61Determine UHI centroid using a 3D Gaussian volume model and MODIS data61Study multi-temporal variation of the magnitude and location of UHI centroid61Study contributions of thermal landscape, NDVI and albedo to the change of UHI

随着我国城市化进程的加快,在长江三角洲、京津冀、珠江三角洲和辽中南等经济社会发展水平最高的地区,大城市的地域空间组织开始从单体型城市的简单形态,向以中心城市为核心的诸多城市和地区相互交融形成的都市圈的复杂形态转变。大上海、大北京、大广州、大沈阳等大都市圈的雏形已经初步形成,同时在一些经济发展水平较高的大中城市的周边地区,一些区域性都市圈和地方性都市圈也在逐渐形成。以这些中心城市为核心的都市圈的形成与发展,对区域经济社会的发展起到了越来越重要的支撑作用。借鉴发达国家的经验,实施都市圈的发展战略已经成为一个共识。正确引导都市圈的建设与发展是我国新形势下城市发展的一个重要课题。本文指出了我国目前都市圈理论研究和规划实践上存在的一些误区,对都市圈的基本概念、地域本质、形成机理、地域结构与空间形态、类型与规模等进行了分析,为我国21世纪都市圈的建设与发展提供科学的理论依据。

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 渭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.

The land surface temperature (LST) pattern is treated as one of the primary indications of environmental impacts of land cover change. Researchers continue to explore the potential contribution of land surface to temperature rising. The LST-land surface relationship is dynamic and varies spatially. Based upon the previous studies, this research assumes that such dynamics is manifested at two levels: (1) the phenomenon level, and (2) its formation mechanism level. The research presents a workflow of exploring such dynamics at both levels. The variogram of the phenomenon and multi-scale analysis of the LST-land surface relationship are mutually interpreted. In the case study of Wuhan, China, the variogram of the LST indicates that the operational scale of the phenomenon is 500–65002m. It suggests the optimal scale to inspect the LST and its cause in the study area. This finding is verified and further inspected through multi-scale analysis of the LST–Impervious Surface Fraction (ISF) relationship at the formation mechanism level. The research also employs the Spatial Autocorrelation model to show how the ISF impacts the LST through scales. A flexible autocorrelation weight matrix is proposed and implemented in the model. The parameters of the model exhibit the thermal sensitivity of land surface and again represent the scale features. The Ordinary Least Square regression is used as the benchmark. Several implications are discussed.

城市热岛效应直接反映着城市的气候特征,这对于研究由城市化发展与环境改变引起的城市气温的变化及保护城市的生态环境具有重要的现实意义。本文利用LandsatTM影像、气象台站资料,基于GIS的空间分析技术及单窗算法,对河谷型城市西宁市的地表温度进行反演,分析了地表温度与NDVI、NDBI的空间对应关系。结果表明:西宁市存在明显的城市热岛效应,热场分布及延伸与西宁市空间扩展布局相一致,热岛范围呈逐年增长的趋势;低、中温区的热岛面积大幅度减少,高温区的热岛范围显著增加;热岛效应冬季最强,夏季次之,秋季有明显减弱的趋势。在河谷型城市的空间格局上,地表温度与NDVI呈负相关关系、与NDBI呈正相关关系。最后,依据热岛时空演化、成因分析和策略研究的思路,从不同角度提出了缓解城市热岛效应的措施和对策,为未来西宁市热环境的改善提供科学参考和决策支持。

This study introduces artificial neural networks (ANNs) for the estimation of land surface temperature (LST) using meteorological and geographical data in Turkey (26–45°E and 36–42°N). A generalized regression neural network (GRNN) was used in the network. In order to train the neural network, meteorological and geographical data for the period from January 2002 to December 2002 for 10 stations (Adana, Afyon, Ankara, Eski06ehir, 02stanbul, 02zmir, Konya, Malatya, Rize, Sivas) spread over Turkey were used as training (six stations) and testing (four stations) data. Latitude, longitude, elevation and mean air temperature are used in the input layer of the network. Land surface temperature is the output. However, land surface temperature has been estimated as monthly mean by using NOAA-AVHRR satellite data in the thermal range over 10 stations in Turkey. The RMSE between the estimated and ground values for monthly mean with ANN temperature(LST ANN ) and Becker and Li temperature(LST B-L ) method values have been found as 0.07702K and 0.09102K (training stations), 0.04502K and 0.00302K (testing stations), respectively.

Urban heat islands (UHIs) have attracted attention around the world because they profoundly affect biological diversity and human life. Assessing the effects of the spatial structure of land use on UHIs is essential to better understanding and improving the ecological consequences of urbanization. This paper presents the radius fractal dimension to quantify the spatial variation of different land use types around the hot centers. By integrating remote sensing images from the newly launched HJ-1B satellite system, vegetation indexes, landscape metrics and fractal dimension, the effects of land use patterns on the urban thermal environment in Wuhan were comprehensively explored. The vegetation indexes and landscape metrics of the HJ-1B and other remote sensing satellites were compared and analyzed to validate the performance of the HJ-1B. The results have showed that land surface temperature (LST) is negatively related to only positive normalized difference vegetation index (NDVI) but to Fv across the entire range of values, which indicates that fractional vegetation (Fv) is an appropriate predictor of LST more than NDVI in forest areas. Furthermore, the mean LST is highly correlated with four class-based metrics and three landscape-based metrics, which suggests that the landscape composition and the spatial configuration both influence UHIs. All of them demonstrate that the HJ-1B satellite has a comparable capacity for UHI studies as other commonly used remote sensing satellites. The results of the fractal analysis show that the density of built-up areas sharply decreases from the hot centers to the edges of these areas, while the densities of water, forest and cropland increase. These relationships reveal that water, like forest and cropland, has a significant effect in mitigating UHIs in Wuhan due to its large spatial extent and homogeneous spatial distribution. These findings not only confirm the applicability and effectiveness of the HJ-1B satellite system for studying UHIs but also reveal the impacts of the spatial structure of land use on UHIs, which is helpful for improving the planning and management of the urban environment.

The temperature of cities continues to increase because of the heat island phenomenon and the undeniable climatic change. The observed high ambient temperatures intensify the energy problem of cities, deteriorates comfort conditions, put in danger the vulnerable population and amplify the pollution problems. To counterbalance the phenomenon, important mitigation technologies have been developed and proposed. Among them, technologies aiming to increase the albedo of cities and the use of vegetative green roofs appear to be very promising, presenting a relatively high heat island mitigation potential. This paper aims to present the state of the art on both the above technologies, when applied in the city scale. Tenths of published studies have been analysed. Most of the available data are based on simulation studies using mesoscale modeling techniques while important data are available from the existing experimental studies. When a global increase of the city albedo is considered, the expected mean decrease of the average ambient temperature is close to 0.3K per 0.1 rise of the albedo, while the corresponding average decrease of the peak ambient temperature is close to 0.9K. When only cool roofs are considered, the analysis of the existing data shows that the expected depression rate of the average urban ambient temperature varies between 0.1 and 0.33K per 0.1 increase of the roofs albedo with a mean value close to 0.2K. As it concerns green roofs, existing simulation studies show that when applied on a city scale, they may reduce the average ambient temperature between 0.3 and 3K. Detailed analysis of many studies reporting a comparison of the mitigation potential of both technologies has permitted the definition of the limits, the boundaries and the conditions under which the considered technologies reach their better performance, in a synthetic way.

One of the key impacts of rapid urbanization on the environment is the effect of urban heat island (UHI). By using the Landsat TM/ETM+ thermal infrared remote sensing data of 1993, 2001 and 2011 to...

利用2006年和2010年8月的TM卫星影像,通过定量遥感反演手段和相对亮温分析方法,反演和分析了西安市城区相应时间点上的热场温度。结果显示:其城区平均气温由2006年的26.28℃提高到2010年的28.14℃,5 a间升高了1.86℃。期间高温区域在市区的西部、西南部和东南部出现了较大范围的扩展,尤其以东南部的扩展范围和强度为大。从相对亮温情况来看,全市缺乏极强热岛,整个市区以弱热岛占绝对优势,2010年和2006年其面积比例分别为61.11%和58.64%;其次为绿岛,其面积比例在2010年和2006年分别为34.08%和35.98%;中等热岛和强热岛所占比例极小。绿岛面积减少的最大区域在西北部的汉长安城遗址南部以及市区东南部的三兆、东伍一带;而强热岛在2010年呈星点状分布在东郊的灞桥纺织城、西二环附近的西安电工铸造厂和西安电压力容器厂、长安区的河池寨等地。从相对亮温热力斑块的转移概率矩阵看,最稳定的热力景观斑块类型为弱热岛斑块和绿岛斑块,2006—2010年,其保持不变的面积分别达79.41%和67.82%;而最不稳定的景观类型为中等热岛和极强热岛,其发生变化的面积比例分别为70.73%和64.18%。从西安市区热场变化的原因来看,城市建设的影响最大,其中人口增长、建成区范围扩张和房地产开发是最主要的影响因素,另外全球变化背景下的区域气温升高也是西安市热岛效应加剧的另一促进因素,而城市绿化建设可以在一定范围内改善局地的热岛效应状况。

A major benefit of multitemporal, remotely sensed images is their applicability to change detection over time. Because of concerns about global and environmental change, these data are becoming increasingly more important. However, to maximize the usefulness of the data from a multitemporal point of view, an easy-to-use, cost-effective, and accurate radiometric calibration and correction procedure is needed. The atmosphere effects the radiance received at the satellite by scattering, absorbing, and refracting light; corrections for these effects, as well as for sensor gains and offsets, solar irradiance, and solar zenith angles, must be included in radiometric correction procedures that are used to convert satellite-recorded digital counts to ground reflectances. To generate acceptable radiometric correction results, a model is required that typically uses in-situ atmospheric measurements and radiative transfer code (RTC) to correct for atmospheric effects. The main disadvantage of this type of correction procedure is that it requires in-situ field measurements during each satellite overflight. This is unacceptable for many applications and is often impossible, as when using historical data or when working in very remote locations. The optimum radiometric correction procedure is one based solely on the digital image and requiring no in-situ field measurements during the satellite overflight. The dark-object subtraction (DOS) method, a strictly image-based technique, is an attempt to achieve this ideal procedure. However, the accuracy is not acceptable for many applications, mostly because it corrects only for the additive scattering effect and not for the multiplicative transmittance effect. This paper presents an entirely image-based procedure that expands on the DOS model by including a simple multiplicative correction for the effect of atmospheric transmittance. Two straightforward methods to derive the multiplicative transmittance-correction coefficient are presented. The COS(TZ) or COST method uses the cosine of the solar zenith angle, which, to a first order, is a good approximation of the atmospheric transmittance for the dates and sites used in this study. The default TAUs method uses the average of the transmittance values computed by using in-situ atmospheric field measurements made during seven different satellite over-flights. Published and unpublished data made available for this study by Moran et al. (1992) are used, and my model results are compared with their results. The corrections generated by the entirely image-based COST model are as accurate as those generated by the models that used in-situ atmospheric field measurements and RTC software.

地表温度是生态系统中极其重要 的环境因子,中小尺度区域的热量资源和热场分布对绿洲冷岛效应的研究以及评价绿洲的稳定性具有重要意义。利用LandsatTM卫星影像,采用单窗算法, 反演了张掖绿洲1999年和2007年两个时期的陆地表面温度(LST),构建了绿洲"冷岛"比例指数,定量分析了张掖地区近8年来绿洲冷岛效应的时序变 化,并且通过对张掖热场的分析,揭示了冷岛效应的空间变化特点。结果表明:①张掖绿洲冷岛效应明显,但近8年来有减弱的趋势;②南北向绿洲内部和荒漠内部 温度差异均在减小,城区温差在扩大;东西向绿洲内部和城区内部温度差异均在减小,荒漠温差在扩大;③水体、耕地、草地、建设用地和林地的平均LST有所降 低,荒漠等未利用土地LST有所升高,其中建筑用地的LST减幅最大。为研究绿洲的光、热、水、土的空间分布及区域变化,评价绿洲的稳定性提供了一种新的 角度,同时对绿洲与荒漠之间的大气物理模型及动力学有一定参考意义。

在分析热红外遥感和现有的劈窗算法的理论基础上,针对MODIS数据对劈窗算法进行了推导.通过对热辐射强度和温度之间的关系计算,对Planck函数进行了线性简化,同时分析了MODIS的波段设置特点.MODIS的近红外波段适宜于反演大气水汽含量,而大气透过率主要从MODIS的近红外波段数据反演得到大气水汽含量,并进而根据水汽含量与大气透过率的关系来进行估算.通过MODIS的可见光波段、近红外和中红外波段数据,完全可以获得地表温度反演所需要的基本参数,从而形成了针对MODIS数据的地表温度反演的劈窗算法.最后以环渤海地区为实验区,对本文提出的方法进行了实际应用分析.

在ENVI和GIS支持下,提出了基于Landsat 8遥感影像的地温反演劈窗算法,提取兰州市中心城区地表温度。利用FNEA和混合光谱分解法确定了兰州市中心城区的城市热岛中心、不透水面和植被盖度,分析了城市热岛空间分布格局以及地表温度与下垫面之间的关系。结果显示:基于Landsat 8数据地温反演的劈窗算法是可行的。兰州中心城区的高温区分布较集中,地表温度与植被呈较强的负相关,与不透水面呈不显著的正相关,与其他非光合物质呈正相关。

为探索西北部中等城市的城市热岛效应规律，利用2004--2010年天水市城、郊自动气象站资料，对天水市城、郊温度差异进行了系统分析。结果表明：天水市城区热岛效应比较显著；冬季是热岛现象最强的季节，夏季是热岛现象最弱的季节；城区热岛现象持续时间为18：00-10：00，占全天的2／3，最大强度达，1．5℃；城区“凉岛”持续时间为11：00—17：00，占全天的1／3，最大强度为0．3℃；地面温度城、郊差比气温更大，40cm以下城区地温全年大于郊区；降雪日及连阴雨天气的中后期，城区基本上维持“凉岛”；强降温天气下，城、郊气温变化基本一致，差别不大。