Evaluation of the surface urban heat island effect in the city of Madrid by thermal remote sensing

The surface urban heat island (SUHI) effect is defined as the increased surface temperatures in urban areas in contrast to cooler surrounding rural areas. In this article, the evaluation of the SUHI effect in the city of Madrid (Spain) from thermal infrared (TIR) remote-sensing data is presented. The data were obtained from the framework of the Dual-use European Security IR Experiment (DESIREX) campaign that was carried out during June and July 2008 in Madrid. The campaign combined the collection of airborne hyperspectral and in situ measurements. Thirty spectral and spatial high-resolution images were acquired with the Airborne Hyperspectral Scanner (AHS) sensor in a 11, 21, and 4 h UTC scheme. The imagery was used to retrieve the SUHI effect by applying the temperature and emissivity separation (TES) algorithm. The results show a nocturnal SUHI effect with a highest value of 5 K. This maximum value agrees within 1 K with the highest value of the urban heat island (UHI) observed using air temperature data (AT). During the daytime, this situation is reversed and the city becomes a negative heat island.     

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.     

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RTI的城市热岛效应变化分析研究-乌鲁木齐市为例

以乌鲁木齐市区为例，利用Landsat TM/ETM+第6波段数据反演地表温度,并计算出其相对温度指数(RTI)。从气温相对性、动态性的角度，对其相对温度指数的变化分布模式进行分析，得出城市热岛的变化特征及其空间分布规律。该方法避免了下垫面复杂性及地-气模型建立的困难性等因素对城市热岛效应变化分析研究的影响。     

Downscaling AVHRR land surface temperatures for improved surface urban heat island intensity estimation

Surface urban heat island (SUHI) is a phenomenon of both high spatial and temporal variability. In this context, studying and monitoring the SUHIs of urban areas through the satellite remote sensing technology, requires land surface temperature (LST) image data from satellite-borne thermal sensors of high spatial resolution as well as temporal resolution. However, due to technical constrains, satellite-borne thermal sensors yield a trade-off between their spatial and temporal resolution; a high spatial resolution is associated with a low temporal resolution and vice versa. To resolve this drawback, we applied in this study four downscaling techniques using different scaling factors to downscale 1-km LST image data provided by the Advanced Very High Resolution Radiometer (AVHRR) sensor, given that AVHRR can offer the highest temporal resolution currently available. The city of Athens in Greece was used as the application site. Downscaled 120-m AVHRR LSTs simulated by the downscaling techniques, were then used for SUHI intensity estimation based on LST differences observed between the main urban land covers of Athens and the city's rural background. For the needs of the study, land cover information for Athens was obtained from the Corine Land Cover (CLC) 2000 database for Greece. Validation of the downscaled 120-m AVHRR LSTs as well of the retrieved SUHI intensities was performed by comparative analysis with time-coincident observations of 120-m LST and SUHI intensities generated from the band 6 of the Thermal Mapper (TM) sensor onboard the Landsat 5 platform. The spatial pattern of the downscaled AVHRR LST was found to be visually improved when compared to that of the original AVHRR LST and to resemble more that of TM6 LST. Statistical results indicated that, when compared to 120-m TM6 LST, the root mean square error (RMSE) in 120-m AVHRR LST generated by the downscaling techniques ranged from 4.9 to 5.3 °C. However, the accuracy in SUHI intensity was found to have significantly improved, with a RMSE value decreasing from 2.4 °C when the original AVHRR LST was utilized, down to 0.94 °C in case that downscaling was applied.     

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。     

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。     

Analysis of Landscape Characteristics of Urban Heat /Sink Islandin Hangzhou based on ASTER Remote Sensing Imagery

Rapid urbanization has significant contributions to the Surface Urban Heat Island (SUHI).Analyzing the SUHI distribution and its impact factors using remote sensing data has received increasing attentions in the past decades,whereas few study has investigated that of the surface Urban Heat Sink Island (SUHI).The paper selects Hangzhou metropolis as a case study to explore SUHI/SUHS spatial patterns and its causes.We first retrieve the Land Surface Temperature (LST) using ASTER thermal infrared remote sensing imagery and extract the region of SUHI/SUHS using the Mean\|Standard deviation method.Landsat8 OLI data is used to classify land use and extract both impervious surface and vegetation information.After that,different landscape patterns within SUHI/SUHS area are analyzed and quantified by using several selected landscape index.The largest impact factors in SUHI/SUHS areas are identified.Finally,we analyze the spatial characteristics of LST using the spatial gradient analysis method,and reveal its relationship with vegetation and impervious surface.The results show that：(1) a large landscape pattern difference exists within SUHI/SUHS area;the impervious surface has the greatest impact on LST of the SUHI area,whereas the vegetation has more obviously cooling effect on LST of the SUHS area than the water body;(2) with the increasing distance from the city center,the same trend was found between the mean LST values and the impervious surface density (positive correlations),whereas the opposite trend between the mean LST values and the vegetation density (negative correlations).And the warming effect of impervious surface is greater than the cooling effect of vegetation in Hangzhou.     

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。      

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。     

土地利用变化下北京市热通量的时空演变

探究土地利用变化对城市热通量的影响,对城市用地规划和城市热岛缓解具有重要指导意义。利用混合像元组分排序对比和分层能量切割方法,通过Landsat系列数据反演的地表参数,结合气象再分析资料,估算了2004、2009、2014和2017年4期9月份的北京市地表瞬时热通量,依据同期的北京市土地利用图,分析了北京市热通量随土地利用变化的时空演变。结果表明：①北京市地表温度和热通量分布具有明显的空间异质性,山区和平原、平原不同土地利用类型之间差异明显;②在不同时期,土地利用类型间的地表温度和热通量的高低次序具有一致性,瞬时潜热通量,林地最高,为347.85~546.95 W/m²,其次为耕地、草地,建设用地最小,为225.23~349.03 W/m²,感热通量和地表温度则相反,建筑用地最高,分别为94.06~189.28 W/m²和25.18~32.25 ℃,耕地和草地次之,水体的最低,分别为28.15~102.55 W/m²和19.25~28.38 ℃;③土地利用类型转变引起的城市热通量变化方面,自然表面转为建设用地时,潜热通量急剧减少,感热通量增加,城区周边耕地的潜热通量受城市热辐射影响而增加,城市绿地能有效缓解城市热岛效应。     

Spatiotemporal Pattern of Urban-rural Aradient Land Cover Changes and Their Impact on Urban Heat Island in Xi′an City Since the 21st Century

Rapid urban expansion had a significant impact in land use/cover change along urban-rural gradient, and the increase of impervious construction land and the reduction of vegetation cover had induced and aggravated the urban heat island effect. Studying the impact of urban-rural gradient land cover change on urban heat island effect was significant for urban planning and construction, improving the comfort of human settlements and enhancing the function of urban ecological services. The surface temperature of Xi'an city was retrieved by mono-window algorithm based on Landsat images, and the thermal field intensity map was obtained by calculating the thermal field variation index, and the gradient land cover changes in urban and rural areas were analyzed with land use data. The results showed that: ①The urban heat island effect in Xi'an showed a trend of first increasing and then decreasing from 2000 to 2015. In 2000, the extremely strong heat island effect area accounted for 10.58% of the research area, and gradually increased to 16.14% in 2011, and then decreased to 9.00% in 2015. ②From 2000 to 2015, the area of construction land increased 412.76 km² and the intensity of extremely strong heat island expanded year by year with the expansion of urban built-up areas. ③About 70% of the non-heat island effect areas were located on farmland and forest land, and the proportion of water area in the non-heat island effect was increasing year by year from 31% to 47%, which showed that the increase of vegetation and water area could effectively alleviate the urban heat island effect.     

长春市城市扩张及其微气候响应

针对目前城市化和热岛效应研究多为单一定性评价和半定量化研究,缺乏耦合分析的问题,提出了一种可行的定量耦合分析方法。分析了长春市建设用地扩张和热岛效应的时空演变特征及其关系。首先收集遥感数据,采用归一化差分复合指数阈值分类法提取建设用地,之后采用辐射传输法反演地表温度,以此为基础计算扩张强度、热岛强度等指数,进而将城市扩张与微气候变化进行相关性分析。结果表明,长春市建设用地1990—2016年间增加了608.85 km^2,2000年后陡增,扩张模式由面状扩张变为沿道路辐射状扩张、飞地式扩张。城市扩张初期,相对热岛效应显著,随着建设用地的扩大,热岛面积增大,但是强度下降。相关性分析表明,单位格网内城市用地所占面积高于40%时地表温度上升明显。     

利用MODIS热红外数据进行广州市夏季热场分析

城市热环境及其热效应是当前城市气候与环境中最为重要的研究内容之一。地表温度（LST）是研究热环境的一个有效手段。相比在我国进行LST研究中广泛使用的AVHRR数据,MODIS数据具有更高的灵敏度和精确度，基于2004年夏季MODIS热红外数据进行广州市城市LST反演，并对LST进行密度分割处理制作城市热场分布图。通过对不同时间LST图像进行对比以及数据统计采分析广州市热环境的空间分布格局。发现广州市城市热岛效应问题极为严重。     

基于NOAA/AVHRR热红外数据的城市热岛强度年内变化特征

采用ENVI/IDL编程技术,实现NOAA/AVHRR数据的校准、几何纠正、云污染识别与剔除、影像特征统计与输出等过程的批处理自动化操作。并以济南市中心城区为例,通过2005~2006年间获取的白天NOAA/AVHRR影像热红外波段调查了济南市区城市热岛强度的年内变化规律与过程。研究结果表明：① 济南市区全年大部分时间存在热岛现象,4~9月份城市热岛效应较为明显,尤以5、7、8月为甚。② 全年城市热岛平均强度2.77℃,最强的热岛效应出现于7月下旬至8月中旬间。③ 从季节分布来看,济南市区夏季热岛效应最明显,春季次之,秋、冬两季较弱。④ 城市热岛强度与城、郊地表温度存在正相关关系,但相关程度较差。     

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.     

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.     

基于多旋翼无人机的热红外地表温度场反演系统设计

对当前城市热岛效应研究所采用的三类主要方法进行了分析,分别得出其优势和存在的不足,提出了一种基于微小型多旋翼无人机平台开发小尺度高分辨率地表温度场观测系统的创新思路,并通过对红外测温原理的研究,设计了热红外无人机地表温度场反演系统,开发了B/S架构的地表温度场反演软件,实现了红外图像实时传输、自动拼图、米级网格化地表温度场反演、实景地图叠加、历史数据回放和数据导出等功能。经多次飞行实践应用表明,该系统运行稳定、数据准确、投资少,机动灵活,使用方便,可弥补传统卫星遥感等观测手段的不足,为开展城市区域热环境精细化观测提供了一种新的技术平台与工具,也为气象无人机的应用开辟了一个新的方向。     

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。     

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.