多源参数在晋江城市热岛分析中的差异性

城市热岛遥感监测分析中,不同参数与指标得到的热岛时空分布结果不同。针对过去研究仅利用遥感反演地表温度或亮度温度监测城市热岛现象,以福建省晋江市为研究区,选择2010年与2014年夏季Landsat数据,结合数字高程数据和气象站点实测数据,反演晋江市域内地表温度与近地表气温。在此基础上,利用多源参数计算多种城市热岛监测指标,并对比分析这些指标用于晋江城市热岛研究的差异性。结果表明:1)2014年较2010年的城市热岛范围扩大,区域内热岛比例显著上升,热岛斑块面积增多且热岛等级增强;2)相对于地表温度,近地表气温用于评价城市热岛空间分布更为合理;3)不同热岛监测指标作用不同,热岛强度、归一化热场强度可以在空间上直接表明热岛空间分布及强弱,热岛比例指数则在数值上反映了不同区域热岛现象的发生概率,热岛源汇指数结合地表热参数和土地覆盖类型,反映了土地利用变化对区域内热岛效应加剧或减缓的贡献程度。     

城市群热岛时空特征与地表生物物理参数的关系研究

以长株潭城市群区域为例,利用2005年3个不同季节的TERRA/MODIS数据提取的地表温度、归一化植被指数(NDVI)和归一化建筑指数(NDBI),分析了城市热岛效应及其随季节的变化,采用归一化植被指数(NDVI)和归一化建筑指数(NDBI)作为反映地表生物物理特征的参数,分析了城市热岛时空特征与地表生物物理参数的关系。研究结果表明,研究区域城市热岛效应的季相变化明显,一年中夏季与春季的城市热岛效应相对显著,城市地表温度高出周边的郊区达8~10℃;而冬季城市热岛效应相对不太明显,城市地表温度高出周边的郊区4℃。地表温度与归一化植被指数(NDVI)的相关性随季节变化较为明显,说明通常将归一化植被指数(NDVI)作为城市地表温度或城市热岛的代用指标是不适宜的;然而,地表温度与归一化建筑指数(NDBI)在不同季节都呈显著的线性关系,而且地表温度与NDBI线性关系的斜率和截距能够很好地指示不同季节城市热岛的强度,这就为定量分析不同季节城市地表温度的变化提供了物理指数,也为利用遥感研究城市热岛效应提供了新的方法与途径。     

使用单窗算法研究北京城区热岛效应

随着全球变暖和城市化进程的加快,大城市城区的热岛效应日益严重。城市下垫面对地表能量交换的影响巨大,引起地表温度分布的不均一性。遥感技术的发展为地表温度的反演提供了可能。近年来人们使用劈窗算法对均一的海面温度的反演很成功,但是受空间分辨率的限制以及陆面的不均一性,陆面温度的反演一直是一个没有解决好的问题。覃志豪提出了一种TM热红外波段单窗算法,可以利用辅助气象资料快速计算出地表温度。本文以北京市城区为研究区,采用LandsetETM第6波段的单窗算法,反演了亮度温度和地表实际温度,分析了城市下垫面情况下NDVI与地表温度的相关关系,并解释了北京城区热岛在空间上的分布及其可能的原因。结果表明:北京市城区热岛效应显著;地表温度与NDVI相关性显著;城区绿地和水体在区域的温度分布中起到重要作用。     

基于遥感的长沙市城市热岛效应时空分析

利用TM/ETM+数据,以长沙市为例,反演了地表温度,并针对不同时相的遥感数据,利用城市热岛强度来反映热岛效应强弱的变化。结果表明,长沙市的热岛空间分布与城市建成区的轮廓相吻合。城市热岛的范围随着城市建设、新建开发区以及道路交通网的发展不断增大,且在东南方向为主要增长方向。根据热岛强度鉴别并提取了强热岛区与正常区,发现长沙市强热岛区的面积已经从1993年的13.18km2扩大至2008年的68.16km2。最后,对长沙市热岛未来的发展趋势进行了预测。     

基于MODIS 数据的南京市夏季城市热岛分析

城市热岛效应是当前城市环境与气候主要研究内容之一。地表温度与气温之间有紧密的联系, 通过遥感反演地表温度已成为研究城市热岛的有效手段。利用MODIS 数据, 获取地表比辐射率与大气透过率2 个基本参数, 运用劈窗算法反演南京市夏季地表温度。基于不同时相的MODIS数据, 对4 幅南京市地表温度反演图像作对比分析, 较好地显示了南京市城市热岛的空间分布、热岛范围和城市热岛强度, 结果表明南京市夏季热岛问题较为严重。     

21世纪以来西安城乡梯度土地覆盖变化及对城市热岛影响时空特征

城市快速扩张导致城乡梯度土地覆盖发生显著的变化,引发不透水地表的增加,植被覆盖的减少,从而加剧了城市热岛强度。研究城乡梯度土地覆盖变化引起的城市热岛效应,并揭示城市热岛的时空特征及强度的变化,对城市规划建设、人居环境改善及提升城市生态系统服务功能具有重要的意义。基于Landsat系列4期影像,利用单窗算法反演西安市地表温度,计算热场变异指数得到热力场强度图并对其进行等级划分,结合土地利用/覆盖类型数据分析城乡梯度土地覆盖变化对城市热岛强度的影响。结果表明：①2000年西安市极强热岛效应区占研究区面积的10.58%,逐渐增加到2011年极强热岛效应区域的面积占比达到16.14%,而后到2015年降低为9.00%,整体上西安市城市热岛效应呈现出了先增长后降低的趋势;②2000年到2015年城乡建设用地面积增加了412.76 km2,极强热岛强度的范围随城市建成区的扩张逐年向外扩展;③无热岛效应区约70%位于耕地和林地,水域在无热岛效应中的占比也在逐年增多,从31%增加到了47%。不透水地表面积占比与地表温度有显著相关性,城乡梯度植被和水体面积的增加可以有效地缓解城市热岛强度。     

南京市热岛效应及效应响应分析

利用南京市2003年TM影像,通过单窗算法提取地表温度信息,总结出南京市热岛分布状况,找出影响较广泛的热岛集聚区。并利用NDVI指数和NDBI指数分别对南京市区进行绿地和建筑用地的提取,分析南京市热岛空间分布特点以及用地各专题信息和热岛分布的相关性。结果显示,南京市区的热岛区多分布在城市下垫面为硬地面,人口或工业密集地区,并且,建筑用地内的植被对于降低地面温度比绿地更明显。     

基于Landsat 8影像和能量平衡的西安市热场格局研究!

基于2014年8月15日的Landsat 8影像,通过劈窗算法反演西安中心城区地表温度,定量测算热岛中心范围。估算多种地表能量分量,分析热环境格局与地表能量分量的关系。结果表明:(1)西安中心城区城市热岛集中分布在人口、居住、商业密集区、经济技术开发区以及植被覆盖较差的区域;(2)感热、波文比与地表温度呈正相关,人为热与温度呈不显著正相关,净辐射、潜热与地表温度呈显著负相关;(3)城市热岛的地表能量结构中感热与潜热差异是构成城市热岛差异的主要原因。     

中小城市地表温度变化与下垫面关系

针对中小城市地表温度变化与下垫面的关系问题,以合肥市城区和六安市金安区为例,基于Landsat-8热红外卫星影像数据,采用大气校正法反演研究区地表温度。对其进行归一化处理后,利用密度分割技术分析研究区城市热场空间格局,以城市热岛比例指数评价了研究区热岛效应,得到热岛效应强度分布情况,并结合地理国情数据,采用数理统计和空间统计相结合的方法,在水体、植被、不透水面3种不同下垫地表覆盖面积比例与地表温度之间建立关联,进而得出城市下垫面布局对热环境的贡献。结果发现,对于中小城市而言,在城市发展的进程中,若能保持区域内植被覆盖面积高占比,即使不透水面的面积占比有所增加,人类活动区域加大,也不一定会导致城市热岛现象的加剧。     

一带一路沿海超大城市热岛时空特征遥感分析

城市热岛不仅影响城市局地及区域气候,而且对城市空气质量、能源消耗、居民健康等有显著的负面作用。利用长时序遥感数据,系统地分析各超大城市热岛的时空特征,能够为城市热岛效应减缓政策的制定提供参考,对带路城市可持续发展具有重要意义。基于2001~2017年MODIS地表温度产品和Landsat土地利用分类数据,以城市热岛强度（Surface Urban Heat Island Intensity, SUHII）作为指标,从季节和年际的角度分析一带一路沿海超大城市2001~2017年热岛效应时空格局的变化。研究结果表明：①2001~2017年期间各超大城市的核心区存在扩张趋势,高强度热岛主要分布在人口活动密集的城市核心区;②年均城市热岛强度最大的城市是卡拉奇,多年SUHII平均值为3.02 ℃,热岛强度显著上升的是金奈（0.07 ℃/a,P<0.1）;③各城市热岛强度存在季节性差异,其中夏季城市热岛强度最大的城市是伊斯坦布尔,SUHII平均值为2.88 ℃,冬季城市热岛强度最大的城市是卡拉奇,SUHII平均值为4.45 ℃。     

Impacts of land-cover types on an urban heat island in Hangzhou,China

This research examined the impact of urban land-cover types on the urban heat island (UHI) in Hangzhou, China. Air temperatures (T_air) measured at a height of 1.5 m at times 00:00, 10:00, 14:00, and 18:00 were used for atmospheric urban heat island (AUHI) analysis. Data from the Environmental and Disaster Monitoring and Forecasting Satellite B (HJ-1B) were utilized to retrieve land surface temperature (LST) for surface urban heat island (SUHI) analysis and to map land-cover distribution. Pearson correlation and partial correlation analyses were performed to investigate the impacts of land-cover types on T_air, LST, and the relationship between T_air and LST. The results show that (1) LST and night-time T_air are sensitive to the amount of impervious surface and vegetation and (2) land-cover types did not significantly influence the correlation between LST and T_air at 10:00, but the amount of impervious surface and vegetation had significant impact at 0:00. This research indicates that the percentage of impervious surface is a good indicator for LST and night-time T_air, and for relating night-time AUHI to satellite-based observations of SUHI. This research also proposed a new method that considers both temperature patterns and land-cover types to explain the spatial variations in AUHI and a new indicator – cooling-distance rate – to help people to select a suitable living place when both work–home distance and work–home temperature difference are factors that they wish to consider.     

环境一号B星热红外波段单通道算法温度反演

文中在考虑环境一号B星(HJ-1B)热红外波段(infrared scanner,IRS4)光谱响应函数和有效波长的基础上,通过MODTRAN4模型模拟,对Jimenez-Munoz和Sobrino(JM&S)单通道算法中的大气函数进行改进,重新计算得到了适合HJ-1B星IRS4地表温度(land surface temperature,LST)反演的3个大气函数公式,并反演了福州地区的地表温度.采用基于星上辐亮度法对反演的地表温度进行精度评价,并将反演的地表温度与JM&S算法、段四波等修正的JM&S算法反演的地表温度进行对比分析.结果表明:使用文中改进后的大气参数对HJ-1B星IRS4进行地表温度反演,可取得较好结果.     

Quantifying how landscape composition and configuration affect urban land surface temperatures using machine learning and neutral landscapes

The urban heat island effect is an important 21st century issue because it intersects with the complex challenges of urban population growth, global climate change, public health and increasing energy demand for cooling. While the effects of urban landscape composition on land surface temperature (LST) are well-studied, less attention has been paid to the spatial arrangement of land cover types especially in smaller, often more diverse cities. Landscape configuration is important because it offers the potential to provide refuge from excessive heat for both people and buildings.We present a novel approach to quantifying how both composition and configuration affect LST derived from Landsat imagery in Southampton, UK. First, we trained a machine-learning (generalized boosted regression) model to predict LST from landscape covariates that included the characteristics of the immediate pixel and its surroundings. The model achieved a correlation between predicted and measured LST of 0.956 on independent test data (n = 102,935) and included predictors for both the immediate and adjacent land use. In contrast to other studies, we found adjacency effects to be stronger than immediate effects at 30 m resolution. Next, we used a landscape generation tool (Landscape Generator) to alter landscape configuration by varying natural and built patch sizes and arrangements while holding composition constant. The generated neutral landscapes were then fed into the machine learning model to predict patterns of LST.When we manipulated landscape configuration, the average city temperature remained the same but the local minima varied by 0.9 °C and the maxima by 4.2 °C. The effects on LST and heat island metrics correlated with landscape fragmentation indices. Moreover, the surface temperature of buildings could be reduced by up to 2.1 °C through landscape manipulation.We found that the optimum mix of land use types is neither at the land-sharing nor land-sparing extremes, but a balance between the two. In our city, maximum cooling was achieved when ~60% of land was left natural and distributed in 7–8 patches km⁻² although this could be location dependent and further work is needed. Opportunities for urban cooling should be required in the planning process and must consider both composition and configuration at the landscape scale if cities are to build capacity for a growing population and climate change.     

Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery

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.     

Characterizing the impact of urban morphology heterogeneity on land surface temperature in Guangzhou,China

The urban morphology is regarded as one of the main reasons for urban heat island (UHI). However, its effect on UHI in city-scale urban areas has seldom been examined. In this paper, we presented a rule-based regression model for investigating the nonlinear relationship between land surface temperature (LST) and urban morphology represented by building height, building density and sky view factor (SVF) across different dates in 2005. Results found that an urban morphology of medium building height and lower density significantly yielded higher LST variation levels, whereas the lowest LST variation levels occurred in high-rise and high-dense building arrays. Compared to building height, building density had a stronger influence on LST. Medium SVF values produced the lowest LST, whereas the largest and smallest SVF values produced the highest LST. Results also showed how rule-based regression model offer great performance in detecting the nonlinear mechanisms of LST as well.     

Effects of Landscape Pattern on Annual Variation of Thermal Environment in Hangzhou

Taking the Hangzhou city as an example,this paper retrieves the urban land surface temperature (LST) using 4 Landsat ETM+/OLI_TIRS images and investigates the effects of landscape pattern on the urban thermal environment change.The hot spot analysis was used to identify both the urban heat island and cold island.Landscape pattern indices were adopted to analyze the relationship between the change of the thermal environment and the landscape pattern.Analysis results show that：(1) The proportions of area in urban heat island and cold island in Hangzhou increase first and then decrease with the alternating four seasons;The urban heat island of Hangzhou is the most significant in summer,and the urban cold island effect is more dominant in autumn;(2) Throughout the year,all kinds of landscape has the highest average land surface temperature in summer and the lowest in winter;As for a variety of landscapes,the construction land has the highest average land surface temperature,while,the water body and forest have the relatively low average land surface temperature;(3)On the landscape level,the selected landscape pattern indices are significantly correlated with average land surface temperature in four seasons,the strength of correlation fluctuates with alternating four seasons and the enlargement of analysis window;On the class level,landscape pattern indices of construction land,water body and forest are significantly and highly correlated with average land surface temperature in different seasons.The research in our paper could help to lay out construction land rationally and execute planning and design on urban green space and waters to effectively alleviate the urban heat island effect of Hangzhou.     

典型地表对长沙主城区地表温度的影响分析

城市热岛是一种城市地区温度比郊区温度高的现象,它可改变城市的自然和社会过程,引发一系列环境问题。利用Landsat 8 TIRS10波段的单通道算法（TIRS10_SC算法）反演了长沙主城区2013年7月、2016年3月、7月和11月4景Landsat 8影像的地表温度,并进一步分析了地表温度的时空分布特征,建设用地、绿地、河流以及不同材质的屋顶等典型地表对主城区地表温度的影响。结果表明：①长沙火车站沿线、高桥大市场和部分工厂在各个时期均为高温区。2016年7月浏阳河周边区域热岛效应相对于2013年7月有所缓解,主要由天气情况的不同以及拆迁改变地表覆盖性质等造成。3月建设用地中比率最大的为中温, 7月建设用地中比率最大的为次高温。3月和7月,绿地中比率最大的为次低温,水体中比率最大的为低温。而11月,建设用地和绿地中比率最大的为中温,水体中比率最大的次高温。②河流周边120 m范围内,由陆地向河流每减少30 m,7月建设用地平均温度减少0.93~1.26 ℃,绿地平均温度减少0.57~0.99 ℃;3月建设用地平均温度减少0.51~0.78 ℃,绿地平均温度减少0.3~0.57 ℃。河流的降温强度与河水温度和距离河流120 m以上的地表温度的差值大小有关。③负的MNDWI（非水体）与地表温度呈正相关关系,正的MNDWI（水体区域）与地表温度3月和7月呈负相关关系,11月呈正相关关系。④地表比辐射率对地表温度反演的结果影响显著,利用NDVI估算地表比辐射率难以区分高反射率的屋顶与其他类型的建设用地。因此,针对高反射率屋顶对地表比辐射率的影响有待进一步研究,以提高城区地表温度的反演精度,为减缓城市热岛效应提供参考依据。     

针对MODIS 数据的地表温度非线性迭代反演方法

地表温度是气象、水文、生态等研究领域中的一个重要参数。构建了MODIS31/ 32 波段的热辐射传输方程, 讨论了方程的数值迭代解法, 提出了针对MODIS 数据地表温度的非线性迭代反演方法, 并介绍了大气透过率和地表比辐射率这两个中间参数的估计方法。误差及敏感性分析表明,提出的方法对大气透过率和地表比辐射率都不敏感, 反演精度优于传统的线性分裂窗算法。     

基于HJ-1B卫星数据的南京市地表温度反演研究

地表温度(LST)是全球变化的过程参数,应用HJ-1B-RS热红外数据,采用辐射传输法(RTE)、覃志豪单窗算法(Qins’)和普适性单通道算法(JM&S)对南京市地表温度进行反演。结果表明:3种算法均能较好地反映南京地区的地表温度趋势。RTE反演精度最高,与MODIS地温产品的差值多集中在2.1 K左右;Qins’的反演结果略低,温差多集中在3.87 K左右;而JM&S的结果明显偏低,温差多集中在5.96 K左右。结合土地利用类型图对地表温度进行分析,RTE温度结果中,温度最高的建设用地与温度最低的水体的温度相差4.1 K;Qins’温度结果中建设用地与水体的温度相差4.38 K;JM&S温度结果中建设用地与水体的温度相差2.15 K。RTE和Qins’更能体现不同土地利用类型之间的温度差异及对城市热岛的贡献。