Spatio-temporal Assessment of Urban Heat Island Effects in Kuala Lumpur Metropolitan City Using Landsat Images

Alteration in climatic pattern has resulted to a steady decline in quality of life and the environment, especially in and around urbanized areas. These areas are faced with increasing surface temperature arising mostly from human activities and other natural sources; hence land surface temperature has become an important variable in global climate change studies. In this paper, Landsat TM/ETM imagery acquired between 1997 and 2013 were used to extract ground brightness temperature and land use/land cover change in Kuala Lumpur metropolis. The main objective of this paper is to examine the effectiveness of quantifying UHI effects, in space and time, using remote sensing data and, also, to find the relationship between UHI and land use change. Four land use types (forest, farmland, built-up area and water) were classified from the Landsat images using maximum likelihood classification technique. The result reveals that Greater KL experienced an increase in average temperature from 312.641°K to 321.112°K which was quite eminent with an average gain in surface temperature of 8.4717°K. During the period of investigation (1997–2013), generally high temperature is been experienced mostly in concentrated built-up areas, the less concentrated have a moderate to intermediate temperature. Again, the study also shows that low and intermediate temperature classes loss more spatial extent from 2,246.89 Km² to 1,164.53 Km² and 6,102.42 Km² to 3,013.63 Km² and a gain of 4,165.963 Km² and 307.098 Km² in moderate and high temperature respectively from 1997 to 2013. The results of this study may assist planners, scientists, engineers, demographers and other social scientists concerned about urban heat island to make decisions that will enhance sustainable environmental practices.     

Spatial and Temporal Dynamics of Urban Heat Island and Their Relationship with Land Cover Changes in Urbanization Process: A Case Study in Suzhou, China

One of the significant environmental consequences of urbanization is the urban heat island (UHI). In this paper, Landsat TM images of 1986 and 2004 were utilized to study the spatial and temporal variations of heat island and their relationships with land cover changes in Suzhou, a Chinese city which experienced rapid urbanization in past decades. Land cover classifications were derived to quantify urban expansions and brightness temperatures were computed from the TM thermal data to express the urban thermal environment. The spatial distributions of surface temperature indicated that heat islands had been largely broadened and showed good agreements with urban expansion. Temperature statistics of main land cover types showed that built-up and bare land had higher surface temperatures than natural land covers, implying the warming effect caused by the urbanization with natural landscape being replaced by urban areas. In addition, the spatial detail distributions of surface temperature were compared with the distribution of land cover by means of GIS buffer analysis. Results show remarkable show good correspondence between heat island variations with urban area expansions.     

Landsat卫星热红外数据地表温度遥感反演研究进展

作为驱动地表与大气之间能量交换的关键物理量,地表温度在众多领域中都发挥着重要作用,包括气候变化、环境监测、蒸散发估算以及地热异常勘探等。Landsat热红外数据因其时间连续性和高空间分辨率等特点被广泛应用于地表温度反演中。本文详细地介绍了Landsat热红外传感器及其可用的数据与产品的现状,梳理了2001年—2020年20年间基于Landsat热红外数据的地表温度遥感反演与应用的相关文献发表及互引情况,系统地综述了基于Landsat热红外数据的地表温度反演算法,包括基于辐射传输方程的算法、单窗算法、普适性单通道算法、实用单通道算法和分裂窗算法等。在此基础上,进一步介绍了每种算法的参数化方案,包括地表比辐射率和大气参数的估算方法。最后针对Landsat热红外数据地表温度遥感反演提出了未来可能的发展趋势与研究方向。     

Comparison and Integration of Radar and Optical Data for Land Use/Cover Mapping

Abstract

This study examined the complementarity of spaceborne radar and optical data for surface feature identification. RADARSAT data sets were assessed independently and in combination with Landsat Thematic Mapper (TM) multispectral data. The primary methodology was spectral signature extraction and the application of a statistical decision rule to classify the surface features for a site near Kericho, Kenya. Relative accuracy of the resultant classifications was established by digital integration and comparison to reference information derived from field visitation. Speckle filtering was a great improvement over the poor results achieved with the unfiltered, original radar data but still not adequate for accurate land cover classification. The extraction and use of Variance texture measures was found to be very advantageous. The overall results were not significant improvements over speckle removal (6% increase) but several individual classes, forest and urban, had excellent results with texture. Combinations of radar with Landsat TM greatly improved results, achieving near perfect classification of all individual classes. The highest overall accuracy was achieved with a merger that included the best individual texture image and six reflectance bands of the TM data. The systematic strategy of this study, determination of the best individual method before introducing the next procedure, was effective in managing a very complex, almost infinite set of analysis possibilities.     

Multi-resolution integration of land cover for sub-pixel estimation of urban impervious surface and forest cover

Abstract

A methodology is presented for estimating percent coverage of impervious surface (IS) and forest cover (FC) within Landsat thematic mapper (TM) pixels of urban areas. High-resolution multi-spectral images from Quickbird (QB) play a key role in the sub-pixel mapping process by providing information on the spatial distributions of ISs and FCs at 2.4 m ground sampling intervals. Thematic classifications, also derived from the Landsat imagery, have then been employed to define relationships between 30 m Landsat-derived greenness values and percent IS and FC. By also utilizing land cover/land use classification derived from Landsat and defining unique relationships for urban sub-classes (i.e. residential, commercial/industrial, open land), confusion between impervious and fallow agricultural lands has been overcome. Test results are presented for Ottawa-Gatineau, an urban area that encompasses many aspects typical of the North American urban landscape. Multiple QB scenes have been acquired for this urban centre, thereby allowing us to undertake an in-depth study of the error budgets associated with the fractional inference process.     

Spatial Analysis of Urban Growth Impacts on Vegetative Greenness with Landsat TM Data

Abstract

Landsat Thematic Mapper (TM) data have been used to monitor land cover types and to estimate biophysical parameters. However, studies examining the spatial relationships between land cover change and biophysical parameters are generally lacking. With the integration of remote sensing and Geographic Information Systems (GIS), these relationships can be better explored. The research reported in this paper applies this integrated approach for detecting urban growth and assessing its impact on vegetative greenness in the Zhujiang Delta, China. Multi‐temporal Landsat TM data were utilized to map urban growth and to extract and identify changes in vegetative greenness. GIS analyses were conducted to examine the changing spatial patterns of urban growth and greenness change. Statistical analyses were then used to examine the impact of urban growth on vegetative greenness. The results revealed that there was a notably uneven urban growth pattern in the delta, and urban development had reduced the scaled Normalized Difference Vegetation Index (NDVI) value by 30% in the urbanized area.     

土地覆盖类型对城市热岛效应的响应分析

利用夏季MODIS地表温度和土地覆盖产品,结合Landsat等辅助遥感数据,分别提取济南、武汉、重庆3个城市2003年、2008年、2013年的土地覆盖与地表温度信息,确定3个城市不同年份的热岛效应等级分布。在此基础上,对济南、武汉、重庆这3个城市的地表温度分布特征、热岛效应等级分布特征与土地覆盖类型各因子之间的关系展开分析。结果表明：城市用地是城市热岛的主要贡献因素,相关系数达到0.42;最能缓解城市热岛效应的是林地,平均相关系数为-0.41;3个城市中最能缓解城市热岛效应的土地覆盖类型并不完全相同：济南市为林地和耕地,武汉市为水体,重庆市为林地。     

热红外遥感浙江地表热环境分布研究

全球气候变暖与人类戚戚相关,研究发现不仅是城市的热岛效应引起了局部区域的地表热环境分布差异,很多地质构造、岩性、土壤、植被等地质及自然地理因素也有影响。本研究选取浙江省作为研究区,利用Landsat 8 OLI/TIRS遥感影像反演地表温度,分析冬季地表热环境分布及其影响因子。结果表明,断裂带附近地表热环境受到断裂带分布影响;岩石和土壤通过不同的地表覆被类型影响地表温度。地表热环境分布与断裂带等自然因素存在一定相关性。     

An analysis of urban expansion and its associated thermal characteristics using Landsat imagery

There has been an increasing interest in mapping and monitoring urban land use/land cover using remote sensing techniques. However, there still exist quite a number of challenges in deriving urban extent and its expansion density from remote sensing data quantitatively. This study utilized Landsat TM/ETM+ remote sensing data to assess urban expansion and its thermal characteristics with a case study in the city of Changsha, China. We proposed a new approach for quantitatively determining built-up area, its expansion density and their respective relationship with land surface temperature (LST) patterns. An urban expansion metric was also developed using a moving window mechanism to identify urban built-up area and its expansion density based on selected threshold values. The study suggested that urban extent and its expansion density, as well as surface thermal characteristics and patterns could be identified through quantitatively derived remotely sensed indices and LST, which offer meaningful characteristics in quantifying urban expansion density and urban thermal pattern. Results from the case study demonstrated that: (1) the built-up area and urban expansion density have significantly increased in the city of Changsha from 1990 to 2001; and (2) the differences of urban expansion densities correspond to thermal effects, where a high percentage of imperviousness is usually associated with the area covered by high surface temperature.     

利用不同地表比辐射率对ETM＋数据地表温度反演对比研究

地表温度作为研究环境生态影响的一个重要指标之一,以Landsat ETM+的热红外波段作为数据源,辅助大气透过率、大气平均气温等参数,利用单窗算法基于混合像元、NDVI、植被和裸露地三种不同地表比辐射率反演泉州市地表温度,对比分析结果表明采用这三种地表比辐射率反演地表温度的方法均是可行的,对于具体采用哪一种地表比辐射率应需要结合实际的研究区域地表地物的实际监测地表温度进行对比后,方可选择合适的地表比辐射率进行地表温度反演,会更具实际意义。反演结果可为城市热岛效应等研究提供支持数据源。     

多时相遥感数据对城市热岛效应的研究——以西安市为例

热岛效应是影响我国城市环境的重要因素,本文基于多时相的Landsat 7 ETM+和Landsat 8 TIRS影像,对西安市2002年和2013年的土地进行分类以及地表温度反演,在此基础上将土地分类影像图和温度分割影像图进行叠加,分析了不同的土地利用类型温度差异、下垫面的空间结构与热场分布的关系以及热场的分布变化与城市变迁的关系。结果表明:不同的土地利用类型的物理属性和生物学属性差异是造成城市热岛效应的内在原因,下垫面空间结构的差异是造成城市热岛效应的外在原因,城市热场的动态变化方向与城市的变迁方向具有一致性。     

基于ASTER GED产品的地表发射率估算

地表发射率是地表温度反演的重要输入参数,为了解决现有地表发射率估算方法在裸露地表精度较差的问题,本文基于最新的ASTER全球地表发射率产品(ASTER GED)和基于植被覆盖度的方法(VCM),提出了一个改进的地表发射率估算方法。首先,利用ASTER GED产品求解裸土发射率,然后,利用ASTER波谱库中的植被发射率和植被覆盖度结合VCM方法计算地表发射率。利用张掖地区2012年11景ASTER TES算法反演的地表发射率产品和实测地表发射率数据进行了验证,同时利用一景Landsat 8 TIRS数据分析了对地表温度反演精度的影响。结果表明该方法估算的地表发射率整体精度较高,可以有效改进裸露地表的发射率估算精度,用于支持利用多种热红外传感器数据生产高精度的地表温度产品。     

张掖绿洲城市热效应的遥感研究

以张掖及周边绿洲为研究区,采用Gilabert大气校正模型对TM图像1～5,7等6个波段进行大气校正。在此基础上,采用基于TM6波段的单窗算法,反演出行星亮度温度和地表真实温度,进一步分析了区域热量分布差异并探讨这一差异形成原因。结果表明:张掖及周边绿洲区存在明显的热量分布差异,热岛现象非常显著,卫星图像反演的地表温度接近实测值,亮度温度和实际温度都与归一化植被指数(NDVI)存在着密切的反相关关系,表明植被在区域热量分布差异中起重要作用,显示出热红外图像与其它波段数据结合,在城市热岛效应及区域生态环境遥感研究方面有很大潜力。     

苏州土地利用及热岛效应的时空变化

选取苏州作为研究区,分别利用1986年和2004年2个时相的Landsat5 TM影像数据对研究区土地利用/覆盖类型进行分类,并采用热红外波段进行地表温度反演,在此基础上分析了下垫面类型与热岛效应的时空变化,深入研究了下垫面类型改变对热岛效应的影响。结果表明,近20a苏州市城区大范围扩张,土地利用变化剧烈,整体表现为水体、植被向不透水下垫面的高强度转化;城市热岛范围由老城区向四周成倍扩大,热岛强度由3.12°增至4.76°,热岛效应变化与土地利用时空变化之间具有明显的对应关系。     

长春地区地表温度异常遥感分析

热红外遥感是一项探测地热资源、植被覆盖、农作物估产等生态环境评价研究的重要技术。本次使用Landsat 7/ETM＋热红外波段（band 6）,基于单通道算法,对长春地区地表温度应用反演,从而为研究该区地热资源、土地覆盖、城市热岛效应及环境评价提供可靠的依据。研究表明,热红外遥感能够有效探测到地表温度异常,而引起其异常的原因有待我们就一步验证和深入研究。     

Estimating spatial variations of total evaporation using multispectral sensors within the uMngeni catchment,South Africa

Total evaporation is of importance in assessing and managing long-term water use, especially in water-limited environments. Therefore, there is need to account for water utilisation by different land uses for well-informed water resources management and future planning. This study investigated the feasibility of using multispectral Landsat 8 and moderate resolution imaging spectroradiometer (MODIS) remote sensing data to estimate total evaporation within the uMngeni catchment in South Africa, using surface energy balance system. The results indicated that Landsat 8 at 30 m resolution has a better spatial representation of total evaporation, when compared to the 1000 m MODIS. Specifically, Landsat 8 yielded significantly different mean total evaporation estimates for all land cover types (one-way ANOVA; F_4.964?=?87.011, p < 0.05), whereas MODIS failed to differentiate (one-way ANOVA; F_2.853?=?0.125, p = 0.998) mean total evaporation estimates for the different land cover types across the catchment. The findings of this study underscore the utility of the Landsat 8 spatial resolution and land cover characteristics in deriving accurate and reliable spatial variations of total evaporation at a catchment scale.     

Land Surface Emissivity and Temperature Determination Using NOAA‐AVHRR Data

Abstract

Studies on land surface processes using remote sensing data gains importance in the context of Geosphere Biosphere Programme. Present study addresses the applicability of split‐window method, in a tropical environment for mapping of surface temperature over heterogeneous surface from satellite data. The accuracy of the method is about +2.2°K, which is reasonable value taking into account the atmospheric attenuation in tropical environment. An attempt has been made to derive emissivity from normalized difference vegetation index (NDVI) by taking into account the fraction of vegetation cover of each pixel, which is determined by satellite data. The emissivity values estimated from satellite data found to be in reasonable agreement with an estimated error of less than 1%. The results of the study indicate the potential use of NDVI as a modulating parameter in the land surface temperature estimation from satellite data.     

Assessing environmental impacts of urban growth using remote sensing

ABSTRACT

This paper provides a study of the changes in land use in urban environments in two cities, Wuhan, China and western Sydney in Australia. Since mixed pixels are a characteristic of medium resolution images such as Landsat, when used for the classification of urban areas, due to changes in urban ground cover within a pixel, Multiple Endmember Spectral Mixture Analysis (MESMA) together with Super-Resolution Mapping (SRM) are employed to derive class fractions to generate classification maps at a higher spatial resolution using an Artificial Neural Network (ANN) predicted Wavelet method. Landsat images over the two cities for a 30-year period, are classified in terms of vegetation, buildings, soil and water. The classifications are then processed using Indifrag software to assess the levels of fragmentation caused by changes in the areas of buildings, vegetation, water and soil over the 30 years. The extents of fragmentation of vegetation, buildings, water and soil for the two cities are compared, while the percentages of vegetation are compared with recommended percentages of green space for urban areas for the benefit of health and well-being of inhabitants. Changes in Ecosystem Service Values (ESVs) resulting from the urbanization have been assessed for Wuhan and Sydney. The UN Sustainable Development Goals (SDG) for urban areas are being assessed by researchers to better understand how to achieve the sustainability of cities.