July) to 3.07 degrees C (January); and (3) on an annual scale, the daytime UHI centroid in August and the nighttime UHI centroid moved toward the northeast by (2.15, 131) km and (0.43, 0.89) km, respectively. There was a dramatic change in the UHI prior to 2008, which was most likely caused by the numerous preparation projects for the 2008 Beijing Olympic Games. Correlation analysis demonstrated that the hot and medium-hot landscapes exhibited positive contributions to the variation of the horizontal location of UHI centroid, and NDVI and albedo showed positive contributions to the variations of daytime (less than 10%) and nighttime (close to 50%) UHI centroids, respectively. Also, we discussed the relationship between Z-dimension of UHI centroid and other UHI indicators, and the impact of missing data. This study presents scientific insights for urban planning and management in Beijing and motivates the investigation of comprehensive changes in UHI of other metropolises worldwide. (C) 2014 Elsevier Inc. All rights reserved.
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| [63] |
Time series decomposition of remotely sensed land surface temperature and investigation of trends and seasonal variations in surface urban heat islands [J].https://doi.org/10.1002/2015JD024354 URL [本文引用: 1] |
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Urban heat island monitoring and analysis using a non-parametric model: A case study of Indianapolis [J].https://doi.org/10.1016/j.isprsjprs.2008.05.002 URL Magsci [本文引用: 1] 摘要
<h2 class="secHeading" id="section_abstract">Abstract</h2><p id="">A procedure for the monitoring an urban heat island (UHI) was developed and tested over a selected location in the Midwestern United States. Nine counties in central Indiana were selected and their UHI patterns were modeled. Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) images taken in 2005 were used for the research. The images were sorted based on cloud cover over the study area. The resulting 94 day and night images were used for the modeling. The technique of process convolution was then applied to the images in order to characterize the UHIs. This process helped to characterize the LST data into a continuous surface and the UHI data into a series of Gaussian functions. The diurnal temperature profiles and UHI intensity attributes (minimum, maximum and magnitude) of the characterized images were analyzed for variations. Skin temperatures within any given image varied between 2–15 <sup>°</sup>C and 2–8 <sup>°</sup>C for the day and night images, respectively. The magnitude of the UHI varied from 1–5 <sup>°</sup>C and 1–3 <sup>°</sup>C over the daytime and nighttime images, respectively. Three dimensional (3-D) models of the day and night images were generated and visually explored for patterns through animation. A strong and clearly evident UHI was identified extending north of Marion County well into Hamilton County. This information coincides with the development and expansion of northern Marion County during the past few years in contrast to the southern part. To further explore these results, an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 2004 land use land cover (LULC) dataset was analyzed with respect to the characterized UHI. The areas with maximum heat signatures were found to have a strong correlation with impervious surfaces. The entire process of information extraction was automated in order to facilitate the mining of UHI patterns at a global scale. This research has proved to be promising approach for the modeling and mining of UHIs from large amount of remote sensing images. Furthermore, this research also aids in 3-D diachronic analysis.</p>
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Spatio-temporal analysis of urban temperature in Bandung City, Indonesia [J].https://doi.org/10.1007/s11252-013-0332-1 URL Magsci [本文引用: 2] 摘要
This study presents an evaluation of urban micro-climate about the exsistence level urban vegetation, in association with the urban temperature (surface temperature) and urban built-up area of Bandung City. The changes in urban vegetation cover, urban temperature, and urban built-up area observed using Landsat 5 TM and Landsat 7 ETM + bands were evaluated on the basis of the WDRVI (Wide Dynamic Range Vegetation Indices), NDBI (Normalized Difference Built-up Index), and SAVI (Soil-Adjusted Vegetation Index). It was found that, due to the uncontrolled urban growth and the removal of urban vegetation cover and urban green space, there was a significant increase in urban temperature, in NDBI, but a decrease in WDRVI. The maximum urban temperatures, NDBI, and the minimum values of WDRVI indices were established in 2009. Therefore the results indicate a significant effect of higher density of impervious surfaces coverage (urban built-up area) contributing significantly to the increase of urban temperature. Again the results also confirm that urban vegetation landscape coverage in the surrounding of industrial area reduced the urban temperature. Based on the results, we recommend the city government to provide more urban green space by cooperating with private land owner, in order to decrease urban temperature and create a healthier living environment for urban inhabitants.
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Remote sensing of urban heat islands from an environmental satellite [J]. |
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Study of the August 2010 heat event in Cyprus [M] |
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Exploring indicators for quantifying surface urban heat islands of European cities with MODIS land surface temperatures [J].https://doi.org/10.1016/j.rse.2011.07.003 URL Magsci [本文引用: 11] 摘要
The term urban heat island describes the phenomenon of altered temperatures in urban areas compared to their rural hinterlands. A surface urban heat island encompasses the patterns of land surface temperatures in urban areas. The classical indicator to describe a surface urban heat island is the difference between urban and rural surface temperatures. However, several other indicators for this purpose have been suggested in the literature. In this study, we compared the eleven different indicators for quantifying surface urban heat islands that were most frequently used in recent publications on remote sensing-based urban heat island assessments. The dataset used here consists of 263 European cities with monthly mean temperatures from MODIS data products for July 2002, January 2003 and July 2003. We found that (i) the indicators individually reveal diurnal and seasonal patterns but show rather low correlations over time, and (ii) for single points in time, the different indicators show only weak correlations, although they are supposed to quantify the same phenomenon. Differentiating cities according to thermal climate zones increased the relationships between the indicators. Thus, we can identify temporal aspects and indicator selection as important factors determining the estimation of urban heat islands. We conclude that research should take into account the differences and instabilities of the indicators chosen for quantifying surface urban heat islands and should use several indicators in parallel for describing the surface urban heat island of a city. (C) 2011 Elsevier Inc. All rights reserved.
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| [69] |
Relationship of land surface and air temperatures and its implications for quantifying urban heat island indicators: An application for the city of Leipzig (Germany) [J].https://doi.org/10.1016/j.ecolind.2012.01.001 URL Magsci [本文引用: 4] 摘要
Urban heat islands (UHIs) describe the phenomenon of altered temperatures that occur in urban areas when compared to their rural surroundings. UHIs influence human well-being, human health and the city as an ecological niche. UHIs can be quantified with meteorological ground measurements of air temperatures or with remotely sensed land surface temperatures (surface urban heat island). Both approaches have advantages and disadvantages and are rarely combined. Further, within these approaches, different indicators for quantifying the UHIs are used. In this methodological study, we (1) combined data on land surface and air temperatures, (2) enriched the debate by suggesting the application of indicators for the two distinct data sets and (3) systematically quantified indicators of all approaches for the city of Leipzig, Germany. A relationship between the land surface and air temperatures was established. However, the results for the single indicators showed that the absolute values of the detected UHI in Leipzig depend on the selected indicator and the data set used. The main conclusion for future studies on UHIs is to use several UHI indicators in parallel to acknowledge the uncertainty of measuring the UHI using a single indicator and either ground measurements or remote sensing. (C) 2012 Elsevier Ltd. All rights reserved.
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Spatial-temporal land-use/land-cover dynamics and their impacts on surface temperature in Chongming Island of Shanghai, China [J].https://doi.org/10.1080/01431161.2015.1043404 URL [本文引用: 1] 摘要
Land-use/land-cover (LULC) changes are occurring at rapid rates on the Chongming Island of Shanghai, China, giving rise to a major concern about environmental impacts. We herein carried out a sound analysis of the LULC dynamics, the conversions among different LULC classes, and land-surface temperature (LST) distribution using remote-sensing data from Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), and Enhanced Thematic Mapper Plus (ETM+) time series spanning the last 35/years (1979-2014). Based on LULC class information and LST, we constructed a temperature/vegetation index space to study the temporal variability of thermal data, vegetation cover, and LULC. The results showed that the LULC change dynamics in Chongming Island have strongly impacted the LST in the recent decade. The spatial position conversion and quantitative change of vegetation cover totalled about 44.4% of LULC-type areas over the Island, and the comprehensive LULC dynamicity changed from 2.97 to 3.95 during the investigated period. Accordingly, significant LST changes took place in the portion of the Chongming Island showing normal temperature range, which accounted for 85.94% of the whole Island area as of 1 August 2000 and that decreased to 50.79% on 6 May 2009, while the surface extents under low- and with ultra-high-temperature ranges increased, respectively, both from 0 of 2000 to 6.67% and 0.41% of 2009. The results indicate that the pixel classes including vegetation cover, wetland, and waterbody, which have larger dynamicity and maximum change vector magnitudes, played a large role in alleviating the effect of the land-surface thermal environment, and were key driving factors contributing to the increasing trend of non-normal temperature range ratio over time. Our findings are expected to provide valuable information for decision-making regarding the development and construction of Chongming Island into an eco-region.
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| [71] |
Long-term and fine-scale satellite monitoring of the urban heat island effect by the fusion of multi-temporal and multi-sensor remote sensed data: A 26-year case study of the city of Wuhan in China [J].https://doi.org/10.1016/j.rse.2015.11.005 URL [本文引用: 4] 摘要
The trade-off between the temporal and spatial resolutions, and/or the influence of cloud cover, makes it difficult to obtain continuous fine-scale satellite data for surface urban heat island (SUHI) analysis. To relieve these difficulties, this study employs multi-temporal and multi-sensor fusion methods for a long-term and fine-scale summer SUHI analysis of the city of Wuhan in China. By integrating several series of satellite images, we generated 26-year (1988 to 2013) high spatial resolution (Landsat-like) summer land surface temperature (LST) data. This series of data was then used for a qualitative and quantitative analysis of the SUHI patterns, evolution characteristics, and mechanisms. This study not only provides a generalized research framework for the long-term and fine-scale analysis of the SUHI effect, but also reveals several findings about the heat distribution and SUHI characteristics in Wuhan. Firstly, our results show that the high temperature and sub-high temperature areas were continuously concentrated from rural to urban areas, but the high temperature area within the old city zones showed an obvious decreasing tendency. Secondly, a more important finding is that the SUHI intensity first increased and then decreased over the 26/years. The maximum temperature difference between the city zone and the rural area was in 2003 (7.19/K for the old city zone, and 4.65/K for the area within the third ring road). Finally, we confirm that the relationships between heat distribution and land cover (especially vegetation and impervious surfaces) were interannually stable, and that the influences of industry, businesses, and residential districts on the SUHI effect were in descending order in Wuhan.
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| [72] |
Observation and analysis of the urban heat island effect on soil in Nanjing, China [J].https://doi.org/10.1007/s12665-011-1501-2 URL Magsci [本文引用: 1] 摘要
The heat island effect in urban meteorology has received significant attention in the recent years. In order to investigate the heat island effect on urban soil, two observation stations were built, respectively, in an urban area and a rural area of Nanjing city, China. The temperatures of underground soil (0-300 cm depth) were recorded continuously for 1 year from June 2009 to June 2010. The data show that the urban soil temperature is generally higher than the rural soil temperature, and reveal an obvious heat island effect in urban soil with average intensity of 2.02A degrees C over the 1-year period. The intensity varies between days, months and seasons: the daily urban heat island intensity (UHII) of soil ranges from 0.37A degrees C to 3.98A degrees C; the monthly UHII of soil ranges from 1.34A degrees C (November) to 3.05A degrees C (July); the order of seasonal UHII is summer (2.45A degrees C) > winter (2.03A degrees C) > spring (1.63A degrees C) > autumn (1.53A degrees C). The temperature data indicate that the maximum influence depth of daily synoptic events on the subsurface temperature is approximately 60 cm; the UHII generally increases with increasing depth. In addition to soil temperature, the temporal-spatial variation of soil moisture in a 100 cm profile depth was also investigated in this study. It is found that the moisture content of urban soil is generally lower than that of rural soil, which reveals an obvious dry island effect with average intensity of -7.2% over the 1-year period; the maximum single-day urban dry island intensity (UDII) in soil is -28.0%; the maximum average monthly UDII is -19.1%, observed in July; the seasonal UDII shows a tendency of summer (-13.8%) > spring (-6.3%) > autumn (-5.2%) > winter (-3.7%). In profile, soil moisture content generally increases with increasing depth, and the maximum UDII is -25.8% at 40 cm depth. In addition, the large-scale measurement results of 600 general points also confirm that the heat island and dry island effects are exist in urban soil.
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Science plan: Urbanization and global environmental change[R]. Bonn, |
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Evaluation of the surface urban heat island effect in the city of Madrid by thermal remote sensing [J].https://doi.org/10.1080/01431161.2012.716548 URL [本文引用: 2] 摘要
The surface urban heat island (SUHI) effect is defined as the increased surface temperatures in urban areas in contrast to cooler surrounding rural areas. In this article, the evaluation of the SUHI effect in the city of Madrid (Spain) from thermal infrared (TIR) remote-sensing data is presented. The data were obtained from the framework of the Dual-use European Security IR Experiment (DESIREX) campaign that was carried out during June and July 2008 in Madrid. The campaign combined the collection of airborne hyperspectral and measurements. Thirty spectral and spatial high-resolution images were acquired with the Airborne Hyperspectral Scanner (AHS) sensor in a 11, 21, and 4/h UTC scheme. The imagery was used to retrieve the SUHI effect by applying the temperature and emissivity separation (TES) algorithm. The results show a nocturnal SUHI effect with a highest value of 5 K. This maximum value agrees within 1 K with the highest value of the urban heat island (UHI) observed using air temperature data (AT). During the daytime, this situation is reversed and the city becomes a negative heat island.
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| [75] |
A remote sensing study of the urban heat island of Houston, Texas [J].https://doi.org/10.1080/01431160110115023 URL [本文引用: 1] 摘要
Radiative surface temperature maps of Houston, Texas were derived from satellite sensor data acquired at approximately 0400 LST on 27 separate occasions over a two-year period. Urban-rural temperature differences were determined for 21 of these cases by modelling the urban heat island as a twodimensional Gaussian surface superimposed on a planar rural background. The purpose of this study was to characterise the complete urban heat island in magnitude and spatial extent without the use of measurements and to determine whether a correlation exists between heat island magnitude and rural temperature. The urban heat island magnitude was found to be inversely correlated with rural temperature, while the spatial extent was found to be independent of both heat island magnitude and rural temperature.
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| [76] |
Satellite-measured growth of the urban heat island of Houston, Texas [J].https://doi.org/10.1016/S0034-4257(03)00007-5 URL [本文引用: 2] 摘要
Growth of the surface temperature urban heat island (UHI) of Houston, TX is determined by comparing two sets of heat island measurements taken 12 years apart. Individual heat island characteristics are calculated from radiative temperature maps obtained using the split-window infrared channels of the Advanced Very High Resolution Radiometer (AVHRR) on board National Oceanic and Atmospheric Administration polar-orbiting satellites. Eighty-two nighttime scenes taken between 1985 and 1987 are compared to 125 nighttime scenes taken between 1999 and 2001. Analysis of the UHI characteristics from these two intervals reveals a mean growth in magnitude of 0.8 K, or 35%. The growth of the mean area of the UHI is found to range between 170 and 650 km, or from 38% to 88%, depending on the method of analysis.
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| [77] |
Remote sensing land surface temperature for meteorology and climatology: A review [J].https://doi.org/10.1002/met.287 URL Magsci [本文引用: 2] 摘要
The last decade has seen a considerable increase in the amount and availability of remotely sensed data. This paper reviews the satellites, sensors and studies relevant to land surface temperature measurements in the context of meteorology and climatology. The focus is on using the thermal infrared part of the electromagnetic spectrum for useful measurements of land surface temperature, which can be beneficial for a number of uses, for example urban heat island measurements. Copyright (C) 2011 Royal Meteorological Society
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Derivation of Birmingham's summer surface urban heat island from MODIS satellite images [J].https://doi.org/10.1002/joc.2261 URL [本文引用: 1] 摘要
Not Available
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Assessment with satellite data of the urban heat island effects in Asian mega cities [J].https://doi.org/10.1016/j.jag.2005.05.003 URL [本文引用: 7] 摘要
This study focuses on using remote sensing for comparative assessment of surface urban heat island (UHI) in 18 mega cities in both temperate and tropical climate regions. Least-clouded day- and night-scenes of TERRA/MODIS acquired between 2001 and 2003 were selected to generate land-surface temperature (LST) maps. Spatial patterns of UHIs for each city were examined over its diurnal cycle and seasonal variations. A Gaussian approximation was applied in order to quantify spatial extents and magnitude of individual UHIs for inter-city comparison. To reveal relationship of UHIs with surface properties, UHI patterns were analyzed in association with urban vegetation covers and surface energy fluxes derived from high-resolution Landsat ETM+ data. This study provides a generalized picture on the UHI phenomena in the Asian region and the findings can be used to guide further study integrating satellite high-resolution thermal data with land-surface modeling and meso-scale climatic modeling in order to understand impacts of urbanization on local climate in Asia.
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| [80] |
Intra-urban relationship between surface geometry and urban heat island: Review and new approach [J].https://doi.org/10.3354/cr027253 URL [本文引用: 2] 摘要
This paper provides a comprehensive review of the intra-urban sky view factor (SVF)-temperature relationship. A new approach to reveal the real connection between SVF and air temperature in an entire city is presented. The results found in the literature are rather contradictory possibly clue the fact that previous investigations were limited to the central or specific parts (e.g. inner city, urban canyons) of cities and used few sites and measurements. Comparisons were often based on element pairs measured at selected sites. In some cases areal means were also discussed, but always in connection with one of the variables examined. For comparison, the present study in Szeged, SE Hungary, utilizes a large number of areal means of SVF and air temperature. The values are related to almost a whole city and based on numerous measurements. The results show a strong relationship in the intra-urban variations of these variables, i.e. urban surface geometry is a significant determining factor of the air temperature distribution inside a city if the selected scale is appropriate. Therefore, investigation of a sufficient number of appropriate-sized areas covering the largest part of a city or the entire city is needed to draw well-established conclusions.
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World urbanization prospects: The 2014 revision, highlights[R]. New York: United Nations,
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Complete urban surface temperatures [J].https://doi.org/10.1175/1520-0450(1997)0362.0.CO;2 URL [本文引用: 1] 摘要
An observation program using ground and airborne thermal infrared radiometers is used to estimate the surface temperature of urban areas, taking into account the total active surface area. The authors call this the complete urban surface temperature. This temperature is not restricted by the viewing biases inherent in remote sensors used to estimate surface temperature over rough surfaces such as cities. Two methods to estimate the complete surface temperature are presented. Results for three different land-use areas in the city of Vancouver, British Columbia, Canada, show significant differences exist between the complete, nadir, and off-nadir airborne estimates of urban surface temperature during daytime. For the sites and times studied, the complete surface temperature is shown to agree with airborne off-nadir estimates of the apparent surface temperature of the most shaded walls. Some implications of using the complete surface temperature to estimate screen level air temperature and to calculate surface sensible heat flux are given. Copyright 1997 American Meteorological Society (AMS). Permissionto use figures, tables, and brief excerpts from this work in scientific and educationalworks is hereby granted provided that the source is acknowledged. Any use of material inthis work that is determined to be “fair use” under Section 107 of the U.S. Copyright Actor that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17USC §108, as revised by P.L. 94-553) does not require the AMS’s permission.Republication, systematic reproduction, posting in electronic form, such as on a web siteor in a searchable database, or other uses of this material, except as exempted by theabove statement, requires written permission or a license from the AMS. Additionaldetails are provided in the AMS Copyright Policy, available on the AMS Web sitelocated at (http://www.ametsoc.org/) or from the AMS at 617-227-2425 orcopyright@ametsoc.org.
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Thermal remote sensing of urban climates [J].https://doi.org/10.1016/S0034-4257(03)00079-8 URL [本文引用: 1] 摘要
Thermal remote sensing has been used over urban areas to assess the urban heat island, to perform land cover classifications and as input for models of urban surface atmosphere exchange. Here, we review the use of thermal remote sensing in the study of urban climates, focusing primarily on the urban heat island effect and progress made towards answering the methodological questions posed by Roth et al. [International Journal of Remote Sensing 10 (1989) 1699]. The review demonstrates that while some progress has been made, the thermal remote sensing of urban areas has been slow to advance beyond qualitative description of thermal patterns and simple correlations. Part of the difficulty lies in the tendency to use qualitatively based land use data to describe the urban surface rather than the use of more fundamental surface descriptors. Advances in the application of thermal remote sensing to natural and agricultural surfaces suggest insight into possible methods to advance techniques and capabilities over urban areas. Improvements in the spatial and spectral resolution of current and next-generation satellite-based sensors, in more detailed surface representations of urban surfaces and in the availability of low cost, high resolution portable thermal scanners are expected to allow progress in the application of urban thermal remote sensing to the study of the climate of urban areas.
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Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends [J].https://doi.org/10.1016/j.isprsjprs.2009.03.007 URL Magsci [本文引用: 3] 摘要
<h2 class="secHeading" id="section_abstract">Abstract</h2><p id="">Thermal infrared (TIR) remote sensing techniques have been applied in urban climate and environmental studies, mainly for analyzing land surface temperature (LST) patterns and its relationship with surface characteristics, assessing urban heat island (UHI), and relating LSTs with surface energy fluxes to characterize landscape properties, patterns, and processes. This paper examines current practices, problems, and prospects in this particular field of study. The emphasis is placed in the summarization of methods, techniques, and applications of remotely sensed TIR data used in urban studies. In addition, some future research directions are outlined. This literature review suggests that the majority of previous research have focused on LST patterns and their relationships with urban surface biophysical characteristics, especially with vegetation indices and land use/cover types. Less attention has been paid to the derivation of UHI parameters from LST data and to the use of remote sensing techniques to estimate surface energy fluxes. Major recent advances include application of sub-pixel quantitative surface descriptors in examining LST patterns and dynamics, derivation of key UHI parameters based on parametric and non-parametric models, and integration of remotely sensed variables with <em>in situ</em> meteorological data for urban surface energy modeling. More research is needed in order to define better “urban surface” from the remote sensing viewpoint, to examine measurement and modeling scales, and to differentiate modeled and measured fluxes.</p>
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Modeling diurnal land temperature cycles over Los Angeles using downscaled GOES imagery [J].https://doi.org/10.1016/j.isprsjprs.2014.08.009 URL Magsci [本文引用: 2] 摘要
Land surface temperature is a key parameter for monitoring urban heat islands, assessing heat related risks, and estimating building energy consumption. These environmental issues are characterized by high temporal variability. A possible solution from the remote sensing perspective is to utilize geostationary satellites images, for instance, images from Geostationary Operational Environmental System (GOES) and Meteosat Second Generation (MSG). These satellite systems, however, with coarse spatial but high temporal resolution (sub-hourly imagery at 3-10 km resolution), often limit their usage to meteorological forecasting and global climate modeling. Therefore, how to develop efficient and effective methods to disaggregate these coarse resolution images to a proper scale suitable for regional and local studies need be explored. In this study, we propose a least square support vector machine (LSSVM) method to achieve the goal of downscaling of GOES image data to half-hourly 1-km ISTs by fusing it with MODIS data products and Shuttle Radar Topography Mission (SRTM) digital elevation data. The result of downscaling suggests that the proposed method successfully disaggregated GOES images to half-hourly 1-km LSTs with accuracy of approximately 2.5 K when validated against with MODIS LSTs at the same over-passing time. The synthetic LST datasets were further explored for monitoring of surface urban heat island (UHI) in the Los Angeles region by extracting key diurnal temperature cycle (DTC) parameters. It is found that the datasets and DTC derived parameters were more suitable for monitoring of daytime- other than nighttime-UHI. With the downscaled GOES 1-km LSTs, the diurnal temperature variations can well be characterized. An accuracy of about 2.5 K was achieved in terms of the fitted results at both 1 km and 5 km resolutions. 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
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| [86] |
The spatial variations of urban land surface temperatures: Pertinent factors, zoning effect, and seasonal variability [J].https://doi.org/10.1109/JSTARS.2008.917869 URL [本文引用: 1] 摘要
Remote sensing of urban land surface temperatures (LSTs) has been conducted based largely on pixel-by-pixel correlation with land use and land cover (LULC) types. Few studies have examined the spatial variations of LST within land use zoning polygons, in spite of its significance on the knowledge of environmental implications or planning practices. This study aimed to analyze the spatial patterns of LSTs and to explore factors contributing to the LST variations in the city of Indianapolis. Four Terra's ASTER images, representing distinct seasons, were used in conjunction with other types of spatial data for the analysis. The potential factors were grouped into the categories of LULC composition, biophysical conditions, intensity of human activities, and landscape pattern. Statistical analyses were conducted to determine the relative importance of each group of the variables. Moreover, the spatial variations of LST were examined at both the residential and general zoning levels, so that the environmental effect of urban planning on LST may be assessed. By analyzing the mean and standard deviation values of normalized LSTs, the seasonal dynamics of LST were finally studied. Results show that the biophysical variables were most significant in explaining the spatial variations of LST. At both zoning levels, LST possessed a weaker relationship with the LULC compositions than with the biophysical variables. Principal component analysis further indicates that the cumulative variance was always larger in residential zoning, implying that the factors contributing to the LST variations in general zoning might be more complex than those for the residential zoning. An interesting finding of this study was in the relationship between LST and the landscape metrics of zoning polygons. It suggests that smaller residential zoning polygons were associated with larger temperature variations, and that the more complex in shape a residential zoning category was, the more intrapolygon variation of LST tended to be. These correlations, however, did not exist in the nonresidential zoning categories. The spatial pattern of LST in Indianapolis may be characterized as concentric in the central part of the city, a hot ring along the Highway 465, and several hot corridors along the radial highways outward to the countryside. The seasonal fluctuation of LST was weak in the central part, but increased towards the countryside. Due to the amount of anthropogenic heat, land use zones with less human activities were found to have a strong seasonal variability, whereas the zones with intensive human activities fluctuated less in LST.
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| [87] |
Managing the adverse thermal effects of urban development in a densely populated Chinese city [J].https://doi.org/10.1016/j.jenvman.2003.11.006 URL PMID: 15160740 Magsci 摘要
<h2 class="secHeading" id="section_abstract">Abstract</h2><p id="">Guangzhou city in South China has experienced an accelerated urban development since the 1980s. This paper examines the impact of the urban development on urban heat islands through a historical analysis of urban–rural air temperature differences. Remote sensing techniques were applied to derive information on land use/cover and land surface temperatures and to assess the thermal response patterns of land cover types. The results revealed an overriding importance of urban land cover expansion in the changes in heat island intensity and surface temperature patterns. Urban development was also related to a continual air temperature increase in the 1980s and 1990s. The combined use of satellite-derived vegetation and land cover distributions with land surface temperature maps provides a potential useful tool for many planning applications. The city's greening campaigns and landscaping designs should consider the different cooling effects of forest, shrubs and grassy lawns for temperature control and should plant more tall trees.</p>
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| [88] |
Review of world urban heat islands: Many linked to increased mortality [J].https://doi.org/10.1115/1.4023176 URL [本文引用: 1] 摘要
Medical and health researchers have shown that fatalities during heat waves are most commonly due to respiratory and cardiovascular diseases, primarily from heat's negative effect on the cardiovascular system. In an attempt to control one's internal temperature, the body’s natural instinct is to circulate large quantities of blood to the skin. However, to perform this protective measure against overheating actually harms the body by inducing extra strain on the heart. This excess strain has the potential to trigger a cardiac event in those with chronic health problems, such as the elderly, Cui et al. Frumkin showed that the relationship of mortality and temperature creates a J-shaped function, showing a steeper slope at higher temperatures. Records show that more casualties have resulted from heat waves than hurricanes, floods, and tornadoes together. This statistic’s significance is that extreme heat events (EHEs) are becoming more frequent, as shown by Stone et al. Their analysis shows a growth trend of EHEs by 0.20 days/year in U.S. cities between 1956 and 2005, with a 95% confidence interval and uncertainty of ±0.6. This means that there were 10 more days of extreme heat conditions in 2005 than in 1956. Studies held from 1989 to 2000 in 50 U.S. cities recorded a rise of 5.7% in mortality during heat waves. The research of Schifano et al. revealed that Rome’s elderly population endures a higher mortality rate during heat waves, at 8% excess for the 65–74 age group and 15% for above 74. Even more staggering is findings of Dousset et al. on French cities during the 2003 heat wave. Small towns saw an average excess mortality rate of 40%, while Paris witnessed an increase of 141%. During this period, a 0.565°C increase above the average minimum nighttime temperature doubled the risk of death in the elderly. Heat-related illnesses and mortality rates have slightly decreased since 1980, regardless of the increase in temperatures. Statistics from the U.S. Census state that the U.S. population without air conditioning saw a drop of 32% from 1978 to 2005, resting at 15%. Despite the increase in air conditioning use, a study done by Kalkstein through 2007 proved that the shielding effects of air conditioning reached their terminal effect in the mid-1990s. Kan et al. hypothesize in their study of Shanghai that the significant difference in fatalities from the 1998 and 2003 heat waves was due to the increase in use of air conditioning. Protective factors have mitigated the danger of heat on those vulnerable to it, however projecting forward the heat increment related to sprawl may exceed physiologic adaptation thresholds. It has been studied and reported that urban heat islands (UHI) exist in the following world cities and their countries and/or states: Tel-Aviv, Israel, Newark, NJ, Madrid, Spain, London, UK, Athens, Greece, Taipei, Taiwan, San Juan, Puerto Rico, Osaka, Japan, Hong Kong, China, Beijing, China, Pyongyang, North Korea, Bangkok, Thailand, Manila, Philippines, Ho Chi Minh City, Vietnam, Seoul, South Korea, Muscat, Oman, Singapore, Houston, USA, Shanghai, China, Wroclaw, Poland, Mexico City, Mexico, Arkansas, Atlanta, USA, Buenos Aires, Argentina, Kenya, Brisbane, Australia, Moscow, Russia, Los Angeles, USA, Washington, DC, USA, San Diego, USA, New York, USA, Chicago, USA, Budapest, Hungary, Miami, USA, Istanbul, Turkey, Mumbai, India, Shenzen, China, Thessaloniki, Greece, Rotterdam, Netherlands, Akure, Nigeria, Bucharest, Romania, Birmingham, UK, Bangladesh, and Delhi, India. The strongest being Shanghai, Bangkok, Beijing, Tel-Aviv, and Tokyo with UHI intensities (UHII) of 3.5–7.0, 3.0–8.0, 5.5–10, 10, and 1265°C, respectively. Of the above world cities, Hong Kong, Bangkok, Delhi, Bangladesh, London, Kyoto, Osaka, and Berlin have been linked to increased mortality rates due to the heightened temperatures of nonheat wave periods. Chan et al. studied excess mortalities in cities such as Hong Kong, Bangkok, and Delhi, which currently observe mortality increases ranging from 4.1% to 5.8% per 165°C over a temperature threshold of approximately 2965°C. Goggins et al. found similar data for the urban area of Bangladesh, which showed an increase of 7.5% in mortality for every 165°C the mean temperature was above a similar threshold. In the same study, while observing microregions of Montreal portraying heat island characteristics, mortality was found to be 28% higher in heat island zones on days with a mean temperature of 2665°C opposed to 2065°C compared to a 13% increase in colder areas.
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| [89] |
Evaluation of the impact of the surrounding urban morphology on building energy consumption [J].https://doi.org/10.1016/j.solener.2010.11.002 URL [本文引用: 1] 摘要
The objective of this research is to assess and to compare how the air temperature variation of urban condition can affect the building energy consumption in tropical climate of Singapore. In order to achieve this goal, a series of numerical calculation and building simulation are utilized. A total of 32 cases, considering different urban morphologies, are identified and evaluated to give better a understanding on the implication of urban forms, with the reference to the effect of varying density, height and greenery density. The results show that GnPR, which related to the present of greenery, have the most significant impact on the energy consumption by reducing the temperature by up to 2C. The results also strongly indicate an energy saving of 4.5% if the urban elements are addressed effectively.
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| [90] |
Development of a 3-D urbanization index using digital terrain models for surface urban heat island effects [J].https://doi.org/10.1016/j.isprsjprs.2013.03.009 URL [本文引用: 1] 摘要
This study assesses surface urban heat island (SUHI) effects during heat waves in subtropical areas. Two cities in northern Taiwan, Taipei metropolis and its adjacent medium-sized city, Yilan, were selected for this empirical study. Daytime and night time surface temperature and SUHI intensity of both cities in five heat wave cases were obtained from MODIS Land-Surface Temperature (LST) and compared. In order to assess SUHI in finer spatial scale, an innovated three-dimensional Urbanization Index (3DUI) with a 5-m spatial resolution was developed to quantify urbanization from a 3-D perspective using Digital Terrain Models (DTMs). The correlation between 3DUI and surface temperatures were also assessed. The results obtained showed that the highest SUHI intensity in daytime was 10.202°C in Taipei and 7.502°C in Yilan. The SUHI intensity was also higher than that in non-heat-wave days (about 502°C) in Taipei. The difference in SUHI intensity of both cities could be as small as only 1.002°C, suggesting that SUHI intensity was enhanced in both large and medium-sized cities during heat waves. Moreover, the surface temperatures of rural areas in Taipei and Yilan were elevated in the intense heat wave cases, suggesting that the SUHI may reach a plateau when the heat waves get stronger and last longer. In addition, the correlation coefficient between 3DUI and surface temperature was greater than 0.6. The innovative 3DUI can be employed to assess the spatial variation of temperatures and SUHI intensity in much finer spatial resolutions than measurements obtained from remote sensing and weather stations. In summary, the empirical results demonstrated intensified SUHI in large and medium-sized cities in subtropical areas during heat waves which could result in heat stress risks of residents. The innovative 3DUI can be employed to identify vulnerable areas in fine spatial resolutions for formulation of heat wave adaptation strategies.
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| [91] |
Assessing the effects of land use spatial structure on urban heat islands using HJ-1B remote sensing imagery in Wuhan, China [J].https://doi.org/10.1016/j.jag.2014.03.019 URL Magsci [本文引用: 1] 摘要
Urban heat islands (UHIs) have attracted attention around the world because they profoundly affect biological diversity and human life. Assessing the effects of the spatial structure of land use on UHIs is essential to better understanding and improving the ecological consequences of urbanization. This paper presents the radius fractal dimension to quantify the spatial variation of different land use types around the hot centers. By integrating remote sensing images from the newly launched HJ-1B satellite system, vegetation indexes, landscape metrics and fractal dimension, the effects of land use patterns on the urban thermal environment in Wuhan were comprehensively explored. The vegetation indexes and landscape metrics of the HJ-1B and other remote sensing satellites were compared and analyzed to validate the performance of the HJ-1B. The results have showed that land surface temperature (LST) is negatively related to only positive normalized difference vegetation index (NDVI) but to Fv across the entire range of values, which indicates that fractional vegetation (Fv) is an appropriate predictor of LST more than NDVI in forest areas. Furthermore, the mean LST is highly correlated with four class-based metrics and three landscape-based metrics, which suggests that the landscape composition and the spatial configuration both influence UHIs. All of them demonstrate that the HJ-1B satellite has a comparable capacity for UHI studies as other commonly used remote sensing satellites. The results of the fractal analysis show that the density of built-up areas sharply decreases from the hot centers to the edges of these areas, while the densities of water, forest and cropland increase. These relationships reveal that water, like forest and cropland, has a significant effect in mitigating UHIs in Wuhan due to its large spatial extent and homogeneous spatial distribution. These findings not only confirm the applicability and effectiveness of the HJ-1B satellite system for studying UHIs but also reveal the impacts of the spatial structure of land use on UHIs, which is helpful for improving the planning and management of the urban environment. (C) 2014 Elsevier B.V. All rights reserved.
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| [92] |
Dynamical monitoring and evaluation methods to urban heat island effects based on RS & GIS [J].https://doi.org/10.1016/j.proenv.2011.09.197 URL Magsci [本文引用: 5] 摘要
At present, research about Urban Heat Island Effects (UHI) is a hot issue in urban climate and ecological environment. Because remote sensing technology has many advantages, it becomes an important means of UHI research. How to use multi-temporal thermal infrared remote sensing data and dynamically monitor and evaluate UHI is a N.-cry interesting research topic. This article took Luzhou City in Sichuan Province, China as an example and explored methods to monitoring and evaluating UHI based on Landsat-5 TM data, which obtained on September 15, 1988, and Landsat-7 ETM+ data which obtained on August 29, 2002.Technical methods and research route established by this article can provide references for other similar research, and have application and popularizing value. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Conference ESIAT2011 Organization Committee.
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| [93] |
Spatial and temporal dynamics of urban heat island and their relationship with land cover changes in urbanization process: A case study in Suzhou, China [J].https://doi.org/10.1007/s12524-011-0073-7 URL Magsci [本文引用: 1] 摘要
One of the significant environmental consequences of urbanization is the urban heat island (UHI). In this paper, Landsat TM images of 1986 and 2004 were utilized to study the spatial and temporal variations of heat island and their relationships with land cover changes in Suzhou, a Chinese city which experienced rapid urbanization in past decades. Land cover classifications were derived to quantify urban expansions and brightness temperatures were computed from the TM thermal data to express the urban thermal environment. The spatial distributions of surface temperature indicated that heat islands had been largely broadened and showed good agreements with urban expansion. Temperature statistics of main land cover types showed that built-up and bare land had higher surface temperatures than natural land covers, implying the warming effect caused by the urbanization with natural landscape being replaced by urban areas. In addition, the spatial detail distributions of surface temperature were compared with the distribution of land cover by means of GIS buffer analysis. Results show remarkable show good correspondence between heat island variations with urban area expansions.
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| [94] |
Detection of the urban heat island in Beijing using HJ-1B satellite imagery [J].https://doi.org/10.1007/s11430-010-4131-8 URL [本文引用: 1] 摘要
Satellite images are used extensively in studying the urban heat island(UHI) phenomenon.We evaluated the suitability of thermal infrared(TIR) data from the HJ-1B satellite for detecting UHI using a case study in Beijing.Two modified algorithms for retrieving the land surface temperature(LST) from HJ-1B data were tested.The results were compared with LST images derived from a Landsat TM thermal band and the MODIS LST output.The spatial pattern of UHI generated using HJ-1B data matched well with that produced using TM and MODIS data.Of the two algorithms,the mono-window algorithm performed better but further tests are necessary.With more frequent coverage than TM and higher spatial resolution than MODIS,the HJ-1B TIR data present a unique opportunity to study thermal environments in cities in China and neighboring countries.
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| [95] |
Application of urban thermal environment monitoring based on remote sensing in Beijing [J].https://doi.org/10.1016/j.proenv.2011.12.214 URL Magsci [本文引用: 2] 摘要
Remote sensing has become an important method for monitoring urban thermal environment, but, it is still difficult to analysis the spatio-temporal change of urban thermal environment quantitatively only by ground surface temperature (or brightness temperature). Three indexes on urban thermal environment monitoring are proposed in this paper: heat island intensity, heat field intensity index and heat island proportion index. Based on MODIS surface temperature products and FY3A/MERSI data of two years, urban thermal environment is monitored by remote sensing method in Beijing. The empirical results verify that three indexes proposed in this paper are meaningful in monitoring of urban heat island. It can not only monitor the intensity and changes of the urban heat island in Beijing effectively, but also, it is positive to carrying out meteorological operations in city thermal environment monitoring. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of the Intelligent Information Technology Application Research Association.
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| [96] |
Comparison of impervious surface area and Normalized Difference Vegetation Index as indicators of surface urban heat island effects in Landsat imagery [J].https://doi.org/10.1016/j.rse.2006.09.003 URL [本文引用: 1] 摘要
This paper compares the normalized difference vegetation index (NDVI) and percent impervious surface as indicators of surface urban heat island effects in Landsat imagery by investigating the relationships between the land surface temperature (LST), percent impervious surface area (%ISA), and the NDVI. Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data were used to estimate the LST from four different seasons for the Twin Cities, Minnesota, metropolitan area. A map of percent impervious surface with a standard error of 7.95% was generated using a normalized spectral mixture analysis of July 2002 Landsat TM imagery. Our analysis indicates there is a strong linear relationship between LST and percent impervious surface for all seasons, whereas the relationship between LST and NDVI is much less strong and varies by season. This result suggests percent impervious surface provides a complementary metric to the traditionally applied NDVI for analyzing LST quantitatively over the seasons for surface urban heat island studies using thermal infrared remote sensing in an urbanized environment.
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| [97] |
Downscaling land surface temperature for urban heat island diurnal cycle analysis [J].https://doi.org/10.1016/j.rse.2011.05.027 URL Magsci [本文引用: 1] 摘要
Urban heat island (UHI) is a phenomenon of high spatial and temporal variabilities. It can develop during night or daytime. UHI monitoring is possible through thermal satellite remote sensing of land surface temperature (LST). LST over large areas (size of a city) can be measured at high temporal resolution merely from instruments on-board geostationary satellites. These can cover the diurnal cycle as they provide data even every 5 min (SEVIRI rapid scanning). Sensors on-board the geostationary satellites have, however, poor spatial resolution. Using high spatial resolution is in many regions most important because LST is a spatially inhomogeneous parameter especially in urban areas. UHI characteristics are correlated with the land cover and micro-relief parameters. These are often available in a higher spatial resolution (e.g. NDVI and EVI). Thus, we employed them to enhance the spatial resolution of the SEVIRI LST over central Europe - using moving window analysis - to 1000 m spatial resolution and temporal resolution of 15 min. For each SEVIRI pixel a multiple regression was run on the low resolution data. Regression equation was then used on the high resolution data in order to estimate LST of high spatial and temporal resolutions. The validation over urban areas showed that the downscaled SEVIRI LST is comparable with the MODIS LST with an average root mean square error of 2.5 K. The obtained results make possible to analyse the diurnal cycle of UHI. (C) 2011 Elsevier Inc. All rights reserved.
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| [98] |
Simple modifications to improve fifth-generation Pennsylvania State University-National Center for atmospheric research mesoscale model performance for the Phoenix, Arizona, metropolitan area [J].https://doi.org/10.1175/1520-0450(2002)041<0971:SMTIFG>2.0.CO;2 URL [本文引用: 1] 摘要
The diurnal temperature cycle in the Phoenix, Arizona, metropolitan area, as represented in the fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5), is examined using a high-resolution 2-km grid spacing to simulate the dry portion of the summer. The model is run for a 48-h period with negligible synoptic forcing and a sufficiently dry atmosphere so that moist convection does not occur. The standard version of MM5 underestimates the magnitude of the diurnal maximum and also causes the maximum to occur too early. This behavior is due to an overestimate of the latent heat flux in the model, resulting from a poor specification of the land cover category and corresponding physical parameters. Adjusting the available moisture improves the modeled temperature maximum at both rural and urban sites, but the model temperatures are still cooler than those observed at the urban site at night because of a poorly represented urban heat island effect. It is the aim of this note to highlight deficiencies associated with running MM5 without any urban parameterization and without correcting the NCEP Eta Model analysis used as initial and boundary conditions.
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| [99] |
Satellite-derived subsurface urban heat island [J].https://doi.org/10.1021/es50211851 URL PMID: 25222374 Magsci [本文引用: 1] 摘要
The subsurface urban heat island (SubUHI) is one part of the overall UHI specifying the relative warmth of urban ground temperatures against the rural background. To combat the challenge on measuring extensive underground temperatures with in situ instruments, we utilized satellite-based moderate-resolution imaging spectroradiometer data to reconstruct the subsurface thermal field over the Beijing metropolis through a three-time-scale model. The results show the SubUHI's high spatial heterogeneity. Within the depths shallower than 0.5 m, the SubUHI dominates along the depth profiles and analyses imply the moments for the SubUHI intensity reaching first and second extremes during a diurnal temperature cycle are delayed about 3.25 and 1.97 h per 0.1 m, respectively. At depths shallower than 0.05 m in particular, there is a subsurface urban cool island (UCI) in spring daytime, mainly owing to the surface UCI that occurs in this period. At depths between 0.5 and 10 m, the time for the SubUHI intensity getting to its extremes during an annual temperature cycle is lagged 26.2 days per meter. Within these depths, the SubUHI prevails without exception, with an average intensity of 4.3 K, varying from 3.2 to 5.3 K.
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| [100] |
Study of the relationships between the spatial extent of surface urban heat islands and urban characteristic factors based on Landsat ETM+ data [J].https://doi.org/10.3390/s8117453 URL PMID: 3787455 [本文引用: 1] 摘要
Ten cities with different population and urban sizes located in the Pearl River Delta, Guangdong Province, P.R. China were selected to study the relationships between the spatial extent of surface urban heat islands (SUHI) and five urban characteristic factors such as urban size, development area, water proportion, mean NDVI (Normalized Vegetation Index) and population density, etc. The spatial extent of SUHI was quantified by using the hot island area (HIA). All the cities are almost at the same latitude, showing similar climate and solar radiation, the influence of which could thus be eliminated during our computation and comparative study. The land surface temperatures (LST) were retrieved from the data of Landsat 7 Enhanced Thematic Mapper Plus (ETM+) band 6 using a mono-window algorithm. A variance-segmenting method was proposed to compute HIA for each city from the retrieved LST. Factors like urban size, development area and water proportion were extracted directly from the classification images of the same ETM+ data and the population density factor is from the official census. Correlation and regression analyses were performed to study the relationships between the HIA and the related factors, and the results show that HIA is highly correlated to urban size (r=0.95), population density (r=0.97) and development area (r=0.83) in this area. It was also proved that a weak negative correlation existed between HIA and both mean NDVI and water proportion for each city. Linear functions between HIA and its related factors were established, respectively. The HIA can reflect the spatial extent and magnitude of the surface urban heat island effect, and can be used as reference in the urban planning.
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| [101] |
Characterizing urban heat islands of global settlements using MODIS and nighttime lights products [J].https://doi.org/10.5589/m10-039 URL [本文引用: 3] 摘要
Not Available
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| [102] |
Surface urban heat island in China's 32 major cities: Spatial patterns and drivers [J].https://doi.org/10.1016/j.rse.2014.05.017 URL Magsci [本文引用: 3] 摘要
Urban heat island (UHI) is a major anthropogenic alteration on Earth environments and its geospatial pattern remains poorly understood over large areas. Using MODIS data from 2003 to 2011, we quantified the diurnal and seasonal surface UHI intensity (SUHII, urban-suburban temperature difference) in China's 32 major cities, and analyzed their spatial variations and possible underlying mechanisms. Results show that the annual mean SUHII varied markedly from 0.01 to 1.87 degrees C in the day and 035 to 1.95 degrees C at night, with a great deal of spatial heterogeneities. Higher SUHIls for the day and night were observed in the southeastern and northern regions, respectively. Moreover, the SUHII differed greatly by season, characterized by a higher intensity in summer than in winter during the day, and the opposite during the night for most cities. Consequently, whether the daytime SUHII was higher or lower than the nighttime SUHII for a city depends strongly on the geographic location and research period. The SUHII's distribution in the day related closely to vegetation activity and anthropogenic heat releases in summer, and to climate (temperature and precipitation) in winter, while that at night linked tightly to albedo, anthropogenic heat releases, built-up intensity, and climate in both seasons. Overall, we found the overwhelming control of climate on the SUHII's spatial variability, yet the factors included in this study explained a much smaller fraction of the SUHII variations in the day compared to night and in summer relative to winter (day vs. night: 57% vs. 72% in summer, and 61% vs. 90% in winter, respectively), indicating more complicated mechanisms underlying the distribution of daytime SUHII, particularly in summer. Our results highlight the different diurnal (day and night) and seasonal (summer and winter) SUHII's spatial patterns and driving forces, suggesting various strategies are needed for an effective UHI effect mitigation. (C) 2014 Elsevier Inc. All rights reserved.
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| [103] |
The footprint of urban heat island effect in China [J].https://doi.org/10.1038/srep11160 URL [本文引用: 2] 摘要
Urban heat island (UHI) is one major anthropogenic modification to the Earth system that transcends its physical boundary. Using MODIS data from 2003 to 2012, we showed that the UHI effect decayed exponentially toward rural areas for majority of the 32 Chinese cities. We found an obvious urban/rural temperature "cliff", and estimated that the footprint of UHI effect (FP, including urban area) was 2.3 and 3.9 times of urban size for the day and night, respectively, with large spatiotemporal heterogeneities. We further revealed that ignoring the FP may underestimate the UHI intensity in most cases and even alter the direction of UHI estimates for few cities. Our results provide new insights to the characteristics of UHI effect and emphasize the necessity of considering city- and time-specific FP when assessing the urbanization effects on local climate.
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| [104] |
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].https://doi.org/10.1007/s10980-013-9950-5 Magsci 摘要
We investigated the seasonal variability of the relationships between land surface temperature (LST) and land use/land cover (LULC) variables, and how the spatial and thematic resolutions of LULC variables affect these relationships. We derived LST data from Landsat-7 Enhanced Thematic Mapper (ETM+) images acquired from four different seasons. We used three LULC datasets: (1) 0.6 m resolution land cover data; (2) 30 m resolution land cover data (NLCD 2001); and (3) 30 m resolution Normalized Difference Vegetation Index data derived from the same ETM+ images (though from different bands) used for LST calculation. We developed ten models to evaluate effects of spatial and thematic resolution of LULC data on the observed relationships between LST and LULC variables for each season. We found that the directions of the effects of LULC variables on predicting LST were consistent across seasons, but the magnitude of effects, varied by season, providing the strongest predictive capacity during summer and the weakest during winter. Percent of imperviousness was the best predictor on LST with relatively consistent explanatory power across seasons, which alone explained approximately 50 % of the total variation in LST in winter, and up to 77.9 % for summer. Vegetation related variables, particularly tree canopy, were good predictor of LST during summer and fall. Vegetation, particularly tree canopy, can significantly reduce LST. The spatial resolution of LULC data appeared not to substantially affect relationships between LST and LULC variables. In contrast, increasing thematic resolution generally enhanced the explanatory power of LULC on LST, but not to a substantial degree.
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