Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies

Remote sensing of urban heat islands (UHIs) has traditionally used the Normalized Difference Vegetation Index (NDVI) as the indicator of vegetation abundance to estimate the land surface temperature (LST)-vegetation relationship. This study investigates the applicability of vegetation fraction derived from a spectral mixture model as an alternative indicator of vegetation abundance. This is based on examination of a Landsat Enhanced Thematic Mapper Plus (ETM+) image of Indianapolis City, IN, USA, acquired on June 22, 2002. The transformed ETM+ image was unmixed into three fraction images (green vegetation, dry soil, and shade) with a constrained least-square solution. These fraction images were then used for land cover classification based on a hybrid classification procedure that combined maximum likelihood and decision tree algorithms. Results demonstrate that LST possessed a slightly stronger negative correlation with the unmixed vegetation fraction than with NDVI for all land cover types across the spatial resolution (30 to 960 m). Correlations reached their strongest at the 120-m resolution, which is believed to be the operational scale of LST, NDVI, and vegetation fraction images. Fractal analysis of image texture shows that the complexity of these images increased initially with pixel aggregation and peaked around 120 m, but decreased with further aggregation. The spatial variability of texture in LST was positively correlated with those in NDVI and in vegetation fraction. The interplay between thermal and vegetation dynamics in the context of different land cover types leads to the variations in spectral radiance and texture in LST. These variations are also present in the other imagery, and are responsible for the spatial patterns of urban heat islands. It is suggested that the areal measure of vegetation abundance by unmixed vegetation fraction has a more direct correspondence with the radiative, thermal, and moisture properties of the Earth's surface that determine LST.     

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。     

不同地表参数变化的上海市热岛效应时空分析

研究地表参数变化与热岛效应的关系对优化城市功能分区以及城市可持续发展具有重要意义。采用上海市2000、2005、2009年3个时期的Landsat ETM+卫星遥感影像,使用归一化不透水面指数(NDISI)、基于指数的植被指数(IVI)、归一化差异水体指数(MNDWI)分别从遥感影像中提取不透水面、植被和水体;然后从时间、空间角度并采用回归分析方法分析了上海市地表参数在这9 a中发生的变化及其对城市热环境造成的影响。结果表明:9 a中城市不透水面面积大幅增加,不透水面增加的代价是植被和水体大范围减少,形成了城市的热岛。上海市整体热岛强度是先增强后缓慢减弱的趋势,且热岛分布从集中型向分散型发展。     

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.     

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.     

Comparative analysis of urban reflectance and surface temperature

Urban environmental conditions are strongly dependent on the biophysical properties and radiant thermal field of the land cover elements in the urban mosaic. Observations of urban reflectance and surface temperature provide valuable constraints on the physical properties that are determinants of mass and energy fluxes in the urban environment. Consistencies in the covariation of surface temperature with reflectance properties can be parameterized to represent characteristics of the surface energy flux associated with different land covers and physical conditions. Linear mixture models can accurately represent Landsat ETM+ reflectances as fractions of generic spectral endmembers that correspond to land surface materials with distinct physical properties. Modeling heterogeneous land cover as mixtures of rock and/or soil Substrate, Vegetation and non-reflective Dark surface (SVD) generic endmembers makes it possible to quantify the dependence of aggregate surface temperature on the relative abundance of each physical component of the land cover, thereby distinguishing the effects of vegetation abundance, soil exposure, albedo and shadowing. Comparing these covariations in a wide variety of urban settings and physical environments provides a more robust indication of the global variability in these parameter spaces than could be inferred from a single study area. A comparative analysis of 24 urban areas and their non-urban peripheries illustrates the variability in the urban thermal fields and its dependence on biophysical land surface components. Contrary to expectation, moderate resolution intra-urban variations in surface temperature are generally as large as regional surface heat island signatures in these urban areas. Many of the non-temperate urban areas did not have surface heat island signatures at all. However, the multivariate distributions of surface temperature and generic endmember fractions reveal consistent patterns of thermal fraction covariation resulting from land cover characteristics. The Thermal-Vegetation (TV) fraction space illustrates the considerable variability in the well-known inverse correlation between surface temperature and vegetation fraction at moderate (< 100 m) spatial resolutions. The Thermal-Substrate (TS) fraction space reveals energetic thresholds where competing effects of albedo, illumination and soil moisture determine the covariation of maximum and minimum temperature with illuminated substrate fraction. The dark surface endmember fraction represents a fundamental ambiguity in the radiance signal because it can correspond to either absorptive (e.g. low albedo asphalt), transmissive (e.g. deep clear water) or shadowed (e.g. tree canopy shadow) surfaces. However, in areas where dark surface composition can be inferred from spatial context, the different responses of these surfaces may still allow them to be distinguished in the thermal fraction space.     

城市不透水面及其与城市热岛的关系研究-以泉州市区为例

利用Landsat TM卫星影像提取了泉州市1989到1996年的城市建成区不透水面,并研究了其与城市热岛之间的关系。根据Ridd(1995)提出的城市建成区不透水面与植被覆盖度有很强的负相关关系的思想,先利用归一化植被指数求出泉州市建成区的植被覆盖度,进而提取了泉州市建成区的不透水面。通过比较所提取的两个时相不透水面信息,可以看出泉州市区不透水面的面积在7年里有了明显的增加,并主要沿研究区东南部扩展。通过将所提取的不透水面信息与利用TM6波段反演的地表温度进行相关分析,可发现二者之间存在着明显的正相关关系。     

Remote sensing of the urban heat island effect across biomes in the continental USA

Impervious surface area (ISA) from the Landsat TM-based NLCD 2001 dataset and land surface temperature (LST) from MODIS averaged over three annual cycles (2003-2005) are used in a spatial analysis to assess the urban heat island (UHI) skin temperature amplitude and its relationship to development intensity, size, and ecological setting for 38 of the most populous cities in the continental United States. Development intensity zones based on %ISA are defined for each urban area emanating outward from the urban core to the non-urban rural areas nearby and used to stratify sampling for land surface temperatures and NDVI. Sampling is further constrained by biome and elevation to insure objective intercomparisons between zones and between cities in different biomes permitting the definition of hierarchically ordered zones that are consistent across urban areas in different ecological setting and across scales.We find that ecological context significantly influences the amplitude of summer daytime UHI (urban-rural temperature difference) the largest (8 °C average) observed for cities built in biomes dominated by temperate broadleaf and mixed forest. For all cities combined, ISA is the primary driver for increase in temperature explaining 70% of the total variance in LST. On a yearly average, urban areas are substantially warmer than the non-urban fringe by 2.9 °C, except for urban areas in biomes with arid and semiarid climates. The average amplitude of the UHI is remarkably asymmetric with a 4.3 °C temperature difference in summer and only 1.3 °C in winter. In desert environments, the LST's response to ISA presents an uncharacteristic “U-shaped” horizontal gradient decreasing from the urban core to the outskirts of the city and then increasing again in the suburban to the rural zones. UHI's calculated for these cities point to a possible heat sink effect. These observational results show that the urban heat island amplitude both increases with city size and is seasonally asymmetric for a large number of cities across most biomes. The implications are that for urban areas developed within forested ecosystems the summertime UHI can be quite high relative to the wintertime UHI suggesting that the residential energy consumption required for summer cooling is likely to increase with urban growth within those biomes.     

Characterizing the impact of urban morphology heterogeneity on land surface temperature in Guangzhou,China

The urban morphology is regarded as one of the main reasons for urban heat island (UHI). However, its effect on UHI in city-scale urban areas has seldom been examined. In this paper, we presented a rule-based regression model for investigating the nonlinear relationship between land surface temperature (LST) and urban morphology represented by building height, building density and sky view factor (SVF) across different dates in 2005. Results found that an urban morphology of medium building height and lower density significantly yielded higher LST variation levels, whereas the lowest LST variation levels occurred in high-rise and high-dense building arrays. Compared to building height, building density had a stronger influence on LST. Medium SVF values produced the lowest LST, whereas the largest and smallest SVF values produced the highest LST. Results also showed how rule-based regression model offer great performance in detecting the nonlinear mechanisms of LST as well.     

Simulation and analysis of urban growth scenarios for the Greater Shanghai Area, China

This research investigates the potential of an integrated Markov chain analysis and cellular automata model to better understand the dynamics of Shanghai’s urban growth. The model utilizes detailed land cover categories to simulate and assess landscape changes under three different scenarios, i.e., baseline, Service Oriented Center, and Manufacturing Dominant Center scenarios. In the study, multi-temporal land use datasets, derived from remotely-sensed images from 1995, 2000, and 2005, were used for simulation and validation. Urban growth patterns and processes were then analyzed and compared with the aid of landscape metrics. This research represents the first scenario-based simulations of the future growth of Shanghai, and is one of the few studies to use landscape metrics to analyze urban scenario-based simulation results with detailed land use categories. The results indicate that the future expansion of both high-density and low-density residential/commercial zones is always located around existing built-up urban areas or along existing transportation lines. In contrast to the baseline and Service Oriented Center scenarios, industrial land under the Manufacturing Dominant Center scenario in 2015 and 2025 will form industrial parks or industrial belts along the transportation channels from Shanghai to Nanjing and Hangzhou. The study’s approach, which combines scenario-based urban simulation modeling and landscape metrics, is shown to be effective in representing, understanding, and predicting the spatial-temporal dynamics and patterns of urban evolution, including urban expansion trends.     

Spectral mixture analysis for mapping abundance of urban surface components from the Terra/ASTER data

Using a linear unconstrained least squares (LSS) method and a non-linear artificial neural network (ANN) algorithm, we conducted a spectral mixture analysis to the Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) image data in Yokohama city, Japan, for mapping the abundance of the urban surface components. ASTER is a newly developed research facility instrument. The regions of interest of four endmembers (Vegetation, Soil, High/Low albedo impervious surfaces) were determined in Maximum Noise Fraction (MNF) feature spaces. The spectral signatures of the four endmembers were then extracted from the ASTER VNIR (15-m resolution) and SWIR (30-m resolution) imagery by referring to high spatial resolution airborne imagery (The Airborne Imaging Spectrometer, AISA, with 2-m resolution) and land use/land cover map for training and testing the LSS and ANN algorithms. Experimental results indicate that ASTER VNIR and SWIR image data are capable of mapping the abundances of urban surface components with a reasonable accuracy and that the ANN outperforms the unconstrained LSS in this spectral mixture analysis.     

Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil

The spatial and spectral variability of urban environments present fundamental challenges to deriving accurate remote sensing products for urban areas. Multiple endmember spectral mixture analysis (MESMA) is a technique that potentially addresses both challenges. MESMA models spectra as the linear sum of spectrally pure endmembers that vary on a per-pixel basis. Spatial variability is addressed by mapping sub-pixel components of land cover as a combination of endmembers. Spectral variability is addressed by allowing the number and type of endmembers to vary from pixel to pixel. This paper presents an application of MESMA to map the physical components of urban land cover for the city of Manaus, Brazil, using Landsat Enhanced Thematic Mapper (ETM+) imagery.We present a methodology to build a regionally specific spectral library of urban materials based on generalized categories of urban land-cover components: vegetation, impervious surfaces, soil, and water. Using this library, we applied MESMA to generate a total of 1137 two-, three-, and four-endmember models for each pixel; the model with the lowest root-mean-squared (RMS) error and lowest complexity was selected on a per-pixel basis. Almost 97% of the pixels within the image were modeled within the 2.5% RMS error constraint. The modeled fractions were used to generate continuous maps of the per-pixel abundance of each generalized land-cover component. We provide an example to demonstrate that land-cover components have the potential to characterize trajectories of physical landscape change as urban neighborhoods develop through time. Accuracy of land-cover fractions was assessed using high-resolution, geocoded images mosaicked from digital aerial videography. Modeled vegetation and impervious fractions corresponded well with the reference fractions. Modeled soil fractions did not correspond as closely with the reference fractions, in part due to limitations of the reference data. This work demonstrates the potential of moderate-resolution, multispectral imagery to map and monitor the evolution of the physical urban environment.     

Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes

Global warming has obtained more and more attention because the global mean surface temperature has increased since the late 19th century. As more than 50% of the human population lives in cities, urbanization has become an important contributor for global warming. Pearl River Delta (PRD) in Guangdong Province, southern China, is one of the regions experiencing rapid urbanization that has resulted in remarkable Urban Heat Island (UHI) effect, which will be sure to influence the regional climate, environment, and socio-economic development. In this study, Landsat TM and ETM+ images from 1990 to 2000 in the PRD were selected to retrieve the brightness temperatures and land use/cover types. A new index, Normalized Difference Bareness Index (NDBaI), was proposed to extract bare land from the satellite images. Additionally, Shenzhen, which has experienced the fastest urbanization in Guangdong Province, was taken as an example to analyze the temperature distribution and changes within a large city as its size expanded in the past decade. Results show that the UHI effect has become more prominent in areas of rapid urbanization in the PRD region. The spatial distribution of heat islands has been changed from a mixed pattern, where bare land, semi-bare land and land under development were warmer than other surface types, to extensive UHI. Our analysis showed that higher temperature in the UHI was located with a scattered pattern, which was related to certain land-cover types. In order to analyze the relationship between UHI and land-cover changes, this study attempted to employ a quantitative approach in exploring the relationship between temperature and several indices, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Bareness Index (NDBaI) and Normalized Difference Build-up Index (NDBI). It was found that correlations between NDVI, NDWI, NDBaI and temperature are negative when NDVI is limited in range, but positive correlation is shown between NDBI and temperature.     

Ground and top of canopy layer urban heat island partitioning on an airborne image

Analyzing the urban heat island from remote sensing thermal images does not distinguish between the various urban levels, i.e., between ground and the top of the urban canopy layer (roofs surfaces). Applying image processing techniques for roofs/ground partitioning and analyzing these levels separately by DISTCORMAT spatial technique reveals that the heat island centers do not coincide. The distance between these centers on a nocturnal image of Johannesburg (South Africa) may reach more than 300 m.     

The influence of socioeconomic and topographic factors on nocturnal urban heat islands: a case study in Shenzhen,China

Earlier studies on urban heat islands (UHIs) focused mostly on the phenomenon during the daytime, when temperature peaks could usually be observed. However, for people living and working in tropical and subtropical cities, night-time air temperatures are also important. Several studies have focused primarily on the impact of biophysical and meteorological factors on nocturnal land surface temperatures (LSTs). Less attention has been paid to study of the influence of socioeconomic and topographic factors on nocturnal UHIs within a city. In this study, the integration of remote sensing (RS), geographic information system (GIS) and landscape ecology methods was used to investigate the relationships between nocturnal UHIs and socioeconomic or topographic factors based on a case study of Shenzhen, China. Nocturnal Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and daytime Landsat Thematic Mapper (TM) images were used to derive and analyse night- and daytime LSTs, respectively. Land-use data were generated by onscreen digitizing, and an abundance of impervious surfaces was produced through a normalized spectral mixture analysis (NSMA) method with TM data. Socioeconomic variables were derived from the China 2000 census data. A 30 m digital elevation model (DEM) was used to calculate elevation and slope grids. The relationships between nocturnal UHIs and socioeconomic or topographic factors were analysed using traditional regression analysis. The results show that the nocturnal and daytime LST patterns in different land-use areas were significantly different. Nocturnal LSTs were closely related to socioeconomic and topographic factors. An increase of 5 K on nocturnal LST of sub-districts was associated with an increase of 66.0% on their impervious surface abundance, 39 810 people per km², 1000 Yuan per month on housing rent, 9.5 km per km² on road density or a decline of 217.5 m on elevation and 17.0° on slope.     

Enhancing and replacing spectral information with intermediate structural inputs: A case study on impervious surface detection

This paper assessed the incorporation of road structural information in the classification process of impervious surface areas. A multi-process classification model was adopted and it consisted of an a priori classifier and an a posteriori classifier. The role of the a priori classifier was to classify the relatively simple portions of the image. This partial classification acted as the basis for the production of linear features using an iterative Radon transform. Spatial statistics derived from the linear features led to road structural intermediate inputs (RSIIs) (for example, distance to the closest segment endpoint). RSIIs were integrated with spectral information on the remaining unclassified pixels and an assessment was done to evaluate whether they would improve a binary impervious classification task. The experimental results on a 2006 Landsat ETM+ image suggested that classification accuracy improved by 8.4% for the portion of the dataset classified with the a posteriori classifier and led to an improvement of 3.2% over the entire dataset. In addition, a more challenging and wide-reaching hypothesis was tested, namely whether RSIIs could completely replace spectral information in portions of the image instead of complementing it. Exclusive use of RSIIs matched or improved classification accuracy obtained solely from spectral information, even when more than half of the validation dataset was forwarded to the a posteriori classifier. This finding offers an important contribution to the remote sensing community, since the proposed methodology handles the missing spectral information problem through exclusive analysis of the given degraded image; no external information, such as spectral information from other times and/or vector data, is needed.     

基于一种改进的多端元混合像元分解方法的三江源中东部地区植被盖度信息的提取

三江源地处青海省南部,近年来生态环境不断恶化、草地退化严重。植被盖度是研究草地退化的重要指标之一,如何快速、准确、大面积地获取植被信息对三江源的生态环境变化监测尤为重要。传统的固定端元混合像元分解(TSMA)不适用于地域辽阔、地物类型复杂多样的三江源区,而多端元混合像元分解(MESMA)允许端元的类型和数量随像元的不同而变化,更符合三江源的实际情况。基于已有相关研究提出一种改进的多端元混合像元分解(IMESMA),即加入端元组分合理性最优端元模型判断规则,分两步对影像进行分解并获得像元的植被盖度信息。第一步利用多端元模型探测出含有植被信息和完全不含植被信息的像元,并对非植被像元进行掩膜,以提高分解精度;第二步仅对含有植被信息的像元进行分解,获得最终的三江源中东部植被盖度信息。通过多端元分布图、混淆矩阵和与实测数据的均方根误差(RMSE)对比表明,相比TSMA,IMESMA考虑了同物异谱现象,并且其分解精度高、分解结果可信,更适合三江源区的植被信息提取。本方法也可用于其他复杂环境地表组分信息的提取。     

Disaggregation of land surface temperature over a heterogeneous urban and surrounding suburban area: a case study in Shanghai,China

Many application fields need land surface temperature (LST) with simultaneous high spatial and temporal resolution, which can be achieved through the disaggregation technique. Most published methods built an assumed scale-independent relationship between LST and predictor variables derived from coarse spatial resolution data. However, LST disaggregation in the heterogeneous areas, especially urban areas, is very difficult to achieve and there are few studies on it. In this article, we propose an adjusted stratified stepwise regression method for temperature disaggregation in urban areas. Landsat Enhanced Thematic Plus (ETM+) data from Shanghai, China, were used to construct remote-sensing indices that are related to LST variance and retrieve LST at 60 and 480 m spatial resolution, respectively. Different stepwise regression models at 480 m resolution were built for two stratified regions according to normalized difference vegetation index (NDVI) distribution, and then each independent variable at 60 m resolution was adjusted to calculate disaggregated LST by considering its relationship with the 480 m resolution image. By using LST retrieved directly from ETM+ band 6 at 60 m spatial resolution as the reference, the proposed method comprising resampling disaggregation, the thermal data sharpening model (TsHARP)/disaggregation procedure for radiometric surface temperature (DisTrad) technique, and the LST-principal component analysis (PCA) regression algorithm were verified and compared. The results show that the temperature distribution estimated using the proposed method is most consistent with that of the reference LST in this heterogeneous study area, and that the precision improves significantly, especially for the low vegetation fraction region.     

Effects of incomplete surface accommodation on non-equilibrium heat transfer in cavity flow: A parallel DSMC study

The impact of incomplete surface accommodation on non-equilibrium heat transfer in a lid-driven cavity is evaluated by a parallel direct simulation Monte Carlo method. A two-dimensional partitioning technique has been employed to parallelize the code and its parallel performance is assessed. The computed results indicate that incomplete surface accommodation significantly impacts all aspects of flow and heat transfer in the cavity like the vortex center, wall heat flux rates, and the heat transfer mechanism itself. In particular, a counter-gradient heat flux pattern exists when the flow is in a non-equilibrium state, under which thermal energy is transferred from a cold region to a hot region. However, the gaseous heat transfer process changes with any increase in incomplete surface accommodation, significantly affecting the direction of heat transfer.