基于MODIS数据的长株潭地区城市热岛时空分析

基于MODIS影像,采用分裂窗算法反演的地表温度对长株潭地区城市热岛空间分布与季相变化特征、影响因子进行定量研究。结果表明,长株潭地区春季和夏季存在明显的城市热岛效应,而冬季和秋季城市热岛并不明显;地表覆盖类型对城市热岛的影响十分明显,长株潭地区春、夏、秋季植被绿地状况与城市热岛呈现明显负相关分布,其中以夏季最为明显,夏季地表温度与NDVI相关系数的平方R2达到0.8193,即植被覆盖对城市地表温度的影响显著。因此,城市植被的分布与季节变化影响着城市热岛的强度与时空分布,揭示出植被绿地对降低城市热岛效应具有重要的作用,大范围的绿地建设能有效降低城市热岛效应。     

基于MODIS的2001年—2012年北京热岛足迹及容量动态监测

利用2001年—2012年MODIS分裂窗算法反演得到的1 km分辨率地表温度产品分析了北京城市热岛效应。首先计算北京2001年—2012年地表温度年平均值,其次利用半径法确定热岛足迹并计算热岛容量。结论如下:(1)热岛足迹及热岛容量昼夜差异明显,2012年白天热岛足迹是夜间的1.5倍,这是由于城市下垫面热特性差异及人为活动的综合影响。(2)2001年—2012年北京城市高温区在空间上向南北扩展,热岛足迹和热岛容量呈阶段性增长。2010年白天热岛足迹最大,半径为28 km,面积是2001年的2.4倍。当热岛足迹相同时,城市绿地和水体功能区的分布和布局方式等因素能够影响热岛容量。城市建设用地和农村居民点对城市热环境贡献率明显高于其他土地利用类型。当建设用地面积比例超过50%时,区域会产生显著的热岛现象。(3)根据北京热岛足迹及容量时空动态变化特征,提出改善城市热岛的措施。     

MODIS数据北京城区热岛监测分析

随着城市化进程的深入,热岛效应问题越来越严重,从而影响城市及周边地区的生态环境与气候,因此备受人们的重视。本文从2000年—2007年724幅1km分辨率的MODIS地表温度产品中选取64幅质量较好,可以代表春、夏、秋、冬四个季节的昼夜影像,制作地表温度图和选取感兴趣区域分析北京城区热岛效应。结果表明,北京市城区温度明显高于周围地区,夏季夜间最高达到3.7℃,秋季白天相对热岛强度较大,夏季、冬季夜间热岛强度要大于白天,尤其冬季较为明显。     

武汉城镇化与热岛效应的定量研究

针对目前城市化进程加快对城市增温和城市热岛效应的促进作用尚不十分明确的问题,提出基于遥感技术的城镇化与热岛效应数量关系的构建。结果表明,1987-2013年间,武汉市中城镇化水平(50%~80%ISA)面积增加了14.5倍,高城镇化水平(80%~100%ISA)面积增加了2.8倍,城镇化范围沿着武汉市主城区和主要干道不断扩张;不透水面值与地表温度表现出明显正相关,不透水面值平均增加1%,可使地表温度增温0.06℃~0.19℃,城镇化水平对武汉城市热岛效应作用明显。研究结果为探讨城市热岛效应的缓解对策提供了基础,对合理规划和管理城市化意义重大。     

兰州市城市热岛效应与地表指数定量分析

针对现有兰州市城市热岛效应研究多以地面气象站的观测资料为数据源,以遥感影像为数据源较少的现状,该文利用Landsat 8影像,研究了兰州市城市热岛效应与修改后的土壤调节植被指数(MSAVI)、增强型水体指数(EWI)、土壤亮度指数(NDSI)、土壤湿度指数(NDMI)之间的关系。结果表明,兰州城市热岛效应较为严重,城市热岛效应与土壤湿度指数、增强型水体指数、土壤亮度指数呈正相关,与修改后的土壤调节植被指数呈负相关。     

Development of a 3-D urbanization index using digital terrain models for surface urban heat island effects

This study assesses surface urban heat island (SUHI) effects during heat waves in subtropical areas. Two cities in northern Taiwan, Taipei metropolis and its adjacent medium-sized city, Yilan, were selected for this empirical study. Daytime and night time surface temperature and SUHI intensity of both cities in five heat wave cases were obtained from MODIS Land-Surface Temperature (LST) and compared. In order to assess SUHI in finer spatial scale, an innovated three-dimensional Urbanization Index (3DUI) with a 5-m spatial resolution was developed to quantify urbanization from a 3-D perspective using Digital Terrain Models (DTMs). The correlation between 3DUI and surface temperatures were also assessed. The results obtained showed that the highest SUHI intensity in daytime was 10.2 °C in Taipei and 7.5 °C in Yilan. The SUHI intensity was also higher than that in non-heat-wave days (about 5 °C) in Taipei. The difference in SUHI intensity of both cities could be as small as only 1.0 °C, suggesting that SUHI intensity was enhanced in both large and medium-sized cities during heat waves. Moreover, the surface temperatures of rural areas in Taipei and Yilan were elevated in the intense heat wave cases, suggesting that the SUHI may reach a plateau when the heat waves get stronger and last longer. In addition, the correlation coefficient between 3DUI and surface temperature was greater than 0.6. The innovative 3DUI can be employed to assess the spatial variation of temperatures and SUHI intensity in much finer spatial resolutions than measurements obtained from remote sensing and weather stations. In summary, the empirical results demonstrated intensified SUHI in large and medium-sized cities in subtropical areas during heat waves which could result in heat stress risks of residents. The innovative 3DUI can be employed to identify vulnerable areas in fine spatial resolutions for formulation of heat wave adaptation strategies.     

天宫一号数据地表温度反演及其在城市热岛效应中的应用

针对天宫一号高光谱成像仪红外波段数据提出了一个单通道地表温度反演算法,算法的输入参数为大气水汽含量和地表发射率.利用模拟数据和黑河流域生态—水文过程综合遥感观测联合试验的地面实测数据对算法进行了精度评价,结果表明算法的均方根误差为2.72 K,能够满足大多数应用研究的需求.以北京市二环以内为研究区域,采用4个时相的天宫一号高光谱红外波段数据进行了城市热岛效应研究,结果表明天宫一号高光谱红外波段数据适合用来进行街区尺度的城市热岛效应研究,具有很大的应用潜力.     

Analysis of urban built-up areas and surface urban heat island using downscaled MODIS derived land surface temperature data

Regional scale urban built-up areas and surface urban heat islands (SUHI) are important for urban planning and policy formation. Owing to coarse spatial resolution (1000 m), it is difficult to use Moderate Resolution Imaging Spectroradiometer (MODIS) Land surface temperature (LST) products for mapping urban areas and visualization, and SUHI-related studies. To overcome this problem, the present study downscaled MODIS (1000 m resolution)-derived LST to 250 m resolution to map and visualize the urban areas and identify the basic components of SUHI over 12 districts of Punjab, India. The results are compared through visual interpretation and statistical procedure based on similarity analysis. The increased entropy value in the downscaled LST signifies higher information content. The temperature variation within the built-up and its environs is due to difference in land use and is depicted better in the downscaled LST. The SUHI intensity analysis of four cities (Ludhiana, Patiala, Moga and Vatinda) indicates that mean temperature in urban built-up core is higher (38.87 °C) as compared to suburban (35.85 °C) and rural (32.41 °C) areas. The downscaling techniques demonstrated in this paper enhance the usage of open-source wide swath MODIS LST for continuous monitoring of SUHI and urban area mapping, visualisation and analysis at regional scale. Such initiatives are useful for the scientific community and the decision-makers.     

典型喀斯特城市的热岛时空变化及其成因分析

利用1989、2003、2018年的Landsat影像对安顺市西秀区的地表温度进行反演,分析研究区在30年发展中的热岛时空变化及其成因。使用基于影像的反演算法,结合分类回归树算法进行地表温度的反演,用气象站数据对反演结果进行精度验证,并建立缓冲区对研究区进行相关性分析等定量分析。结果表明：研究区受喀斯特地貌的影响,除主城区外,郊区也存在大量高温区;近30年研究区热效应与不透水面、绿地的面积有极显著相关;1989-2003年研究区城市热岛面积随城市扩张逐渐增大,但2018年主城区城市热岛现象几乎完全消失,排除气象因素和城市形态因子影响的可能后,发现这与安顺市城市绿化的大力进行有密切关系。     

On urban heat island of Beijing based on landsat TM data

Land surface temperature (LST) of Beijing area was retrieved from Landsat TM thermal band data utilizing a radiative transfer equation and the urban heat island (HUI) effects of Beijing and its relationship with land cover and normalized difference vegetation index (NDVI) were discussed. The result of LST showed that the urban LST was evidently higher than the suburban one. The average urban LST was found to 4. 5°C and 9°C higher than the suburban and outer suburban temperature, respectively, which demonstrated the prominent UHI effects in Beijing. Prominent negative correlation between LST and NDVI was found in the urban area, which suggested the low percent vegetation cover in the urban area was the main cause of the urban heat island.     

On Urban Heat Island of Beijng Based on Landsat TM Data

IntroductionThe scientists have begun to retrieve land sur-face temperature (LST) fromsatellite data sincethe launch of TIROS-Ⅱin 60s of the 20th centu-ry . With the development of remote sensingtechnology and its application, more and moreLST retrieval …     

Assessment with satellite data of the urban heat island effects in Asian mega cities

This study focuses on using remote sensing for comparative assessment of surface urban heat island (UHI) in 18 mega cities in both temperate and tropical climate regions. Least-clouded day- and night-scenes of TERRA/MODIS acquired between 2001 and 2003 were selected to generate land-surface temperature (LST) maps. Spatial patterns of UHIs for each city were examined over its diurnal cycle and seasonal variations. A Gaussian approximation was applied in order to quantify spatial extents and magnitude of individual UHIs for inter-city comparison. To reveal relationship of UHIs with surface properties, UHI patterns were analyzed in association with urban vegetation covers and surface energy fluxes derived from high-resolution Landsat ETM+ data. This study provides a generalized picture on the UHI phenomena in the Asian region and the findings can be used to guide further study integrating satellite high-resolution thermal data with land-surface modeling and meso-scale climatic modeling in order to understand impacts of urbanization on local climate in Asia.     

基于影像IB算法的沈阳市热岛效应分析

本文利用2006年、2010年沈阳市地区TM遥感数据,采用影像IB算法反演地表温度,分析了沈阳市热岛效应的空间分布特征、变化现象以及地表温度与归一化植被指数(NDVI)、归一化建筑指数(NDBI)之间的相关性。研究表明沈阳市热岛效应总体呈现由市中心向四周逐渐扩张的空间特征,地表温度与归一化植被指数(ND-VI)存在紧密的负线性相关关系,地表温度与归一建筑指数(NDBI)存在正相关关系。     

Statistical analysis of surface urban heat island intensity variations: A case study of Babol city,Iran

The urban heat island is considered as one of the most important climate change phenomena in urban areas, which can result in remarkable negative effects on flora, concentration of pollutants, air quality, energy and water consumption, human health, ecological and economic impacts, and even on global warming. The variation analysis of the surface urban heat island intensity (SUHII) is important for understanding the effect of urbanization and urban planning. The objective of this study was to present a new strategy based on the Shannon’s entropy and Pearson chi-square statistic to investigate the spatial and temporal variations of the SUHII. In this study, Landsat TM, ETM+, OLI and TIRS images, MODIS products, meteorological data, topographic and population maps of the Babol city, Iran, from 1985 to 2017, and air temperature data recorded by ground recorder devices in 2017 were used. First, Single-Channel algorithm was used to estimate land surface temperature (LST), and the maximum likelihood classifier was employed to classify Landsat images. Then, based on LST maps, surface urban heat island ratio index was employed to calculate the SUHII. Further, several statistical methods, such as the degree-of-freedom, degree-of-sprawl and degree-of-goodness, were used to analyse the SUHII variation along different geographic directions and in various time periods. Finally, correlation between various parameters such as air temperature, SUHII, population variation and degree-of-goodness index values were investigated. The results indicated that the SUHII value increased by 24% in Babol over different time periods. The correlation coefficient yielded 0.82 between the values of the difference between the mean air temperature of the urban and suburbs and the SUHII values for the geographic directions. Furthermore, the correlation coefficient between the population variation and the degree-of-goodness index values reached 0.8. The results suggested that the SUHII variation of Babol city had a high degree-of-freedom, high degree-of-sprawl and negative degree-of-goodness.     

Spatially non-stationary effect of underlying driving factors on surface urban heat islands in global major cities

Urban heat island (UHI) effect is among the most typical characteristics of urban climate. The analysis of surface UHI (SUHI) mechanisms has received the most extensive attention in the world. Here, we quantify the diurnal and seasonal SUHI intensity (SUHII) in global 419 major cities during the period 2003-2013. A geographically weighted regression (GWR) was established to assess the relationships between SUHII and several driving factors, and it further was compared to the ordinary least square (OLS) and stepwise multiple linear regression (SMLR) models. We show that GWR model has higher determination coefficient (R²) than OLS and SMLR models (Time: summer daytime, summer night, winter daytime and winter nighttime; GWR: 0.805, 0.458, 0.699 and 0.582; OLS: 0.732, 0.347, 0.473 and 0.320; SMLR: 0.732, 0.341, 0.468 and 0.316), indicating the spatially non-stationarity in the relationships. During the day, both vegetation activity and tree cover fraction have stronger cooling effect on SUHI in the summer of Asia. At night, there are stronger albedo effects on SUHI in the summer of Eastern Asia and Western North America and in the winter of Eastern Asia. Furthermore, temperature has stronger effect on daytime SUHI in Africa, Europe and South America in summer, and precipitation has stronger effect on nighttime SUHI in Africa and Europe in summer. Our results emphasize the spatial variation of the relationships between SUHII and relevant driving factors across global major cities, further indicating that the spatially non-stationary effect of driving factors on SUHII need to be considered in the future.     

A simplified urban-extent algorithm to characterize surface urban heat islands on a global scale and examine vegetation control on their spatiotemporal variability

We develop a new algorithm, the simplified urban-extent (SUE) algorithm, to estimate the surface urban heat island (UHI) intensity at a global scale. We implement the SUE algorithm on the Google Earth Engine platform using Moderate Resolution Imaging Spectroradiometer (MODIS) images to calculate the UHI intensity for over 9500 urban clusters using over 15 years of data, making this one of the most comprehensive characterizations of the surface UHI to date. The results from this algorithm are validated against previous multi-city studies to demonstrate the suitability of the method. The dataset created is then filtered for elevation differentials and percentage of urban area and used to estimate the diurnal, monthly, and long-term variability in the surface UHI in different climate zones. The global mean surface UHI intensity is 0.85 °C during daytime and 0.55 °C at night. Cities in arid climate show distinct diurnal and seasonal patterns, with higher surface UHI during nighttime (compared to daytime) and two peaks throughout the year. The diurnal variability in surface UHI is highest for equatorial climate zone (0.88 °C) and lowest for arid zone (0.53 °C). The seasonality is highest in the snow climate zone and lowest for equatorial climate zone. While investigating the change in the surface UHI over a decade and a half, we find a consistent increase in the daytime surface UHI in the urban clusters of the warm temperate climate zone (0.04 °C/decade) and snow climate zone (0.05 °C/decade). Only arid climate zones show a statistically significant increase in the nighttime surface UHI intensity (0.03 °C/decade). Globally, the change is mainly seen during the daytime (0.03 °C/decade). Finally, the importance of vegetation differential between urban and rural areas on the spatiotemporal variability is examined. Vegetation has a strong control on the seasonal variability of the surface UHI and may also partly control the long-term variability. The complete UHI data are available through this website (https://yceo.yale.edu/research/global-surface-uhi-explorer) and allows the user to query the UHI of urban clusters using a simple interface.     

基于COST模型的热岛效应时空演变特征分析

本文选取中等城市绵阳为研究对象,以2000年、2007年TM/ETM+遥感影像为数据源,首先应用COST模型完成大气校正;然后采用Artis等提出的算法进行地表温度反演,并利用均值标准差法进行温度等级划分,获得绵阳市地表温度分布图和温度等级分布图。结果表明:①2000-2007年热岛区与建成区在空间发展趋势上基本一致,其中2000年热岛区面积为11.49 km2,到2007年增加为43.12 km2,热岛区面积所占比例增加36.36%;②7年间温度等级升高的区域面积为56.09km2,占建成区总面积的64.47%,其中从温度较低区转化为热岛区比例占42.40%。     

Dynamics and controls of urban heat sink and island phenomena in a desert city: Development of a local climate zone scheme using remotely-sensed inputs

This study aims to determine the dynamics and controls of Surface Urban Heat Sinks (SUHS) and Surface Urban Heat Islands (SUHI) in desert cities, using Dubai as a case study. A Local Climate Zone (LCZ) schema was developed to subdivide the city into different zones based on similarities in land cover and urban geometry. Proximity to the Gulf Coast was also determined for each LCZ. The LCZs were then used to sample seasonal and daily imagery from the MODIS thermal sensor to determine Land Surface Temperature (LST) variations relative to desert sand. Canonical correlation techniques were then applied to determine which factors explained the variability between urban and desert LST.Our results indicate that the daytime SUHS effect is greatest during the summer months (typically ∼3.0 °C) with the strongest cooling effects in open high-rise zones of the city. In contrast, the night-time SUHI effect is greatest during the winter months (typically ∼3.5 °C) with the strongest warming effects in compact mid-rise zones of the city. Proximity to the Arabian Gulf had the largest influence on both SUHS and SUHI phenomena, promoting daytime cooling in the summer months and night-time warming in the winter months. However, other parameters associated with the urban environment such as building height had an influence on daytime cooling, with larger buildings promoting shade and variations in airflow. Likewise, other parameters such as sky view factor contributed to night-time warming, with higher temperatures associated with limited views of the sky.     

Coupling urbanization analyses for studying urban thermal environment and its interplay with biophysical parameters based on TM/ETM+ imagery

Studies of urbanization and urban thermal environment are now attracting wide interests among scientists all over the world. This study investigated the influences of urbanization on urban thermal environment as well as the relationships of thermal characteristics to other biophysical variables in Guangzhou, China utilizing three dates of Landsat TM/ETM+ images acquired in 1990, 2000, and 2005, respectively. Vegetation abundances and percent impervious surfaces were derived by means of linear spectral mixture model, and a method for effectively enhancing impervious surface has been developed to accurately examine the urban enlargement. As a key parameter for studying urban thermal characteristics, the land surface temperature (LST) was also retrieved from thermal infrared band of each TM/ETM+ dataset. Based on these parameters, the urban expansion, urban heat island effect and the relationships of LSTs to other biophysical parameters were then analyzed. Results indicated that the area ratio of impervious surface in Guangzhou increased significantly, which grew from 20.56% in 1990, to 34.72% in 2000, and further to 41.12% in 2005, however, the intensity of urban heat island was not always enlarged in observed years. In addition, Geostatistical analyses showed that the mean-centre of the impervious surface was moving towards the northwest during 1990–2005. And correlation analyses revealed that, at the pixel-scale, the association of LSTs to other two variables (vegetation abundance and percent impervious surface) was not straightforward, while LSTs possessed a strong positive correlation with percent impervious surfaces and negative correlation with vegetation abundances at the regional-scale, respectively. This study provided an integrated research scheme and the findings can be very useful for urban ecosystem modeling.     

Validation of the SEBS-derived sensible heat for FY3A/VIRR and TERRA/MODIS over an alpine grass region using LAS measurements

In this study, sensible heat (H) calculation using remote sensing data over an alpine grass landscape is conducted from May to September 2010, and the calculation is validated using LAS (large aperture scintillometers) measurements. Data from two remote sensing sensors (FY3A-VIRR and TERRA-MODIS) are analysed. Remote sensing data, combined with the ground meteorological observations (pressure, temperature, wind speed, humidity) are fed into the SEBS (Surface Energy Balance System) model. Then the VIRR-derived sensible heat (VIRR_SEBS_H) and MODIS-derived sensible heat (MODIS_SEBS_H) are compared with the LAS-estimated H, which are obtained at the respective satellite overpass time. Furthermore, the similarities and differences between the VIRR_SEBS_H and MODIS_SEBS_H values are investigated. The results indicate that VIRR data quality is as good as MODIS data for the purpose of H estimation. The root mean square errors (rmse) of the VIRR_SEBS_H and MODIS_SEBS_H values are 45.1098 W/m² (n = 64) and 58.4654 W/m² (n = 71), respectively. The monthly means of the MODIS_SEBS_H are marginally higher than those of VIRR_SEBS_H because the satellite overpass time of the TERRA satellite lags by 25 min to that of the FT3A satellite. Relative evaporation (EFr), which is more time-independent, shows a higher agreement between MODIS and VIRR. Many common features are shared by the VIRR_SEBS_H and the MODIS_SEBS_H, which can be attributed to the SEBS model performance. In May–June, H is over-estimated with more fluctuations and larger rmse, whereas in July–September, H is under-estimated with fewer fluctuations and smaller rmse. Sensitivity analysis shows that potential temperature gradient (delta_T) plays a dominant role in determining the magnitude and fluctuation of H. The largest rmse and over-estimation in H occur in June, which could most likely be attributed to high delta_T, high wind speed, and the complicated thermodynamic state during the transitional period when bare land transforms to dense vegetation cover.