Article

Profile and concentric zonal analysis of relationships between land use/land cover and land surface temperature: Case study of Shenyang, China

Authors:
  • Shenyang Institute of Atmospheric Environment, Chinese Meteorological Administration
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Abstract

In this study, relationships between land use/land cover (LULC) types and land surface temperature (LST) patterns in Shenyang, China were investigated using Landsat 8 image. Typical characteristics of LST in summer time and temperature variations over all LULC types were firstly examined. Afterwards, spatial distribution of LST in radical and circumferential directions was characterized based on profile and concentric zonal analysis, in which contributions of LULC types to LST were studied. In addition, models that can effectively predict LST distribution were developed based on multiple linear regression method (MLRM) and the partial least squares regression method (PLSRM). Results indicate that (1) LST of the study area mainly ranges from 32 °C to 41 °C, where building and road are main drivers leading to hot temperatures. (2) Temperature profiles vary greatly with LULC types, while urban expansion along central axis causes to horizontal arrangement of building and road, resulting in hot but small-fluctuated LST temperature in 0° and 180°. (3) Mean and standard deviation of LST in concentric zones are 36.0 ± 4.2 °C, and LST over all LULC types follows the following pattern: building > road > bare land > agricultural land > green land > water bodies. Due to large proportions of building and road, Buffer 3, 4 and 5 undergo the highest temperature. Although green land and water bodies show significant capability to mitigate UHI effects, cooling effects are unapparent when their sizes are small. (4) Both models developed on MLRM and PLSRM have high degree of accuracy, while the latter is more reasonable for different coefficients can respectively indicate negative and positive roles of LULC types in affecting surface temperature. In general, this study can assist urban planners and policy makers to understand relationships between local LULC types versus LST patterns, and can help them predict UHI patterns along rapid urban expansion, and then make rational urban planning decisions for UHI mitigation.

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... On the other hand, satellite-based remote sensing can easily acquire real-time land surface parameters over extensive areas, but the accuracy of these products exhibits significant variability due to variations in satellite platforms, sensor configurations, spectral bands, retrieval algorithms, and surface environmental conditions. Consequently, the general accuracy performance of international mainstream satellite remote sensing LST products varies considerably, and their applicability to different surface environmental conditions also differs [7]. Continuous enhancement of instrument performance and retrieval techniques, based on an accurate understanding of product accuracy through authenticity testing and assessment, is essential for optimizing these products [8]. ...
... With the application of FY-4A products in China's meteorological business in recent years, technicians generally reflect that the accuracy of the product is still short of that of the international mainstream products. The authority does not provide a quality report for FY-4A/AGRI LST; however, relevant studies indicate that this product exhibits certain detection errors and a general underestimation phenomenon [18,19], with larger detection errors compared to mainstream Landsat and MODIS products [7,20]. The comparison of FY-4A and MODIS LST products in relevant studies also confirms their high correlation, with a significantly increased difference in higher LST during summer and autumn [21]. ...
... The distribution of ubRMSE shows that the random error has no spatial distribution characteristics in Hunan Province, the remote sensing detection accuracy is no longer greatly affected by topographic factors after removing the systematic error, but the accuracy of FY-4A LST on a water body is still relatively low. In the aforementioned validation analysis, FY-4A LST exhibited a high overall error level and an unexpectedly significant underestimation compared to in situ measurements at high LSTs, surpassing MODIS [20], Landsat [7], and Himawari-8 [18] counterparts in terms of error level. The phenomenon under investigation may be attributed to various factors. ...
Article
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... In complex urban areas, LST is influenced by multiple factors [31,32]. Previous studies have examined the influence of land cover, urban morphology, human activities, meteorological conditions, and other factors on LST [33][34][35][36][37][38][39]. Many studies have shown that LST is closely related to land cover type [3,38,40,41]. ...
... Previous studies have examined the influence of land cover, urban morphology, human activities, meteorological conditions, and other factors on LST [33][34][35][36][37][38][39]. Many studies have shown that LST is closely related to land cover type [3,38,40,41]. Vegetation and water bodies usually have low LST, while buildings and roads have high LST [3,38]. LST is also negatively correlated with vegetation coverage and water body area [42,43], and positively correlated with an impervious surface area [41]. ...
... Many studies have shown that LST is closely related to land cover type [3,38,40,41]. Vegetation and water bodies usually have low LST, while buildings and roads have high LST [3,38]. LST is also negatively correlated with vegetation coverage and water body area [42,43], and positively correlated with an impervious surface area [41]. ...
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... GWR is a regression analysis method for spatial data. Unlike traditional global regression models, GWR allows model parameters to vary spatially, better capturing heterogeneity and local associations in geographic spatial data [41]. The GWR model is expressed as: ...
... Exploring the impact of urban spatial morphology on land surface temperature from both 2D and 3D perspectives [41]. However, the impact of the urban heat island effect can extend across the entire urban area, including remote rural regions and even adjacent cities, often overlooking the systematic spatial morphology of cities [42]. ...
Article
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The increasing population density and impervious surface area have exacerbated the urban heat island effect, posing significant challenges to urban environments and sustainable development. Urban spatial morphology is crucial in mitigating the urban heat island effect. This study investigated the impact of urban spatial morphology on land surface temperature (LST) at the township scale. We proposed a six-dimensional factor system to describe urban spatial morphology, comprising Atmospheric Quality, Remote Sensing Indicators, Terrain, Land Use/Land Cover, Building Scale, and Socioeconomic Factors. Spatial autocorrelation and spatial regression methods were used to analyze the impact. To this end, the township-scale data of Linyi City from 2013 to 2022 were collected. The results showed that LST are significantly influenced by urban spatial morphology, with the strongest correlations found in the factors of land use types, landscape metrics, and remote sensing indices. The global Moran’s I value of LST exceeds 0.7, indicating a strong positive spatial correlation. The High-High LISA values are distributed in the central and western areas, and the Low-Low LISA values are found in the northern regions and some scattered counties. The Geographically Weighted Regression (GWR) model outperforms the Spatial Error Model (SEM) and Ordinary Least Squares (OLS) model, making it more suitable for exploring these relationships. The findings aim to provide valuable references for town planning, resource allocation, and sustainable development.
... The spatial distribution of LST results from changes in LULC types (Zhao et al. 2017). The change in LULC and LST results from expanding urban areas (Mustafa et al. 2020). ...
... The methodological framework adopted for implementing objectives in the current study has been used in many research, which deals with the relationship between LST, LCLU, and other indexes (Zhao et al. 2017;Kumari et al. 2018;Sahana et al. 2018). Landsat 5 TM and Landsat 8 OLI /TIRS images were enhanced and spatially analyzed. ...
Article
Iraq is one of the five countries most affected by high temperatures, low precipitation, drought, and desertification hazards. In this research, Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) images of Basra, southern Iraq, were used from 1986 to 2021. The relationships between Land Surface Temperature (LST), Normalized Difference Vegetation Index, and Normalized Difference Built-up Index were examined to determine the impacts of LST on Land Use/Land Cover (LULC) changes and to estimate future changes under projected temperature and precipitation scenarios for Representative Concentration Pathways (RCP4.5 and RCP8.5) scenarios from 2010 to 2091. The results indicated significant changes in different LULC categories in Basra from 1986 to 2021. Orchards and swampy areas (especially in Hawiza, Msahab, and Salal marshes) decreased by 45%, mostly converting to built-up or barren areas. The sand area increased by 15.6%. The built-up area increased rapidly from 1217 to 1371 km2, a 12.7% increase. Most of the built-up and barren areas in the north, center, and south of Basra province recorded LST values less than 50 °C, especially in gas-flaring areas in petroleum locations. The overall accuracy of LULC was 90% in 1986 and 88% in 2021, while the kappa coefficients were 0.797 in 1986 and 0.848 in 2021. Based on RCP4.5 and RCP8.5 scenarios, the values of the temperature increase in both scenarios by 1.7 °C in 2050 and 2.2 °C in 2091 in Basra. Due to Basra's significance to Iraq’s economy, society, and politics, the findings of this study will be helpful to city planners and decision-makers in future development of Basra province.
... This classification, widely tested in previous studies [29], helps to establish solid correlations between urban typologies and climate performance [30,31], which also affects directly buildings' cooling energy [32,33]. Other studies have focused on the land use-land cover (LULC) classification [34] and its relationship with LST [35], while others have established automatic machine-learning UHI predictions based on LULC [36]. Regarding this, the Copernicus' CORINE programme has developed a very detailed openaccess LULC classification map [37], whose results are useful to urban research. ...
... Discussing first the whole city case study scale, and according to Figure 3b (LULC) and Figure 4 (LST), what first becomes apparent is the strong relationship between the kind of land cover and its LST. This was already pointed out by previous research [36]. These classes tend to exhibit more uniform climatic behaviour within the city due to the commonly present materials and their properties concerning heat fluxes (albedo and emissivity), heat capacity (thermal mass), as well as the typical presence of vegetation and urban density (SVF), among others. ...
Article
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One of the most significant urban challenges focuses on addressing the effects of urban overheating as a consequence of climate change. Several methods have been developed to characterize urban heat islands (UHIs); however, the most widely used involve complex planning, huge time consumption, and substantial human and technical resources on field monitoring campaigns. Therefore, this study aims to provide an easily accessible and affordable remote sensing method for locating urban hotspots and addresses a multi-criteria assessment of urban heat-related parameters, allowing for a comprehensive city-wide evaluation. The novelty is based on leveraging the potential of the last Landsat 9 satellite, the application of kernel spatial interpolation, and GIS open access data, providing very high-resolution land surface temperature images over urban spaces. Within GIS workflow, the city is divided into LCZs, thermal hotspots are detected, and finally, it is analyzed to understand how urban factors, such as urban boundaries, building density, and vegetation, affect urban scale LST, all using graphical and analytical cross-assessment. The methodology has been tested in Seville, a representative warm Mediterranean city, where variations of up to 10 °C have been found between homogeneous residential areas. Thermal hotspots have been located, representing 11% of the total residential fabric, while results indicate a clear connection between the urban factors studied and overheating. The conclusions support the possibility of generating a powerful affordable tool for future research and the design of public policy renewal actions in vulnerable areas.
... Compounding these issues are escalating temperatures, primarily fuelled by rapid urbanisation. 1 Counteracting these global challenges-encompassing climate change, health inequity and sustainable urbanisation-green areas or urban vegetation are deemed critical. In this vein, the United Nations Sustainable Development Goal 11 target 7 stipulates the provision of universal access to secure, inclusive, and accessible green and public spaces, especially for vulnerable populations, by 2030. 2 The health implications of high temperatures are profound, posing substantial risks to individuals across all age groups. ...
... 4 Urban green areas have emerged as a potential counter to heat, demonstrated by research evidencing their critical role in thermal mitigation. 1 5 For instance, a study in China underscored the efficient cooling effect of green spaces. 1 Vegetation, through its added shading effect, significantly cools night-time temperatures in urban regions while trees contribute to daytime temperature regulation. 6 Green spaces have also been linked to mental well-being, with their health advantages attributed to community cohesion, physical activity enhancement and mental well-being improvement. ...
Article
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Objectives The objective of this review was to scrutinise the impact of urban green spaces on heat-related morbidity and mortality. Design This systematic review was meticulously carried out following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines Data sources A comprehensive search was conducted across PubMed, Scopus and Google Scholar including studies from January 2000 to December 2022. Eligibility criteria Studies that examined the influence of urban green spaces on heat-related morbidity and mortality, including randomised controlled trials, observational and modelling studies, were included. Data extraction and synthesis A total of 3301 publications were initially identified, out of which 12 studies met the inclusion criteria and were selected for analysis. The selected studies were predominantly from high-income and upper-middle-income nations (95%). Results The research points towards a pattern where regions abundant in green spaces report lower rates of heat-related morbidity and mortality in contrast to those with sparse greenery. Additionally, urban vegetation appears to exert a positive influence on mental health and well-being, potentially aiding in offsetting the adverse health repercussions of high temperatures. Conclusion Urban green spaces play a vital role in mitigating heat-related health risks, offering a potential strategy for urban planning to address climate change and enhance public health. Additional research is required to thoroughly comprehend the magnitude of urban greenery’s impact on heat-related morbidity and mortality, as well as its interplay with other variables, including air pollution, socioeconomic status, among others.
... For instance, the characterization of land use/land cover has acquired wide acceptance in explaining surface-based thermal environments [22], [23]. In summer, buildings and roads are often characterized as hotspots, while water bodies and vegetated zones are mostly distinguished as cold spots [24], [25]. Moreover, local climate zone (LCZ) scheme was developed to standardize local morphological characteristics and to differentiate urban temperatures and heat vulnerability of different local areas [26], [27]. ...
... The LST retrieval followed a common process. After atmospheric correction of reflective and thermal bands, the LST was retrieved based on the split window algorithm from the only spectral band of Thermal InfraRed Sensor (TIRS) 10 in Landsat 8 [24], [32]. According to (1), the digital number was converted to the spectral radiance at the top of the atmosphere. ...
Article
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The local climate zone (LCZ) classification scheme provides a standardized method to characterize urban morphological characteristics and urban thermal environments. However, its capability to differentiate urban temperatures has not been well examined. This study investigated the LCZ-based land surface temperature (LST) in five megacities, including Shenyang, Beijing, Xi'an, Nanjing, and Nanchang. The results indicate that the LCZ scheme might conceal areas with the most critical heat risks, if the maximum LST was not used. The built-dominated zones often contributed to urban temperature increase, but it was not always true. The non-built-dominated zones, mostly lowered urban temperatures, while they could contribute to urban temperature increase depending on seasonal and urban context. Both hot and cold non-built-dominated zones varied significantly with city and season. Some zones were the hottest in one season, but changed to be the coldest in another season. LCZ scheme showed good capability to differentiate the temperatures of built-dominated zones, while its capability to characterize non-built-dominated zones was weaker. In Beijing, the LCZ capability to characterize the temperature of non-built-dominated zones was below 70%, and was only 16.67% in summer. Therefore, urban planners, designers, and managers should prudently adopt LCZ scheme to rank the priorities for integrating cooling interventions in both built-dominated and non-built-dominated zones. It is important to not copy the LCZ–based LST pattern of other cities or seasons when making decisions. Overall, this study provides a reference to understand LCZ capability and make proper decisions for urban heat mitigation, adaptation, and management.
... The effects of global warming are shown by changes in temperature and increasingly unstable environmental conditions 2 . One of the main triggers of global warming is urbanization and increased human activity in big cities 3 . Urbanization is one of the main causes of many environmental problems, such as Urban Heat Island (UHI) 4 . ...
... These studies discuss many aspects, one of which is how changes in land use cover affect changes in SUHI, such as those conducted by 9,10 . Other studies try to see the spatiotemporal pattern of SUHI distribution in various regions as conducted by 3,8,11 . Other studies also examine the use of thermal indices to see SUHI conditions quantitatively, therefore SUHI effect can be measured in a certain scale, as conducted by 6,[12][13][14][15] . ...
Conference Paper
The effects of global warming are shown by increasing temperatures in various regions, especially urban areas. Beside urbanization, topography of a region also had an effect on Land Surface Temperature (LST). LST is the main indicator of Surface Urban Heat Island (SUHI) effect. SUHI can be quantified into a standard value by utilizing the Urban Thermal Field Variance Index (UTFVI). The effect of SUHI in urban areas close to the coast is usually influenced by the topography, one of which is elevation and distance from the sea. But the extent it affected the SUHI still needed to be explored. Through this research we analyze how elevation and distance from the beach affected SUHI in the coastal city of Makassar and the regency bordering it. We used elevation data from DEM to match the spatial resolution of the LST. The distance from the beach is calculated from visual observation of the same imagery. Relationship between these variables was analyzed by applying the Ordinary Least Square (OLS) analysis. The UTFVI value variable in this study acts as the dependent variable, while the elevation and distance values act as independent variables. The results of the OLS analysis show that the distance and elevation variables have a simultaneous influence on the UTFVI variable with a regression coefficient of 74.04%. These two effects show a negative influence, where the greater the elevation and distance values, the lower the UTFVI value. Future studies should explore more topographical variables and utilized more complex spatial statistics analysis.
... The continuous change of the urban underlying surface towards impermeable surfaces is a distinctive feature of the urbanization process, which may lead to several urban issues [66]. On the one hand, during heavy rainfall, the impermeable surfaces prevent rainwater from effectively infiltrating the ground, causing urban areas to be unable to The main sources of NAPS increases were APS and ES. ...
... The continuous change of the urban underlying surface towards impermeable surfaces is a distinctive feature of the urbanization process, which may lead to several urban issues [66]. On the one hand, during heavy rainfall, the impermeable surfaces prevent rainwater from effectively infiltrating the ground, causing urban areas to be unable to discharge excess water in a short period, of time leading to urban floods or waterlogging. ...
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... More recently, several studies have established the strong impacts of LULC change on surface temperature and reported that the relative rise in LST depends on LULC change (Das et al., 2020;Guechi et al., 2021). Land surface temperature is dependent on LULC patterns, according to Zhao et al. (2017), and it is well known that variations in LULC cause major changes in LST (Barbieri et al., 2018;Stemn & Kumi-Boateng, 2020;Wang et al., 2021). This demonstrates that any anthropogenic activities that alter the LULC of a specific area pose a serious threat to LST changes in that location, and as a result of the rapid rate of global urbanization, LST studies continue to be significant and garner significant study attention. ...
... The highest LST from bare land is also reported over Dire Dawa City, Ethiopia (Haylemariyam, 2018). Likewise, Zhao et al., (2017) in Shenyang, China, reported lower LST from river and lakes followed by vegetation, revealing the cooling effects of water and vegetation on thermal environments. Pal and Ziaul (2017) in English Bazar Municipality also reported the lowest LST from water bodies and the highest LST from impervious areas. ...
Article
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Much of the urbanization that occurs in Africa creates the potential for technological development and economic growth but is also a breeding ground for environmental and health problems. This study was undertaken to evaluate the urban-induced land use/land cover (LULC) change and its contribution to the land surface temperature (LST) and urban heat fluxes from 2001 to 2021. More specifically, the study analyzed different scenarios of LULC change and retrieved the LST to evaluate the trends of the urban heat flux (UHI) in response to the urban-induced LULC change. The analysis of LULC change from 2001 to 2021 indicated that built-up and bare land showed the highest rate of increase at the expense of declining open spaces, agricultural land, and vegetation areas. The built-up areas in Nekemte and Jimma City increased by 929.25 ha (172.75%) and 2285.64 ha (226.93%) over the investigated period, respectively. The highest changes in LULC are seen in built-up areas followed by agricultural land, while the smallest changes are shown by water body followed by bare land. Built-up areas showed the highest net gain, while agricultural land experienced the greatest loss. In areas where the vegetation cover is low, low LST was depicted, and high LST was shown in areas where built-up areas were concentrated in both cities. Due to the LULC changes, the average LST increased by 1.9 °C and 2.2 °C in Nekemte and Jimma City, respectively, over the last 21 years. The urbanization-induced LULC change does not only cause changes in the hydrological process but also changes in the thermal variations and urban heat stress of the two urban centers. The result indicates that the increases in vegetation and green areas are significant in improving the heat stress and thermal characteristics of urban areas. Overall, to achieve sustainable urban development, the integration of land use with urban planning policies could be critical to the resilience of local environment and urban ecosystem.
... Rural areas often exhibit lower T and higher RH than urban areas due to differences in multiple surface characteristics, such as radiation (albedo and radiance), LULC, and aerodynamics (roughness) in rural areas [32,33]. It should be noted that the peak Ea usually appears several hours before and after sunrise and sunset in rural areas, as reported in many studies [11,12,17]. ...
Article
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An urban canopy’s humidity significantly affects thermal comfort, public health, and building energy efficiency; however, it remains insufficiently understood. This study employed 3-year (2020–2022) fixed measurements from Guangzhou to investigate the temporal patterns of relative humidity (RH), vapor pressure (Ea), and vapor pressure deficit (VPD) across eight local climatic zones (LCZs). Clear and distinct patterns in the humidity characteristics among the LCZs were revealed on multiple timescales. The RH and VPD of each zone were higher in summer than in winter, with peak RH observed in LCZ A (83.45%) and peak VPD in LCZ 3 (13.6 hPa). Furthermore, a significant daytime urban dry island (UDI) effect in the summer and a nighttime urban moisture island (UMI) effect in the winter were observed in terms of the Ea difference between urban and rural areas. The strongest UMI occurred during winter nights in LCZ 8, with a peak intensity of 0.8 hPa, while the UDI was more frequent during summer days in LCZ 1, with a maximum value of −1.2 hPa; meanwhile, compact areas had a slightly higher frequency of UDI than open areas. Finally, the effects of the urban heat island (UHI) and wind speed (V) on UMI were analyzed. During the daytime, a weak correlation was observed between the UHI and UMI. Wind enhanced the intensity of the nighttime UMI. This research offers further insights into the canopy humidity characteristics in low-latitude subtropical cities, thereby contributing to the establishment of a universal model to quantify the differences in moisture between urban and rural areas.
... Several studies have investigated the relationship between land use land cover (LULC) and LST, focusing on how urban planning can mitigate urban UHI. For instance, Zhao et al. explored the effectiveness of cool and hot sources in either enhancing or mitigating LST under different temperature backgrounds, highlighting how different land uses such as water bodies, greenery, and developed areas affect temperature [47]. Similarly, He et al. used a concentric zonal analysis to investigate LULC-LST relationships in Shenyang, China, and found that buildings and roads are primary contributors to higher temperatures, while vegetation and water bodies act as cooling sources [48]. ...
Article
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Information about land surface temperature (LST) plays a crucial role in environmental studies, as it provides essential data for understanding climate patterns, assessing ecosystem health, and predicting environmental changes. Understanding the relationship between land cover types and LST is crucial across all disciplines that deal with LST data. It helps researchers identify trends in global warming, heatwaves, and cooling effects, which can influence biodiversity, agriculture, and water resources. The accuracy of LST calculations heavily depends on the quality of the data used. However, most satellite thermal data used for LST estimations are in coarse spatial resolution. This study aims to explore the complex interaction between land cover types, considering factors such as proportion and neighboring effects, and LST recalculation by integrating the estimated LST from Landsat thermal band and Spot imagery classification. A machine learning model was employed to quantify the contribution of each Spot pixel to the LST estimated from TIRS data, classifying it as either heating or cooling. The Al Morjan and Al Hamra districts in Jeddah, Saudi Arabia, were used as case studies. The results showed that Spot images achieved a classification accuracy of over 95%, whereas Landsat images did not exceed 77%. The average heating and cooling factors from neighboring pixels were 1.06 and 0.96, respectively. The study demonstrates the improved spatial distribution of LST, with overall temperature increases across all land cover classes. The findings of this study could aid in identifying environmental imbalances and developing effective solutions.
... The urban surface thermal environment is closely related to landscape patterns. Previous research has extensively explored the relationship between landscape patterns and the urban surface thermal environment from various perspectives, such as establishing the empirical relationships, spatiotemporal patterns, and unveiling the key driving mechanisms (Fu and Weng, 2016;Zhao et al., 2017;Carrasco et al., 2020). However, much of this research has been predominantly focused on static viewpoints, with limited exploration to capture its dynamics. ...
... This has been demonstrated in real-world examples, such as the expansion of buildings and roads and the reduction of water bodies, trees, and agricultural land. These observations support the notion that LULC changes are responsible for the UHI effect (Zhao et al., 2017). The thermal characteristics of the urban surface materials and the lack of surface evaporation increase the outgoing longwave radiation resulting in increased LST over urban areas (Dousset and Luvall, 2019;Li et al., 2019). ...
Article
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Rapid urbanisation over the years has led to the loss of natural land cover, thereby affecting Land Surface Temperature (LST) distribution in urban areas. This study aims to analyse LST anomalies (calculated as the deviation from the normal) over selected Indian cities and check if critical land cover changes can be identified. LST from Landsat Thermal Infrared (TIR) images acquired in March, April and May from 1988 to 2020 were used to estimate LST anomalies. Positive LST anomalies were observed mainly over barren and impervious areas; however, some areas showed a negative anomaly where the barren lands were converted to vegetated areas. The study has demonstrated that while some developed areas exhibit a positive anomaly indicative of significant changes or development, there are instances where the conversion of barren land to developed (i.e. built up) areas has resulted in a negative anomaly. Developed areas that are closer to the water creek or mangroves were associated with lower anomaly values indicating the cooling effect of the water body and vegetation. Conversely, the core urban areas generally exhibited higher LST values with positive anomalies indicating a warming effect. These findings can be used by city planners to identify hotspot areas and develop more effective strategies and policies to address the challenges of urban heat. They also highlight the regions that require infrastructural resources and policy changes to reduce the temperature.
... A large amount of original natural and semi natural surfaces is replaced by artificial structures with hard paving materials (impervious surfaces), such as buildings, roads, squares, bridges, etc. [4]. This severely imbalanced land use structure significantly changes the thermal energy storage and re-emission process, sensible/latent heat distribution, and heat flux of the underlying surface in urban areas [5], leading to a continuous increase in heat storage within the city [4,6]. As a result, cities are exposed to the risk of frequent high temperatures, posing a huge threat to the urban ecological environment, social economy, and human health [1,[7][8][9][10]. ...
Preprint
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The Urban heat island (UHI) effect has evolved into one of the key environmental problems affecting urban ecological environment and sustainable development. Based on 52 Urban Thermal Heat spots (UTHSs) with significant differences between land use structure and UGI spatial layout within the influence range of UHI in Shanghai, Landsat-8/9 satellite images were used to construct a high-dimensional data set reflecting the impact of built environment components on urban thermal environment. Descriptive statistical analysis was used to analyze the spatial difference qualitatively. Using stepwise regression model and partial least square regression (PLSR) model, the complex response relationship between UGI pattern differentiation and urban thermal environment in three spatial stratification ranges of UTHSs was quantitatively analyzed. Overall, the statistical explanatory power of the partial least square regression PLSR model is due to the stepwise regression model. The PLSR model points out that moderately increasing the average building height, CA, PLAND, LSI and LPI play a positive role in inhibiting/slowing down the growth of LST (land surface temperature), and the cooling effect of index weights decreases in order. However, the interaction effects of CA×Cohesion×AI×LPI and PLAND×CA×Cohesion×AI×LPI exert relatively small weight on the cooling effect, and according to the results, suggestions such as UGI structure and urban construction layout optimization can effectively alleviate the urban heat island effect are proposed.
... In these areas, natural and semi-natural surfaces are often replaced by artificial structures with hard paving materials, such as buildings, roads, and bridges [4]. This severely unbalanced land use structure significantly alters the thermal energy storage and re-emission processes, sensible/latent heat distribution, and subsurface heat fluxes in urban areas [5], leading to a continuous increase in heat storage within the city [4,6]. Regarding the spatial extent of urban built-up areas, the formation of the UHI effect is closely related to human activities and land use/cover conditions. ...
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The urban heat island (UHI) effect has evolved into one of the key environmental problems affecting the urban ecological environment and sustainable development. Based on 52 Urban Thermal Heat spots (UTHSs) with significant differences between land use structure and urban green infrastructure (UGI) spatial layout within the influence range of UHI in Shanghai, Landsat-8/9 satellite images were used to construct a high-dimensional dataset reflecting the impact of built environment components on urban thermal environment. Descriptive statistical analysis was used to analyze the spatial difference qualitatively. Using the stepwise regression (SWR) model and partial least square regression (PLSR) model, the complex response relationship between UGI’s structure/spatial pattern differentiation and urban thermal environment in three spatial stratification ranges of UTHSs was quantitatively analyzed. Overall, the statistical explanatory power of the PLSR model is much better than the stepwise regression model. The PLSR model points out that moderately increasing the average building height, class area (CA), percentage of landscape (PLAND), landscape shape index (LSI), and largest patch index (LPI) play a positive role in inhibiting the growth of land surface temperature (LST), and the cooling effect of index weights decreases in order. However, the interaction effects of the box-cox transformed indices with underlines, e.g., CA × Cohesion × AI × LPI and PLAND × CA × Cohesion × AI × LPI, exert relatively small weight on the cooling effect. According to the results, suggestions such as optimization of the UGI structure and urban construction layout were proposed, which can effectively mitigate the UHI effect.
... Applying zonal analysis to evaluate the performance of the RFI derived from DEM surfaces provides a detailed and localized approach for assessing topographical variations. This technique segments a geographical landscape into distinct zones using specific criteria, such as elevation ranges, slope categories, or aspect orientations [54][55][56]. When applied to the RFI, this tool enables an in-depth exploration of the topographical changes within each zone, ensuring a precise evaluation rather than a generalized overview. ...
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Soil resource management is fundamentally integral to environmental sustainability and agricultural productivity. The digital elevation model (DEM) is the fundamental data for analyzing landform surfaces, which introduces an opportunity to obtain a broad spectrum of terrain factors to simplify interpreting the patterns and processes in the geoscience field. The accuracy and resolution of DEM are crucial for their effective use, and many algorithms have been developed to interpolate digital elevation data from a set of known points. Although primary topographic variables derived from grid datasets are important, secondary variables, such as the relief index (RFI), play a more critical role in understanding the complicated relationship between soil properties and landform attributes. The RFI is attained from a DEM by calculating the elevation range within a given neighborhood surrounding a central cell. It is an essential predictor of soil natural resource management that measures the degree of differentiation surface relief. In addition, it is beneficial for perceiving the landscape and its management. This study presents a comprehensive zonal analysis comparing the RFI values derived from multiple interpolation-based DEMs. It investigates deterministic and geostatistical interpolators, such as inverse distance weighted and natural neighbor across distinct zones with diverse topographical characteristics. The findings indicated a high correlation between the RFI and the reliability of the DEM, and the natural neighbor technique provided superior performance against others. The results revealed that the choice of spatial interpolation technique significantly affects the accuracy and reliability of RFI models.
... Recent satellite-scale studies reported that the influence of the TRD effect can reach 5 K in urban regions during summer daytime [8,9] and an average of 4 K for sparse vegetation canopies in summer [10]. This severe discrepancy will induce large uncertainty and limit subsequent studies, such as urban heat island and urban planning studies [11,12]. Therefore, it is necessary to correct the current LST products to a reference direction, which is typically the nadir direction [13,14]. ...
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Land surface temperature (LST) is a fundamental parameter in global climate, environmental, and geophysical studies. Remote sensing is an essential approach for obtaining large-scale and frequently updated LST data. However, due to the wide field of view of remote sensing sensors, the observed LST with diverse view geometries suffers from inconsistency caused by the thermal radiation directionality (TRD) effect, which results in LST products being incomparable, especially during daytime. To address this issue and correct current off-nadir LSTs to nadir LSTs, a semi-physical time-evolved kernel-driven model (TEKDM) is proposed, which depicts multitemporal TRD patterns during the daytime. In addition, we employ a Bayesian optimization method to calibrate seven unknown parameters in the TEKDM. Validation results using the U.S. Climate Reference Network (USCRN) sites show that the RMSE (MBE) for GOES-16 and MODIS off-nadir LST products is reduced from 3.29 K (−2.0 K) to 2.34 K (−0.02 K), with an RMSE reduction of 0.95 K (29%) and a significant reduction in systematic bias. Moreover, the proposed method successfully eliminates the angular and temporal dependence of the LST difference between the satellite off-nadir LST and in situ nadir LST. In summary, this study presents a feasible approach for estimating the high-accuracy nadir LST, which can enhance the applicability of LST products in various domains.
... The cooling effect of urban blue space (areas dominated by surface water bodies) and urban green space (areas dominated by vegetation cover) is increasingly recognized as a promising nature-based solution to alleviate the UHI phenomenon 12-15 . Urban water bodies can reduce ambient surface/air temperature, and form an "urban cooling island" (UCI) in summer daytime due to the great specific heat capacity and evaporation effect [16][17][18][19] . Through the exchange of air convection, the cooler air originating from an urban water body is transported to the surrounding areas, and the cooling distance can reach 1000 m 20,21 . ...
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Urban water bodies can effectively mitigate the urban heat island effect and thus enhance the climate resilience of urban areas. The cooling effect of different water bodies varies, however, the cooling heterogeneity of different sections of a single watercourse or river network is rarely considered. Based on various satellite images, geospatial approaches and statistical analyses, our study confirmed the cooling heterogeneity from spatial and seasonal perspectives of the Suzhou Outer-city River in detail in the urban area of Suzhou, China. The cooling effect of the river was observed in the daytime in four seasons, and it is strongest in summer, followed by spring and autumn, and weakest in winter. The combination of the width of the river reach, the width and the NDVI value of the adjacent green space can explain a significant part of the cooling heterogeneity of the different river sections in different seasons. Land surface temperature (LST) variations along the river are more related to the width of the river reach, but the variations of the cooling distance are more related to the adjacent green space. The cooling effect of a river reach could be enhanced if it is accompanied by green spaces. In addition, the cooling effect of a looping river is stronger on the inside area than on the outside. The methodology and results of this study could help orient scientific landscape strategies in urban planning for cooler cities.
... Vegetation, with its natural ability to provide shade and facilitate evapotranspiration, acts as a cooling agent, reducing LST (Naikoo et al. 2020). As urban areas expand, vegetation is often the first casualty, leading to reduced evapotranspiration and increased heat retention (Zhao et al. 2017). Çorumluoğlu (2023) identified industrial regions, roads, bare lands and certain urban land parts as primary contributors to urban heat islands (UHI) and Urban Hot Spots (UHS) in Izmir. ...
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Rapid urbanisation has led to significant environmental and climatic changes worldwide, especially in urban heat islands where increased land surface temperature (LST) poses a major challenge to sustainable urban living. In the city of Abha in southwestern Saudi Arabia, a region experiencing rapid urban growth, the impact of such expansion on LST and the resulting microclimatic changes are still poorly understood. This study aims to explore the dynamics of urban sprawl and its direct impact on LST to provide important insights for urban planning and climate change mitigation strategies. Using the random forest (RF) algorithm optimised for land use and land cover (LULC) mapping, LULC models were derived that had an overall accuracy of 87.70%, 86.27% and 93.53% for 1990, 2000 and 2020, respectively. The mono-window algorithm facilitated the derivation of LST, while Markovian transition matrices and spatial linear regression models assessed LULC dynamics and LST trends. Notably, built-up areas grew from 69.40 km² in 1990 to 338.74 km² in 2020, while LST in urban areas showed a pronounced warming trend, with temperatures increasing from an average of 43.71 °C in 1990 to 50.46 °C in 2020. Six landscape fragmentation indices were then calculated for urban areas over three decades. The results show that the Largest Patch Index (LPI) increases from 22.78 in 1990 to 65.24 in 2020, and the number of patches (NP) escalates from 2,531 in 1990 to an impressive 10,710 in 2020. Further regression analyses highlighted the morphological changes in the cities and attributed almost 97% of the LST variability to these urban patch dynamics. In addition, water bodies showed a cooling trend with a temperature decrease from 33.76 °C in 2000 to 29.69 °C in 2020, suggesting an anthropogenic influence. The conclusion emphasises the urgent need for sustainable urban planning to counteract the warming trends associated with urban sprawl and promote climate resilience.
... More than half of the global population resides in urban settlement (United Nations, Department of Economic & Social Affairs, 2018), and thus in localized hotspots covering less than 3 % of the Earth's land surface (Liu et al., 2014). Urbanization not only manifests by changes in land use/land cover (Zhao et al., 2017), such as the transformation of natural landscapes into built-up areas, but also involves the utilization of land resources in both horizontally and vertically Zambon et al., 2019). For instance, average height of buildings in China is now taller at 10.35 m, compared to Europe at 7.37 m and the US at 6.69 m . ...
... Two primary determinants of these thermal anomalies are alterations in urban land-use changes and the extraction of mineral resources [8]. Moreover, mining cities have undergone significant changes in their surface thermodynamic properties, resulting in changes in land surface temperature (LST) and contributing to the UHI (urban heat island) effect [9]. LST varies across different land-use types due to differences in their surface reflectance and surface roughness [10]. ...
... Focusing on LST, literature findings show that this variable is directly influenced by a number of factors including vegetation, soil content, presence of pervious and impervious surfaces, and construction material used in roads and buildings (Weng and Lu, 2008). Specifically, while vegetation is the most accountable mitigating factor of LST in urban areas (Alavipanah et al., 2015), buildings and roads have been identified as the surface types experiencing the highest LST values, primarily due to the increased heat storage capability of their construction materials (Zhao et al., 2017). Despite this well-established framework, the LULC-LST relationship may significantly vary according to the background climate, with temperature differences generally decreasing with increasing background temperature (He et al., 2019). ...
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Cities have been identified as a landmark for climate change, being among the direct targets of its negative feedbacks. The combined effect of climate change and rapidly growing urbanization is exacerbating the urban heat island phenomenon in cities worldwide. The availability of multiple geo-data sources including satellite remote sensing products is significantly empowering the investigation of its driving factors. This is a crucial step to implement ad hoc mitigation and adaptation strategies. In view of the above, the goal of this study is to measure the effect of a motorway on the Land Surface Temperature (LST) space-time patterns by leveraging Landsat 5 and 8 thermal data of the period from 2006 to 2022. The study area is around the motorway A58 and connected roads in the Metropolitan City of Milan (northern Italy). LST patterns are investigated along the motorway track and in the neighbouring areas before and after the motorway construction, in both the cold and warm seasons. Results show that the motorway significantly affects the LST distribution during summer with a median increase of 2.5 °C along the road track with respect to the surrounding area. The warming effect is also recorded in the road buffers with decreasing LST with increasing distance from the road. On the contrary, no meaningful variation in terms of LST is measured in winter. These experiments provide insightful measures of the effect of a highway on the local climate conditions in an urban area, thus representing crucial pieces of information for driving evidence-based urban planning activities.
... Two primary determinants of these thermal anomalies are alterations in urban land-use changes and the extraction of mineral resources [8]. Moreover, mining cities have undergone significant changes in their surface thermodynamic properties, resulting in changes in land surface temperature (LST) and contributing to the UHI (urban heat island) effect [9]. LST varies across different land-use types due to differences in their surface reflectance and surface roughness [10]. ...
Article
The global impact of coal mining and associated activities on land use/land cover (LULC) changes is significant. This study used Landsat satellite images from 1990 to 2020 to assess LULC changes and their impact on land surface temperature (LST) in four districts of Chhattisgarh state, India. Over three decades, Korba and Raigarh districts saw expansion in coal mines, built-up areas, and water bodies, while forest areas diminished by 711.3 km2 and 212.87 km2, respectively. Koriya district saw coal mine expansion of 5.68 km2 (1990–2010), later declining to 2.85 km2, alongside growth in built-up regions, and forest cover reduction by 251.31 km2 in 2020. Surguja district experienced coal mine and built-up area expansion (1990–2020), with initial forest decline of 160.21 km2 in 2010 followed by recovery in 2020. LST was determined using the Mono-window algorithm. LST increased during winter and summer, with the most significant rise in summer. Vegetation-rich regions had lower LST, while coal mines had the highest temperatures. There was a positive relationship between mining land patch size and patch temperatures. This study underscores the need for vegetation restoration in mining areas, particularly abandoned sites, and sustainable mining practices to mitigate coal mining's warming effects.
... Green spaces (including forests, urban parks, green roofs, and other vegetated areas) can cool the air through evaporation and also reduce the ground temperature by reducing the solar radiation directly reaching the ground through shade [24]. Blue spaces (including water-covered areas such as rivers, lakes, artificial water features, etc.) offer great cooling capacity due to their high thermal capacity (i.e., they heat up more slowly than air and other substances for the same amount of solar radiation) and evaporation and convection processes [25,26]. Studies have also shown that UBGS cooling is controlled by environmental factors such as building height/density [27,28]. ...
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Urban high-temperature disasters have gradually emerged as a significant threat to human society. Therefore, it is crucial to assess and identify areas at risk of such disasters and implement urban planning measures aimed at mitigating their impact. Additionally, a multitude of studies have demonstrated the significant cooling effect of urban blue-green spaces (UGBS), which play a pivotal role in urban environments. Incorporating a UBGS layout into planning and evaluation processes has substantial potential for mitigating high-temperature disasters. This paper presents the construction of a set of assessment processes for mitigating urban high-temperature disaster risk using a UBGS structure layout specifically for the main urban area of Harbin, China. We employed GIS and multi-source remote sensing imagery to develop local climate zone (LCZ) maps applicable to the designated study area. The differentiated impact of UBGS factors on high-temperature disaster risk was determined using the multi-scale geographical weighted regression model (MGWR). The results showed the following: (a) There was an overall low risk level, with 19.61% of the high-risk areas concentrated within the second ring road, forming a spatial pattern characterized by “one line, one cluster”. (b) The risk of the building category LCZs was generally higher than that of the natural category LCZs. The risk of the architectural LCZs could be summarized as the risk of low-density LCZs being smaller than that of the high-density LCZs, except LCZ 5. The mean value of the LCZ 2 and LCZ 5 types was the highest. (c) Through indicator screening, AREA_MN, SHAPE_MN, PD, and NP were found to be significant determinants influencing the risk, and the effectiveness and spatial differentiation of these main factors exhibited notable disparities. (d) By comparing different LCZ types, we concluded that the mitigation effect of these factors on risk may be interfered with by building height (BH); NP may be positively interfered with by BH; and PD and SHAPE_MN may be negatively interfered with by BH. The research results provided a new perspective and practical scientific basis for high-temperature disaster risk-mitigation planning based on UBGSs under LCZ classification.
... Different buffer distances were used according to the area's characteristics in various studies in the literature [69]. After examining the LST maps, this study decided to create four buffer zones of 50 meters. ...
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The rapid urbanization of cities has led to a radical change in the use of land in cities with the accompanying climate change. It has formed urban heat islands, which have begun to be characterized by climatic conditions such as temperature, precipitation, humidity and wind that differ from the surroundings of cities. It is observed that the heat island effect is felt more intensively day by day, especially in large cities such as Ankara, where urbanization is increasing. It is possible to determine and analyze the urban heat island effect using satellite technologies. Google Earth Engine (GEE) is an online platform that enables remote sensing users to efficiently perform big data analysis without increasing the demand for local computing resources. In this study, NDVI (Normalized Difference Vegetation Index) and LST (Land Surface Temperature) spectral indices were analyzed using Google Earth Engine, remote sensing and GIS techniques in four important parks with different sizes and plant diversity located in the urban area of Ankara. The NDVI and LST results were then analyzed with zonal statistics. Although studies have shown that urban parks create a temperature change effect of about 1ºC, it has been observed that the temperature difference is about 3 °C in this study. These results show that the urban heat island effect is increasing in Ankara province, where the effects of climate change are seen rapidly.
... Although this study provides strong evidence of non-linear and synergistic effects of green spaces on active travel, there are certain limitations. First, due to data unavailability, physical environmental factors such as the presence/absence of bicycle lanes, road surface maintenance, adequacy of signage, and temperature, are not included in this study (Zhao et al., 2017(Zhao et al., , 2021. Second, we use cross-sectional data, which only allows us to analyze the correlation between variables but not infer causality. ...
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The relationship between green spaces and active travel has been extensively studied. However, the majority of previous studies relied on small datasets concerning active travel and inadequately explored non-linear and/or synergistic effects. This study uses multi-source data and interpretable machine learning techniques to identify the non-linear and synergistic effects of green spaces in Chengdu (China) on two types of active travel: cycling and running. Crowdsourced data from Strava collected in December 2021 is used to measure city-wide active travel levels. Meanwhile, green spaces are evaluated from two viewpoints: overhead view and eye level, with the latter assessed using Baidu Street View imagery. The findings demonstrate that green spaces can account for up to 20% of the variance in active travel. Generally, the effect of the area of green spaces on active travel is positive. When the area of green spaces reaches a certain threshold, its effect becomes marginal and even negative. The green view index displays complex effects on cycling. Furthermore, this study identifies synergistic effects among predictors (e.g., green view index & river line length).
... Most studies have examined the driving forces of polycentric urban development from the perspective of natural and socioeconomic factors [40]. The focus on natural factors includes land cover [41], topographical features [42], elevation grades [43], landscape fragmentation [44] and the patterns of rivers/lakes [45]. The focus on socioeconomic factors includes urban functions and human activities such as transportation networks [46], population distribution [47], economic size [48] and employment figures [14]. ...
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As a crucial government strategy for spatial management and resource allocation, administrative division adjustment (ADA) provides interesting insights in the investigation of the polycentric urban structure (POLY). Using high-resolution geographic grid population data, this study aims to interpret complex linkages between ADA and POLY. Specifically, this research explores the dynamic evolution of POLY and ADA, investigates the spatiotemporal impact of ADA on POLY based on geographically and temporally weighted regression models and analyzes the differences in socioeconomic driving forces for POLY in cities with and without ADA. The results demonstrate that the value of POLY had a rising trend during the study periods. In terms of spatial pattern, POLY has a higher value in the Eastern region and a lower value in the Western region. The influences of ADA on POLY are also characterized by spatiotemporal heterogeneity. The impact of ADA on POLY has a higher value in Eastern and Western China and a lower value in Central and Northeastern China. In addition, the impacts of socioeconomic factors on POLY between cities with and without ADA differed significantly in Central and Western China while differing insignificantly in Eastern and Northeastern China. To promote the balanced development of administrative institutional structures and urban spatial transformation, ADA should be selectively implemented to facilitate POLY following the level of population, economic and productive development in each region.
... The UHI effect is a common urban climatic phenomenon, and its creation and severity are directly connected to the type of land cover (Zhao et al. 2017;AlDousari et al. 2022). The phenomenon occurs when the temperature in the city is greater than that in the suburbs at the same time (Harmay et al. 2021). ...
Article
Green space in cities has been reducing rapidly due to the intensive urban expansion, which contributes to surface temperature growth, leading to numerous challenges in management and planning. This work applied U-Net and cellular automaton-artificial neural network (CA-ANN) models to classify and predict the land use and land cover (LULC) change in Ho Chi Minh, the largest city in Vietnam. The present study indicates that the LULC in this city has changed remarkedly for 27 years when the urban green space (UGS) performed a gradual decline. The urban expansion is mainly in the north and northeast direction. The UGS and temperature are negatively correlated since the UGS decline contributes to a temperature increase from 1995 to 2022 in the study area. The temperature is high in all urban areas, being highest in industrial zones or areas with manufacturing activities. There is a different picture of temperature between the inner-city area and the other areas according to the density of green spaces. Based on the CA-ANN model, this work can predict the LULC change in 2035 as the urban land will increase, but the UGS will reduce and the expansion direction being to the east, northeast and northwest. Our findings suggest that remote sensing and U-Net models may be used to investigate urban heat islands and urbanization, as well as to analyze geographical and temporal changes. These results would be helpful for planners and managers to pay more attention to long-term plans for sustainable urban development and management in this city.
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This study examines the interrelationship between land use, urban growth patterns, and the urban heat island effect in Istanbul, Turkey, utilising Landsat images spanning the period from 1990 to 2018. The land surface temperatures are derived from Landsat images, and the urban growth patterns are obtained using the Corine Land Cover and Global Human Settlement databases. Urban growth patterns are classified into four categories: high-rise high-density, high-rise low-density, low-rise high-density, and low-rise low-density. It is observed that the urban built-up areas in Istanbul have more than doubled during the study period, while the agricultural and forest areas have undergone a significant decrease. In consequence, there has been a notable increase in land surface temperatures (LST). The findings of the study indicate that artificial surfaces, particularly continuous urban fabric, industrial and commercial units, and airports, have exhibited the highest LST over time. A statistical analysis reveals a relationship between the growth pattern and surface temperature changes. The development patterns of high-rise low-density and low-rise low-density do not significantly contribute to the formation of urban heat islands. In contrast, high-rise high-density development and low-rise high-density development exert a pronounced influence on the formation of urban heat islands. Furthermore, a negative correlation was observed between vegetation coverage and LST, whereas a positive correlation was noted between building density and imperviousness and LST. The urban heat island effect in Istanbul, a major global metropolitan area with a population of approximately 16 million, is exhibiting a continuous increase due to the dynamics of urban growth. The findings of this study can inform the formulation of urban growth strategies for the forthcoming years, thereby facilitating thermal comfort.
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Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) products are essential data sources for global and regional climate change research. Currently, several versions of the MODIS LST product have been released, yet the performance differences and uncertainties they introduce in land surface studies remain insufficiently addressed. To bridge this gap, this study focuses on four distinct versions of the LST product: MxD11A1 Collection 5 (C5), Collection 6 (C6), Collection 6.1 (C6.1), and MxD21A1 Collection 6.1 (MxD21). The spatial resolution of all product generations is 1 km, and the temporal resolution is 0.5 days. This study provides a comprehensive analysis of the errors arising from different generations of these products in various land surface process studies. The error assessment includes cross-comparisons between product versions and evaluations of the absolute errors generated. Absolute errors in evaluation data were collected from 13 surface sites within the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project during the period 2013–2018. Cross-validation results show that the largest difference between C5 and C6.1 occurs over bare land, with an RMSE of approximately 1.45 K, while there is no significant change between C6 and C6.1. MOD21 shows considerable variation compared to C6.1 at night across different land cover types, with RMSE over cropland exceeding 2 K. The temperature difference between MOD21 and C6.1 is more pronounced at night (2.01 K) than during the day (0.30 K). Validation results based on temperature indicate that C5 has greater uncertainty compared to C6, especially over bare land, where errors are 2.06 K and 1.06 K, respectively. Furthermore, MxD21 demonstrates significant day–night performance discrepancies, with an average bias of 0.10 K at night, while daytime errors over bare land can reach 2 K, potentially influenced by atmospheric conditions. Based on the research in this paper, it is possible to clarify the performance of different versions of MODIS products, reflecting the appropriateness of their past applications; on the other hand, it is recommended to prioritize the use of the MxD11A1 C6 and C6.1 products for monitoring and applications in bare soil areas to ensure higher accuracy. Furthermore, for day and night monitoring, it may be beneficial to alternate between the MxD11A1 and MxD21A1 products to fully leverage their respective advantages and enhance overall monitoring effectiveness.
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This study reveals the temporal and spatial evolution characteristics of the winter nighttime urban heat island (UHI) effect in the case of Beijing, China. The land surface temperature (LST) is retrieved by radiative transfer equation by using the remote sensing data from Landsat ETM+/OLITIRS from 2007 to 2017 for the winter nighttime period, and LST is then divided by the mean-standard deviation method into different levels of thermal landscapes. A combination of the migration calculation of gravity center and multi-directional profile analysis is used to study the directional differentiation characteristics of LST and the migratory characteristics of the gravity center of UHI. Finally, the overall temporal and spatial evolution characteristics of winter nighttime surface urban heat island (SUHI) in Beijing are studied, and the possible reasons for the changes are discussed. Results show that Beijing’s UHI effect first increased and subsequently decreased from 2007 to 2017. The winter heat island in the urban area developed from low-density agglomeration to high-density agglomeration to low-density diffusion. Additionally, the high-level thermal landscapes migrated to the southwest along with the city center of gravity, and the expansion rate is fastest in the southwest, which is directly linked to the changes in the urban construction land. Moreover, the overall spatial distribution of winter nighttime LST is high in the east and south and low in the west and north, and is influenced by topography, land cover, urbanization, anthropogenic heat, and other factors as well.
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Mineral extraction in resource-based cities has caused serious damage to the original ecology, resulting in poor regional vegetation growth, reduced carbon sequestration capacity, and reduced ecosystem resilience. Especially in resource-based cities with fragile ecology, the overall anti-interference ability of the environment is relatively worse. Seeking ecological network optimization solutions that can improve vegetation growth conditions on a large scale is an effective way to enhance the resilience of regional ecosystems. This paper introduces carbon sequestration indicators and designs a differential ecological networks (ENs) optimization model (FTCC model) to achieve the goal of improving ecosystem resilience. The model identifies the patches that need to be optimized and their optimization directions based on the differences in ecological function-topology-connectivity-carbon sequestration of the patches. Finally, the resilience of the ecological network before and after optimization was compared, proving that the model is effective. The results show that the sources in the Yulin ENs form three main clusters, with connectivity between clusters relying on only a few patches. The patches in the northeastern and southwest clusters are large but their ecological functions need to be improved. After optimization, 16 new stepping stones were added, 38 new corridors were added, and the ecological function of 39 patches was enhanced. The optimized ecological network resilience was improved in terms of structure, function, and carbon sinks, and carbon sinks increased by 6364.5 tons. This study provides a reference for measures to optimize landscape space and manage ecosystem resilience enhancement in resource-based cities.
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The growth of urban areas has a significant impact on land use by replacing areas of vegetation with residential and commercial areas and their related infrastructure; this escalates the land surface temperature (LST). Rapid urban growth has occurred in Duhok City due to enhanced political and economic growth during the period of this study. The objective is to investigate the effect of land use changes on LST; this study depends on data from three Landsat images (two Landsat 5-TM and Landsat OLI_TIRS-8) from 1990, 2000 and 2016. Supervised classification was used to compute land use/cover categories, and to generate the land surface temperature (LST) maps the Mono-window algorithm was used. Images were also used to create the normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), normalized difference bareness index (NDBAI) and normalized difference water index (NDWI) maps. Linear regression analysis was used to generate relationships between LST with NDVI, NDBI, NDBAI and NDWI. The study outcome proves that the changes in land use/cover have a significant role in the escalation of land surface temperatures. The highest temperatures are associated with barren land and built-up areas, ranging from 47°C, 50°C, 56°C while lower temperatures are related to water bodies and forests, ranging from 25°C, 26°C, 29°C respectively, in 1990, 2000 and 2016. This study also proves that NDVI and NDWI correlate negatively with low temperatures while NDBI and NDBAI correlate positively with high temperatures.
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The spatio-temporal patterns of land use/land cover changes (LUCC) can significantly affect the distribution and intensity of the urban heat island (UHI) effect. However, few studies have mapped a clear picture of the influence of LUCC on UHI. In this study, both qualitative and quantitative models are employed to explore the effect of LUCC on UHI. UHI and LUCC maps were retrieved from Landsat data acquired from 1984, 1992, 2000, 2007, and 2014 to show their spatiotemporal patterns. The results showed that: (1) both the patterns of LUCC and UHI have had dramatic changes in the past 30 years. The urban area of Changchun increased more than four times, from 143.15 km2 in 1984 to 577.45 km2 in 2014, and the proportion of UHI regions has increased from 15.27% in 1984 to 29.62% in 2014; (2) the spatiotemporal changes in thermal environment were consistent with the process of urbanization. The average LST of the study area has been continuously increasing as many other land use types have been transformed to urban regions. The mean temperatures were higher in urban regions than rural areas over all of the periods, but the UHI intensity varied based on different measurements; and (3) the thermal environment inside the city varied widely even within a small area. The LST possesses a very strong positive relationship with impervious surface area (ISA), and the relationship has become stronger in recent years. The UHI we employ, specifically in this study, is SUHI (surface urban heat island).
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Knowledge of wind effects is of great significance in structural, environmental, and architectural fields, where excessive relevance among wind pressure, building load, and natural ventilation has been formerly confirmed. Within the scope of high-rise buildings, functions of their layout, separation and height in altering wind pressure have been inquired on purpose, while a few investigations in relation to impacts of plane dimensions have been explored. This study consequently intends to ascertain wind pressure distributions on and around various squared-shaped tall buildings by the application of Computational Fluid Dynamics techniques. To start with, models established by the Common Advisory Aeronautical Research Council (CAARC) were simulated, for the purpose of correctness comparison, and reliability verification. Hereafter, wind pressure distributing on buildings was predicted under two scenarios, namely height-width (HW) and height-thickness (HT). Results evidenced that both HW ratio and HT ratio exerted great influence on wind characteristics of buildings. Positive pressure on building surface generally varied greatly, where a narrower windward tended to suffer higher wind pressures, while a larger one was corresponding to severer negative wind effects. The thickness played little influence on altering positive wind pressure. Prominently, pressure distributed on leeward surfaces showed great differences, whereas wind effects on leeward and side surface were strengthened. Likewise, both positive and negative effects around buildings were magnified by larger widths, while negative effects became feeble along the increasing building thickness.
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The urban heat island (UHI) is mainly a nocturnal phenomenon but it also appears during the day in Mexico City. This UHI may affect the human thermal comfort which can influence human productivity and morbidity in the spring/summer period. A simple phenomenological model based on the energy balance was developed to generate theoretical support of the UHI mitigation in Mexico City focused on the latent heat flux change by increasing tree coverage to reduce sensible heat flux and air temperature. Half hourly data of the urban energy balance components were generated in a typical residential/commercial neighborhood of Mexico City, and then parameterized using easily measured variables (air temperature, humidity, pressure, visibility). Canopy conductance was estimated every hour in four tree species and transpiration was estimated using sap flow technique and parameterized by the envelope function method. Averaged values of net radiation, energy storage, sensible and latent heat flux were around 449, 224, 153 and 72 W m-2, respectively. Daily tree transpiration ranged 3.64─4.35 Ld-1. To reduce air temperature by 1 °C in the studied area, 63 large Eucaliptus camaldulensis would be required per hectare, whereas to reduce the air temperature by 2 °C only 24 large Liquidambar styraciflua trees would be required. This study suggests increasing tree canopy cover in the city cannot mitigate UHI adequately, but requires choosing the most appropriate tree species to solve this problem. Also it is imperative to include these types of studies in the tree selection and urban development planning to adequately mitigate UHI.
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Differences between the energy balance of cities and their non-urban surroundings exist due to modification of surface properties. In temperate and subtropical climates, these differences are manifest as the Urban Heat Island (UHI) effect. However in more arid environ ments man-made modifications of the environment can cause urban cooling relative to the surrounding landscape particularly during the dry season. This research examines the spatial formation of the daytime Surface Urban Cool Island (SUCI) effect of Erbil city in Iraq, as a case study of cities in semi-arid climates. Six satellite images acquired by Landsat 8 during the period from 1st July to 19th September 2013 are used to retrieve Land Surface Temperature (LST), identify Land Use/Land Cover (LULC) classes and investigate the spatial variation of LST and the SUCI intensity. In order to find out the key drivers of the observed patterns of LST, the relationship with wetness, brightness, bareness, built-up and vegetation index maps are examined. The results indicate that densely built-up areas, such as central districts of the city, green areas and water bodies, had lower LST acting as cool islands, compared to the non-urbanized area around the city. In contrast, the airport, open spaces and new low-density housing developments on the outskirts of the city, experienced higher LST and showed an SUHI effect. A very strong inverse relationship is evident between surface temperature and wetness index (r = -0.9; p < 0.01). A strong positive cor-46 relation (r = 0.75; p < 0.00001) is apparent with the brightness index. In contrast, between surface temperature and the greenness index a moderate negative correlation was found (r = -0.39; p < 0.01) for a typical dry season day. The results show that during the daytime residential areas in the city centre recorded an LST of 46.2 ± 1.74 °C. Urban Cool Island Intensity (UCII) of the city ranged from 3.5 to 4.6 °C compared to a 10 km buffer zone around the city. This study shows that during the dry season in some cities, such as Erbil, the surface wetness is the main determinant of the UCI effect, and not vegetation cover.
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Urban heat islands (UHI) are defined. The importance of distinguishing between different types is stressed and a simple classification scheme is forwarded. Emphasis in this paper is upon the heat island in the urban boundary layer (UBL) above roof-level (the UHIUBL). The observed characteristics of the daytime and nocturnal UHIUBL are illustrated including the evolution of its thermal structure and the transition between the day and night régimes. Effects of weather controls on the UBL are mentioned. The essential physics underlying the genesis of the daytime and nocturnal UHIUBL is outlined including radiative flux divergence, heating from below due to the altered surface energy balance, and heating from above due to entrainment. UHIUBL effects on urban airflow and air pollution dispersion are numerous. Examples given include effects on thermal turbulence, atmospheric stability, convective structures, nocturnal inversions, mixed layer depth, local circulation systems, plume trajectories, intra- and inter-urban fumigation, humidity and rates of chemical reactions and biogenic emission.
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The City of Phoenix (Arizona, USA) developed a Tree and Shade Master Plan and a Cool Roofs initiative to ameliorate extreme heat during the summer months in their arid city. This study investigates the impact of the City's heat mitigation strategies on daytime microclimate for a pre-monsoon summer day under current climate conditions and two climate change scenarios. We assessed the cooling effect of trees and cool roofs in a Phoenix residential neighborhood using the microclimate model ENVI-met. First, using xeric landscaping as a base, we created eight tree planting scenarios (from 0% canopy cover to 30% canopy cover) for the neighborhood to characterize the relationship between canopy cover and daytime cooling benefit of trees. In a second set of simulations, we ran ENVI-met for nine combined tree planting and landscaping scenarios (mesic, oasis, and xeric) with regular roofs and cool roofs under current climate conditions and two climate change projections. For each of the 54 scenarios, we compared average neighborhood mid-afternoon air temperatures and assessed the benefits of each heat mitigation measure under current and projected climate conditions. Findings suggest that the relationship between percent canopy cover and air temperature reduction is linear, with 0.14 °C cooling per percent increase in tree cover for the neighborhood under investigation. An increase in tree canopy cover from the current 10% to a targeted 25% resulted in an average daytime cooling benefit of up to 2.0 °C in residential neighborhoods at the local scale. Cool roofs reduced neighborhood air temperatures by 0.3 °C when implemented on residential homes. The results from this city-specific mitigation project will inform messaging campaigns aimed at engaging the city decision makers, industry, and the public in the green building and urban forestry initiatives.
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The cooling effects of urban parks, which form "Park Cool Island" (PCI), can help decrease land surface temperature (LST) and mitigate urban heat island (UHI) effects. PCI intensity largely depends on the characteristics of urban parks. The relationship between PCI intensity and urban park characteristics such as urban park size has been well documented. However, it is still unclear how urban forest structures in parks affect PCI intensity and particularly whether the relationship changes across seasons. In this study, PCI intensity for 33 parks in Changchun, China was obtained from Landsat-5 Thematic Mapper (TM) data and then correlated with urban park characteristics such as the size derived from "Systeme Probatoire d'Observation dela Tarre" (SPOT) satellite data and the forest structures of parks derived from the field-based survey to uncover the relationship between urban park characteristics and PCI intensity. Our results suggested that (1) The PCI intensity varied across seasons and the cooling effect of parks in summer was higher than that in autumn. (2) The increase of urban park size was still an effective measure to mitigate UHI. However, urban park size was non-linearly correlated to PCI intensity. (3) Not only by increasing urban park size, but also by optimizing urban park shape and forest structures in parks can increase PCI intensity. (4) The relationship between PCI intensity and urban park characteristics changed across seasons and seasons should be considered when exploring the relationship between them. These findings can deepen the understanding of PCI formation and provide useful information for urban planners about how to design urban parks to maximize their PCI intensity and mitigate UHI effects.
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The paper presents the results of shading analysis which was carried out as part of a wider comparative analysis of two sites with different characteristics in terms of street geometry and urban density. The first experiment site was a traditional settlement in the island of Tinos, Greece, and the second was a relatively newly built part of the capital city of the island. Also a parametric shading analysis was carried out in order to examine a number of parameters that influence shading conditions in urban canyons.
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Although urban growth in the city of Glasgow, UK, has subsided, urban morphology continues to generate local heat islands. We present a relatively less data-intense method to classify local climate zones (LCZ) and evaluate the effectiveness of green infrastructure options in tackling the likely overheating problem in cold climate urban agglomerations such as the Glasgow Clyde Valley (GCV) Region. LCZ classification uses LIDAR data available with local authorities, based on the typology developed by Stewart and Oke (2012). LCZ classes were then used cluster areas likely to exhibit similar warming trends locally. This helped to identify likely problem areas, a sub-set of which were then modelled for the effect of green cover options (both increase and reduction in green cover) as well as building density options. Results indicate green infrastructure could play a significant role in mitigating the urban overheating expected under a warming climate in the GCV Region. A green cover increase of approximately 20% over the present level could eliminate a third to a half of the expected extra urban heat island effect in 2050. This level of increase in green cover could also lead to local reductions in surface temperature by up to 2 °C. Over half of the street users would consider a 20% increase in green cover in the city centre to be thermally acceptable, even under a warm 2050 scenario. The process adopted here could be used to estimate the overheating problem as well as the effectiveness green infrastructure strategies to overcome them.
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In this paper, a new method for estimation of vertical leaf area density (LAD) profile of tree canopy using portable scanning lidar is proposed. In this method, which we refer to as the voxel-based canopy profiling (VCP) method, several measurement points surrounding the canopy and optimally inclined laser beams are adopted to facilitate full laser beam illumination of whole canopy up to the internal. After the scanning, each data obtained from each measurement point are co-registered and the 3-D information is reproduced as the voxel attributes in the 3-D voxel array. Based on the voxel attributes, contact frequency of laser beams on leaves is computed and LAD in each horizontal layer is obtained. In addition, influence of non-photosynthetic tissues and leaf inclination angle on the LAD estimation are corrected. Using the method, good agreement between estimated and actual LAD was obtained in an individual tree of Camellia sasanqua. Next, the method was applied to broad leaved woody canopy of Japanese zelkova (Zelkova serrata (Thunb.) Makino). In the experiment, LAD profiles had different accuracy depending on each quadrat established on the measurement plot and on the laser incident angles. From the results, it was shown that the number of laser beam incidences N and G( c) (the mean projection of a unit leaf area on a plane perpendicular to the direction of the laser beam) are the factors to influence the accuracy of LAD estimation.
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We investigated the seasonal variability of the relationships between land surface temperature (LST) and land use/land cover (LULC) variables, and how the spatial and thematic resolutions of LULC variables affect these relationships. We derived LST data from Landsat-7 Enhanced Thematic Mapper (ETM+) images acquired from four different seasons. We used three LULC datasets: (1) 0.6 m resolution land cover data; (2) 30 m resolution land cover data (NLCD 2001); and (3) 30 m resolution Normalized Difference Vegetation Index data derived from the same ETM+ images (though from different bands) used for LST calculation. We developed ten models to evaluate effects of spatial and thematic resolution of LULC data on the observed relationships between LST and LULC variables for each season. We found that the directions of the effects of LULC variables on predicting LST were consistent across seasons, but the magnitude of effects, varied by season, providing the strongest predictive capacity during summer and the weakest during winter. Percent of imperviousness was the best predictor on LST with relatively consistent explanatory power across seasons, which alone explained approximately 50 % of the total variation in LST in winter, and up to 77.9 % for summer. Vegetation related variables, particularly tree canopy, were good predictor of LST during summer and fall. Vegetation, particularly tree canopy, can significantly reduce LST. The spatial resolution of LULC data appeared not to substantially affect relationships between LST and LULC variables. In contrast, increasing thematic resolution generally enhanced the explanatory power of LULC on LST, but not to a substantial degree.
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Remote sensing of land surface temperature (LST) from the thermal band data of Landsat Thematic Mapper (TM) still remains unused in comparison with the extensive studies of its visible and near-infrared (NIR) bands for various applications. The brightness temperature can be computed from the digital number (DN) of TM6 data using the equation provided by the National Aeronautics and Space Administration (NASA). However, a proper algorithm for retrieving LST from the only one thermal band of the sensor still remains unavailable due to many difficulties in the atmospheric correction. Based on thermal radiance transfer equation, an attempt has been made in the paper to develop a mono-window algorithm for retrieving LST from Landsat TM6 data. Three parameters are required for the algorithm: emissivity, transmittance and effective mean atmospheric temperature. Method about determination of atmospheric transmittance is given in the paper through the simulation of atmospheric conditions with LOWTRAN 7 program. A practicable approach of estimating effective mean atmospheric temperature from local meteorological observation is also proposed in the paper when the in situ atmospheric profile data is unavailable at the satellite pass, which is generally the case in the real world especially for the images in the past. Sensitivity analysis of the algorithm indicates that the possible error of ground emissivity, which is difficult to estimate, has relatively insignificant impact on the probable LST estimation error i T, which is sensible to the possible error of transmittance i ‰ 6 and mean atmospheric temperature i T a . Validation of the simulated data for various situations of seven typical atmospheres indicates that the algorithm is able to provide an accurate LST retrieval from TM6 data. The LST difference between the retrieved and the simulated ones is less than 0.4°C for most situations. Application of the algorithm to the sand dunes across the Israel-Egypt border results in a reasonable LST estimation of the region. Based on this LST estimation, spatial variation of the interesting thermal phenomenon has been analysed for comparison of LST difference across the border. The result shows that the Israeli side does have significantly higher surface temperature in spite of its denser vegetation cover than the Egyptian side where bare sand is prevalent.
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Evaluating how park characteristics affect the formation of a park cool island (PCI) is the premise of guiding green parks planning in mountain cities. The diurnal variation of PCI intensity was achieved, and correlations between PCI intensity and park characteristics such as park area, landscape shape index (LSI), green ratio and altitude were analyzed, using 3 010 temperature and humidity data from measurements in six parks with typical park characteristics in Chongqing, China. The results indicate that: 1) the main factor determining PCI intensity is park area, which leads to obvious cool island effect when it exceeds 14 hm2; 2) there is a negative correlation between PCI intensity and LSI, showing that the rounder the park shape is, the better the cool island effect could be achieved; 3) regression analysis of humidity and PCI intensity proves that photosynthesis midday depression (PMD) is an important factor causing the low PCI intensity at 13:00; 4) the multivariable linear regression model proposed here could effectively well predict the daily PCI intensity in mountain cities.
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Trees play an important role in mitigating heat stress on hot summer days, mainly due to their ability to provide shade. However, an important issue is also the reduction of solar radiation caused by trees in winter, in particular at high latitudes. In this study, we examine the transmissivity of total and direct solar radiation through crowns of single street trees in Göteborg, Sweden. One coniferous and four deciduous trees of species common in northern European cities were selected for case study. Radiation measurements were conducted on nine clear days in 2011–2012 in foliated and leafless tree conditions using two sunshine pyranometers— one located in shade of a tree and the other one on the roof of an adjacent building. The measurements showed a significant reduction of total and direct shortwave radiation in the shade of the studied trees, both foliated and leafless. Average transmissivity of direct solar radiation through the foliated and defoliated tree crowns ranged from 1.3 to 5.3 % and from 40.2 to 51.9 %, respectively. The results confirm the potential of a single urban tree to reduce heat stress in urban environment. However, the relatively low transmissivity through defoliated trees should be considered while planning street trees in high latitude cities, where the solar access in winter is limited. The results were used for parameterisation of SOLWEIG model for a better estimation of the mean radiant temperature (Tmrt). Measured values of transmissivity of solar radiation through both foliated and leafless trees were found to improve the model performance.
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The Urban Heat Island (UHI) effect already produces elevated temperatures in city centres therefore urban design has a key role to pay in reducing the UHI to create safe and pleasant places in which to live and work. Increased surface porosity and bodies of surface water have a role to play in increasing potential cooling through evaporation. Urban rivers may, therefore, have a place in reducing the UHI. This paper investigates the effectiveness that small urban rivers may have in reducing the UHI effect and also examines the role that the urban form on the banks of a river can play in propagating or reducing this potential cooling. The results from a field survey during spring and summer are presented for a river in Sheffield, UK. The level of cooling is related to the ambient air temperature, increasing at higher temperatures. However, there are also seasonal dependencies and relationships linked to the river water temperature, incident solar radiation, wind speed and relative humidity. A mean level of daytime cooling of over 1.5 °C was found above the river in spring, but this was reduced in summer when the river water temperature was warmer. The urban form on the river bank influenced the levels of cooling felt away from the river bank.
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Three-dimensional meteorological simulations have been conducted to investigate the potential impact of urban surface characteristic modifications on local climate. Results for a base case simulation for the Los Angeles basin are compared to results from cases in which urban albedo or vegetative cover are increased. The methodology for determining the distribution and magnitude of these simulated surface modifications is presented. Increasing albedo over downtown Los Angeles by 0.14 and over the entire basin by an average of 0.08 decreased peak summertime temperatures by as much as 1.5°C. This level of albedo augmentation also lowered boundary layer heights by more than 50 m and reduced the magnitude and penetration of the sea breeze. A second simulation, in which vegetative cover was increased, showed qualitatively similar impacts. The results from these simulations indicate a potential to reduce urban energy demand and atmospheric pollution by 5% 10% through application of reasonable surface modification strategies.
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NOAA AVHRR satellite infra-red data are used to display the surface radiant temperature heat islands of Vancouver, British Columbia, Seattle, Washington, and Los Angeles, California. Heat island intensities are largest in the day-time and in the warm season. Day-time intra-urban thermal patterns are strongly correlated with land-use; industrial areas are warmest and vegetated, riverine or coastal areas are coolest. Nocturnal heat island intensities and the correlation of the surface radiant temperature distribution with land use are less. This is the reverse of the known characteristics of near-surface air temperature heat islands. Several questions relating to the interpretation and limitations of satellite data in heat island analysis and urban climate modelling are addressed.
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The regional-scale climatic impact of urbanization is examined using two land cover parameters, fractional vegetation cover (Fr) and surface moisture availability (Mo). The parameters are hypothesized to decrease as surface radiant temperature (To) increases, forced by vegetation removal and the introduction of non-transpiring, reduced evaporating urban surfaces. Fr and Mo were derived from vegetation index and To data compared from the Advanced Very High Resolution Radiometer (AVHRR), and then correlated to a percentage of urban land cover obtained from a supervised classification of Landsat TM imagery. Data from 1985 through 1994 for an area near State College, PA, USA, was utilized. Urban land cover change (at the rate of >3 per cent km2 per year) was statistically significant when related to a decrease in normalized values of Fr and increase in normalized values of To. The relationship between urbanization and Mo, however, was ill-defined due to variations in the composition of urban vegetation. From a nomogram of values of Fr and To, a Land Cover Index (LCI) is proposed, which incorporates the influence of local land cover surrounding urbanized pixels. Such an index could allow changes in land use at neighbourhood-scale to be input in the initialization of atmospheric and hydrological models, as well as provide a new approach for urban heat island analyses. Furthermore, the nomogram can be used to qualify urbanization effects on evapotranspiration rates.
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One mechanism for climate change is the collected impact of changes in land cover or land use. Such changes are especially significant in urban areas where much of the world's population lives. Satellite observations provide a basis for characterizing the physical modifications that result from urbanization. In particular, the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on the National Aeronautics and Space Administration (NASA) Terra satellite measures surface spectral albedos, thermal emissivities, and radiative temperatures. A better understanding of these measurements should improve our knowledge of the climate impact of urbanization as well as our ability to specify the parameters needed by climate models to compute the impacts of urbanization. For this purpose, it is useful to contrast urban areas with neighboring nonurban surfaces with regard to their radiative surface temperatures, emissivities, and albedos. Among these properties, surface temperatures have been most extensively studied previously in the context of the ``urban heat island'' (UHI). Nevertheless, except for a few detailed studies, the UHI has mostly been characterized in terms of surface air temperatures.To provide a global analysis, the zonal average of these properties are presented here measured over urban areas versus neighboring nonurban areas. Furthermore, individual cities are examined to illustrate the variations of these variables with land cover under different climate conditions [e.g., in Beijing, New York, and Phoenix (a desert city of the United States)]. Satellite-measured skin temperatures are related to the surface air temperatures but do not necessarily have the same seasonal and diurnal variations, since they are more coupled to surface energy exchange processes and less to the overlying atmospheric column. Consequently, the UHI effects from skin temperature are shown to be pronounced at both daytime and nighttime, rather than at night as previously suggested from surface air temperature measurements. In addition, urban areas are characterized by albedos much lower than those of croplands and deciduous forests in summer but similar to those of forests in winter. Thus, urban surfaces can be distinguished from nonurban surfaces through use of a proposed index formed by multiplying skin temperature by albedo.
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Remotely sensed imagery is ideally used to monitor and detect land cover changes that occur frequently in urban and peri-urban areas as a consequence of incessant urbanization. It is a lengthy process to convert satellite imagery into land cover map using the existing methods of manual interpretation and parametric image classification digitally. In this paper we propose a new method based on Normalized Difference Built-up Index (NDBI) to automate the process of mapping built-up areas. It takes advantage of the unique spectral response of built-up areas and other land covers. Built-up areas are effectively mapped through arithmetic manipulation of re-coded Normalized Difference Vegetation Index (NDVI) and NDBI images derived from TM imagery. The devised NDBI method was applied to map urban land in the city of Nanjing, eastern China. The mapped results at an accuracy of 92.6% indicate that it can be used to fulfil the mapping objective reliably. Compared with the maximum likelihood classification method, the proposed NDBI is able to serve as a worthwhile alternative for quickly and objectively mapping built-up areas.
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Natural ventilation has been an important strategy for the improvement of indoor air quality and human thermal comfort, and the reduction of energy consumption of buildings. Many investigations have been conducted to examine the natural ventilation of low, multi-rise buildings rather than tall buildings. Starting from enlarging the wind pressure difference to create wind-driven natural ventilation, this paper aims to analyze characteristics of surface pressure coefficients over tall buildings and to identify the influence of building shapes on coefficient distribution. Taking oval-shaped high-rise buildings as examples, this paper numerically investigated the effects of height-width ratio (HWR) and height-thickness ratio (HTR) on mean wind pressure coefficients (Cm) of building surfaces. Results indicated that windward side of oval-shaped buildings suffered from positive wind pressure, while side, top and back surfaces were basically in negative pressure areas. The absolute values of Cm on building surfaces increased as the decrease of HWR. On the contrary, Cm near central axis of side surfaces showed opponent trend due to fluid separation. In HTR scenario, Cm on windward and top surfaces were greatly affected, increasing along the HTR values. However, with the decrease of HTR, properties of wind field on leeward surface changed. Through this work, the architects and HVAC engineers can get a master plan of in which place they can set possible openings for the creation of possible ventilation paths.
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To explore impacts of urban surface characteristics on spatiotemporal pattern of land surface temperature (LST), LST was retrieved from the thermal infrared band of satellite images and five indices were selected and extracted from remote sensing images in different time periods respectively acquired from 1992 to 2014. The correlation analysis (pixel by pixel) and linear regression analysis showed that although Normalized Difference Built-up Index (NDBI) and LST had the highest correlation coefficient, a combination of Modified Normalized Difference Water Index (MNDWI) and Normalized Difference Vegetation Index (NDVI) yielded the best regression results (the mean of R-squared value increased 0.1). These results showed that both NDBI and NDVI-MNDWI would be acceptable indicators of LST, but NDVI-MNDWI could be better. Moreover, urban heat island (UHI) intensity (represented by LST) analysis showed that the highest UHI intensity appeared in the April, while the lowest UHI intensity index emerged in June. These results suggested that both UHI intensity and UHI intensity index might be closely related to land surface moisture. Furthermore, the regions of UHI intensity index greater than 0.5 were unchanged essentially from 2000 to 2014. So, these regions would be considered as key areas where the UHI could be focused on elimination.
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Local and global climate change increases the ambient temperature of cities by several degrees with important consequences on energy consumption, health and the economy. Advanced urban mitigation technologies contribute to decrease the ambient temperature and counterbalance the impact of urban heat islands. The present paper analyses and presents in a comparative way the mitigation potential of the known mitigation technologies using performance data from about 220 real scale urban rehabilitation projects. The average and peak temperature drop of reflective technologies, greenery, evaporative systems, earth to air heat exchangers and their combinations is calculated and presented. The mitigation potential of the main systems like cool roofs, cool pavements, green roofs, urban trees, pools and ponds, sprinklers, fountains, and evaporative towers, is analysed. It is found that the potential of the main mitigation technologies is considerable and can counterbalance UHI effects partly or fully. The average peak temperature drop calculated for all projects is close to 2 K, while the corresponding decrease of the average ambient temperature is close to 0.74 K. Almost 31% of the analysed projects resulted in a peak temperature drop below 1 K, 62% below 2 K, 82% below 3 K and 90% below 4 K.
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The urban heat island (UHI) phenomenon and the outdoor thermal comfort in a planned city need to be reviewed and studied as a climatic issue in the design process. Increasing the temperature and discomfort conditions would be unjustified and not acceptable, unlike the temperature and the discomfort outdoors in a non-planned city that is natural. This study aimed to investigate the UHI phenomenon and outdoor thermal comfort on a micro-scale of the different areas in a planned city. A mobile survey and fixed station measurements were performed to investigate the intra-urban air temperature within the city. The thermal comfort condition of the different hot spots of the urban area in the city was investigated by using Envi-met V4 Beta software. The results indicate that the maximum UHI occurred during the afternoon and reached 3 °C in low-rise residential buildings. The high-rise residential buildings and the Boulevard street are 4 °C lower than low-rise buildings and 1 °C lower than nearby suburban areas. The city’s human thermal comfort exceeds the natural range of 30 °C. However, the high-rise residential buildings and the Boulevard street are thermally comfortable most of the daytime hours, while low-rise buildings suffer from a long period of heat stress. The diffuse, reflected solar radiation and the surface temperature have an influence on increasing the Physiologically Equivalent Temperature (PET) thermal index within the city, while the wind velocity and building height are the essential variables reducing the PET thermal index.
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Urban heat island and global warming increase significantly the ambient temperature. Higher temperatures have a serious impact on the electricity consumption of the building sector increasing considerably the peak and the total electricity demand. The present paper aims to collect, analyze and present in a comparative way existing studies investigating the impact of ambient temperature increase on electricity consumption. Analysis of eleven studies dealing with the impact of the ambient temperature on the peak electricity demand showed that for each degree of temperature increase, the increase of the peak electricity load varies between 0.45% and 4.6%. This corresponds to an additional electricity penalty of about 21 (±10.4) W per degree of temperature increase and per person. In parallel, analysis of fifteen studies examining the impact of ambient temperature on the total electricity consumption, showed that the actual increase of the electricity demand per degree of temperature increase varies between 0.5% and 8.5%.
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Using time series Landsat TM/ETM+ imagery and demographic data of Shanghai for 1997 and 2008, the relationship between land use/land cover (LULC) change and population shift and their effects on the spatiotemporal patterns of urban heat islands (UHIs) were quantitatively examined using an integrated approach of remote sensing, geographical information systems (GIS), and statistical analysis. The results showed that this city has experienced unprecedented urban growth and sprawl during the study period. The developed land increased by 219.50%, approximately 72.52% of which was converted from former cropland (24.79%), fallow land (21.21%), forest and shrub (18.97%), bare land (6.62%), and water (0.93%). Furthermore, in combination with the detection of LULC change, an analysis of the spatially differential growth rates for developed land area and population size revealed an urban suburban exurban gradient pattern of population shifting, as evidenced by a sharp increase in developed land area within the middle sub-zones at the urban fringe and the exurban sub-zones beyond the outer traffic ring. Consequently, changes in LULC and population shifts resulted in significant variation in the spatiotemporal patterns of the UHIs due to the loss of water bodies and vegetated surfaces. In the foreseeable future, substantial population growth and urban expansion will continue, especially in the rapidly urbanizing suburban and exurban areas, and thus, the extent and magnitude of UHI effects will continue expanding as well. The relationships between land use, the UHI effect, and regional climate change require that the underlying mechanisms, patterns, and processes of land conversion as well as the response of urban climate should be addressed throughout official decision-making processes. Thus, the planners and decision-makers could fully evaluate the environmental consequences of different land development scenarios and therefore improve the scientific basis of future planning and regulations.
Article
Surface radiant temperatures derived from Landsat TM thermal infrared images of 13 December, 1989, 03 March, 1996, and 29 August 1997 were used to study the urban heat island (UHI) phenomenon in Guangzhou, China. To examine the spatial distribution of surface radiant temperatures, transects were drawn and analyzed from each temperature image. Moreover, the fractal dimensions of these transects were computed using the divider method, so that the spatial variability of surface radiant temperatures caused by the thermal behavior of different land-cover types and landscape pattern characteristics can be better understood. The effect of urban development on the geographical distribution of surface radiant temperatures and thus on the UHI was also investigated. The results revealed two major heat islands, one in the southwest and the other in the east of the city. The areal extent of the UHIs varied as the season changed. The transact derived from the spring image had the lowest fractal dimension while that from the summer image the highest value. Urban development increased the spatial variability of radiant temperatures, resulting in higher fractal dimension values. The thermal surfaces have become more spatially uneven and the textures more complex.
Book
Forewords Acknowledgements Executive summary 1. Introduction Part I. Defining the Risk Framework: 2. Cities, disasters and climate risk 3. Urban climate: processes, trends and projections Part II. Urban Sectors: 4. Climate change and urban energy systems 5. Climate change, water and wastewater 6. Climate change and urban transportation systems 7. Climate change and human health in cities Part III. Cross-Cutting Issues: 8. The role of urban land in climate change 9. Cities and climate change: the challenges for governance Annex: list of contributors Index.
Article
Remote sensing data from MODIS, ASTER and LANDSAT 7 sensors were used to assess land cover-temperature interactions in the Abu Dhabi metropolitan area over a 10-year period between 2000 and 2010 with a multi-sensor approach. Low resolution data from MODIS sensor with high revisiting time have been used to analyze the daily variation of Land Surface Temperature (LST), the derived Surface Urban Heat Island (SUHI), and the Normalized Difference Vegetation Index (NDVI) at city level. Medium resolution data from ASTER and LANDSAT 7 sensors have been used for spot assessment of the above mentioned parameters at district level. With medium resolution satellites, LST and NDVI have been analyzed in correspondence of different level of Impervious Surface Areas (ISAs) over the study period. With both datasets, the obtained results showed an inversion of the standard SUHI phenomenon during daytime, where the downtown areas appear colder compared to the suburbs. Throughout the study period, the trend has been replicated and seasonality is also observed, where the inversion of SUHI is accentuated mainly in the summer months with a daily difference of 5-6 K compared to 2-3 K during the winter season, while the standard SUHI can be observed during the night with values of downtown 2-3 K higher than the suburbs. Spot analysis of single images confirmed this trend, adding the contribution of ISA to an average increment of 1 K during winter and 2 K during the summer.
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