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Journal of Geo-information Science >

2018 , Vol. 20 >Issue 3: 385 - 395

DOI: https://doi.org/10.12082/dqxxkx.2018.170342

Remote Sensing Analysis of Changes of Urban Thermal Environment of Fuzhou City in China in the Past 20 Years

  • HOU Haoran , 1 ,
  • DING Feng , 1, 2, 3, * ,
  • LI Qinsheng 1
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  • 1. School of Geographical Sciences, Fujian Normal University, Fuzhou 350007, China
  • 2. Key Laboratory of Humid Subtropical Eco-geographical Process, Ministry of Education, Fujian Normal University, Fuzhou 35007, China
  • 3. Fujian Provincial Engineering Research Center for Monitoring and Assessing Terrestrial Disasters, Fuzhou 350007, China
*Corresponding author: DING Feng, E-mail:

Received date: 2017-07-22

  Request revised date: 2017-12-22

  Online published: 2018-03-20

Supported by

Natural Science Foundation of Fujian Province, China, No.2017J01463, 2009J01210

Education Department of Fujian Province, China, No.JA09059.

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《地球信息科学学报》编辑部 所有
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Abstract

Urbanization is taking place at an unprecedented rate around the world, particularly in China in the latest two decades. The effects of the intensive land-use / land-cover changes on urban surface temperatures and the consequences of these changes to human health are becoming progressively larger problems. Fuzhou, which is the capital city of Fujian province, is located in the coastal area of southeastern China. It has experienced a significant change of urban thermal environment during this period, and was recently named as one of the “new four furnace cities” in China. To study the process of changes in the thermal environment of Fuzhou city in the past 22 years, three Landsat images acquired in the years of 1994, 2003 and 2016, respectively, were used. HUTS is a widely used thermal sharpener method, which uses Normalized Difference Vegetation Index (NDVI) and surface albedo (α) to downscale the spatial resolution of thermal infrared data. It was applied to obtain LST images of higher spatial resolution (30 m) in the study area. The three downscaled LST images were then normalized, rescaled and overlaid to produce difference images to find out the changes of the thermal environment between different dates. Furthermore, by introducing simulations of different landscape patterns, these changes were evaluated and analyzed from the perspective of landscape ecology. The research results showed that, in 1994-2016, the high temperature area was increased from 35.75 km2 to 184.11 km2 with the city expansion. High temperature patches were expanded from city center to suburbs. On the other hand, the area and density of the high temperature patches were remarkably declined with the urban renewal. The Urban-Heat-Island Ratio Index (URI) rose up from 0.39 to 0.52, indicating that the urban heat island effect in the study area has been enhanced. Overall, the thermal environment of main urban area in Fuzhou has changed greatly in the past 22 years. Aggregation of high temperature patches was mitigated in Gulou, Taijiang and southern Jin'an Districts. Meanwhile, the temperature increased significantly in most area of Cangshan District, Mawei District and Minhou County due to rapid urbanization.

Key words： urban thermal environment; thermal sharpening; land surface temperature; landscape metrics; Fuzhou

Cite this article

HOU Haoran , DING Feng , LI Qinsheng . Remote Sensing Analysis of Changes of Urban Thermal Environment of Fuzhou City in China in the Past 20 Years[J]. Journal of Geo-information Science, 2018 , 20(3) : 385 -395 . DOI: 10.12082/dqxxkx.2018.170342

1 引言

采用遥感技术监测城市热岛效应和城市热环境变化是近年来城市环境监测和城市生态研究的热点领域[1,2,3,4,5]。随着城市化进程的快速发展,人为改造的下垫面和人为活动的热量排放打破了原有的热平衡,使得城市及其周边区域的生态环境发生了很大变化[6],因此,开展城市热环境变化研究对于保护生态环境、建设宜居城市具有重要指导意义[7]。然而,对于城市这种下垫面异质性很大的区域,使用遥感方法对其热环境进行研究却受到很多因素的制约,其中最为主要的因素是现有热红外遥感数据空间分辨率的限制[8]。目前研究热环境所需要的热红外数据,空间分辨率普遍在100 m左右,在这个尺度上,可以正确区分城市与郊区的热环境差异,却无法划分城市内部的道路、高建筑密度区域、低建筑密度区域、公园和具有一定规模的绿地的热环境差异[9]。
空间降尺度技术是解决现有困境的有效途径之一,它通过对同一传感器的不同分辨率波段数据进行整合,从而实现提高热红外影像空间分辨率的目标[10]。运用空间降尺度算法可以发掘遥感影像的潜力,获得与可见光波段空间分辨率相同的热红外影像,这对于提高城市热环境的监测水平具有重要意义[11]。本文以福建省福州市为例,采用现有文献中表现较好的空间降尺度HUTS算法,获取空间分辨率为30 m的1994年5月12日、2003年5月29日和2016年7月27日3个时相的地表温度影像,结合土地利用、城市规划等数据定量分析近20年福州市建成区的热环境发展变化情况,主要包括：随着建成区拓展地表温度分布的时空演变,不同等级的温度分布区域的扩张与收缩,城市热岛的变化情况等,同时引入多个景观指数分析建成区中高温斑块在近20年的变化情况。

2 研究区及数据源

本文以福建省福州市为研究区（图1）,范围以《福州市城市总体规划（2011-2020）》中确定的福州市中心城区为主,主要包括福州市的鼓楼区、台江区、仓山区、马尾区以及晋安区和闽侯县的部分地区,2016年区内常住人口约350万[12],是福州市近20年来快速发展的核心区域。福州全年冬短夏长,气候湿润,属亚热带海洋性季风气候。研究区所在位置属典型的河口盆地地貌,地势西高东低,盆地四周被群山所环抱,闽江穿城而过。
Fig. 1 Location of the study area (Landsat 7 imagery acquired on May 29, 2003, RGB432)

图1 福州市位置图（2003年5月29日Landsat 7影像,RGB432标准假彩色合成）

表1是本文选用的3幅Landsat影像的具体信息和参数,考虑到地表温度的季相特征,均选用成像时间为夏季的影像,影像的轨道条带号均为119/42。3幅影像成像质量较好,云量较小且云大多位于影像边缘,不影响使用。数据经过辐射校正、几何校正和掩膜去云处理[13,14],精度满足研究需要。
Tab. 1 Landsat data used in this study

表1 本研究使用的Landsat数据

日期 卫星 传感器 多光谱波段
分辨率/m		 热红外波段
分辨率/m
1994-05-12 Landsat 5 TM 30 120
2003-05-29 Landsat 7 ETM+ 30 60
2016-07-27 Landsat 8 OLI/TIRS 30 100

3 研究方法

3.1 地表温度反演算法

本文采用普适性单通道算法反演地表温度,该算法最早由Jiménez-Muñoz 等提出[15],对Landsat 5数据的应用效果较好,后又根据Landsat 7和Landsat 8数据特性进行了改进[16,17,18]。该算法的优点是普适性较强,所需的大气参数只有一个大气水汽含量w,降低了因所需大气参数较多而引起的估算误差。以Landsat 5数据为例,具体求算公式如下[15]：
LST = γ / [ ε - 1 ( φ 1 × L sensor + φ 2 ) + φ 3 ] + δ （1）
T sensor = K 2 / ln ( K 1 / L sensor + 1 ) （2）
γ = 1 / [ c 2 · L sensor ( λ 4 · L sen sor / c 1 + λ - 1 ) / T sensor ] （3）
δ = T sensor - γ · L sensor （4）
式中：LST为地表温度,K;ε表示地表比辐射率;Lsensor表示卫星高度上所观测到的热红外波段辐射亮度值;φ1、φ2和φ3为大气水汽含量w的函数。对于2000年1月19号以后的Landsat数据,w可直接从美国宇航局NASA提供的全球大气参数库获得（https://atmcorr.gsfc.nasa.gov/）;对于此日期之前的Landsat数据,则可根据文献[19]中提出的全国范围的大气水汽含量估算模型进行估算。Tsensor表示星上亮温;K1和K2为常量;对于Landsat 5,K1=607.76 W/(m2·sr·μm),K2=1260.56 K;对于Landsat 7,K1=666.09.76 W/(m2·sr·μm),K2=1282.71 K;对于Landsat 8 TIRS波段10,K1=774.89 W/(m2.sr.μm),K2=1321.08 K;c1、c2为Plank辐射常数,c1=1.19104×108 W/(m2·sr·μm),c2=1.43877×104 μm·K;λ为有效作用波长,对于Landsat 5、Landsat 7的第6波段以及Landsat 8的TIRS波段10,λ=11.45 μm。此外,需要说明的是,经过修正,Landsat 7和Landsat 8数据的单通道算法求算过程与Landsat 5数据的求算过程略有不同,具体参见文献[16,17,18,19]。

3.2 空间降尺度算法

本文采用现有文献中表现较好的HUTS算法作为地表温度影像降尺度的算法[8-9,20-21]。该算法最早由Dominguez 等[21]于2011年提出,它在TsHARP等算法的基础上引进一个新参数地表反照率（α）,由归一化植被指数（NDVI）和地表反照率（α）作为回归核,通过120 m/60 m/100 m分辨率的NDVI和α与120 m/60 m/100 m分辨率的LST数据拟合出一个以NDVI和α为自变量,LST为因变量的高次回归方程,再通过30 m分辨率的NDVI和α求出30 m分辨率的LST。现有研究表明,相较于DisTrad和TsHARP等的算法,该算法在城市建成区等地表异质性较大的区域效果更好[9,21]。需要说明的是,Landsat 8数据在发布时其热红外波段就已被USGS重采样为30 m空间分辨率,故本研究先将其退化为100 m分辨率,与重采样至100 m分辨率的NDVI和α共同作为HUTS算法的训练数据。
图2为HUTS算法的流程图。其中,归一化植被指数（NDVI）的计算公式为[22]：
NDVI = ρ NIR - ρ R ρ NIR + ρ R （5）
式中：ρNIR和ρR分别为近红外波和红光波段的地表反射率。
Fig. 2 Procedures of the HUTS method

图2 HUTS算法流程

Landsat 5和Landsat 7的反照率计算公式为[23]：
α = 0.356 × ρ 1 + 0.130 × ρ 2 + 0.373 × ρ 3 + 0.085 × ρ 4 + 0.072 × ρ 5 - 0.0018 （6）
式中：ρ1为Landsat 5和Landsat 7的蓝光波段地表反射率;ρ2为Landsat 5和Landsat 7的红光波段地表反射率;ρ3为近红外波段地表反射率;ρ4和ρ5为2个短波红外波段地表反射率。
而Landsat 8的反照率的计算则采用Smith等[24]于2010年提出的方法,该方法因简单易操作,获得了耶鲁大学的推荐[25]和较多学者的采用[26,27],具体如下：
α = ( 0.356 × ρ 2 ' + 0.130 × ρ 4 ' + 0.373 × ρ 5 ' + 0.085 × ρ 6 ' + 0.072 × ρ 7 ' - 0.0018 ) / 1.016 （7）
式中：ρ′2为Landsat 8的蓝光波段地表反射率;ρ′4为Landsat 8的红光波段地表反射率;ρ′5为近红外波段地表反射率;ρ′6和ρ′7为2个短波红外波段地表反射率。得到LST、NDVI与α的100 m/120 m影像后,即可用这3个量去求解式（8）的系数向量P：
LST = P 1 × NDV I 4 + P 2 × NDV I 3 × α + P 3 × NDV I 2 × α 2 + P 4 × NDVI × α 3 + P 5 × α 4 + P 6 × NDV I 3 + P 7 × NDV I 2 × α + P 8 × NDVI × α 2 + P 9 × α 3 + P 10 × NDV I 2 + P 11 × NDVI × α + P 12 × α 2 + P 13 × NDVI + P 14 × α + P 1 5 （8）
通过对LST、NDVI和α做多元回归分析,求得系数向量P1-P15的值,得到用于降尺度的函数关系式。将30 m分辨率的NDVI和α影像代入到函数关系式中,即可得到降尺度的30 m分辨率的LST影像。对于回归关系产生的误差,可以通过在降尺度的30 m分辨率的LST影像上叠加残差影像加以修正。表2是本研究采用的3幅影像求得的函数关系式及相关系数R2。
Tab. 2 Regression equations and correlation coefficients of the HUTS method

表2 HUTS算法拟合方程与相关系数R2

日期 函数关系式 R2
1994-05-12 LST=-102.968×NDVI4-81.018×NDVI3×α-452.939×NDVI2×α2-3470.743×NDVI×α3-6711.404×α4+ 185.304×NDVI3+124.925×NDVI2×α+1989.608×NDVI×α2+7339.745×α3-98.337×NDVI2-208.730×NDVI× α-2543.358×α2+3.451×NDVI+307.084×α+287.112 0.693
2003-05-29 LST=-83.076×NDVI4-210.457×NDVI3×α+1191.840×NDVI2×α2+1632.957×NDVI×α3-5267.525×α4 +202.028×NDVI3-145.967×NDVI2×α-2091.112×NDVI×α2+4130.058×α3
-126.477×NDVI2+527.870×NDVI×842.025×α2-9.252×NDVI+21.155×α+301.247		 0.722
2016-07-27 LST=18.931×NDVI4-173.263×NDVI3×α+22.455×NDVI2×α2+480.325×NDVI×α3-26.323×α4+8.589×NDVI3 +264.135×NDVI2×α-275.787×NDVI×α2+64.599×α3-47.393×NDVI2-52.674×NDVI×α-64.338×α2 +11.217×NDVI+27.213×α+307.319 0.734

3.3 城市建成区的提取与城市拓展强度

城市建成区是指开发起来的、集中连片的公共设施完善的地区,包括城郊已经开发起来的,公共设施基本完善的地区[28],这类地区在遥感影像上主要表现为连片分布的建设用地。城市建成区提取的关键是判断建成区边界,本文采用影像分类法与手工勾绘相结合的方法提取城市建成区。考虑到Landsat多波段影像30 m的空间分辨率和影像质量,参考中国科学院土地资源分类系统[29],通过人工选取训练区,使用支持向量机（SVM）分类方法提取福州市的城镇用地,在此基础上手工勾绘建成区矢量边界。
城市拓展强度（G）是指单位时间内某一区域区内的城市化强度,公式为[30]：
G = ΔA / TA × T - 1 × 100 （9）
式中：ΔA为某时段城市净增面积/km2;TA为研究区总面积/km2;T为该时段长度。该指标最初被用于研究北京地区的城市化过程[31],是利用GIS和遥感技术对城市建成区空间拓展分析的有效手段,现已在城市化研究中广泛应用。

3.4 城市热岛比例指数

徐涵秋等[32]于2003年提出的城市热岛比例指数（Urban-Heat-Island Ratio Index, URI）可以定量衡量热岛效应的强烈程度,该指数已被国家环境保护部和住房城乡建设部引用,得到了权威认可,并被广泛应用于城市热环境变化的研究中[9,30,33-37]。该指数通过计算热岛与建成区面积的比例,并赋权重来表征热岛发育程度,能更科学地对比不同年份间城市热岛的变化,指数值越大,热岛效应越严重。该指数反映了热岛面积与城市建成区面积的比例关系,其计算公式为：
URI = 1 100 m ∑ i = 1 n w i p i （10）
式中：URI为热岛比例指数;wi为权重,取第i级的等级;pi为第i级的面积占比/%;m为正规化的等级数;n为温度高于郊区的城区范围内的温度等级数。

4 结果与分析

4.1 降尺度结果评价

图3是通过HUTS算法降尺度获得的3个时相的30 m分辨率的LST影像与原本60m/100 m/120 m分辨率的LST影像重采样到30 m分辨率的影像的比较,为方便展示,选用了3个时相的影像中大小均为1.2 km×1.2 km的区域。从图3中可以看出：① 经过降尺度处理后的影像展现出更丰富的细节,对城市建成区中住宅区、道路和绿化植被构成的混合区域的表达更为清晰;② 相较而言,未经降尺度而直接重采样为30 m分辨率的影像,虽然与降尺度后得到的影像空间分辨率一致,但在其城市建成区内部的纹理特征细节表现较为笼统和模糊。因此,经过空间降尺度算法的LST影像,能更清晰地展现建成区内部热环境的发展变化细节,因而也更有利于对城市热环境状况及其变化作进一步的深入分析。
Fig. 3 1.2 km×1.2 km subarea from images in 1994, 2003 and 2016

图3 3个时相（1.2 km × 1.2 km）不同处理结果图

为定量评价降尺度的结果,引入表3中的几个数学统计量,其中均方根误差（RMSE）可以灵敏地测出n维空间中2个测度向量的相似性。本研究采用RMSE指标衡量降尺度得到的地表温度影像与原始地表温度影像之间的差异[38],RMSE的计算公式如下[39]：
R M SE = N - 1 ∑ i = 1 N ( LS T p - LST ) 2 1 2 （11）
式中：RMSE的单位为K;N为像元个数;LSTp为降尺度的影像像元值;LST为原始影像像元值。除了RMSE外,表3还给出了降尺度影像和原始地表温度影像的一些其他指标。可以看出,降尺度后的影像与原始影像差异不大,最大值和最小值的差异主要来源于少数离群的极端值,此类像元占总像元的比例小于0.2%。2016年影像的RMSE值最大,为1.507 K,其余2个时相的RMSE值分别为1.010 K和0.781 K,总体上,降尺度效果较好,满足研究要求。
Tab. 3 Statistics of downscaled images and RMSE (K)

表3 降尺度影像的统计信息和均方根误差（RMSE）（K）

统计量 1994-05-12 2003-05-29 2016-07-27
原始 降尺度 原始 降尺度 原始 降尺度
最大值 303.1 304.3 306.8 304.2 316.5 314.5
最小值 283.1 282.2 289.2 287.4 297.7 297.8.
均值 296.7 296.7 295.7 295.7 305.7 305.5
标准差 0.940 0.651 1.878 1.604 2.858 2.554
RMSE 0.781 1.010 1.507

4.2 研究区近20 年来热环境变化

通过空间降尺度的HUTS算法获得研究区30 m分辨率的1994年、2003年和2016年3个时相地表温度影像（图4）。从图4可以发现,随着福州市的快速发展,其热环境也相应发生了巨大变化。
Fig. 4 LST imageries of the study area

图4 福州市3个时相的地表温度影像

（1）1994年,市中心在地表温度影像上亮度极高,与周围建筑密度较低的郊区和林地、耕地等植被覆盖地区亮度差异巨大。且市中心鼓楼区、台江区和晋安区的部分区域高亮斑块密度极高,仓山区北部靠近闽江的区域也出现高亮斑块聚集的情况,总体可看出高亮斑块呈沿主要交通线分布的态势。
（2）到了2003年,市中心高亮区域面积显著增长,向西与闽侯县相连,向东与马尾区连为一体、与长乐市区隔闽江相望。闽侯县的部分区域像元亮度提高,与其周边郊区已有明显差异。与此同时,市中心与郊区的斑块亮度差别有所减小,市中心高亮斑块内部的亮度差异有所扩大。
（3）2016年,市中心高亮区域继续扩张,高温中心呈现从单中心向多中心分化的趋势。仓山区中部、马尾区沿江地带、原属闽侯县的福州市高新区、长乐市区等都出现了高亮斑块聚集的情况。此外,市中心与郊区的斑块亮度差异继续减小,同时高亮斑块内部的亮度差异继续扩大。

4.3 福州市建成区的空间拓展和热岛变化

图5为3个时相的福州市建成区提取结果,表4是反映城市拓展状况的统计指标。
Fig. 5 Spatial and temporal changes of urban built-up area of Fuzhou from1994 to 2016

图5 福州市建成区时空动态变化

Tab. 4 Built-up areas and its related indicators of Fuzhou in 1994, 2003 and 2016, respectively

表4 福州市各年份建成区面积及其相关指标

年份 1994-05-12 2003-05-29 2016-07-27 1994-2003年 2003-2016年
面积/km2 73.08 180.38 274.83 107.30 94.45
年均增长率/% - - - 16.31 3.64
城市扩展强度 - - - 6.12 2.57
为进一步定量分析福州市建成区的热环境变化,本文对3个时相的地表温度影像做正规化处理,在保留各时相地表温度影像空间分布格局的同时,实现对不同年份相近季相影像的年际比较[32,35]：
N i = T i - T min T max - T min （12）
式中：Ni为正规化后的像元i的值;Ti为像元i的温度值;Tmin是影像中所有像元的温度值中的最小值;Tmax是影像中所有像元的温度值中的最大值,正规化处理后像元i的值分布在[0,1]之间。对于正规化后的3期地表温度影像,使用密度分割的方法将其划分为7个等级（图6）,表5为3个时相7个温度等级的面积和占比。
Fig. 6 Images of graded radiant temperature of urban built-up area in three years

图6 3个时相建成区内温度等级分布图

Tab. 5 Area and percentage of each LST level and URI indexes of urban built-up areas in 1994, 2003 and 2016

表5 福州市建成区各年地表温度等级的面积、比例和URI

温度等级 1994-05-12 2003-05-29 2016-07-27
面积/km2 占比/% 面积/km2 占比/% 面积/km2 占比/%
1级（低温） 0.75 1.02 3.64 2.02 2.56 0.93
2级（较低温） 3.76 5.15 7.48 4.15 10.61 3.86
3级（次中温） 7.57 10.36 21.03 11.66 24.51 8.92
4级（中温） 25.23 34.53 43.58 24.16 53.04 19.30
5级（次高温） 19.57 26.78 59.00 32.71 111.64 40.62
6级（高温） 10.39 14.23 36.31 20.13 60.85 22.14
7级（特高温） 5.79 7.93 9.33 5.17 11.62 4.23
URI 0.39 0.46 0.52
由图6可知,建成区主要分布有特高温、高温和次高温这3个等级,代表了城市热岛的范围,据此,可确定m为7,n为3,特高温、高温和次高温这3个等级的级值分别为7、6和5,计算得到的URI见表5。
由表5可以发现：1994-2016年,福州市建成区的较低温、中温和特高温区域的占比持续减少,其中中温区域占比降幅最大,为15.23%;高温和次高温区域占比持续增多,其中又以次高温区域占比增幅13.84%为最大;次中温、较低温和低温区域在过去近20年的波动较小,占比变化不大。在近20 a间,福州市建成区的URI呈现上升的趋势,其中1994-2003年上升0.07,2003-2016年上升0.06,这说明在过去约20年福州市建成区的热岛效应强烈程度在不断上升。
为进一步定量探索建成区20 a间的热环境变化情况,对3个时相的温度等级影像做叠加处理,获得1994-2003年、2003-2016年2幅温度等级变化影像（图7）。由图7（a）可以看到：1994-2003年,鼓楼区、马尾区、台江区的部分地区和仓山区中部沿闽江南岸一带温度等级普遍下降,部分绿地覆盖地区如茶亭、乌山等温度等级下降了1-2级;温度等级升高的地区主要集中在鼓楼区北部、晋安区南部、仓山区西北部和闽侯县大学城附近,增幅普遍在1-2级,其中也有少量增幅达3级的地区,究其原因,主要是因为在1994-2003年间,其土地利用类型变化较大,在经历城市化过程后地表温度快速上升成为温度等级较高的区域。由图7（b）可知：2003-2016年,温度等级下降的趋势在市中心进一步扩大,除了台江区大部分地区外,鼓楼区南部、仓山区西北部沿江区域、晋安区南部和马尾区沿江地区等地温度等级也有下降,屏山公园、西湖公园和金牛山附近温度等级下降较大;温度升高的区域主要集中在仓山区西南部和中东部的闽江沿岸、晋安区东部至马尾区的西部、以及闽侯县与鼓楼区交界地区和大学城与海西高新科技产业园附近地区,上升幅度在1-2级之间。
Fig. 7 Spatio-temporal variations of the LST levels in built-up area of Fuzhou

图7 福州市建成区内温度等级时空变化

4.4 热岛斑块的发展变化

为定量探索建成区内热岛斑块的发展变化,本文引入景观生态学中景观格局的研究方法,通过计算3个时相不同水平的景观指数,对分级的正规化温度影像进行统计和分析。
景观指数是能够高度浓缩景观格局信息,反映其结构组成和空间配置某些方面的简单定量指标。通过景观指数可以对景观的组成特征、空间配置、动态变化等进行定量研究。结合本研究区具体情况,本文在景观水平上选择蔓延度指数（CONTAG）,在斑块类型水平上选择斑块密度（PD）和聚合度指数（AI）进行统计计算[36-37,40-42]。
表6为3个时相的蔓延度指数（CONTAG）,这一指数反映了热环境中不同温度等级的团聚程度,也就是不同温度等级间的连通性。由表6可知,1994-2003年蔓延度指数呈上升趋势,在这9年间同一温度等级的斑块聚集程度提高,即同一温度等级的斑块相互连通集中分布于一个区域的状况有所增加,而不同温度等级的斑块间连通性下降,即不同温度等级的斑块相邻混合分布的情况有所减少。2003-2016年蔓延度指数呈下降趋势,说明这一时期与前一时期热环境的变化趋势相反,即同一温度等级的斑块聚集程度下降,不同温度等级的斑块间连通性提高。
Tab. 6 Contagion indexes (CONTAG) in 1994, 2003 and 2016, respectively

表6 3个时相的蔓延度指数（CONTAG）

1994-05-12 2003-05-29 2016-07-27
CONTAG 40.72 52.38 41.91
表7为3个时相高温度等级斑块的斑块密度（PD）和聚合度指数（AI）。斑块密度反映某一类型斑块的破碎化程度,斑块密度越大则说明单个斑块的面积越小,这一温度等级的斑块破碎化程度越高。聚合度指数反映某一类型斑块的聚集水平,当一个温度等级的斑块多为离散分布的小斑块时,则AI值较小;当一个温度等级的斑块集中分布或斑块相互联通时,则AI值较大。综合这2个指标可知,在过去20年：
Tab. 7 Patch Density (PD) and Aggregation Index (AI) of high temperature patches

表7 3个时相热岛温度等级斑块密度（PD）和聚合度指数（AI）

温度等级 1994-05-12 2003-05-29 2016-07-27
PD AI PD AI PD AI
5级（次高温） 53.75 62.53 36.40 67.33 17.52 67.42
6级（高温） 76.08 50.79 24.68 79.45 67.44 64.80
7级（特高温） 73.68 68.77 113.70 51.75 102.20 37.25
（1）次高温斑块经历了从分散的破碎斑块拓展融合成较大面积斑块的过程,斑块密度大幅下降,集中分布水平有一定提高。且在此时期内,次高温区域的面积与占比均大幅提高,说明该类型斑块从较小的离散分布的斑块变为聚集成片分布的斑块,已成为高温度等级斑块的主要组成部分,它的发展变化对福州市建成区高温区域的发展变化有重要作用。
（2）高温斑块经历了从破碎到融合再到破碎的过程,斑块密度先降后升,聚合度指数先升后降。这说明在1994-2003年这一时期高温斑块经历了拓张、聚合的过程,构成成片覆盖的高温区域,成为热岛斑块的主要部分;而在2003-2016年这一时期虽然高温斑块的面积与面积占比均上升,但它不再聚集成片覆盖一个区域而是变为较为破碎的斑块广泛分布于次高温斑块中。
（3）特高温斑块经历了进一步破碎化的过程,斑块密度大幅提高,同时聚合度指数大幅下降超过30,且斑块面积和面积占比均略有下降,这说明在过去20年特高温斑块从较为集中分布变为零星分布于其他温度等级的斑块中,也进一步验证了从图6观察到的趋势：特高温区域的斑块在过去约20年变得破碎而分散。

5 结论

利用HUTS算法得到的更高分辨率城市地表温度影像,具备更清晰的细节和纹理,能够突出原始分辨率热红外影像所不能表达的细小温度差异,从而为本研究开展更高精度的热环境变化分析提供了坚实的基础。归一化的地表温度影像可以去除不同影像之间的季相差异,实现时间轴上不同年份之间的热环境对比分析。进一步引入景观生态学中的景观指数,定量分析研究区3种高温度等级斑块的发展变化,特别是近20年不同等级斑块的形态变化。
结果表明,在过去20年,随着城市化的快速发展,福州市中心城区的热环境发生了较大的变化。一方面,高温度等级区域随着城市扩展而扩展,同时伴随着城市化过程部分原为低温度等级的区域不断向高温度等级区域转变,高温度等级区域占建成区的比重从不足50%提高甚至超过了65%,温度等级升高的区域多集中在鼓楼区北部、晋安区中东部、仓山区和闽侯县;另一方面,随着城市更新,在上世纪90年代已完成城市化的鼓楼区南部和台江区有大量的高温度等级区域在过去20年经历了温度等级下降的过程,仓山区的西北部和马尾区的部分地区在最近10年也经历了温度等级下降的过程。与此同时,福州市的城市热岛比例指数（URI）则从0.39上升为0.52,说明这一时期福州市的热岛效应明显加强,特高温、高温和次高温3个等级区域的总占比不断提高,面积不断扩大。景观指数分析结果表明,同一温度等级的斑块集中连片分布的情况减少,不同温度等级的斑块相间分布成为主流。高温和特高温斑块均历经了破碎化、离散化的过程,市中心的高温集聚现象有所缓解,次高温斑块成为高温度等级斑块的主体。
福州城市热环境在过去20年的明显变化与建成区下垫面状况的变化密不可分,主要是城市拓展和城市规划带来的建筑密度的变化和城市绿地的增加。未来研究中,可以结合长时间尺度的土地利用类型数据、城市3D模型等数据,进一步探索热环境变化的驱动因素,为城市规划和市政建设提供科学决策支持。

The authors have declared that no competing interests exist.

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Bonafoni S.Downscaling of Landsat and MODIS land surface temperature over the heterogeneous urban area of Milan[J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2016,9(5):2019-2027.Remotely sensed images of land surface temperature (LST) with high spatial resolution are required for various environmental applications. For instance, finer resolutions (FRs) are essential to capture thermal details in urban textures. To meet the requirements of sharper and sharper images, this study carries out a downscaling from coarser spatial resolution LST images to FRs using relationships between LST and spectral indexes (SIs) representative of different land cover types over the heterogeneous area of Milan. Different regressive schemes were applied to downscale LST of Landsat Thematic Mapper (TM) and Terra MODIS images during four summer passages. The regressions were first evaluated on Landsat images aggregated at 960 m resolution and downscaled to 480, 240, and 120 m. For the four Landsat scenes, the best regression models include both vegetation and built-up/soil SIs: the root mean square (rms) error, around 1 K for 480 m and 2 K for 120 m, is clearly below the LST standard deviation of each reference image, assumed as LST spatial variability. Then, contemporary MODIS data were downscaled from 960 m to the above FRs, and the best models include again both vegetation and built-up/soil SIs. The rms error is higher than the correspondent Landsat one (in some cases exceeds 3 K), but always below the LST spatial variability. A compression of the range of LST values for the MODIS-downscaled images was found with respect to the Landsat disaggregated images: this shortcoming in the LST retrieval affects the MODIS downscaling accuracy.

DOI

[9]
范鹏宇. 基于热红外遥感影像的空间降尺度研究[D].福州:福建师范大学,2013.

[Fan P Y.Spatial downscaling of land surface temperature retrieved from remotely sensed thermal infrared imagery[D]. Fuzhou: Fujian Normal University Master's thesis, 2013.]

[10]
Anderson M C, Norman J M, Kustas W P, et al.A thermal-based remote sensing technique for routine mapping of land-surface carbon, water and energy fluxes from field to regional scales[J]. Remote Sensing of Environment, 2008,112(12):4227-4241.Robust yet simple remote sensing methodologies for mapping instantaneous land-surface fluxes of water, energy and CO 2 exchange within a coupled framework add significant value to large-scale monitoring networks like FLUXNET, facilitating upscaling of tower flux observations to address questions of regional carbon cycling and water availability. This study investigates the implementation of an analytical, light-use efficiency (LUE) based model of canopy resistance within a Two-Source Energy Balance (TSEB) scheme driven primarily by thermal remote sensing inputs. The LUE model computes coupled canopy-scale carbon assimilation and transpiration fluxes, and replaces a Priestley aylor (PT) based transpiration estimate used in the original form of the TSEB model. In turn, the thermal remote sensing data provide valuable diagnostic information about the sub-surface moisture status, obviating the need for precipitation input data and prognostic modeling of the soil water balance. Both the LUE and PT forms of the model are compared with eddy covariance tower measurements acquired in rangeland near El Reno, OK. The LUE method resulted in improved partitioning of the surface energy budget, capturing effects of midday stomatal closure in response to increased vapor pressure deficit and reducing errors in half-hourly flux predictions from 16 to 12%. The spatial distribution of CO 2 flux was mapped over the El Reno study area using data from an airborne thermal imaging system and compared to fluxes measured by an aircraft flying a transect over rangeland, riparian areas, and harvested winter wheat. Soil respiration contributions to the net carbon flux were modeled spatially using remotely sensed estimates of soil surface temperature, soil moisture, and leaf area index. Modeled carbon and water fluxes from this heterogeneous landscape compared well in magnitude and spatial pattern to the aircraft fluxes. The thermal inputs proved to be valuable in modifying the effective LUE from a nominal species-dependent value. The model associates cooler canopy temperatures with enhanced transpiration, indicating higher canopy conductance and carbon assimilation rates. The surface energy balance constraint in this modeling approach provides a useful and physically intuitive mechanism for incorporating subtle signatures of soil moisture deficiencies and reduced stomatal aperture, manifest in the thermal band signal, into the coupled carbon and water flux estimates.

DOI

[11]
Hu X F, Weng Q H.Impervious surface area extraction from IKONOS imagery using an object-based fuzzy method[J]. Geocarto International, 2011,26(1):3-20.The study of impervious surfaces is crucial to the sustainable development of urban areas due to its strong impact on urban environments. Remotely sensed high-resolution imagery has the advantage of providing more spatial details; however, digital image processing algorithms have not been well developed to accommodate this advantage and other characteristics of such imagery. In this article, an object-based fuzzy classification approach for impervious surface extraction was developed and applied to two pan-sharpened multi-spectral IKONOS images covering the residential and central business district (CBD) areas of Indianapolis, Indiana, USA. Fuzzy rules based on spectral, spatial and texture attributes, were developed to extract impervious surfaces. An accuracy assessment was performed for the final maps. The results indicated that the spatial patterns of extracted features were in accordance with those in the original images and the boundaries of features were appropriately delineated. Impervious surfaces were extracted with an accuracy of 95% in the residential area and 92% in the CBD area. Road extraction achieved accuracy a bit lower, with 93% and 90% from the residential and CBD area, respectively. Buildings were extracted with an accuracy of 94% from the residential area while 89% from the CBD area. It is suggested that the CBD area had a higher spectral complexity, building displacement and the shadow problem, leading to a more difficult estimation and mapping of impervious surfaces.

DOI

[12]
福州市统计局.2016福州统计年鉴[EB/OL]. .

[Fuzhou City Bureau of Statistics. 2016 Fuzhou Statistical Yearbook[EB/OL]. ]

[13]
赵英时. 遥感应用分析原理与方法(第二版)[M].北京:科学出版社,2013.

[Zhao Y S.Principles and methods of remote sensing application analysis (second edition)[M]. Beijing: Science Press, 2013.]

[14]
Jensen J R.2015. Introductory digital image processing: aremote sensing perspective (4th ed). Upper Saddle River, New Jersey: Prentice Hall.本书详细介绍航空以及航天遥感数字图像经典处理过程。深入浅出, 所有算法都以简单的代数知识介绍。

DOI

[15]
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):2015-2023.1] 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 0204m 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 (0203 090908 10.8 0204m) and ATSR-2 channel 2 (0203 090908 11 0204m), and lower than 1.5 K for Landsat Thematic Mapper (TM) band 6 (0203 090908 11.5 0204m).

DOI

[16]
Jiménez-Muñoz J C, Cristobal 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...

DOI

[17]
Jiménez-Muñoz J C, Sobrino J A, Skoković D, et al. Land surface temperature retrieval methods from Landsat-8 thermal infrared sensor data[J]. IEEE Geoscience & 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 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.

DOI

[18]
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 on retrieval of land surface temperature of Landsat thermal data with single-channel algorithm[J]. Geomatics and Information Science of Wuhan University, 2015,40(4):487-492.]

[19]
王炳忠,申彦波.我国上空的水汽含量及其气候学估算[J].应用气象学报,2012,23(6):763-768.该文根据中国高空气候标准值 (1971—2000年)逐月数据集124个探空站资料,计算出各站的整层大气水汽含量,并绘制出年水汽含量分布,除青藏高原地区外,其余地区基本上呈纬 向分布。继而配合我国地面气候标准值逐月数据集的水汽压和地面气压数据,在对水汽压进行相应的订正后,将其与整层水汽含量进行相关分析,拟合出全国普遍适 用的、统一的或分月的线性经验表达式。拟合结果与实测值之间的均方根误差为0.25 cm。文中还详细讨论了多项式不同次数对拟合结果的影响,结果表明:与数据点走向拟合较好的多项式,次数高其结果并非误差最小。利用经地面气压订正的地面 水汽压(x)与整层水汽含量(y)的拟合公式为y=0.185x+0.093,其最大优点是站点无论高低、不分地域普遍适用。

DOI

[Wang B Z, Shen Y B.Atmospheric vapor content over china and its climatological evaluation method[J]. Journal of Applied Meteorological Science, 2012,23(6):763-768.]

[20]
全金玲,占文凤,陈云浩,等.遥感地表温度降尺度方法比较——性能对比及适应性评价[J].遥感学报,2013,17(2):374-387.在归纳现有遥感地表温度降尺度方法的基础上,选取3种代表性方法:Normalized Difference Vegetation Index(NDVI)、Pixel Block Intensity Modulation (PBIM)和Linear Spectral Mixture Model (LSMM)方法进行实验比较,并建立了一种纹理相似性度量指标CO-RMSE (Co-Occurrence Root Mean Square Error).结果表明:(1)NDVI方法受季节影响最严重,不适于春、冬季,其次为PBIM方法；(2)LSMM方法受分辨率限制最大,低分辨率时丢失大量纹理信息,NDVI方法在较高分辨率时优于PBIM方法,较低分辨率时则相反；(3)3种方法的适用区域分别为植被与裸土像元并存区域,山区和反照率变化较大区域,以及类别间温差较大区域；(4)NDVI方法操作最简单,LSMM方法最复杂.分析认为,尺度因子是决定方法性能的关键,应根据季节、分辨率、地表覆盖、应用目的和操作性等综合选择.

DOI

[Quan J L, Zhan W F, Chen Y H, et al.Downscaling remotely sensed land surface temperature: A comparison of typical methods[J]. Journal of Remote Sensing, 2013,17(2):374-387.]

[21]
Dominguez A, Kleissl J, Luvall J C, et al.High-resolution urban thermal sharpener (HUTS)[J]. Remote Sensing of Environment, 2011,115(7):1772-1780.78 The relationship between albedo, NDVI, and land surface temperature (LST) in an urban area is examined. 78 We develop a sharpening method (HUTS) for low resolution LST satellite data based on higher resolution albedo and NDVI.

DOI

[22]
Rouse J W J, Haas R H, Schell J A, et al. Monitoring vegetation systems in the great plains with ERTS[A]. NASA Special Publication, 1973,351:309.

[23]
Liang S L.Narrowband to broadband conversions of land surface albedo I: Algorithms[J]. Remote Sensing of Environment, 2001,76:213-238.Land surface broadband albedo is a critical variable for many scientific applications. High-resolution narrowband satellite observations contain important information that enables us to map land surface albedo globally, and validate the coarse-resolution albedo products from the broadband sensors using ground oint/plot measurements. However, the conversions from narrowband to broadband albedos of a general surface type have not been well established. Most studies compute total shortwave albedo based on either the empirical relations between surface total shortwave albedo measurements and satellite observations or radiative transfer simulations with the limited number of surface reflectance spectra because of the computational constraints. As a result, many conversion formulae for the same sensors are quite different. In this study, we applied an approach that decouples surface reflectance spectra from the real-time radiative transfer simulations so that many different surface reflectance spectra and the atmospheric conditions can be effectively incorporated. The conversion formulae, based on extensive radiative transfer simulations, are provided in this paper for calculating the total shortwave albedo, total-, direct-, and diffuse-visible, and near-infrared broadband albedos for several narrowband sensors, including ASTER, AVHRR, ETM+/TM, GOES, MODIS, MISR, POLDER, and VEGETATION. Some of these formulae were compared with the published formulae of the same sensors and further validations using extensive ground measurements will be discussed in the companion paper.

DOI

[24]
Smith R B.The heat budget of the earth's surface deduced from space [EB/OL]. .

[25]
Yale University. Yale guide to Landsat 8 image processing[EB/OL]. .

[26]
Sibandze P, Mhangara P, Odindi J, et al.A comparison of normalized difference snow index (NDSI) and normalized difference principal component snow index (NDPCSI) techniques in distinguishing snow from related land cover types[J]. South African Journal of Geomatics, 2014,3(2):197-209.ABSTRACT Snow is a common global meteorological phenomenon known to be a critical component of the hydrological cycle and an environmental hazard. In South Africa, snow is commonly limited to the country's higher grounds and is considered one of the most destructive natural hazards. As a result, mapping of snow cover is an important process in catchment management and hazard mitigation. However, generating snow maps using survey techniques is often expensive, tedious and time consuming. Within the South African context, field surveys are therefore not ideal for the often highly dynamic snow covers. As an alternative, thematic cover ypes based on remotely sensed data-sets are becoming popular. In this study we hypothesise that the reduced dimensionality using Principal Components Analysis (PCA) in concert Normalized Difference Snow Index (NDSI) is valuable for improving the accuracy of snow cover maps. Using the recently launched 11 spectral band Landsat 8 dataset, we propose a new technique that combines the principal component imager generated using PCA with commonly used NDSI, referred to as Normalised Difference Principal Component Snow Index (NDPCSI) to improve snow mapping accuracy. Results show that both NDPCSI and NDSI with high classification accuracies of 84.9% and 76.8% respectively, were effective in mapping snow. Results from the study also indicate that NDSI was sensitive to water bodies found on lower grounds within the study area while the PCA was able to de-correlate snow from water bodies and shadows. Although the NDSI and NDPCSI produced comparable results, the NDPCSI was capable of mapping snow from other related land covers with better accuracy. The superiority of the NDPCSI can particularly be attributed to the ability of principal component analysis to de-correlate snow from water bodies and shadows. The accuracy of both techniques was evaluated using a higher spatial resolution Landsat 8 panchromatic band and Moderate Resolution Imaging Spectroradiometer (MODIS) data acquired on the same day. The findings suggest that NDPCSI is a viable alternative in mapping snow especially in heterogeneous landscape that includes water bodies.

DOI

[27]
Lamchin M, Lee J Y, Lee W K, et al.Assessment of land cover change and desertification using remote sensing technology in a local region of Mongolia[J]. Advances in Space Research, 2016,57:64-77.Desertification is a serious ecological, environmental, and socio-economic threat to the world, and there is a pressing need to develop a reasonable and reproducible method to assess it at different scales. In this paper, the Hogno Khaan protected area in Mongolia was selected as the study area, and a quantitative method for assessing land cover change and desertification assessment was developed using Landsat TM/ETM+ data on a local scale. In this method, NDVI (Normalized Difference Vegetation Index), TGSI (Topsoil Grain Size Index), and land surface albedo were selected as indicators for representing land surface conditions from vegetation biomass, landscape pattern, and micrometeorology. A Decision Tree (DT) approach was used to assess the land cover change and desertification of the Hogno Khaan protected area in 1990, 2002, and 2011. Our analysis showed no correlation between NDVI and albedo or TGSI but high correlation between TGSI and albedo. Strong correlations (0.77 0.92) between TGSI and albedo were found in the non-desertification areas. The TGSI was less strongly correlated with albedo in the low and non desertification areas, at 0.70 and 0.92; respectively. The desertification of the study area is increasing each year; in the desertification map for 1990 2002, there is a decrease in areas of zero and low desertification, and an increase in areas of high and severe desertification. From 2002 to 2011, areas of non desertification increased significantly, with areas of severe desertification also exhibiting increase, while areas of medium and high desertification demonstrated little change.

DOI

[28]
Masek J G, Lindsay F E, Goward S N.Dynamics of urban growth in the Washington DC metropolitan area, 1973-1996, from Landsat observations[J]. International Journal of Remote Sensing, 2000,21(18):3473-3486.Like other human-induced landcover changes, urbanization represents a response to specific economic, demographic, or environmental conditions. We use the Washington D.C. area as a case study to relate satellite-derived estimates of urban growth to these economic and demographic drivers. Using the Landsat data archive we have created a three epoch timeseries for urban growth for the period 1973-1996. This map is based on a NDVI-differencing approach for establishing urban change filtered with a landcover classification to minimize confusion with agriculture. Results show that the built-up area surrounding Washington DC has expanded at a rate of 22km2 per year during this period, with notably higher growth during the late-1980s. Comparisons with census data indicate that the physical growth of the urban plan, observable from space, can be reasonably correlated with regional and national economic patterns.

DOI

[29]
肖鹏峰,刘顺喜,冯学智,等.基于中分辨率遥感图像的土地利用与覆盖分类系统构建[J].中国土地科学,2006,20(2):33-38.

[Xiao P F, Liu S X, Feng X Z, et al.A land use/cover classification system based on medium resolution remote sensing data[J]. China Land Science, 2006,20(2):33-38.]

[30]
李乐,徐涵秋.杭州市城市空间拓展及其热环境变化[J].遥感技术与应用,2014,29(2):264-272.lt;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>

DOI

[Li L, Xu H Q.Urban expansion and thermal environmental changes in Hangzhou City of East China[J]. Remote Sensing Technology and Application, 2014,29(2):264-272.]

[31]
何春阳,史培军,陈晋,等.北京地区城市化过程与机制研究[J].地理学报,2002,57(3):363-371.在长时间序列高分辨率Landsat TM/MSS数据的支持下,对北京地区1975～1997年城市化基本过程和驱动机制进行了分析研究.基本结论如下:(1)北京地区城市化过程主要表现为中心大区和边缘次级中心区的面状城市化、中心大区和边缘次级中心区之间沿交通干线的线状城市化以及中心大区与边缘次级中心区之间的点状城市化3种基本模式,其中,中心大区在城市边缘区的面状城市化过程在区域内居于优势地位.(2)北京地区城市化过程和城市格局的形成是地形、交通等内在适应性因素和经济因素、政府行为、文化传统、突发事件等外在驱动因素共同作用的结果.其中,城市规划、产业发展政策等政府行为和3000年城市发展形成的旧有城市格局和古都风貌从根本上决定了现代北京城市发展的基本过程.

DOI

[He C Y, Shi P J, Chen J, et al.Process and mechanism of urbanization in Beijing area[J]. Acta Geographica Sinica, 2002,57(3):363-371.]

[32]
徐涵秋,陈本清.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].遥感技术与应用,2003,18(3):129-133.lt;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>

DOI

[Xu H Q, Chen B Q.An image processing technique for the study on urban heat island changes using different seasonal remote sensing data[J]. Remote Sensing Technology and Application, 2003,18(3):129-133.]

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中华人民共和国国家环保部.中华人民共和国环境保护行业标准(试行):HJ/T192-2015[S].北京:中国环境科学出版社,2015.

[Ministry of Environmental Protection of People's Republic of China. Technical criterion for ecosystem status evaluation HJ/T192-2015[S]. Beijing: China Environmental Science Press, 2015.]

[34]
中华人民共和国住房城乡建设部.城市生态建设环境绩效评估导则(试行)[S].北京:中国建筑工业出版社,2015.

[Ministry of Housing and Urban-Rural of People's Republic of China. Environmental performance evaluation of urban ecological construction[S]. Beijing: China Architecture Building Press, 2015.]

[35]
Xu H Q, Chen B Q.Remote sensing of the urban heat island and it's change in Xiamen City of SE China[J]. Journal of Environmental Sciences, 2004,16(2):276-281.IntroductionOverthepast 5 0years ,therapidgrowthoftheurbanareasintheworld(Masek ,2 0 0 0 )hasalteredthesurfaceenergybalanceandresultedinaclimaticphenomenonknownasanurbanheatisland (UHI) .TheresultinghighertemperaturecausedbytheUHIhaseffectsonmeteorologya

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[36]
乔治,田光进.北京市热环境时空分异与区划[J].遥感学报,2014,18(3):715-734.城市热环境空间区划是采用分区管理的思路来缓解城市社会经济发展与热环境之间矛盾的技术基础。本文构建城市热环境区划模型的思路为：（1）将不同时相的MODIS地表温度数据产品进行正规化、分级，分析2008年北京城市热环境时空分布特征。（2）构建城市热环境影响因素评价体系，并通过空间主成分分析计算得到热环境影响主成分因子。（3）通过自组织映射神经网络，利用热环境影响主因子，进一步对热环境进行空间区划。结果表明，北京夜间较白天城市热岛分布层次感明显，夏季白天较其他季节高温区聚合程度高。区域下垫面组成要素直接影响热环境，北京城市热环境的主成分因子依次为植被覆盖、地形地貌、城市下垫面建设规模和人为热排放，并依此将北京划为7个热环境区域，根据各个分区热环境成因机制差异分别提出热环境改善和调控措施。

DOI

[Qiao Z, Tian G J.Spatio-temporal diversity and regionalization of the urban thermal environment in Beijing[J]. Journal of Remote Sensing, 2014,18(3):715-734.]

[37]
王乐. 基于RS的西安市地表温度反演及城市热环境研究[D].西安:长安大学,2015.

[Wang L.Inversion of land surface temperature of Xi'an and the research of urban thermal environment based on RS[D]. Xi'an: Chang'an University, 2015.]

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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 effects based on the dynamics of biophysical descriptors of urban surfaces[J]. Acta Ecologic Sinica, 2011,31(14):3890-3901.]

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