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。     

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。     

城市热岛效应地表通量空间分布研究

城市化进程的加快促使更多大中城市的产生以及城市面积的扩展,导致更加严重的城市热岛现象。为了更加深入理解城市热岛效应产生根源,以西安市城区为例采用美国陆地卫星遥感数据反演或估算地表温度、植被指数以及地表通量等变量,不仅采用传统的地表温度参数理解城市热岛现象,还着重分析城市建成区和郊区的地表通量空间分布格局及其与地表温度的关系。研究发现西安城市建成区与郊区之间热环境存在显著的差异,地表温度不仅与植被覆盖状况具有密切的关系,还与地表潜热通量和实际蒸散发变量存在显著的反相关关系。详细分析表明拥有众多工厂企业的西安市莲湖区热岛效应尤为显著,而位于市中心的新城区次之,具有较大面积郊区的灞桥区热岛效应并不明显。因此城市绿地不仅影响城市建成区的地表温度空间分布,还对地表通量以及实际蒸散发的空间格局产生重要的影响,在调节城市热岛效应方面具有重要的作用。
     

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。     

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。     

杭州市城市空间扩展及其热环境变化

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。     

基于混合光谱分解的兰州城市热岛与下垫面空间关系分析

利用Landsat ETM+数据,采用混合像元线性光谱分解方法提取的城市植被覆盖度与不透水面表征城市下垫面,通过单窗算法反演地表真实温度,对兰州市中心城区的夏季城市热岛强度与城市下垫面的空间分布关系进行相关分析。结果显示,利用中等分辨率ETM+影像对兰州中心城区不透水面和植被盖度分布提取,其成本较低,精度令人满意;兰州城区植被覆盖、不透水面与热岛强度的分布呈空间正自相关,地表温度的空间依赖性极强,与植被盖度和不透水面在空间方向上的相关性差异较大。     

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。     

不同地表参数变化的上海市热岛效应时空分析

研究地表参数变化与热岛效应的关系对优化城市功能分区以及城市可持续发展具有重要意义。采用上海市2000、2005、2009年3个时期的Landsat ETM+卫星遥感影像,使用归一化不透水面指数(NDISI)、基于指数的植被指数(IVI)、归一化差异水体指数(MNDWI)分别从遥感影像中提取不透水面、植被和水体;然后从时间、空间角度并采用回归分析方法分析了上海市地表参数在这9 a中发生的变化及其对城市热环境造成的影响。结果表明:9 a中城市不透水面面积大幅增加,不透水面增加的代价是植被和水体大范围减少,形成了城市的热岛。上海市整体热岛强度是先增强后缓慢减弱的趋势,且热岛分布从集中型向分散型发展。     

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 ℃。     

ASTER‐based study of the night‐time urban heat island effect in Metro Manila

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) was used to derive land surface temperatures to quantify the night‐time urban heat island (UHI) effect in Metro Manila. Temperature differences between Metro Manila and its adjacent rural towns were compared to determine heat island intensity and analyse spatial variation of surface temperature. Transects were drawn across from the rural to the urban region to characterize the UHI profile and the Normalized Difference Vegetation Index (NDVI) was used to examine the relationship between amount of vegetation and temperature. The thermal images revealed the highest UHI intensity to be 2.96°C with the presence of a heat island existing in the central part of the city. The transects described the cross‐sectional heat island profile characterized by gradients of ‘cliffs’, ‘plateaus’ and a ‘peak’ occurring in the city centre. The study also showed an inverse relationship between NDVI and temperature, which suggests that increasing the amount of plants in cities can reduce the UHI effect.     

Modelling the diurnal variations of urban heat islands with multi-source satellite data

Examination of the diurnal variations in surface urban heat islands (UHIs) has been hindered by incompatible spatial and temporal resolutions of satellite data. In this study, a diurnal temperature cycle genetic algorithm (DTC-GA) approach was used to generate the hourly 1 km land-surface temperature (LST) by integrating multi-source satellite data. Diurnal variations of the UHI in ‘ideal’ weather conditions in the city of Beijing were examined. Results show that the DTC-GA approach was applicable for generating the hourly 1 km LSTs. In the summer diurnal cycle, the city experienced a weak UHI effect in the early morning and a significant UHI effect from morning to night. In the diurnal cycles of the other seasons, the city showed transitions between a significant UHI effect and weak UHI or urban heat sink effects. In all diurnal cycles, daytime UHIs varied significantly but night-time UHIs were stable. Heating/cooling rates, surface energy balance, and local land use and land cover contributed to the diurnal variations in UHI. Partial analysis shows that diurnal temperature range had the most significant influence on UHI, while strong negative correlations were found between UHI signature and urban and rural differences in the normalized difference vegetation index, albedo, and normalized difference water index. Different contributions of surface characteristics suggest that various strategies should be used to mitigate the UHI effect in different seasons.     

Impacts of land use and socioeconomic patterns on urban heat Island

Intensive land surface change and human activities induced by rapid urbanization are the major causes of the urban heat island (UHI) phenomenon. In this article, we examined the spatial variability of UHI and its relationships with land use and socioeconomic patterns in the Baltimore–DC metropolitan area. Census data, road network as well the digital elevation model (DEM) and average water surface percentage were selected to analyse the correlation between spatial patterns of UHI and socioeconomic factors. The impervious surface (coefficient of determination R² = 0.89) and normalized difference vegetation index (R² = 0.81) were the two most important landscape factors, and population density (R² = 0.57) was the most influential socioeconomic variable in contributing to the UHI intensity. Generally, the socioeconomic variables had smaller influence on the UHI intensity than the landscape variables. Based on the patch analysis, most of the socioeconomic variables influenced the UHI intensity indirectly through changing the physical environment (e.g. impervious surface or forest cover). The selected landscape and socioeconomic variables, except impervious surface percentage, demonstrated third-order polynomial correlation with the UHI intensity. The higher correlations were found within certain ranges such as forest percentage from 0% to 30% and population density from 0 to 5000 km^–2. This research provides a case study to understand the urban land surface, vegetation, and microclimate for urban management and planning.     

Remote-sensing image-based analysis of the patterns of urban heat islands in rapidly urbanizing Jinan,China

According to the UN Population Reference Bureau, 1.4 billion more people will have settled in urban areas by 2030. One of the key environmental effects of rapid urbanization is the urban heat island (UHI) effect. Understanding the mechanism of surface UHIs associated with land-use/land-cover (LULC) change patterns is important for improving the ecology and sustainability of cities. In this article, time series Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+) data were used to extract LULC data and land surface temperature (LST) data for the city of Jinan, China, from 1987 to 2011, a period during which the city experienced rapid urbanization. With the aid of a geographical information system (GIS) and remote sensing (RS) approach, the changes in this urban area’s LULC were explored, and the impact of these changes on the spatiotemporal patterns and underlying driving forces of the surface UHI effect were further quantitatively characterized. The results show that significant changes in land use and land cover occurred over the study period, with loss of farmland, forest, and shrub vegetation to urban use, leading to spatial growth of impervious surfaces. Consequently, the land surface characteristics and spatiotemporal patterns of the UHI have changed drastically. According to the seasonal and inter-annual variations in intensity of UHIs, mean differences in UHI intensity between city centre, peri-urban, and nearby rural areas were stronger during summer and spring and weaker during winter and autumn. Spatially, there were significant LST gradients from the city centre to surrounding rural areas. The city centre exhibited higher LSTs and remarkable variation in LSTs, while the surrounding rural areas exhibited lower LSTs and lower variation in LSTs. Moreover, the analysis of LSTs and indices showed that great differences of temperature even existed in a LULC type except for variations between different LULC types. In addition, a local-level analysis revealed that the intensity of the UHI effect is proportional to the size of the urban area, the population density, and the frequent occurrence of certain activities.     

基于TM和GIS的合肥市热环境研究

利用2007年10月5日覆盖合肥市的TM影像反演了NDVI和LST,并结合GIS技术和城市形态分维理论,分析了合肥市热环境布局以及不同热环境等级与各土地利用类型的关系。结果表明:合肥市地表温度具有东南高、西北低的热岛效应存在,热岛效应的中心并未出现在城市中心区域。LST与NDVI呈明显负相关关系(相关系数为-0.734),NDVI值每升高0.1,LST约降低0.93℃。城建面积加权环境效应贡献指数(WHI)为1.14,对热环境的作用(正向)程度最高;耕地的WHI为-0.51,对热环境的作用(负向)程度最高,且耕地在合肥市各热环境等级中都发挥了独特的作用。      

Monitoring of urban heat island effect in Beijing combining ASTER and TM data

This paper focuses on the monitoring of the urban heat island (UHI) effect with temporal and spatial variation, combining Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Thematic Mapper (TM) data. Our study area is located in the central urban area of Beijing, which mainly refers to the areas within the fifth ring road. For detecting UHI changes over the years 2002–2006, three ASTER images in the summers of 2003, 2004 and 2006 and two TM datasets in the summers of 2002 and 2005 were collected. For monitoring UHI changes with the seasons, three ASTER images and one TM image in 2004 in winter, spring, summer and autumn, respectively, were employed. To calculate the urban heat island intensity, the land surface temperatures were retrieved iteratively for ASTER data and using a generalized single-channel method for the TM image. Four separated regions located in four directions outside the fifth ring road were selected as representing rural comparative regions. Their averaged land surface temperature was regarded as the rural comparative temperature. The UHI intensity was computed by the difference between the pixel urban land surface temperature in the urban area and the comparative temperature in the rural area. Detection of the UHI effect over 2002 to 2006 indicated that most of the areas with high UHI effect were the industrial land use regions and the areas having a high density of buildings, roads, transportations and residents; and the areas without UHI effect were located around the regions with large areas of grassland, trees and water bodies. Our results also showed that the UHI effect was not proportional to urbanization over time. Statistical UHI data during 20 July to 20 September in 2003–2008 also support this point. The monitoring of the UHI effect over seasons (winter, spring, summer and autumn) showed that the urban area of Beijing city had a high UHI effect except in winter, when the urban area of Beijing was in an urban heat sink; the UHI effect increased in spring, summer and autumn.     

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。     

Impacts of landscape structure on surface urban heat islands: A case study of Shanghai, China

Urbanization is taking place at an unprecedented rate around the world, particularly in China in the past few decades. One of the key impacts of rapid urbanization on the environment is the effect of urban heat island (UHI). Understanding the effects of landscape pattern on UHI is crucial for improving the ecology and sustainability of cities. This study investigated how landscape composition and configuration would affect UHI in the Shanghai metropolitan region of China, based on the analysis of land surface temperature (LST) in relation to normalized difference vegetation index (NDVI), vegetation fraction (Fv), and percent impervious surface area (ISA). Two Landsat ETM+ images acquired on March 13 and July 2, 2001 were used to estimate LST, Fv, and percent ISA. Landscape metrics were calculated from a high spatial resolution (2.5 × 2.5 m) land-cover/land-use map. Our results have showed that, although there are significant variations in LST at a given fraction of vegetation or impervious surface on a per-pixel basis, NDVI, Fv, and percent ISA are all good predictors of LST on the regional scale. There is a strong negative linear relationship between LST and positive NDVI over the region. Similar but stronger negative linear relationship exists between LST and Fv. Urban vegetation could mitigate the surface UHI better in summer than in early spring. A strong positive relationship exists between mean LST and percent ISA. The residential land is the biggest contributor to UHI, followed by industrial land. Although industrial land has the highest LST, it has limited contribution to the overall surface UHI due to its small spatial extend in Shanghai. Among the residential land-uses, areas with low- to-middle-rise buildings and low vegetation cover have much high temperatures than areas with high-rise buildings or areas with high vegetation cover. A strong correlation between the mean LST and landscape metrics indicates that urban landscape configuration also influences the surface UHI. These findings are helpful for understanding urban ecology as well as land use planning to minimize the potential environmental impacts of urbanization.     

Effect of vegetation and waterbody on the garden city concept: An evaluation study using a newly developed city,Putrajaya, Malaysia

The garden city concept was adopted in the development of a new tropical city, Putrajaya, aimed at mitigating the effect of urban thermal modification associated with urbanisation, such as urban heat island (UHI). WRF/Noah/UCM coupled system was used to estimate the urban environment over the area and the individual thermal contributions of natural land use classes (vegetation and waterbody). A control experiment including all land use types describing the urban conditions of Putrajaya city agreed well with the observations in the region. A series of experiments was then conducted, in which vegetation and waterbody were successively replaced with an urban land use type, providing the basis for an assessment of their respective effect on urban thermal mitigation. Surface energy components, 2-m air temperature (T2m) and mixing ratio (Q2m), relative humidity (RH) and UHI intensity (UHII) showed variations for each land use class. Overall, an increase in urban surfaces caused a corresponding increase in the thermal conditions of the city. Conversely, waterbody and vegetation induced a daily reduction of 0.14 and 0.39 °C of T2m, respectively. RH, UHI and T2m also showed variations with urban fractions. A thermal reduction effect of vegetation is visible during mornings and nights, while that of water is minimally shown during daytime. However, during nights and mornings, canopy layer thermal conditions above waterbody remain relatively high, with a rather undesirable effect on the surrounding microclimate, because of its high heat capacity and thermal inertia.