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Journal of Geo-information Science >

2017 , Vol. 19 >Issue 5: 713 - 722

DOI: https://doi.org/10.3724/SP.J.1047.2017.00713

Orginal Article

A Study of Urban Heat Island Intensity Based on “Local Climate Zones”

  • LIN Zhongli ,
  • XU Hanqiu , *
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  • 1. College of Environment and Resources, Fuzhou University, Fuzhou 350116, China;2. Institute of Remote Sensing Information Engineering, Fuzhou University, Fuzhou 350116, China;3. Fujian Provincial Key Laboratory of Remote Sensing Soil Erosion and Disaster Protection, Fuzhou University, Fuzhou 350116, China
*Corresponding author: XU Hanqiu, E-mail:

Received date: 2016-07-25

  Request revised date: 2016-11-09

  Online published: 2017-05-20

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Abstract

In the context of city expansion and raise of awareness of climate change, urban planners are looking for methods and tools to take the urban heat island （UHI） into account. Urban heat island intensity （UHII） is an important metric used in measuring UHI effect. Nevertheless, its quantitative measurement has not yet been clearly addressed. Due to the limitation of meteorological stations either in number or location, the traditional method of calculating the temperature difference between urban and rural areas based on the meteorological station data fails to accurately describe the UHII of a city. In order to solve this problem, a classification schema “Local Climate Zones” （LCZ） was proposed by Steward and Oke. Nowadays, the satellite remote sensing imagery is widely used to reveal urban heat island phenomenon. Therefore, this paper applied the new framework of LCZ to the study of UHII in Fuzhou City, located in the center of the Fuzhou basin, southeast China, using remote sensing technology. Fuzhou City has witnessed a rapid urban expansion since the late 1970s. The fast expansion of the city has caused severe UHI phenomenon in the city. Thus, it has become the top one furnace city in China. This study reveals that LCZ based on remote sensing technology can effectively distinguish the thermal contrasts among all LCZ classes. Such contrasts are governed largely by height and spacing of buildings, pervious surface fraction, trees density and soil wetness. In addition, the LCZ can fully disclose the distribution patterns of UHI. In this study, we revealed a UHIILCZ of 6.73℃ for Fuzhou city on 27 September 2015, which indicates a significant UHI in the city.

Key words： urban heat island intensity; local climate zones; remote sensing; Fuzhou

Cite this article

LIN Zhongli , XU Hanqiu . A Study of Urban Heat Island Intensity Based on “Local Climate Zones”[J]. Journal of Geo-information Science, 2017 , 19(5) : 713 -722 . DOI: 10.3724/SP.J.1047.2017.00713

1 引言

当前,城市化进程正在全球范围内以前所未有的速度进行。据联合国（United Nations, UN）最新人口统计数据显示,2014年全球已有54%的人口居住在城市,预测到2050年,城市人口比例将达到66%[1]。城市化给全世界带来繁荣和进步的同时,也给全球的生态环境带来了明显的负面影响,其中城市热岛效应表现尤为突出,已经引起了全球范围内的普遍关注[2-5]。城市热岛（Urban Heat Island, UHI）是指城市气温高于郊区的现象[6],而城郊之间的温度差就被称之为城市热岛强度（Urban Heat Island Intensity, UHII）,它是刻画城市热岛程度的一个重要和最常用的指标。在以往的城市热岛研究中,很多城市热岛强度的计算方法无法进行科学地界定[7]。由此可见,如何科学地表征城市热岛强度一直是城市热岛研究中的难点。目前,较为常用的评价方法是直接利用城乡温度差以衡量城市热岛强度[8-11],也有一些学者利用城市建成区与建成区缓冲区的温度差值[12-14],以及利用不同不透水面比例区域间的温度差值[15],来估算城市热岛强度。但是这些计算方法面临的困难是如何客观选取能够分别代表城区和郊区的温度。同时,建立一个适用于不同城市间热岛定量化评价统一体系,也是当前研究中的一个重要课题[7,16]。
鉴此,Stewart和Oke [16]提出了一种基于局地气候区的分类体系（Local Climate Zones, LCZ）,该分类体系旨在为城市热岛的研究提供一个适用于全球不同城市热岛强度比较的分类准则。目前,基于LCZ的城市热岛研究已在国外各城市逐渐开展[17-20],Stewart等[17]利用气象数据和LCZ体系分别对加拿大的温哥华（2008-2010年）、瑞典的乌普萨拉（1948-1949年）和日本的长野（2001-2002年）的城市热岛强度进行研究,结果显示,基于LCZ方法计算出这3个城市的城市热岛强度（记为：UHIILCZ）分别为6、3和1 K。Alexander和Mills [18]对爱尔兰的都柏林2010年8-9月的城市热岛强度进行研究,结果表明,在理想的天气状况下UHIILCZ为4.3~4.8 K。Ng等[19]研究得到新加坡2014年1-3月的UHIILCZ 为2.01 ℃。Lehnert等[20]就捷克奥洛穆茨的14个气象站的LCZ类别划分进行了讨论。Bechtel等[21]利用遥感影像分类技术,在全球范围内建立了各大城市的LCZ分类数据库,为利用遥感数据进行LCZ分类开创了先河。应用卫星遥感数据可以大大弥补基于有限地面气象站观测点在空间分布上的不足,实现对城市地表热岛空间分布的全面观测。
总的来看,LCZ是国际上新兴的一种城市热岛强度定量计算方法。它比简单地利用少量城郊气象站数据计算的城市热岛强度更为科学。目前,LCZ在国际上的应用已在逐渐增多,但在中国却还未曾有报道。因此,本文尝试将LCZ分类体系与卫星遥感数据相结合,应用于福建省会城市福州的城市热岛研究中,以期深入探究LCZ分类体系在城市热环境卫星遥感研究中的优势。

2 数据与方法

2.1 研究区与遥感数据

福州市位于福州盆地中心,四周被海拔600~1000 m的群山所环抱。福州属于亚热带海洋性季风气候,温暖湿润,雨量充沛,年平均气温16~20 ℃,年平均降水量达900~2100 mm。近年来,福州因城市热岛效应严重,而被列为中国的新“火炉”之首[22]。因此,福州市具有研究LCZ分类与UHIILCZ的典型性。本文选择福州市的5个主要行政区（鼓楼、台江、仓山、晋安和马尾）作为研究区,总面积为1013.41 km2（图1）。
Fig. 1 Location of the study area and its Landsat 8 image (2015-09-27)

图1 研究区位置及其Landsat 8影像(2015-09-27)

注：红色矩形柜区域为本文研究区

本文选用2015年9月27日过境的Landsat 8影像作为遥感影像数据源,为了减少地形、光照和大气等因素对光谱信息的影响,对影像进行辐射校正,将灰度值（DN）转换为传感器处反射率（at-sensor reflectance）[ 23-25]。

2.2 LCZ分类体系

LCZ是一种基于局地气候理念的分类体系,它将区域气候按下垫面类型的不同,分为若干局地气候小区,然后分别从中选择出可代替城区的气候小区和代表郊区的气候小区,通过计算出二者的温度差来表征城市热岛强度[16,26]。LCZ分类体系由建筑类型和土地覆盖类型构成,在2个类型下又细分17个次级标准类,建筑类型包括LCZ 1~LCZ 10,土地覆盖类型包括LCZ A~LCZ G。由于气候变化、农业耕作和季节循环会引起土地覆盖特性的变化,因此土地覆盖类型又被赋予b、s、d、w等 4种可变 特性（表1）。
Tab. 1 Local climate zones scheme

表1 LCZ分类体系[16]

建筑类型 定义 土地覆盖类型 定义
密集混合的高层建筑（10层以上）;几乎无树木;不透水路面;建筑材质为混凝土、钢材、石头和 玻璃 茂密的落叶林和（或）常绿林;地表覆盖大量可透水面（低矮的植被）;区域功能为天然林、苗圃林或城市公园
密集混合的中层建筑（3-9层）;几乎无树木;不透水路面;建筑材质为石头、砖、瓦片和混凝土 稀疏的落叶林和（或）常绿林;地表覆盖大量可透水面（低矮的植被）;区域功能为天然林、苗圃林或城市公园
密集混合的低层建筑（1-3层）;几乎无树木;不透水路面;建筑材质为石头、砖、瓦片和混凝土 开阔分布的灌木、矮树丛和矮小的树木;地表覆盖大量可透水面（裸土或沙）;区域功能为天然灌木林地或农用地
开阔分布的高层建筑（10层以上）;地表覆盖大量可透水面（低矮的植被、稀疏的树木）;建筑材质为混凝土、钢材、石头和玻璃 草地或草本植物/作物。几乎无树木;区域功能为草地、农用地或城市公园
开阔分布的中层建筑（3-9层）;地表覆盖大量可透水面（低矮的植被、稀疏的树木）;建筑材质为混凝土、钢材、石头和玻璃 岩石或不透水路面;几乎无植被;区域功能为天然荒漠（岩石）或城市交通运输干道
开阔分布的低层建筑（1-3层）;地表覆盖大量可透水面（低矮的植被、稀疏的树木）;建筑材质为木头、砖、石头、瓦片和混凝土 土或沙;几乎无植被;区域功能为天然沙漠或农用地
密集混合的单层建筑;几乎无树木;夯实的土质路面;轻质建筑材质（木头,茅草和波纹状板材） 大面积开阔的水体,如海和湖;或小面积水体,如河、水库和池塘
开阔分布的低层大型建筑（1-3层）;几乎无树木;不透水道面;建筑材质为钢材、混凝土、金属和 石头 土地覆盖的可变特性
（因气候变化,农业耕作和季节循环所引起的土地覆盖特性的变化）
自然环境中零散的中、小型建筑;地表覆盖大量可透水面（低矮的植被、稀疏的树木） b 光秃的树木 冬季少叶落叶林
s 积雪覆盖 积雪覆盖厚度大于10 cm
中低层工业建筑（塔、贮水池、堆积物）;几乎无树木;不透水路面或夯实的土质路面;建筑材质为金属、钢材和混凝土 d 干燥地表 焦土（如火烧迹地）
w 湿润地表 浸水土壤

2.3 基于LCZ的遥感影像分类

为了能够准确地从Landsat 8影像中分出LCZ各类,本文借助同期Google Earth高分辨率影像,对研究区的LCZ类别进行鉴引。确定出研究区存在的13个LCZ类别,并在此基础上选取各类别的分类训练区（表2）。
Tab. 2 LCZ identification based on Landsat 8 and Google Earth images

表2 研究区主要LCZ类别Landsat 8与Google Earth影像对照

Landsat 8影像 Google Earth影像 Landsat 8影像 Google Earth影像
LCZ 1 密集高层建筑 LCZ A 茂密树木
LCZ 2 密集中层建筑 LCZ B 稀疏树木
LCZ 3 密集低层建筑 LCZ C 灌木和矮树
LCZ 5 开阔中层建筑 LCZ D 低矮植被
LCZ 8 大型低层建筑 LCZ E 道路
LCZ 10 工业厂房 LCZ F 裸土或沙
LCZ G 水体
由于LCZ是一种基于气象学的分类体系[16],而遥感是以土地覆盖为基础的分类,因此很难用遥感方法来实现准确的LCZ分类。鉴此,本文采用分层分类法,先从遥感影像中分出基本大类（水体、植被、非植被）,然后参考同期的Google Earth高分辨率影像,采用分层分类和人工目视解译相结合的方法来调试确定每一类别的最佳阈值,以区分出各LCZ类别。
分类流程如下：①首先构建修正归一化差值水体指数（MNDWI）[27]、归一化差值植被指数（NDVI）[28]、建筑用地指数（IBI）[29]、归一化差值不透水面指数（NDISI）[30]和裸土指数（BI）[31],形成5个衍生指数波段,然后将其线性拉伸到0-255 之间;②使用MNDWI指数区分出研究区的水体（LCZ G）与非水体;③利用NDVI指数从非水体区中分出植被与非植被;④对于植被的各类别（茂密树木（LCZ A）、稀疏树 木（LCZ B）、灌木和矮树（LCZ C）和低矮植被（LCZ D））,利用NDVI与BI指数的比值（图2）以及植 被覆盖度（Fractional Vegetation Cover, FVC） [32]来进一步区分;⑤对于非植被类型,利用原始波段与衍生指数波段的阈值组合,采用逐层判别和人工目视解译相结合的方法,依次分出大型低层建筑（LCZ 8）、工业厂房（LCZ 10）、裸土或沙（LCZ F）、开阔中层建筑（LCZ 5）、密集中层建筑（LCZ 2）、密集高 层建筑（LCZ 1）、密集低层建筑（LCZ 3）、裸露的岩石或道路（LCZ E）;⑥将以上各类与同期的Google Earth高分辨率影像逐类叠加,进行必要的人工 修改。图3给出以上各步骤的流程图和分层分类法的阈值。
Fig. 2 Spectral signatures of NDVI/BI of LCZ vegetation types

图2 LCZ植被类型NDVI/BI光谱特征曲线

Fig. 3 Flow chart of the hierarchical classification of LCZ

图3 LCZ遥感分层分类流程图

2.4 城市热岛强度计算

在Stewart和Oke [16]的城市热岛强度（UHIILCZ）定义中,郊区的温度是以LCZ D （低矮植被类）的平均温度来代表,而城区的温度则必须在诸多的LCZ建筑类型中,选择其中的某一类来代表,通常是以占建成区面积比例最大的建筑类别来代表。然后计算郊区和城区类别之间的平均温度差来获得城市热岛强度UHIILCZ,即：
UHI I LCZ = LS T LCZ   X - LS T LCZ   D （1）
式中：LSTLCZ X为代表城区的LCZ建筑类型X的平均温度;LSTLCZ D为代表郊区的低矮植被类型的平均 温度。

2.5 地表温度的反演

Landsat 8卫星的热红外传感器（TIRS）具有2个热红外波段（10、11波段）,但由于TIRS 11波段的定标参数尚不稳定,因此,USGS暂不鼓励使用劈窗算法,而建议采用TIRS 10波段以类似TM/ETM+的单波段方法来计算地表温度[25,33-34]。因此,本文选用TIRS 10波段来计Landsat 8的地表温度,算法采用Jiménez-Muñoz和Sobrino的单通道算法[35-36],计算公式如下：
LST = γ ε - 1 ( ψ 1 L sensor + ψ 2 ) + ψ 3 + δ （2）
式中：Lsensor为传感器辐射值（W·m-2·sr-1·μm-1）;ψ1、 ψ2、ψ3是通过大气水汽含量计算的大气参数,可以从文献[35]、[36]中计算获得;γ和δ是基于Planck函数的2个参数,计算公式为：
γ ≈ T sensor 2 ( b γ L sensor ) （3）
δ ≈ T sensor - T sensor 2 b γ （4）
其中：
T sensor = c 2 λln [ c 1 ( λ 5 L sensor ) + 1 ] （5）
式中：Tsensor为传感器处亮度温度值（K）;λ为热红外波段的中心波长或有效作用波长（μm）;c1、c2是Planck辐射常数,分别为1.19104×108 W·μm4·m-2·sr-1和14 387.7 μm·K;bγ是算法系数,Landsat 8第10波段的bγ=1324 K [37];ε是地表比辐射率,可采用文献[34]的方法估算Landsat 8第10波段的ε。对所得反演结果与地面气象站实测的地表温度进行验证,误差为0.35 ℃。

3 结果与讨论

3.1 结果分析

根据图3的分类流程,获得了研究区LCZ分类结果图（图4）,并对分类结果进行统计（表3）。精度验证利用同期Google Earth影像,采用随机采样法,选出了1200个验证点,验证得到的分类总精度为82.92%,Kappa系数为0.806（附表A）,满足精度要求。
Fig. 4 LCZ classification image of the study area

图4 研究区LCZ分类结果图

Tab. 3 Statistics of LCZ classification results

表3 LCZ分类结果统计

LCZ类型 研究区范围 建成区范围
面积/km2 比例/% LST/℃ 面积/km2 比例/%
LCZ 1 密集高层建筑 21.29 2.10 37.38 19.48 8.42
LCZ 2 密集中层建筑 68.01 6.71 39.67 64.44 27.86
LCZ 3 密集低层建筑 52.62 5.19 39.23 33.52 14.49
LCZ 5 开阔中层建筑 21.88 2.16 37.89 19.24 8.32
LCZ 8 大型低层建筑 2.23 0.22 42.45 2.23 0.96
LCZ 10 工业厂房 16.79 1.66 41.82 15.14 6.54
LCZ A 茂密树木 546.41 53.92 28.24 4.61 1.99
LCZ B 稀疏树木 78.22 7.72 28.64 6.31 2.73
LCZ C 灌木和矮树 53.55 5.28 32.72 15.45 6.68
LCZ D 低矮植被 44.52 4.39 32.94 8.61 3.72
LCZ E 裸露的岩石或道路 30.47 3.01 39.23 22.33 9.65
LCZ F 裸土或沙 14.66 1.45 37.96 9.38 4.06
LCZ G 水体 62.76 6.19 29.05 10.56 4.57
合计 1013.41 100.00 - 231.33 100.00
Tab. A Confusion matrix for LCZ classification results

附表A LCZ分类误差矩阵

验证数据
LCZ类型 1 2 3 5 8 10 A B C D E F G 行合计 使用者精度/%
分类数据 1 84 9 0 0 1 1 0 0 0 1 4 0 15 115 73.04
2 8 173 9 4 0 3 0 0 3 0 9 1 0 210 82.38
3 1 15 88 0 2 4 0 1 0 0 4 6 0 121 72.73
5 1 12 0 39 0 2 0 0 2 1 1 1 0 59 66.10
8 0 0 0 1 13 0 0 0 0 0 0 0 0 14 92.86
10 0 4 5 0 0 34 0 0 0 0 2 0 0 45 75.56
A 0 0 0 0 0 0 236 14 4 0 0 0 0 254 92.91
B 0 0 0 0 0 0 4 29 8 1 0 0 0 42 69.05
C 1 0 0 4 0 0 0 4 52 2 0 2 3 68 76.47
D 2 0 0 0 0 0 0 1 2 37 0 0 1 43 86.05
E 0 4 0 0 0 0 0 0 2 0 40 5 0 51 78.43
F 0 2 2 0 0 3 0 0 0 0 0 28 0 35 80.00
G 0 0 0 0 0 0 0 0 0 0 1 0 142 143 99.30
列合计 97 219 104 48 16 47 240 49 73 42 61 43 161 1200
生产者精度/% 86.60 79.00 84.62 81.25 81.25 72.34 98.33 59.18 71.23 88.10 65.57 65.12 88.20
总精度/% 82.93
Kappa系数 0.806
从图4和表3可以看出,研究区范围内,LCZ A （茂密树木）所占的比例面积最大,为53.92% （546.41 km2）,LCZ B （稀疏树木）次之,为7.72% （78.22 km2）,但城市建成区内成片树木分布十分缺乏。建筑类型以LCZ 2 （密集中层建筑）所占比例最大,为6.71%,面积为68.01 km2,LCZ 3 （密集低层建筑）以5.19% （52.62 km2）次之,说明研究区的建筑类型以中、低层为主,且多为密集的连片建筑。
将LCZ分类图与反演获得的LST影像叠加,统计得到各类LCZ的平均地表温度（表3）,其中代表郊区的LCZ D的平均温度为32.94 ℃。为了客观地从6个LCZ建筑类型中选择出代表城区温度的类别,将城市建成区（图1）范围内的LCZ分类图进行面积统计。从表3可得,福州市建成区以密集中层建筑类（LCZ 2）所占比例面积最大,为27.86%（64.44 km2）,因此可选择LCZ 2的平均温度（39.67 ℃）代表城区温度,然后与代表郊区的LCZ D的平均温度相减,由此获得福州的城市热岛强度,即：
UHI I LCZ = LS T LCZ2 - LS T LCZd （6）
以上结果表明,福州市的城市热岛强度高达6.73 ℃,城市热岛效应十分显著。

3.2 讨论

城市热岛强度是指城郊之间温度差,其计算方法简单,即把城市的温度减去郊区的温度就可以求得城市热岛强度。但是如何科学地确定城市、郊区的温度却是一个长期困惑业界的问题。传统的方法是将位于城市气象站的温度减去位于郊区气象站的温度来获得城市热岛强度[8,10]。但在中国实施起来有一定的难度,原因之一是中国大部分城市气象站少,而且几乎都不设在郊区,因此无法获得郊区的温度。例如,福州市没有郊区气象站,所以无法按这一方法计算热岛强度。另一原因是即使有郊区气象站,但由于近年的城市扩展,这些气象站现在也都位于城区之内,无法代表郊区温度,因此传统计算城市热岛强度的方法现在几乎不可行。当前常用的是将城市建成区的平均温度减去郊区的平均温度来确定城市热岛强度,但是如何确定城市的平均温度,特别是如何确定郊区的平均温度也缺乏一种科学的方法,其确定往往因人而异,其结果带有很大的主观性,也无法相互对比。以遥感方法为例,通常以城市建成区的边界外推一定距离的缓冲区范围作为郊区,然后以其平均温度作为郊区温度来求出热岛强度[12-14]。以福州市为例,本次分别以2、4、6 km为距离,设定了3个缓冲区,分别计算它们的平均温度作为郊区温度,然后与福州建成区的平均温度相减,获得了3个城市热岛强度值,分别为7.8、8.8和9.2 ℃。显然缓冲区距离的设定直接影响了热岛强度的计算,使其结果带有很大的主观性,难以对比。如果将以上计算结果与本文用LCZ方法求出的热岛强度值6.7 ℃对比,只有2 km缓冲区的热岛强度计算结果最为接近,但仍有1.1 ℃之差。由于LCZ方法的城郊温度是依靠分类结果来客观确定,不带有主观因素,因此其计算结果客观可靠,且便于不同城市或同一城市不同年份之间的对比,已成为当前欧洲以及世界很多城市普遍采用的热岛强度计算方法[17-20]。

4 结论

LCZ分类体系能根据城市建筑表面结构、材质与土地覆盖类型对各地类的热特性进行有效地区分,为城市热岛的研究提供了一个适用于全球不同城市热岛强度比较的分类准则。利用城乡LCZ类别间的温度差代替传统的城乡温度差可以对城市间的热岛强度进行科学地定量化计算。本文基于LCZ分类体系,实现了LCZ与卫星遥感数据的结合,弥补了有限的地面观测点在空间分布上的不足,由此计算出的城市热岛强度（UHIILCZ）可以更客观、准确地评价城市热岛效应的状况。
将LCZ分类体系应用于福州市的热岛研究,分别得到代表城区的LCZ 2和代表郊区的LCZ D的平均地表温度,二者的温度差为6.73 ℃,表明2015年9月27日福州的热岛强度为6.73 ℃,反映了福州城市热岛效应十分显著,改善城市热环境,缓解城市热岛效应已迫在眉睫。

The authors have declared that no competing interests exist.

References

Publishing order | Descend order by publishing year | Descend order by cited within
[1]
United Nations.World urbanization prospects: the 2014 revision[M]. Population Division, Department of Economic and Social Affairs, Now York, 2015.

[2]
Georgescu M, Moustaoui M, Mahalov A, et al.Summer-time climate impacts of projected megapolitan expansion in Arizona[J]. Nature Climate Change, 2013,3(1):37-41.Efforts characterizing the changing climate of southwestern North America by focusing exclusively on the impacts of increasing levels of long-lived greenhouse gases omit fundamental elements with similar order-of-magnitude impacts as those owing to large-scale climate change(1,2). Using a suite of ensemble-based, multiyear simulations, here we show the intensification of observationally based urban-induced phenomena and demonstrate that the direct summer-time climate effects of the most rapidly expanding megapolitan region in the USA-Arizona's Sun Corridor-are considerable. Although urban-induced warming approaches 4 degrees C locally for the maximum expansion scenario, impacts depend on the particular trajectory of development. Cool-roof implementation reduces simulated warming by about 50%, yet decreases in summertime evapotranspiration remain at least as large as those from urban expansion without this mode of adaptation. The contribution of urban-induced warming relative to mid- and end-of-century climate change illustrates strong dependence on built environment expansion scenarios and emissions pathways. Our results highlight the direct climate impacts that result from newly emerging megapolitan regions and their significance for overcoming present challenges concerning sustainable development(3,4).

[3]
Weng Q.Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2009,64(4):335-344.lt;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 &ldquo;urban surface&rdquo; from the remote sensing viewpoint, to examine measurement and modeling scales, and to differentiate modeled and measured fluxes.</p>

DOI

[4]
张艳,鲍文杰,余琦,等.超大城市热岛效应的季节变化特征及其年际差异[J].地球物理学报,2012,55(4):1121-1128.本文以超大城市上海为例,分析了近50年四季城郊温差的总体变化趋势,同时利用城市化进程中4个年份(1987、1990、1997和2004年)9个气象站的气温数据,重点研究了上海地区热岛效应的季节变化特征及年际差异.结果表明,近50年来上海城郊温差逐年显著增长,年平均热岛天数频率为86.0%,年平均热岛强度为1.17 ℃,秋季热岛频率和强度高于其他季节,累积热岛强度也最大.不同时刻热岛的特征表明,夜间热岛(2∶00,20∶00时刻)累积强度在四季都较大,在春、夏季14∶00时刻热岛累积强度较大,而在秋、冬季8∶00时刻热岛累积强度较大.进一步分析表明,热岛效应的四季差异主要在于较强热岛和强热岛出现频率的差异;秋季大气最稳定的F类型比例较高可能是热岛效应更加显著的原因之一.四个年份对比分析表明,1997年之前的三个年份,热岛效应的四季差异比较显著.之后,随着年代的推移,四季累积热岛强度逐渐趋于均化,并且夏季低强度热岛有向中强热岛和强热岛转化的趋势,一定程度上反映了夏季人为热的贡献.

DOI

[Zhang Y, Bao W J, Yu Q, et al.Study on seasonal variations of the urban heat island and its interannual changes in a typical Chinese megacity[J]. Chinese Journal of Geophysics, 2012,55(4):1121-1128. ]

[5]
徐涵秋. 基于城市地表参数变化的城市热岛效应分析[J].生态学报, 2011,31(14):3890-3901.以不透水面、植被、水体为代表的地表参数的变化决定了城市的热环境质量。针对福州从一个非"火炉"城市一跃成为中国新三大"火炉"之首,对福州市1976-2006年间的地表参数变化及其对城市热环境的影响进行研究。通过Landsat卫星影像反演了福州市1976、1986、1996、2006年的不透水面、植被、水体、地面温度等主要地表参数,并对其进行空间叠加分析和相关关系的定量分析。研究发现:不透水面对地面温度的影响可接近或超过植被和水体之和,查明了福州城市主要地表参数在这30a里发生的变化及其对城市热环境的影响。总的看来,城市地表不透水面斑块的增加和集聚、植被和水体面积的减少和破碎,以及通风不畅,是造成福州成为"火炉"城市的主要因素。

[Xu H Q.Analysis on urban heat island effect based on the dynamics of urban surface biophysical descriptors[J]. Acta Ecological Sinica, 2011,31(14):3890-3901. ]

[6]
Mnaley G.On the frequency of snowfall in metropolitan England[J]. Quarterly Journal of the Royal Meteorological Society, 1958,84:70-72.No abstract is available for this article.

DOI

[7]
Stewart I D.A systematic review and scientific critique of methodology in modern urban heat island literature[J]. International Journal of Climatology, 2011,31(2):200-217.In the modern era of urban climatology, much emphasis has been placed on observing and documenting heat island magnitudes in cities around the world. Urban climate literature consequently boasts a remarkable accumulation of observational heat island studies. Through time, however, methodologists have raised concerns about the authenticity of these studies, especially regarding the measurement, definition and reporting of heat island magnitudes. This paper substantiates these concerns through a systematic review and scientific critique of heat island literature from the period 1950-2007. The review uses nine criteria of experimental design and communication to critically assess methodological quality in a sample of 190 heat island studies. Results of this assessment are discouraging: the mean quality score of the sample is just 50 percent, and nearly half of all urban heat island magnitudes reported in the sample are judged to be scientifically indefensible. Two areas of universal weakness in the literature sample are controlled measurement and openness of method: one-half of the sample studies fail to sufficiently control the confounding effects of weather, relief or time on reported 'urban' heat island magnitudes, and three-quarters fail to communicate basic metadata regarding instrumentation and field site characteristics. A large proportion of observational heat island literature is therefore compromised by poor scientific practice. This paper concludes with recommendations for improving method and communication in heat island studies through better scrutiny of findings and more rigorous reporting of primary research. Copyright. (C) 2010 Royal Meteorological Society

DOI

[8]
Zhao L, Lee X, Smith R B, et al.Strong contributions of local background climate to urban heat islands[J]. Nature, 2014,511(7508):216-219.The urban heat island(UHI), a common phenomenon in which surface temperatures are higher in urban areas than in surrounding rural areas, represents one of the most significant human-induced changes to Earth's surface climate(1,2). Even though they are localized hotspots in the landscape, UHIs have a profound impact on the lives of urban residents, who comprise more than half of the world's population(3). A barrier to UHI mitigation is the lack of quantitative attribution of the various contributions to UHI intensity(4) (expressed as the temperature difference between urban and rural areas, Delta T). A common perception is that reduction in evaporative cooling in urban land is the dominant driver of DT (ref. 5). Here we use a climate model to show that, for cities across North America, geographic variations in daytime Delta T are largely explained by variations in the efficiency with which urban and rural areas convect heat to the lower atmosphere. If urban areas are aerodynamically smoother than surrounding rural areas, urban heat dissipation is relatively less efficient and urban warming occurs (and vice versa). This convection effect depends on the local background climate, increasing daytime Delta T by 3.06 +/- 0.3 kelvin (mean and standard error) in humid climates but decreasing Delta T by 1.5 +/- 0.2 kelvin in dry climates. In the humid eastern United States, there is evidence of higher Delta T in drier years. These relationship simply that UHIs will exacerbate heat wave stress on human health in wet climates where high temperature effects are already compounded by high air humidity(6,7) and in drier years when positive temperature anomalies may be reinforced by a precipitation temperature feedback(8). Our results support albedo management as a viable means of reducing Delta T on large scales(9,10).

DOI PMID

[9]
Zhou D, Zhao S, Liu S, et al.Surface urban heat island in China's 32 major cities: Spatial patterns and drivers[J]. Remote Sensing of Environment, 2014,152:51-61.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.

DOI

[10]
王郁,胡非.近10年来北京夏季城市热岛的变化及环境效应的分析研究[J].地球物理学报,2006,49(1):61-68.根据1993～2003年北京地区气象台站7、8月的温度资料，分析研究了近10年来北京夏季城市热岛的变化及其环境效应.结果表明：北京夏季城市热岛的水平范围扩大到近郊区和远郊区的通州，分布特征也由“单中心”转变为“多中心”；平均热岛强度呈逐渐增强趋势，在夏季出现了强热岛；北京夏季出现热岛和强热岛的天数激增，7月最大热岛强度也呈逐年上升趋势；热岛的强度和水平分布都有明显的日变化；由于热岛效应使城区增温显著，北京夏季的高温日（Tmax≥35℃）也逐年增多. 本文还指出朝阳区气象观测站由于周围高大植被的影响，观测资料已不具备城区代表性.同时也证明绿化对降低城市热岛效应是极为有效的.本文的研究成果对北京城市发展和规划有一定的科学参考价值.

DOI

[Wang Y, Hu F.Variations of the urban heat island in summer of the recent 10 years over Beijing and its environment effect[J]. Chinese Journal of Geophysics, 2006,49(1):61-68. ]

[11]
许辉熙. 成都平原中等城市的热岛效应动态特征对比研究[J].测绘与空间地理信息, 2015,38(1):13-19.

[Xu H X.Comparison on dynamic characteristics of urban heat island effects in medium - sized cities in Chengdu Plain[J]. Geomatics and spatial information technology, 2015,38(1):13-19. ]

[12]
Peng S, Piao S, Ciais P, et al.Surface urban heat island across 419 global big cities[J]. Environmental Science and Technology, 2012,46(2):696-703.Urban heat island is among the most evident aspects of human impacts on the earth system. Here we assess the diurnal and seasonal variation of surface urban heat island intensity (SUHII) defined as the surface temperature difference between urban area and suburban area measured from the MODIS. Differences in SUHII are analyzed across 419 global big cities, and we assess several potential biophysical and socio-economic driving factors. Across the big cities, we show that the average annual daytime SUHII (1.5 1.2 C) is higher than the annual nighttime SUHII (1.1 0.5 C) (P < 0.001). But no correlation is found between daytime and nighttime SUHII across big cities (P = 0.84), suggesting different driving mechanisms between day and night. The distribution of nighttime SUHII correlates positively with the difference in albedo and nighttime light between urban area and suburban area, while the distribution of daytime SUHII correlates negatively across cities with the difference of vegetation cover and activity between urban and suburban areas. Our results emphasize the key role of vegetation feedbacks in attenuating SUHII of big cities during the day, in particular during the growing season, further highlighting that increasing urban vegetation cover could be one effective way to mitigate the urban heat island effect.

DOI PMID

[13]
Zhou B, Rybski D, Kropp J P.On the statistics of urban heat island intensity[J]. Geophysical Research Letters, 2013,40(20):5486-5491.We perform a systematic study of all cities in Europe to assess the Urban Heat Island (UHI) intensity by means of remotely sensed land surface temperature data. Defining cities as spatial clusters of urban land cover, we investigate the relationships of the UHI intensity, with the cluster size and the temperature of the surroundings. Our results show that in Europe, the UHI intensity in summer has a strong correlation with the cluster size, which can be well fitted by an empirical sigmoid model. Furthermore, we find a novel seasonality of the UHI intensity for individual clusters in the form of hysteresis-like curves. We characterize the shape and identify apparent regional patterns.

DOI

[14]
Tan M, Li X.Quantifying the effects of settlement size on urban heat islands in fairly uniform geographic areas[J]. Habitat International, 2015,49(4):100-106.City size is closely related to urban heat island intensity (UHII). To examine the relationship more accurately, it is necessary to eliminate the effects of landforms and climatic differences on urban heat islands (UHIs), through selecting settlement clusters in a large plain within a similar biome as the study area. This study selected 1124 land use clusters (cities, towns, and big rural settlements) and demarcated surrounding buffer areas; each buffer width equaled the radius of the clusters. The results showed that UHI increased with growth in cluster size, and the relationship could be described using a logarithmic function. For clusters with an area >2km 2 , the city size accounted for about 60% of the variance in UHII during the night and only about 30% during the day. For clusters with areas of 10km 2 .

DOI

[15]
Imhoff M L, Zhang P, Wolfe R E, et al.Remote sensing of the urban heat island effect across biomes in the continental USA[J]. Remote Sensing of Environment, 2010,114(3):504-513.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.

DOI

[16]
Stewart I D, Oke T R.Local climate zones for urban temperature studies[J]. Bulletin of the American Meteorological Society, 2012,93(12):1879-1900.Abstract The new 'local climate zone' (LCZ) classification system provides a research framework for urban heat island studies and standardizes the worldwide exchange of urban temperature observations.

DOI

[17]
Stewart I D, Oke T R, Krayenhoff E S.Evaluation of the “local climate zone” scheme using temperature observations and model simulations[J]. International Journal of Climatology, 2014,34(4):1062-1080.Local climate zones' (LCZs) comprise a new and systematic classification of field sites for heat island studies. The classification divides urban and rural landscapes into 17 standard classes, each defined by structural and land cover properties that influence air temperature at screen height. This study is the first to evaluate the conceptual division of LCZs with temperature observations and simulation results from surface-atmosphere models. Results confirm that thermal contrasts exist among all LCZ classes, and that such contrasts are governed largely by building height and spacing, pervious surface fraction, tree density, and soil wetness. Therefore, partitioning of landscapes into structural and land cover classes, or LCZs,' is deemed justified for the purposes of field site classification in heat island studies. Also justified is the use of inter-zone temperature difference (TLCZ X-Y) to quantify heat island magnitude. To further improve the LCZ system, we encourage other researchers to observe and model the climatic conditions of its varied classes. Especially useful would be tests using field data from different urban and rural environments to those in this study, and running more advanced urban canopy models with demonstrated predictive capability.

DOI

[18]
Alexander P J, Mills G.Local climate classification and Dublin's urban heat island[J]. Atmosphere, 2014,5(4):755-774.A recent re-evaluation of urban heat island (UHI) studies has suggested that the urban effect may be expressed more meaningfully as a difference between Local Climate Zones (LCZ), defined as areas with characteristic dimensions of between one and several kilometers that have distinct effects on climate at both micro-and local-scales (city streets to neighborhoods), rather than adopting the traditional method of comparing urban and rural air temperatures. This paper reports on a UHI study in Dublin (Ireland) which maps the urban area into LCZ and uses these as a basis for carrying out a UHI study. The LCZ map for Dublin is derived using a widely available land use/cover map as a basis. A small network of in-situ stations is deployed into different LCZ across Dublin and additional mobile temperature traverses carried out to examine the thermal characteristics of LCZ following mixed weather during a 1 week period in August 2010. The results show LCZ with high impervious/building coverage were on average >4 C warmer at night than LCZ with high pervious/vegetated coverage during conditions conducive to strong UHI development. The distinction in mean LCZ nocturnal temperature allows for the generation of a heat map across the entire urban area.

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[19]
Ng Y X Y, Chua L H C, Irvine K N. A study of urban heat island using "local climate zones" - the case of Singapore[J]. British Journal of Environment and Climate Change, 2015,5(2):116-133.Aims: The study of urban heat islands and traditionally relies on simplistic descriptors such as "urban" and "rural". While these descriptors may be evocative of the landscape, they are insufficient in providing information like its site properties which have direct impacts on the surface-layer climate. The newly developed "Local Climate Zones" (LCZ) characterization scheme from Oke and Stewart...

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[20]
Lehnert M, Geletič J, Husák J, et al.Urban field classification by “local climate zones” in a medium-sized Central European city: The case of Olomouc (Czech Republic)[J]. Theoretical and Applied Climatology, 2015,122:531-541.The stations of the Metropolitan Station Network in Olomouc (Czech Republic) were assigned to local climatic zones, and the temperature characteristics of the stations were compared. The classification of local climatic zones represents an up-to-date concept for the unification of the characterization of the neighborhoods of climate research sites. This study is one of the first to provide a classification of existing stations within local climate zones. Using a combination of GIS-based analyses and field research, the values of geometric and surface cover properties were calculated, and the stations were subsequently classified into the local climate zones. It turned out that the classification of local climatic zones can be efficiently used for representative documentation of the neighborhood of the climate stations. To achieve a full standardization of the description of the neighborhood of a station, the classification procedures, including the methods used for the processing of spatial data and methods used for the indication of specific local characteristics, must be also standardized. Although the main patterns of temperature differences between the stations with a compact rise, those with an open rise and the stations with no rise or sparsely built areas were evident; the air temperature also showed considerable differences within particular zones. These differences were largely caused by various geometric layout of development and by unstandardized placement of the stations. For the direct comparison of temperatures between zones, particularly those stations which have been placed in such a way that they are as representative as possible for the zone in question should be used in further research.

DOI

[21]
Bechtel B, Alexander P J, Böhner J, et al.Mapping local climate zones for a worldwide database of the form and function of cities[J]. ISPRS International Journal of Geo-Information, 2015,4(1):199-219.Progress in urban climate science is severely restricted by the lack of useful information that describes aspects of the form and function of cities at a detailed spatial resolution. To overcome this shortcoming we are initiating an international effort to develop the World Urban Database and Access Portal Tools (WUDAPT) to gather and disseminate this information in a consistent manner for urban areas worldwide. The first step in developing WUDAPT is a description of cities based on the Local Climate Zone (LCZ) scheme, which classifies natural and urban landscapes into categories based on climate-relevant surface properties. This methodology provides a culturally-neutral framework for collecting information about the internal physical structure of cities. Moreover, studies have shown that remote sensing data can be used for supervised LCZ mapping. Mapping of LCZs is complicated because similar LCZs in different regions have dissimilar spectral properties due to differences in vegetation, building materials and other variations in cultural and physical environmental factors. The WUDAPT protocol developed here provides an easy to understand workflow; uses freely available data and software; and can be applied by someone without specialist knowledge in spatial analysis or urban climate science. The paper also provides an example use of the WUDAPT project results.

DOI

[22]
天气网.四大火炉新排名福州居首[N/OL]. 2014. .

[23]
Chander G, Markham B.Revised Landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003,41(11):2674-2677.Effective May 5, 2003, Landsat-5 (L5) Thematic Mapper (TM) data processed and distributed by the U.S. Geological Survey (USGS) Earth Resources Observation System (EROS) Data Center (EDC) will be radiometrically calibrated using a new procedure and revised calibration parameters. This change will improve absolute calibration accuracy, consistency over time, and consistency with Landsat-7 (L7) Enhanced Thematic Mapper Plus (ETM+) data. Users will need to use new parameters to convert the calibrated data products to radiance. The new procedure for the reflective bands (1-5,7) is based on a lifetime radiometric calibration curve for the instrument derived from the instrument's internal calibrator, cross-calibration with the ETM+, and vicarious measurements. The thermal band will continue to be calibrated using the internal calibrator. Further updates to improve the relative detector-to-detector calibration and thermal band calibration are being investigated, as is the calibration of the Landsat-4 (L4) TM.

DOI

[24]
Chavez P S.Image-based atmospheric corrections-revisited and improved[J]. Photogrammetric Engineering and Remote Sensing, 1996,62(9):1025-1035.Abstract A major benefit of multitemporal, remotely sensed images is their applicability to change detection over time.(...) However, to maximize the usefulness of data from multitemporal point of view, an easy-to-use, cost-efective, and accurate radiometric calibration and correction procedure is needed.

DOI

[25]
USGS. Landsat 8 (L8) Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS)[OL]. , 2013.

[26]
Stewart I D, Oke T R.Newly developed “thermal climate zones” for defining and measuring urban heat island magnitude in the canopy layerC]//[Eighth Symposium on Urban Environment, Phoenix, AZ, 2009.Urban heat island (UHI) magnitude is the most widely recognized indicator of city climate modification in the environmental sciences. Understood broadly as a nocturnal "urban-rural" air temperature difference at screen height, UHI magnitude has been measured and reported in thousands of cities and towns worldwide since the early 1900s. The popular use of this indicator, however, has created considerable confusion in climate literature over the dichotomous classification of measurement sites--as "urban" or "rural"--thereby defining heat island magnitude. This confusion stems from UHI investigators relying intuitively on standard dictionary meanings of "urban" and "rural" to describe and classify their sites. Over time, complacency with these meanings has blurred the physical and cultural peculiarities of the "urban" and "rural" sites chosen by investigators to quantify UHI magnitude. The need for methodological rigor behind computations of UHI magnitude is now a pressing concern. We address this issue through a new and more purposeful classification of "urban" and "rural" measurement sites.

[27]
Xu H.Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery[J]. International Journal of Remote Sensing, 2006,27(14):3025-3033.The normalized difference water index (NDWI) of McFeeters (1996) was modified by substitution of a middle infrared band such as Landsat TM band 5 for the near infrared band used in the NDWI. The modified NDWI (MNDWI) can enhance open water features while efficiently suppressing and even removing built-up land noise as well as vegetation and soil noise. The enhanced water information using the NDWI is often mixed with built-up land noise and the area of extracted water is thus overestimated. Accordingly, the MNDWI is more suitable for enhancing and extracting water information for a water region with a background dominated by built-up land areas because of its advantage in reducing and even removing built-up land noise over the NDWI.

DOI

[28]
Rouse J W, Haas R H, Schell J A, et al.Monitoring vegetation systems in the great plains with ERTSC]//[Proceedings of the Third ERTS Symposium, Nasa SP-351, Washington DC, USA, 1973,1:309-317.Not Available

[29]
Xu H.Extraction of urban built-up land features from Landsat imagery using a thematic-oriented index combination technique[J]. Photogrammetric Engineering and Remote Sensing, 2007,73(12):1381-1391.This paper proposes a technique to extract urban built-up land features from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) imagery taking two cities in southeastern China as examples. The study selected three indices, Normalized Difference Built-up Index (NDBI), Modified Normalized Difference Water Index (MNDWI), and Soil Adjusted Vegetation Index (SAVI) to represent three major urban land-use classes, built-up land, open water body, and vegetation, respectively. Consequently, the seven bands of an original Landsat image were reduced into three thematic-oriented bands derived from above indices. The three new bands were then combined to compose a new image. This considerably reduced data correlation and redundancy between original multispectral bands, and thus significantly avoided the spectral confusion of the above three land-use classes. As a result, the spectral signatures of the three urban land-use classes are more distinguishable in the new composite image than in the original seven-band image as the spectral clusters of the classes are well separated. Through a supervised classification, a principal components analysis, or a logic calculation on the new image, the urban built-up lands were finally extracted with overall accuracy ranging from 91.5 to 98.5 percent. Therefore, the technique is effective and reliable. In addition, the advantages of SAVI over NDVI and MNDWI over NDWI in the urban study are also discussed in this paper.

DOI

[30]
Xu H Q.Analysis of impervious surface and its impact on urban heat environment using the normalized difference impervious surface index (NDISI)[J]. Photogrammetric Engineering & Remote Sensing, 2010,76(5):557-565.

[31]
Rikimaru A.Landsat TM data processing guide for forest canopy density mapping and monitoring modelC]//[International Tropical Timber Organization (ITTO) workshop on utilization of remote sensing in site assessment and planning for rehabilitation of logged-over forest, Bangkok, Thailand, 1996:1-8.

[32]
Carlson T N, Ripley D A.On the relation between NDVI, fractional vegetation cover, and leaf area index[J]. Remote Sensing of Environment, 1997,62(3):241-252.ABSTRACT We use a simple radiative transfer model with vegetation, soil, and atmospheric components to illustrate how the normalized difference vegetation index (NDVI), leaf area index (LAI), and fractional vegetation cover are dependent. In particular, we suggest that LAI and fractional vegetation cover may not be independent quantitites, at least when the former is defined without regard to the presence of bare patches between plants, and that the customary variation of LAI with NDVI can be explained as resulting from a variation in fractional vegetation cover. The following points are made: i) Fractional vegetation cover and LAI are not entirely independent quantities, depending on how LAI is defined. Care must be taken in using LAI and fractional vegetation cover independently in a model because the former may partially take account of the latter; ii) A scaled NDVI taken between the limits of minimum (bare soil) and miximum fractional vegetation cover is insenstive to atmospheric correction for both clear and hazy conditions, at least for viewing angles less than about 20 degrees from nadir; iii) A simple relation between scaled NDVI and fractional vegetation cover, previously described in the literature, is further confirmed by the .simulations; iv) The sensitive dependence of LAI on NDVI when the former is below a value of about 2 4 may be viewed as being due to the variation in the bare soil component.

DOI

[33]
徐涵秋,林中立,潘卫华.单通道算法地表温度反演的若干问题讨论——以Landsat系列数据为例[J].武汉大学学报·信息科学版, 2015,40(4):487-492.

[Xu H Q, Lin Z L, Pan W H.Some issues in land surface temperature retrieval of Landsat thermal data with the single-channel algorithm[J]. Geomatics and information science of Wuhan University, 1997,62(3):241-252. ]

[34]
徐涵秋. 新型Landsat8卫星影像的反射率和地表温度反演[J].地球物理学报,2015,58(3):741-747.Landsat 8卫星自2013年2月发射以来,其影像的定标参数经过了不断调整和完善,针对Landsat 8开发的各种算法也相继问世.本文采用最新的参数、算法和引入COST算法建立的大气校正模型,对Landsat 8多光谱和热红外波段进行了处理,反演出它们的反射率和地表温度,并与同日的Landsat 7数据和实测地表温度数据进行了对比.结果表明,现有Landsat 8多光谱数据的定标参数和大气顶部反射率反演算法已有很高的精度,本文引入COST算法建立的Landsat 8大气校正模型也与Landsat 7的COST模型所获得的结果几乎相同,相关系数可高达0.99.但是现有针对Landsat 8提出的地表温度反演算法仍不理想,已提出的劈窗算法误差都较大.鉴于TIRS 11热红外波段的定标参数仍不理想,因此在现阶段建议采用单通道算法单独反演TIRS 10波段来求算地表温度,但要注意根据大气水汽含量的情况选用正确的大气参数计算公式.

DOI

[Xu H Q.Retrieval of the reflectance and the land surface temperature of the newly-launched Landsat 8 satellite[J]. Chinese Journal of Geophysics, 2015,58(3):741-747. ]

[35]
Jiménez-Muñoz J C, Cristóbal J, Sobrino J A, et al. Revision of the single-channel algorithm for land surface temperature retrieval from Landsat thermal-infrared data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009,47(1):339-349.This paper presents a revision, an update, and an extension of the generalized single-channel (SC) algorithm developed by Jimenez-Munoz and Sobrino (2003), which was particularized to the thermal-infrared (TIR) channel (band 6) located in the Landsat-5 Thematic Mapper (TM) sensor. The SC algorithm relies on the concept of atmospheric functions (AFs) which are dependent on atmospheric transmissivity and upwelling and downwelling atmospheric radiances. These AFs are fitted versus the atmospheric water vapor content for operational purposes. In this paper, we present updated fits using MODTRAN 4 radiative transfer code, and we also extend the application of the SC algorithm to the TIR channel of the TM sensor onboard the Landsat-4 platform and the enhanced TM plus sensor onboard the Landsat-7 platform. Five different atmospheric sounding databases have been considered to create simulated data used for retrieving AFs and to test the algorithm. The test from independent simulated data provided root mean square error (rmse) values below 1 K in most cases when atmospheric water vapor content is lower than 2 g middotcm. For values higher than 3 g middotcm, errors are not acceptable, as what occurs with other SC algorithms. Results were also tested using a land surface temperature map obtained from one Landsat-5 image acquired over an agricultural area using inversion of the radiative transfer equation and the atmospheric profile measuredat the sensor overpass time. The comparison with this ldquoground-truthrdquo map provided an rmse of 1.5 K.

DOI

[36]
Jiménez-Muñoz J C, Sobrino J A. A generalized single-channel method for retrieving land surface temperature from remote sensing data[J]. Journal of Geophysical Research, 2003,108(D22):4688, doi: 10.1029/2003JD003480.Many papers have developed algorithms to retrieve land surface temperature from at-sensor and land surface emissivity data. These algorithms have been specified for different thermal sensors on board satellites, i.e., the algorithm used for one thermal sensor (or a combination of thermal sensors) cannot be used for other thermal sensor. The main goal of this paper is to propose a generalized single-channel algorithm that only uses the total atmospheric water vapour content and the channel effective wavelength (assuming that emissivity is known), and can be applied to thermal sensors characterized with a FWHM (Full-Width Half-Maximum) of around 1 μm actually operative on board satellites. The main advantage of this algorithm compared with the other single-channel methods is that in-situ radiosoundings or effective mean atmospheric temperature values are not needed, whereas the main advantage of this algorithm compared with split-window and dual-angle methods is that it can be applied to different thermal sensors using the same equation and coefficients. The validation for different test sites shows root mean square deviations lower than 2 K for AVHRR channel 4 (λ≈ 10.8 μm) and ATSR-2 channel 2 (λ≈ 11 μm), and lower than 1.5 K for Landsat Thematic Mapper (TM) band 6 (λ≈ 11.5 μm).

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[37]
Jimenez-Munoz J C, Sobrino J A, Skokovic D, et al. Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor data[J]. IEEE Geoscience and Remote Sensing Letters, 2014,11(10):1840-1843.The importance of land surface temperature (LST) retrieved from high to medium spatial resolution remote sensing data for many environmental studies, particularly the applications related to water resources management over agricultural sites, was a key factor for the final decision of including a thermal infrared (TIR) instrument on board the Landsat Data Continuity Mission or Landsat-8. This new TIR sensor (TIRS) includes two TIR bands in the atmospheric window between 10 and 12 mu m, thus allowing the application of split-window (SW) algorithms in addition to single-channel (SC) algorithms or direct inversions of the radiative transfer equation used in previous sensors on board the Landsat platforms, with only one TIR band. In this letter, we propose SC and SW algorithms to be applied to Landsat-8 TIRS data for LST retrieval. Algorithms were tested with simulated data obtained from forward simulations using atmospheric profile databases and emissivity spectra extracted from spectral libraries. Results show mean errors typically below 1.5 K for both SC and SW algorithms, with slightly better results for the SW algorithm than for the SC algorithm with increasing atmospheric water vapor contents.

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