Satellite-derived urban heat islands from three coastal cities and the utilization of such data in urban climatology

Abstract

NOAA AVHRR satellite infra-red data are used to display the surface radiant temperature heat islands of Vancouver, British Columbia, Seattle, Washington, and Los Angeles, California. Heat island intensities are largest in the day-time and in the warm season. Day-time intra-urban thermal patterns are strongly correlated with land-use; industrial areas are warmest and vegetated, riverine or coastal areas are coolest. Nocturnal heat island intensities and the correlation of the surface radiant temperature distribution with land use are less. This is the reverse of the known characteristics of near-surface air temperature heat islands. Several questions relating to the interpretation and limitations of satellite data in heat island analysis and urban climate modelling are addressed.     

Comparing surface- and canopy-layer urban heat islands over Beijing using MODIS data

The canopy-layer urban heat island (CLHI) and the surface-layer urban heat island (SLHI) of Beijing, the capital city of China, were compared on the spatial scale of a city and the temporal scale of a year in this study. A differential temperature vegetation index (DTVX) method was improved by suggesting a new parameterization scheme for estimating daytime air temperature (T_a); a binary linear regression equation was developed for estimating night-time T_a from Moderate Resolution Imaging Spectroradiometer (MODIS) land-surface temperature (T_s) and vegetation indices data during 2009–2010. Validations using weather station observations show that the spatially distributed T_a can be obtained with an accuracy of approximately 2 K. Comparisons between the CLHI and the SLHI indicate that the CLHI agrees well with the SLHI during night-time, but they have a greater difference during daytime either in heat island intensity or in spatial distribution pattern. The SLHI?CLHI intensity difference during daytime has a noticeable seasonal variation, which is small and negative in cold seasons, but large and positive in warm seasons, whereas that at night-time has no significant seasonal variations. The difference in the evapotranspiration cooling effects between urban and rural areas may be the predominant factor that drives the SLHI?CLHI difference.     

Modelling peak-hour urban freight movements with limited data availability

Modelling the complexity of urban freight transport requires large amounts of data related to supply chain management, delivery practices, tour configuration, time windows, etc., but when all this detailed data is not available local authorities still need models that represent this type of transport and its contribution to congestion and environmental impacts. We present here an improvement on other recent works, consisting of a demand model for B2B and home deliveries during the morning peak hour that uses only very limited data to estimate the number of delivery vehicles entering and leaving each zone of the city. We then calculate the trip distribution using an entropy maximisation approach, and solve the resulting model using simulated annealing. We apply this model to a case study in the city of Seville, in Spain, and compare its results to those produced by a gravity model, and with actual traffic counts.     

HJ-1B卫星遥感数据在重庆市城市热环境监测的应用分析

本文以MODIS反演大气透射率,以HJ-1B/CCD分类结果反演地表比辐射率,并基于单窗算法,利用HJ-1B/IRS4数据反演地表温度.在此基础上,提取研究区的热场变异指数来分析重庆热岛空间分布特征,并就NDVI与NDBI对热岛效应的影响进行了分析.其结果如下:1)重庆城市热岛大致位于中梁山、铜锣山之间,呈东北、西南走向分布;2)热岛中心不在市中心,而是集中在大渡口工业园区、江北机场这些能耗大、人口密集区域,热岛强度范围在5?C-10?C之间;3)接近长江、嘉陵江水域的建筑用地密集区域,其热岛效应并不明显;4)NDVI与热岛强度呈负相关关系,NDBI与热岛强度呈现较为明显的正相关关系,二者对热岛都有重要影响,而NDBI的影响更大.因此,利用HJ-1B数据监测城市热环境,能较好地揭示重庆城市热岛空间分布特征,为城市环境监测与改善提供参考.     

基于多源卫星遥感的灾害性气象观测数据监测系统设计

灾害性气象的频发不仅给人们的生活带来了极大的影响,而且还危及生态环境的稳定。为减小实测气象数据与真实数据样本之间误差,实现对灾害性气象观测数据的准确监测,设计基于多源卫星遥感的灾害性气象观测数据监测系统。设置温湿度传感器及防辐射罩,按照数据信息传输网络布局形式,将风速风向传感器、雨量传感器、太阳能控制器接入既定模块单元之中,完成监测系统的硬件设计。利用多源卫星遥感技术,定义多源影像空间,通过分解数字卫星图像的方式,确定监测极值点所处位置,并计算相似性度量指标的具体数值,完成基于多源卫星遥感的灾害性气象图像配准处理。根据上位机组网模型,完善WINSOCK控件的连接形式,实现监测系统的执行程序编制,联合相关传感器元件,完成基于多源卫星遥感的灾害性气象观测数据监测系统设计。实验结果表明,多源卫星遥感技术设计系统的湿度测量曲线、气压测量曲线、风速测量曲线与真实气象数据样本之间的误差值均未超过2%,能够准确监测灾害性气象观测数据。     

Exploring indicators for quantifying surface urban heat islands of European cities with MODIS land surface temperatures

The term urban heat island describes the phenomenon of altered temperatures in urban areas compared to their rural hinterlands. A surface urban heat island encompasses the patterns of land surface temperatures in urban areas. The classical indicator to describe a surface urban heat island is the difference between urban and rural surface temperatures. However, several other indicators for this purpose have been suggested in the literature. In this study, we compared the eleven different indicators for quantifying surface urban heat islands that were most frequently used in recent publications on remote sensing-based urban heat island assessments. The dataset used here consists of 263 European cities with monthly mean temperatures from MODIS data products for July 2002, January 2003 and July 2003. We found that (i) the indicators individually reveal diurnal and seasonal patterns but show rather low correlations over time, and (ii) for single points in time, the different indicators show only weak correlations, although they are supposed to quantify the same phenomenon. Differentiating cities according to thermal climate zones increased the relationships between the indicators. Thus, we can identify temporal aspects and indicator selection as important factors determining the estimation of urban heat islands. We conclude that research should take into account the differences and instabilities of the indicators chosen for quantifying surface urban heat islands and should use several indicators in parallel for describing the surface urban heat island of a city.     

基于AF-SVR的城市快速路多源交通信息融合研究

针对单一检测器所得到的交通数据不能够全面准确地反映实际的交通状态,提出一种基于AF-SVR模型的城市快速路多源交通信息融合的方法。首先通过将相同路段中不同检测器的速度数据作为学习样本输入到支持向量机回归模型(Support Vector Regression,SVR)中进行训练。然后利用鱼群算法(Artificial Fish,AF)对支持向量机回归模型中的参数进行优化,获得最优的信息融合模型,用于多源交通信息的融合,输出为能准确反映真实交通状态的速度数据,并用人工采集的速度数据作为真值进行验证。最后将此方法应用于成都市三环快速路路段上的多源交通信息融合,取得了令人满意的结果。     

Quantifying urban land use and impact on soils in Egypt using diurnal satellite imagery of the Earth surface

Conversion of agricultural land to urban use represents a potential loss of agricultural productivity, especially in areas where arable land is in short supply. Using derived products from both daytime (Landsat sensor data) and night-time imaging systems (U.S. Air Force Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS)) we examined the impacts of urbanization on soils in Egypt; a country with very limited agricultural land. We concluded that urban land cover types to occupy 3.7% of the total area of Egypt and that over 30% of the soils most suitable for agriculture are under urban land cover. Analysis of multiyear historical DMSP/OLS data sets (digitized from paper images) proved unreliable for long-term urban growth estimates.     

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.     

Calculation of the sensible heat flux of the global ocean using satellite data

The sensible heat flux of the global ocean is derived using satellite data of Special Sensor Microwave/Imager (SSM/I) precipitable water, Advanced Very High Resolution Radiometer (AVHRR) sea-surface temperature (SST) and scatterometer wind speed. Prior to heat flux derivation, the air temperature over the sea surface in the global ocean is obtained with an iterative solving technique applied to a simplified equation that specifies the relationship among boundary-layer parameters. It is found that a bias exists between the calculated air temperature and the climatology data, which is corrected by a linear model based on the climatology of air temperature. Using the corrected air temperature and the bulk formula, we calculate the sensible heat flux from January 1992 to October 1998. The heat flux calculation is consistent with previous results. An error analysis suggests that based on the bulk formula, the uncertainty caused by the air temperature is comparable with the error components from the SST and wind. Empirical orthogonal function (EOF) analysis is used to extract the temporal and spatial characteristics of the calculated sensible heat flux. The first EOF is characterized by a winter and summer oscillation with an annual cycle, the second shows another annual cycle in spring and autumn oscillation, the third EOF is related to the El Niño and La Niña cycle, reflecting a certain relationship between El Niño Southern Oscillation (ENSO) events and the global ocean sensible heat flux, and the fourth EOF indicates an increasing trend with a quasi-biennial oscillation and atmospheric influences.     

Near-surface thermal structure and surface diurnal warming in the Adriatic Sea using satellite and drifter data

An extensive set of in situ temperature data collected by surface drifters is combined with satellite-derived sea surface temperature images to study the difference between the pseudo-bulk and bulk temperatures (ΔT_pb-b) in the Adriatic Sea for the period 21 September 2002-31 December 2003. The variations of this temperature difference are described as a function of local wind speed and incoming solar radiation provided by a local area atmospheric model. The daily sea surface temperature variability is also assessed by computing the temperature difference between the daily maximal and minimal values (ΔT_day-night). The data show that the smaller the wind speed and the larger the solar radiation, the larger ΔT_pb-b. The temperature difference reached the highest value (∼5 °C) on a hot day (more than 600 W/m²) of May 2003 in weak wind condition (around 3 m/s). For strong winds (speed > 6 m/s) the dependence on both the wind and solar radiations vanishes as the temperature difference approaches zero because the near-surface water becomes thermally homogenous due to the wind-induced vertical mixing. Strong diurnal warming of the sea surface, as derived by the pseudo-bulk estimates, and a strong near-surface stratification were found during the spring/summer season. Monthly mean statistics show that the diurnal cycle of the pseudo-bulk and bulk temperature starts to become significant already in February and March. Subsequently (from April to August) both the diurnal warming and the stratification are maximal (monthly means of ΔT_day-night ∼1-2 °C and of ΔT_pb-b ∼0.5 °C ), while in fall and early winter the ΔT_pb-b values are quite small (monthly means near 0 °C) and the ΔT_day-night monthly means are bounded by 0.5-1.5 °C. Maximal amplitudes of the diurnal cycle can exceed 4 °C (mostly in spring-summer) for both the pseudo-bulk and bulk temperatures. However, the monthly means of ΔT_day-night is generally twice as large for the pseudo-bulk estimates (∼2 °C) with respect to the bulk layer (∼1 °C). The diurnal warming of the sea surface, as derived by the pseudo-bulk temperature, occurs at about 14:30 local time, that is more than 2 h after the maximal sun elevation and an hour earlier than the bulk temperature maximum at 20-40 cm depth.     

Individual differences on diurnal variations of the task performance

In order to understand psychophysiological background of the individual differences on the diurnal variation of task performance, the task performance (i.e. the number of a simple adding calculation performed in one minute) was measured from 09:30 in the morning to 21:30 in the late evening for 9 healthy university male students. Heart rate (HR) and body temperature were also continuously recorded for 35 hours. There were two different types in the diurnal variation of the task performance; the morning type who shows the best performance in the morning, and the evening type who shows it in the evening. The body temperature curve during daytime in the evening type goes gradually up to the evening, on the contrary, that in the morning type rapidly rises to the maximum point in the morning or the afternoon. HR during daytime were higher in the morning type than the evening type. These results indicate that the morning type has a tendency of the higher mental tension compared with the evening type. And they also suggest that the individual differences on the task performance with a large mental concentration are caused by some kinds of subject's psychosomatic state.     

Sensible and latent heat flux in the tropical Pacific from satellite multi-sensor data

Satellite multi-sensor data is used to derive sensible heat flux (SHF), Bowen ratio (Bo), and thus latent heat flux (LHF) in the tropical Pacific. The temperature differences at the air-sea interface are determined empirically for regions where strong deep air convection is present due to the buoyancy force. The vertical airflow results in surface wind divergence, which is estimated from scatterometer wind vector fields. The areas of positive temperature differences between sea surface temperature and atmospheric temperature estimated using surface wind divergence and in situ measurements are highly consistent in the tropical Pacific, especially in two convergence zones, i.e., the Intertropical Convergence Zone (ITCZ), and the South Pacific Convergence Zone (SPCZ). The bulk formulated SHF, determined by surface wind divergence (SHF_BWD), is compared with a long-term time series (January 1993-December 1999) of the SHF from the National Centers for Environmental Prediction (NCEP) reanalyzed model data (SHF_M). In the ITCZ, the correlation coefficients and the Root Mean Square (RMS) differences between SHF_BWD and SHF_M are over 70% and in the order of less than 4 W/m², respectively. Furthermore, the bulk formulated latent heat flux (LHF_B) is obtained using the empirical Bowen ratio (Bo_E) and SHF_BWD. The Bo_E for the tropical Pacific was estimated using only sea surface temperature, which is valid when the vapor is saturated near sea surface. The RMS differences between SHF_BWD and LHF_B are estimated as 3.5 and 39.3 W/m², respectively. In addition, the differences of SHF_BWD and LHF_B during the boreal winters are smaller than that in the boreal summers. Finally, in order to study spatial and temporal variations, we apply Empirical Orthogonal Function (EOF) analysis to SHF_BWD and LHF_B and compare with EOF analysis of Coupled Ocean and Atmosphere Data Set (COADS)'s SHF and LHF.     

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.     

Mitigating urban heat islands: A method to identify potential wind corridor for cooling and ventilation

In summer, the urban heat island effect causes unbearable warmth in the cities of Taiwan. This study maps out ventilation paths that are expected to draw in breezes from suburban areas and the sea to mitigate the urban heat island effect. Wind corridors are estimated based on the concept that wind moves along paths of low urban roughness, as defined by the frontal area index (FAI) and the least cost path (LCP) methodology followed in this study. The primary wind corridor was proposed with a main function of identifying wind that minimizes net daytime heating through a redistribution of the total surface heat flux. Land use that affects urban temperature is discussed by indicating the areas of potential rises and drops in air temperature in order to describe the temperature influences in both windward and leeward areas. The horizontal resolution of the calculation unit was a hundred meters, which is called an urban block scale in this study. At that resolution, detailed analyses can be discussed in a detailed plan scheme. A slightly more sophisticated CFD model confirms the more simplistic GIS-based mode, and thus, these results have the potential to guide urban wind corridor planning efforts. The land use map of potential temperature influences is superimposed with the current wind corridors to determine the exact areas in which to execute the mitigation strategies. Four sites are recommended for urban surface improvement according to their land uses, respectively. Furthermore, a potential cooling route on the east side of the study area was suggested for the FAI reduction strategy.     

Estimating spatial variations in soil organic carbon using satellite hyperspectral data and map algebra

This study evaluated the effectiveness of using Hyperion hyperspectral data in improving existing remote-sensing methodologies for estimating soil organic carbon (SOC) content on farmland. The study area is Big Creek Watershed in Southern Illinois, USA. Several data-mining techniques were tested to calibrate and validate models that could be used for predicting SOC content using Hyperion bands as predictors. A combined model of stepwise regression followed by a five hidden nodes artificial neural network was selected as the best model, with a calibration coefficient of determination (R ²) of 78.9% and a root mean square error (RMSE) of 3.3 tonnes per hectare (t ha^?1). The validation RMSE, however, was found to be 11.3 t ha^?1. Map algebra was implemented to extrapolate this model and produce a SOC map for the watershed. Hyperspectral data improved marginally the predictability of SOC compared to multispectral data under natural field conditions. They could not capture small annual variations in SOC, but could measure decadal variations with moderate error. Satellite-based hyperspectral data combined with map algebra can measure total SOC pools in various ecosystem or soil types to within a few per cent error.     

Spatio‐temporal modelling and analysis of urban heat islands by using Landsat TM and ETM+ imagery

In this paper we propose a method for characterizing UHIs by using a non‐parametric model. Landsat 5 and 7 images obtained over the city of Indianapolis were used for analysis of UHI over space and time. The images were subjected to kernel convolution implementing Gaussian bi‐variate function for non‐parametric characterization of the UHI. The convoluted images were then analyzed, for pattern over space and over time. It was found that the spread of the UHI in Indianapolis increased from 13.90 km to 21.63 km in the west‐east direction and from 10.98 km to 15.90 km in the north‐south direction from 1985 to 2000. The changes in the UHI from 1995 to 2000 were evident in all cardinal directions. The model aided in successfully characterizing UHI in terms of its location, spread, and the rate of increase, facilitating a clearer image of UHI through space and time.     

Assessing tundra-taiga boundary with multi-sensor satellite data

Monitoring the dynamics of the circumpolar boreal forest (taiga) and Arctic tundra boundary is important for understanding the causes and consequences of changes observed in these areas. This ecotone, the world's largest, stretches for over 13,400 km and marks the transition between the northern limits of forests and the southern margin of the tundra. Because of the inaccessibility and large extent of this zone, remote sensing data can play an important role for mapping the characteristics and monitoring the dynamics. Basic understanding of the capabilities of existing space borne instruments for these purposes is required. In this study we examined the use of several remote sensing techniques for characterizing the existing tundra-taiga ecotone. These include Landsat-7, MISR, MODIS and RADARSAT data. Historical cover maps, recent forest stand measurements and high-resolution IKONOS images were used for local ground truth.It was found that a tundra-taiga transitional area can be characterized using multi-spectral Landsat ETM+ summer images, multi-angle MISR red band reflectance images, RADARSAT images with larger incidence angle, or multi-temporal and multi-spectral MODIS data. Because of different resolutions and spectral regions covered, the transition zone maps derived from different data types were not identical, but the general patterns were consistent.     

Estimation of storage heat flux in an urban area using ASTER data

The urban heat island phenomenon occurs as a result of the mixed effects of anthropogenic heat discharge, increased use of artificial impervious surface materials, and decreased vegetation cover. These factors modify the heat balance at the land surface and eventually raise the atmospheric temperature. It is important to quantify the surface heat balance in order to estimate the contributions of these factors. The present authors propose the use of storage heat flux to represent the heat flux between the land surface and the inside of the canopy for the heat balance analysis based on satellite remote sensing data. Surface heat fluxes were estimated around the city of Nagoya, Japan using Terra ASTER data and meteorological data. Seasonal and day-night differences in heat balance were compared using ASTER data acquired in the daytime on July 10, 2000, and January 2, 2004 and in the nighttime on September 26, 2003. In the central business and commercial districts, the storage heat flux was higher than those in the surrounding residential areas. In particular, in winter, the storage heat flux in the central urban area was 240 to 290 W m^− 2, which was much larger than the storage heat fluxes in residential areas, which ranged from 180 to 220 W m^− 2. Moreover, the negative storage heat flux in the central urban area was greater at night. This tendency implies that the urban surface stores heat during the daytime and discharges it at night. Extremely large negative storage heat flux occurred primarily in the industrial areas for both daytime and nighttime as a result of the enormous energy consumption by factories.