Urban heat island intensity during winter over metropolitan cities of India using remote-sensing techniques: impact of urbanization

ABSTRACT

The present study aims to quantify spatial relationship of land-use and land-cover (LULC) changes and land surface temperature (LST) using remote-sensing and geographical information system techniques over 10 major metropolitan cities of India. For this purpose, Landsat 7 Enhanced Thematic Mapper Plus images of these cities during winter period from 2001 to 2013 are used. Statistical analysis of the LULC classification has shown overall accuracy ranging between 85% and 88%. The LULC classification and estimated LST using the satellite imageries reveals the presence of multiple urban heat islands (UHIs) and their increase in number in all cities. Significant increase in built-up/urban areas are noticed at the expense of vegetated lands and barren lands over Lucknow, Nagpur, and Jaipur, whereas in Hyderabad and Bengaluru the built-up area and the dry/barren lands are observed to be increasing, at the expense of crop/grass lands. Higher UHI intensities in the range of 8.9–10.3°C are noticed over Mumbai, Nagpur, and Hyderabad compared to the other cities. Higher temperature zones (hotspots) are found to be increasing in the built-up area as well as in barren lands. Varying increase in UHI intensity among the cities is noticed which may have implications in the regional climate over the cities.     

Modelling the diurnal variations of urban heat islands with multi-source satellite data

Examination of the diurnal variations in surface urban heat islands (UHIs) has been hindered by incompatible spatial and temporal resolutions of satellite data. In this study, a diurnal temperature cycle genetic algorithm (DTC-GA) approach was used to generate the hourly 1 km land-surface temperature (LST) by integrating multi-source satellite data. Diurnal variations of the UHI in ‘ideal’ weather conditions in the city of Beijing were examined. Results show that the DTC-GA approach was applicable for generating the hourly 1 km LSTs. In the summer diurnal cycle, the city experienced a weak UHI effect in the early morning and a significant UHI effect from morning to night. In the diurnal cycles of the other seasons, the city showed transitions between a significant UHI effect and weak UHI or urban heat sink effects. In all diurnal cycles, daytime UHIs varied significantly but night-time UHIs were stable. Heating/cooling rates, surface energy balance, and local land use and land cover contributed to the diurnal variations in UHI. Partial analysis shows that diurnal temperature range had the most significant influence on UHI, while strong negative correlations were found between UHI signature and urban and rural differences in the normalized difference vegetation index, albedo, and normalized difference water index. Different contributions of surface characteristics suggest that various strategies should be used to mitigate the UHI effect in different seasons.     

Thermal anomalies associated with faults: a case study of the Jinhua–Quzhou basin of Zhejiang Province,China

Faults provide the path for geothermal natural convection and partially influence the ground surface thermal environment. The land surface temperatures (LSTs) near a fault are higher than in other areas and can indicate the strike trend of an underground fault. However, these anomalies of higher LSTs are not located accurately in the fault centre but near it with some offset, and these LST data may include other thermal information that needs to be eliminated prior to analysis. In this study, LSTs were retrieved from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) thermal infrared (TIR) images and enhanced with land cover classification and elimination. The spatial patterns of the enhanced images were compared with geophysical prospecting tectonic profiles and with regional geological tectonic maps, revealing the spatial correspondence between the thermal anomalies and the faults. The results indicated that the thermal anomalies are located near the faults and are consistent with the faults' dip planes.     

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.     

Monitoring of urban heat island effect in Beijing combining ASTER and TM data

This paper focuses on the monitoring of the urban heat island (UHI) effect with temporal and spatial variation, combining Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Thematic Mapper (TM) data. Our study area is located in the central urban area of Beijing, which mainly refers to the areas within the fifth ring road. For detecting UHI changes over the years 2002–2006, three ASTER images in the summers of 2003, 2004 and 2006 and two TM datasets in the summers of 2002 and 2005 were collected. For monitoring UHI changes with the seasons, three ASTER images and one TM image in 2004 in winter, spring, summer and autumn, respectively, were employed. To calculate the urban heat island intensity, the land surface temperatures were retrieved iteratively for ASTER data and using a generalized single-channel method for the TM image. Four separated regions located in four directions outside the fifth ring road were selected as representing rural comparative regions. Their averaged land surface temperature was regarded as the rural comparative temperature. The UHI intensity was computed by the difference between the pixel urban land surface temperature in the urban area and the comparative temperature in the rural area. Detection of the UHI effect over 2002 to 2006 indicated that most of the areas with high UHI effect were the industrial land use regions and the areas having a high density of buildings, roads, transportations and residents; and the areas without UHI effect were located around the regions with large areas of grassland, trees and water bodies. Our results also showed that the UHI effect was not proportional to urbanization over time. Statistical UHI data during 20 July to 20 September in 2003–2008 also support this point. The monitoring of the UHI effect over seasons (winter, spring, summer and autumn) showed that the urban area of Beijing city had a high UHI effect except in winter, when the urban area of Beijing was in an urban heat sink; the UHI effect increased in spring, summer and autumn.     

Assessment of land surface temperature in relation to landscape metrics and fractional vegetation cover in an urban/peri-urban region using Landsat data

Land surface temperature (LST) is essentially considered to be one of the most important indicators used for assessment of the urban thermal environment. It is quite evident that land-use/land-cover (LULC) and landscape patterns have ecological implications at varying spatial scales, which in turn influence the distribution of habitat and material/energy fluxes in the landscape. This article attempts to quantitatively analyse the complex interrelationships between urban LST and LULC landscape patterns with the purpose of elucidating their relation to landscape processes. The study employed an integrated approach involving remote-sensing, geographic information system (GIS), and landscape ecology techniques on bi-temporal Landsat Thematic Mapper images of Southwestern Sydney metropolitan region and the surrounding fringe, taken at approximately the same time of the year in July 1993 and July 2006. First, the LULC categories and LST were extracted from the bi-temporal images. The LST distribution and changes and LST of the LULC categories were then quantitatively analysed using landscape metrics and LST zones. The results show that large differences in temperature existed in even a single LULC category, except for variations between different LULC categories. In each LST zone, the regressive function of LST with fractional vegetation cover (FVC) indicated a significant relationship between LST and FVC. Landscape metrics of LULC categories in each zone in relation to the other zones showed changing patterns between 1993 and 2006. This study also illustrates that a method integrating retrieval of LST and FVC from remote-sensing images combined with landscape metrics provides a novel and feasible way to describe the spatial distribution and temporal variation in urban thermal patterns and associated LULC conditions in a quantitative manner.     

Characterizing fractional vegetation cover and land surface temperature based on sub-pixel fractional impervious surfaces from Landsat TM/ETM+

Estimating the distribution of impervious surfaces and vegetation is important for analysing urban landscapes and their thermal environment. The application of a crisp classification of land-cover types to analyse urban landscape patterns and land surface temperature (LST) in detail presents a challenge, mainly due to the complex characteristics of urban landscapes. In this article, sub-pixel percentage impervious surface areas (ISAs) and fractional vegetation cover (FVC) were extracted from bitemporal Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) data by linear spectral mixture analysis (LSMA). Their accuracy was assessed with proportional area estimates of the impervious surface and vegetation extracted from high-resolution data. A range approach was used to classify percentage ISA into different categories by setting thresholds of fractional values and these were compared for their LST patterns. For each ISA category, FVC, LST, and percentage ISA were used to quantify the urban thermal characteristics of different developed areas in the city of Fuzhou, China. Urban LST scenarios in different seasons and ISA categories were simulated to analyse the seasonal variations and the impact of urban landscape pattern changes on the thermal environment. The results show that FVC and LST based on percentage ISA can be used to quantitatively analyse the process of urban expansion and its impacts on the spatial–temporal distribution patterns of the urban thermal environment. This analysis can support urban planning by providing knowledge on the climate adaptation potential of specific urban spatial patterns.     

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.     

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.     

Evaluation of dynamic linkages between evapotranspiration and land-use/land-cover changes with Landsat TM and ETM+ data

Complexity embedded in coastal management leads to numerous questions as to how inherent spatial and temporal linkages among evapotranspiration (ET), depth to groundwater and land-use/land-cover change (LUCC) could affect the dynamics among these seemingly unrelated events. This article aims to address such unique dynamics in the nexus of physical geography and ecohydrology. To understand such dynamic linkages, a case study was carried out in a fast growing coastal region – the southern Laizhou Bay in Shandong Province, China – by identifying the coastal LUCC at the decadal scale in association with the variations of ET with the aid of Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) data. In such a coastal landscape evolutionary assessment, findings show that the major patterns of land use and land cover (LULC) in the study area are farmland, saline-alkali land, developed land, salt land and beach land. Over a 20-year time frame, declining groundwater trends were observed, while ET increased gradually with changing LULC. By using the surface energy balance algorithm for land (SEBAL) with Landsat TM/ETM+ images and additional environmental data, the concomitant response of ET variations due to LUCC becomes lucid among three significantly correlated pairs including fractional vegetation cover (FVC), land surface temperature (LST) and soil heat flux. The dynamic linkages between ET and LULC were finally confirmed with such a pair-wise analysis.     

Dynamic monitoring of land-use/land-cover change and urban expansion in Shenzhen using Landsat imagery from 1988 to 2015

To analyse changes in human settlement in Shenzhen City during the past three decades, changes in land use/land cover (LULC) and urban expansion were investigated based on multi-temporal Landsat Thematic Mapper/Enhanced Thematic Mapper Plus/Operational Land Imager (TM/ETM+/OLI) images. Using C4.5-based AdaBoost, a hierarchical classification method was developed to extract specific classes with high accuracy by combining a specific number of base-classifier decisions. Along with a classification post-processing approach, the classification accuracy was greatly improved. The statistical analysis of LULC changes from 1988 to 2015 shows that built-up areas have increased 6.4-fold, whereas cultivated land and forest continually decreased because of rapid urbanization. Urban expansion driven by human activities has considerably affected the landscape change of Shenzhen. The urban-expansion pattern of Shenzhen is a mixture of three urban-expansion patterns. Among these patterns, traffic-driven urban expansion has been the main form of urban expansion for some time, especially in the Non-Special Economic Zone. In addition, by taking 8 to 10 year periods as time intervals, urban expansion in Shenzhen was divided into three stages: the early-age urbanization stage (1988–1996), the rapid urbanization stage (1996–2005), and the intensive urbanization stage (2005–2015). For different stages, the state of urban expansion is different. In long-term LULC dynamic monitoring and urban-expansion detection, it was possible to obtain 11 LULC maps, which took 2 to 4 years as a research interval. With regard to the short research periods, LULC changes and urban expansion were investigated in detail.     

Spatial-temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran

Rapid changes of land use and land cover (LULC) in urban areas have become a major environmental concern due to environmental impacts, such as the reduction of green spaces and development of urban heat islands (UHI). Monitoring and management plans are required to solve this problem effectively. The Tabriz metropolitan area in Iran, selected as a case study for this research, is an example of a fast growing city. Multi-temporal images acquired by Landsat 4, 5 TM and Landsat 7 ETM+ sensors on 30 June 1989, 18 August 1998, and 2 August 2001 respectively, were corrected for radiometric and geometric errors, and processed to extract LULC classes and land surface temperature (LST). The relationship between temporal dynamics of LST and LULC was then examined. The temperature vegetation index (TVX) space was constructed in order to study the temporal variability of thermal data and vegetation cover. Temporal trajectory of pixels in the TVX space showed that most changes due to urbanization were observable as the pixels migrated from the low temperature-dense vegetation condition to the high temperature-sparse vegetation condition in the TVX space. The uncertainty analysis revealed that the trajectory analysis in the TVX space involved a class-dependant noise component. This emphasized the need for multiple LULC control points in the TVX space. In addition, this research suggests that the use of multi-temporal satellite data together with the examination of changes in the TVX space is effective and useful in urban LULC change monitoring and analysis of urban surface temperature conditions as long as the uncertainty is addressed.     

Spatial–temporal patterns of urban anthropogenic heat discharge in Fuzhou,China, observed from sensible heat flux using Landsat TM/ETM+ data

The urban heat island (UHI) effect is the phenomenon of increased surface temperatures in urban environments compared to their surroundings. It is linked to decreased vegetation cover, high proportions of artificial impervious surfaces, and high proportions of anthropogenic heat discharge. We evaluated the surface heat balance to clarify the contribution of anthropogenic heat discharges into the urban thermal environment. We used a heat balance model and satellite images (Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) images acquired in 1989 and 2001), together with meteorological station data to assess the urban thermal environment in the city of Fuzhou, China. The objective of this study was to estimate the anthropogenic heat discharge in the form of sensible heat flux in complex urban environments. In order to increase the accuracy of the anthropogenic heat flux analysis, the sub-pixel fractional vegetation cover (FVC) was calculated by linear spectral unmixing. The results were then used to estimate latent heat flux in urban areas and to separate anthropogenic heat discharge from heat radiation due to insolation. Spatial and temporal distributions of anthropogenic heat flux were analysed as a function of land-cover type, percentage of impervious surface area, and FVC. The accuracy of heat fluxes was assessed using the ratios of sensible heat flux (H), latent heat flux (L), and ground heat flux (G) to net radiation (R _n), which were compared to the results from other studies. It is apparent that the contribution of anthropogenic heat is smaller in suburban areas and larger in high-density urban areas. However, seasonal disparities of anthropogenic heat discharge are small, and the variance of anthropogenic heat discharge is influenced by urban expansion, land-cover change, and increasing energy consumption. The results suggest that anthropogenic heat release probably plays a significant role in the UHI effect, and must be considered in urban climate change adaptation strategies. Remote sensing can play a role in mapping the spatial and temporal patterns of UHIs and can differentiate the anthropogenic heat from the solar radiative fluxes. The findings presented here have important implications for urban development planning.     

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.     

ASTER‐based study of the night‐time urban heat island effect in Metro Manila

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) was used to derive land surface temperatures to quantify the night‐time urban heat island (UHI) effect in Metro Manila. Temperature differences between Metro Manila and its adjacent rural towns were compared to determine heat island intensity and analyse spatial variation of surface temperature. Transects were drawn across from the rural to the urban region to characterize the UHI profile and the Normalized Difference Vegetation Index (NDVI) was used to examine the relationship between amount of vegetation and temperature. The thermal images revealed the highest UHI intensity to be 2.96°C with the presence of a heat island existing in the central part of the city. The transects described the cross‐sectional heat island profile characterized by gradients of ‘cliffs’, ‘plateaus’ and a ‘peak’ occurring in the city centre. The study also showed an inverse relationship between NDVI and temperature, which suggests that increasing the amount of plants in cities can reduce the UHI effect.     

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.     

Spatial–temporal land-use/land-cover dynamics and their impacts on surface temperature in Chongming Island of Shanghai,China

Land-use/land-cover (LULC) changes are occurring at rapid rates on the Chongming Island of Shanghai, China, giving rise to a major concern about environmental impacts. We herein carried out a sound analysis of the LULC dynamics, the conversions among different LULC classes, and land-surface temperature (LST) distribution using remote-sensing data from Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), and Enhanced Thematic Mapper Plus (ETM+) time series spanning the last 35 years (1979–2014). Based on LULC class information and LST, we constructed a temperature/vegetation index space to study the temporal variability of thermal data, vegetation cover, and LULC. The results showed that the LULC change dynamics in Chongming Island have strongly impacted the LST in the recent decade. The spatial position conversion and quantitative change of vegetation cover totalled about 44.4% of LULC-type areas over the Island, and the comprehensive LULC dynamicity changed from 2.97 to 3.95 during the investigated period. Accordingly, significant LST changes took place in the portion of the Chongming Island showing normal temperature range, which accounted for 85.94% of the whole Island’s area as of 1 August 2000 and that decreased to 50.79% on 6 May 2009, while the surface extents under low- and with ultra-high-temperature ranges increased, respectively, both from 0 of 2000 to 6.67% and 0.41% of 2009. The results indicate that the pixel classes including vegetation cover, wetland, and waterbody, which have larger dynamicity and maximum change vector magnitudes, played a large role in alleviating the effect of the land-surface thermal environment, and were key driving factors contributing to the increasing trend of non-normal temperature range ratio over time. Our findings are expected to provide valuable information for decision-making regarding the development and construction of Chongming Island into an eco-region.     

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.     

Identification and analysis of urban surface temperature patterns in Greater Athens, Greece, using MODIS imagery

Thermal infrared images are being acquired by satellites for more than two decades enabling studies of the human-induced Urban Heat Island (UHI) phenomenon. As a result, the requirement of the scientific community for fast and efficient methods for extracting and analyzing the thermal patterns from a vast volume of acquired data has emerged. The present paper proposes an innovative object-based image analysis procedure to extract thermal patterns for the quantitative analysis of satellite-derived Land Surface Temperature (LST) maps. The spatial and thermal attributes associated with these objects are then calculated and used for the analyses of the intensity, the position and the spatial extent of UHIs. A case study was conducted in the Greater Athens Area, Greece. More than 3000 LST images of the area acquired by MODIS sensor over a decade were analyzed. Three daytime hot-spots were identified and studied (Megara, Elefsina-Aspropyrgos and Mesogeia). They were all found to exhibit similar behavior, gradually increasing their maximum temperature during the summer season and reaching their maxima in mid-July. The hot-spots' thermal intensities compared to a suburban area were of 9-10 °C and were found to be highly correlated to their areal extent. During the night-time, Athens center developed a typical UHI spatially coinciding with the dense urban fabric. The nighttime maximum LST peaked (on average) at the end of July, two weeks later than the daytime surface patterns. The mean spatial extent of UHI in Athens was 55.2 km², whilst its mean intensity was 5.6 °C. The proposed automatic extraction process can be customized for other cities and potentially used for comparison of LST patterns and UHI behavior between different cities.     

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.