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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... More than 50% of the population and infrastructure are within the three-ring area (the core city, Figure 1), consisting of Heping, Shenhe, Dadong, Huanggu, Tiexi and some parts of other districts such as Dongling, Yuhong and Hunnan New District, with the area of about 455 km 2 . Within the three-ring area, the SUHII could reach 4-5 °C [23]. ...
... accessed on 19 October 2021). First, the population within the threering area saw an upward trend from 2.998 million in 1985 to 3.772 million in 2015 [23]. Second, upon the three-ring area, the urbanized area of central city has been expanding towards different directions, forming a new urban pattern (built-up area A, 635.36 km 2 in area in Figure 1). ...
... More than 50% of the population and infrastructure are within the three-ring area (the core city, Figure 1), consisting of Heping, Shenhe, Dadong, Huanggu, Tiexi and some parts of other districts such as Dongling, Yuhong and Hunnan New District, with the area of about 455 km 2 . Within the three-ring area, the SUHII could reach 4-5 • C [23]. ...
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This study investigated monthly variations of surface urban heat island intensity (SUHII) and the applicability of the local climate zones (LCZ) scheme for land surface temperature (LST) differ-entiation within three spatial contexts including urban, rural and their combination in Shenyang, China, a city with a monsoon-influenced humid continental climate. The monthly SUHII and LST of Shenyang were obtained through 12 LST images, with one in each month (within the period between 2018 and 2020), retrieved from the Thermal InfraRed Sensor (TIRS) 10 in Landsat 8 based on split window algorithm. Non-parametric analysis of Kruskal-Wallis H test and a multiple pairwise-comparison were adopted to investigate the monthly LST differentiations with LCZs. Overall, the SUHII and the applicability of LCZ scheme exhibited spatiotemporal variations. July and August were the two months when Shenyang underwent strong heat island effects. Shen-yang underwent a longer period of cool than heat island effects, occurring from November to May. June and October were the transition months of cool-heat and heat-cool island phenome-non, respectively. The SUHII analysis was dependent on the definition of urban and rural boundaries, where a smaller rural buffering zone resulted in a weaker SUHI or surface urban cool island (SUCI) phenomenon and a larger urban area corresponded to a weaker SUHI or SUCI phenomenon as well. The LST of LCZs did not follow a fixed order, where in July and August, the LCZ-10 (Heavy industry) had the highest mean LST, followed by LCZ-2 (Compact midrise) and then LCZ-7 (Lightweight low-rise). In comparison, LCZ-7, LCZ-8 (Large low-rise) and LCZ-9 (Sparsely built) had the highest LST from October to May. The LST of LCZs varied with urban and rural contexts, where LCZ-7, LCZ-8, and LCZ -10 were the three built LCZs that had the highest LST within urban context, while LCZ-2, LCZ-3 (Compact low-rise), LCZ-8, LCZ-9 and LCZ-10 were the five built LCZs that had the highest LST within rural context. The suitability of LCZ scheme for temperature differentiation varied with the month, where from July to October, the LCZ scheme had the strongest capability and in May it had the weakest capability. Urban context also made a difference to the suitability, where compared with the whole study area (the com-bination of urban and rural areas), the suitability of built LCZs in either urban or rural contexts weakened. Moreover, the built LCZs had a higher level of suitability in urban context compared with rural context, while the land-cover LCZs within rural had a higher level of suitability.
... Climate is the most important aspect that defines a lifestyle, and it is one of the essential components that must be taken into account in constructing structures within an ecological environment. It has been observed from pieces of literature that accelerated urbanization and global warming are factors underlying the great conurbations and land-use changes in many cities of the world (He et al., 2020;Javadinejad et al., 2021;Liu et al., 2017;Massad et al., 2019;Talebmorad et al., 2021;Zhang et al., 2020), and they have resulted in urban heat island (UHI) phenomenon, in which the temperatures of urban areas are considerably higher than those of suburban or rural regions (Oke, 1995;Zhao et al., 2017). This phenomenon is ubiquitous because it is found in cities of all climatic regions, including cities in high-altitude regions or with colder seasons (for example, Harbin, in Northeast China). ...
... Moreover, in high-density built-up areas, the temperature is discovered to be significantly higher than that in rural areas, open green areas, and forest areas (Bozdogan Sert et al., 2021;Cetin, 2019Cetin, , 2020aCetin et al., 2019;Zeren Cetin & Sevik, 2020;. As the urban heat island rises, it adds significantly to climate warming and intense heat waves (Huang & Lu, 2015;Neethu & Ramesh, 2022;Tewari et al., 2019;Wang & Li, 2021;Zhou & Shepherd, 2010) and negatively impacts air quality, loss of biological control, water resources, urban thermal environment, increases energy consumption, outdoor thermal comfort, and human health and wellbeing (Adiguzel et al., 2022;Cetin, 2015Cetin, , 2016Cetin et al., 2018Cetin et al., , 2019Liu et al., 2020;Ostad-Ali-Askar et al., 2018;Talebmorad et al., 2021;Zhao et al., 2017). UHIs affect the quality of life and the livability of cities (Adiguzel et al., 2020;Cetin, 2019Cetin, , 2020aZeren Cetin & Sevik, 2020;. ...
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At the local and regional climate scale, one of the most studied environmental issues is urban heat island (UHI). UHI is a thermal anomaly caused by temperature differences between urban and rural settings, which adds heat to the atmosphere and makes people feel uncomfortable. This study explores the influence of new land-cover data on UHI simulations using the high-resolution Weather Research and Forecasting (WRF) model coupled with the single-layer urban canopy model (SLUCM) in the city of Harbin. A comparison was performed between the new Tsinghua University (TU) land cover dataset with the default United States Geological Survey (USGS) and Moderate Resolution Imaging Spectroradiometer (MODIS) land cover datasets. The results of this study revealed that the new TU land cover data had better representation and more realistic land cover changes than the default datasets. The diurnal, seasonal, and long-term nighttime UHIs of air and surface temperatures were higher than the daytime UHIs for both downtown Harbin and the satellite towns. We discovered that coal-burning during winter had a significant influence on UHI in Harbin. Moreover, the results from our buffer revealed a rapid increase in the UHIs of satellite towns, thus revealing the need to focus on the effects of UHI in satellite towns in the future. Therefore, the timely updating of land cover datasets in the WRF model and implementing mitigation strategies will help improve the urban climatic comfort.
... Many studies revealed industrial areas as local thermal hot-spot areas, characterized by the highest daytime LST values (Roth et al., 1989;Stathopoulou et al., 2006;Geletič et al., 2016;Rashash Ali and Mohammed, 2016;Wang et al., 2016;Zhao et al., 2017;Mujabar and Rao, 2018;Rao et al., 2018;Almalki and Al-Namazi, 2019;Wang et al., 2019;Zhang et al., 2019;Dahiru and Hashim, 2020;Ejiagha et al., 2020;Portela et al., 2020;Wu et al., 2020;Choudhury et al., 2021). In addition, it was recently demonstrated that these thermal anomalies were exacerbated by some combinations of several urban features, such as the urban morphology, unfavorable surface thermal characteristics (e.g. ...
... The higher daytime LST of industrial areas was observed in many previous studies (Roth et al., 1989;Stathopoulou et al., 2006;Geletič et al., 2016;Rashash Ali and Mohammed, 2016;Wang et al., 2016;Zhao et al., 2017;Mujabar and Rao, 2018;Rao et al., 2018;Almalki and Al-Namazi, 2019;Wang et al., 2019;Zhang et al., 2019;Dahiru and Hashim, 2020;Ejiagha et al., 2020;Lemus-Canovas et al., 2020;Portela et al., 2020;Wu et al., 2020;Choudhury et al., 2021), clearly showing the impact that industrial sites have on the modification of the urban thermal pattern. Recent studies (Ghosh and Das, 2018;Morabito et al., 2018;Cao et al., 2020;Morabito et al., 2021) also investigated the potential LST-related thermal drivers by using a focal buffer approach ranging from about 50 and 300 m. ...
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... With the rapid development of urbanization and global warming, urban expansion in the past led to a dramatic change in the underlying surface (1,2). The problems of the urban thermal environment, such as the urban heat island (UHI) effect and extreme weather events, arose worldwide (3). Additionally, in recent years, the development pattern of smart growth made some metropolitan areas of high density and intensive, where the urban and environmental problems caused by high temperature also became more serious (4). ...
... The temperature of farmland is higher than that of green space. They also emphasized that although the water and the green space have a good cooling effect, the capacity is not obvious if their area is small (3). And some researchers are focusing on the cooling effect of a certain land cover or element, such as water (18), forests (19,20), and wetlands (21)(22)(23). ...
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With the rapid process of urbanization and global warming, many metropolises are vulnerable to high temperatures in summer, threatening the health of residents. However, green spaces can generate a cooling effect to mitigate the urban heat island effect in big cities. They can also help to improve the living quality and wellbeing of surrounding residents. In this paper, we utilized the radiative transfer equation algorithm, k-means clustering algorithm, big data crawling, and spatial analysis to quantify and map the spatial distribution, cooling capacity, and cooling contribution for surrounding communities of 1,157 green spaces within Beijing Fifth Ring Road, a typical metropolitan area. The findings showed that (1) the area proportion of the heat island in the study area is larger than that of the cooling island. Accounting for only about 30% area in the study area, the green spaces reduce the average land surface temperature by 1.32°C. (2) The spatial features of green space, such as area and shape complexity, have a significant influence on its cooling effect. (3) Four clusters of green spaces with specific spatial features and cooling capacity were identified. And there were differences among these clusters in green space cooling contribution for the surrounding communities. (4) The differences in green space cooling contribution also existed in different urban zones. Specifically, the middle zone performed significantly better than the inner and outer zones. (5) We furthered in finding that some green spaces with medium and high cooling contributions need to improve their cooling capacity soon, and some green spaces with low cooling contributions or no contributions have a good potential for constructing new communities in the future. Our study could help planners and government understand the current cooling condition of green spaces, to improve their cooling capacity, mitigate the urban heat island effect, and create a comfortable and healthy thermal environment in summer.
... Scholars in many fields have shown keen concern about temperature and its influence. For example, they observed the urban heat island (UHI) phenomenon, He, Zhao [2] and Zhao, He [3] used the largest city in the northeast of China, Shenyang City, as a case to examined and analyzed environmental temperatures and land surface temperature (LST), and have reached some significant conclusions. To cease the further rise in global temperature, many countries have been working hard to reduce greenhouse gas emissions, including China. ...
... In Eq (4), lnLCP it as the explained variable represents the low-carbon innovation of provinces i in time t. And Eq (5) is to add lnLCI it to Eq (3). The mediation effect is tested by stepwise regression. ...
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As the largest carbon dioxide emitter, China is working towards the direction of a green economy. As an irreplaceable part of establishing a green economy, the low-carbon city pilot (LCCP) policy is implemented in many large cities in China, and the scope of implementation will be further expanded. However, to date, there has been an absence of empirical studies basing on prefecture-level cities about the evaluation of China’s LCCP policy. Evaluating and optimizing the LCCP policy is constructive to achieve the goal of China’s green economic transition. In this paper, we evaluated the effect of the LCCP policy on China’s low-carbon economic transition by using the difference-in-difference (DID) approach which can effectively alleviate endogenous problems and better evaluate this effect and the panel data of 210 prefecture-level cities in China from 2008 to 2016. The empirical analysis revealed that the LCCP policy inhibited China’s low-carbon economic transition in general. Specifically, the policy worked well in the eastern region but failed in the central region and western region by studying the regional heterogeneity and influence mechanism. The reason is that the LCCP policy can stimulate low-carbon innovation with the help of innovation offset effects in the eastern region, but it failed to do so in the central region and western region. In addition, this paper analyzed the performance of three types of policy tools adopted by local governments to implement the policy, we found that market-economic tools are valuable to improving the low-carbon economic transition in pilot areas, but command-mandatory tools and voluntary tools have failed to achieve the expected objectives. The research results of this article can provide policy recommendations for optimizing the low-carbon policy and provide a reference for countries that are determined to develop a green economy.
... In the last stage, the evaluations that could be observed over the maps were verified with the help of statistical analyses. It is seen that correlation analysis and regression models are used in many scientific studies investigating the effects of different land use/cover types on surface temperatures (He et al., 2019;Tran et al., 2017;Chen & Zhang, 2017;Estoque & Murayama, 2017;Zhao et al., 2017;Sun et al., 2012a). ...
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In this study, the climatic interactions between parks and the urban pattern surrounding them were investigated by sampling from two urban parks which are located in the Kadıköy district of Istanbul. Parks are very close to each other but their surrounding areas are different from each other in terms of the building density and the urban pattern. The analyses were carried out by examining the relationships between surface temperatures, distances to the park, and zones. Surface temperature/park distance correlations were examined in different zones by the bivariate method at a 0.01 significance level, and it was observed that the parks displayed different correlations in different pattern types and again each pattern type exhibited different graphics in temperature/distance scatter plots. When the results were evaluated from a climatic perspective, it was seen that a street with trees was more effective than a park area that has a weak plantation. On the other hand, the results also showed that the building geometries can cause the formation of cool islands. So, the study revealed that street planting and urban design are as important as the landscape design of park areas in terms of the cooling of the city.
... Faced with the intensified SUHI effect, scholars also paid attention to its driving forces. According to previous studies, the primary potential drivers of SUHI could be divided into several groups, including climate background [28], [29], urban area size and configuration [30]- [32], land use type [33], [34], heat release emission, and so on [35]. However, some of them are still under dispute [36]. ...
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The urban heat island (UHI) phenomenon, arising from rapid urbanization, has become a crucial research topic across various fields due to its adverse impacts on the ecological environment and human well-being. This study investigated the spatiotemporal patterns of summer UHI from 2001 to 2018 in Beijing-Tianjin-Hebei (BTH) urban agglomeration, and also examined the influence of natural and social factors on summer UHI by using the spatial regression model and ordinary regression model. We find that the mean summer UHI intensity in August was the highest at 0.76, followed by July and June (0.57 and 0.08, respectively). The results of spatiotemporal trend analysis reveal that the summer UHI of more than one-third of research districts and counties (68 of 200) have the significant increasing trends. The largest significant increasing trend was observed in Dongli District, Tianjin (0.17/year). Meanwhile, the summer UHI exhibited an apparent spatial pattern. Most of the high UHIs were dispersedly located in the southeast plain area, while low UHIs were mainly congregated in the northwest mountain area. For the relationships between summer UHI and influencing factors, different models have different the goodness of fit. Compared with the ordinary regression model, the spatial regression model performed better. And the optimal model indicated that the proportion of impervious surface and average temperature should take lead role for the summer UHI. The findings are of great help for understanding the features of summer UHI dynamic and provide a theoretical basis for optimizing urban agglomeration planning.
... The overall terrain has a trend of sloping from northeast to southwest and both sides to the middle. There is a temperate semi-humid continental climate with four distinct seasons [47]. By the end of 2019, Shenyang had a permanent resident population of 8.322 million and an urbanization rate of 81%. ...
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For megacities, they are in a period of transformation from extensive development to smart growth. Recognizing new characteristics and new changes of the residential space in megacities under the backdrop of new development has great practical significance for realizing the sustainable development of the city. As the only megacity in Northeast China, Shenyang was selected to be the research object, with 1989–2018 as the research period. The research comprehensively used multiple spatial representation methods and statistical methods to study the residential space pattern and driving factors in Shenyang City. The results showed that: (1) Residential space expansion can be divided into four stages: slow development, rapid expansion, speedy expansion, and stable extension. (2) The residential space structure presented a spatial evolution characteristic of overall expansion, forming multiple secondary core density centers. The east-west direction had a larger extension range than the northeast-southwest direction. There was an axisymmetric zonal distribution on both sides of the Hun River. (3) The agglomeration of different residential forms was obvious, and the spatial heterogeneity was increasingly stronger. (4) Urban planning measures and economic strength were the main driving forces of residential space expansion.
... On the one hand, changes in land use/land cover and rapid urban expansion inevitably transform a large amount of ecological land, such as forests, grasslands and waters, into construction land, resulting in the fragmentation of habitats that are suitable for biological survival, which not only affects the changes in ecosystem structure but also influences the content of greenhouse gases in the atmosphere and changes the regional atmospheric chemistry [2]. Then, this destroys ecosystem climate regulation and other service functions, leading to a series of climate change problems, such as ozone layer holes, glacier melting and frequent extreme weather, and the sustainable development of cities is threatened and challenged [3]. On the other hand, climate change, such as global temperature increases, also leads to the degradation of ecosystem functions and habitat fragmentation, thus affecting the quality of ecosystem habitats [4]. ...
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... In this study, popular Landsat 8 remote sensing images were used to retrieve the LST to characterize the thermal environment. Buffer analysis has proven to be applicable for studying thermal environments [45,46]. However, the different choices of width and interval of buffers could affect and add more uncertainty to the results. ...
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Urban parks have been proven to cool the surrounding environment, and can thus mitigate the urban heat island to an extent by forming a park cooling island. However, a comprehensive understanding of the mechanism of park cooling islands is still required. Therefore, we studied 32 urban parks in Jinan, China and proposed absolute and relative indicators to depict the detailed features of the park cooling island. High-spatial-resolution GF-2 images were used to obtain the land cover of parks, and Landsat 8 TIR images were used to examine the thermal environment by applying buffer analysis. Linear statistical models were developed to explore the relationships between park characteristics and the park cooling island. The results showed that the average land surface temperature (LST) of urban parks was approximately 3.6 °C lower than that of the study area, with the largest temperature difference of 7.84 °C occurring during summer daytime, while the average park cooling area was approximately 120.68 ha. The park cooling island could be classified into four categories—regular, declined, increased, and others—based on the changing features of the surrounding LSTs. Park area (PA), park perimeter (PP), water area proportion (WAP), and park shape index (PSI) were significantly negatively correlated with the park LST. We also found that WAP, PP, and greenness (characterized by the normalized difference vegetation index (NDVI)) were three important factors that determined the park cooling island. However, the relationship between PA and the park cooling island was complex, as the results indicated that only parks larger than a threshold size (20 ha in our study) would provide a larger cooling effect with the increase in park size. In this case, increasing the NDVI of the parks by planting more vegetation would be a more sustainable and effective solution to form a stronger park cooling island.
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Urbanization has become a hot issue in context of environmental and socio-political scenarios which is being addressed at every forum internationally. The classification results of Islamabad showed that the total area of Islamabad was about 899.56 sq.km. The area wise percentages of various landuse features remained very diverse in the period from 2000 to 2020. The area of barren land was highest in the year 2000 that was around 63 % of total LULC. Barren land faced major shift and the area reduced from 63 % to 40% in 2020. The built-up area has increased rapidly over the course of 20 years which was 4% in 2000 and increased up to 36% in 2020, which is alarming for any country. This study reveals that the settlement area has increased by 54 percent between 2000 and 2020. The geological map of study site is showing that Islamabad is located on the fault lines which are dangerous and the earthquake may hit this area any time which leads to huge disaster in coming future therefore this region is unsuitable for megastructures even for setting up small localities. The drainage network is showing that most of water channels were found in SE direction clearly narrating that the trend of population was also in the same direction. This resemblance signifies that people rush toward water channels to manage their daily routine in a better way. Slope map is showing that there were gentle slopes in southern parts of study site while steep slopes were observed in extreme north. Steep slopes are considered unsuitable for living therefore about 99% of urban settlements were found toward gentle slopes. Hazard map is showing that the NE of study site is dangerous for human settlements because this area was found prone to earthquakes. Luckily most of urban settlements were not found on this site and it is
... LST deals with the urban heat island effect and it changes significantly in an urban area (Hao, Li, and Deng 2016;Tran et al. 2017). Various land features influence LST differently (Shigeto 1994;Estoque, Murayama, and Myint 2017;Zhao et al. 2017;Mahato and Pal 2018;Mushore et al. 2019). Land conversion process changes the intensity of LST (Wen et al. 2017;. ...
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Land surface temperature (LST) is a significant component of the ecological health of any city and the LST is closely related to the normalized difference vegetation index (NDVI). The present study evaluates the seasonal variability of the relationship of LST with NDVI by using a large dataset of Landsat sensors for different seasons from 1991-92 to 2018-19. Pearson’s correlation coefficient technique was used to obtain the LST-NDVI relationship. The study also compares the ecological and thermal status of the city by applying the urban thermal field variance index (UTFVI). The results found that the mean LST increased considerably. The post-monsoon season produces the best correlation (-0.59), followed by the monsoon season (-0.53), pre-monsoon season (-0.45), and winter season (-0.22). Apart from this relationship, the ecological status of the city has also been estimated. Almost an equal portion of lands are under the excellent and worst categories of ecological condition. This study is beneficial for future ecological planning in any tropical city.
... All of the transect profiles demonstrate a pattern of lower temperatures near water and vegetation surfaces, increasing sharply along built-up and barren land sections. The temperature variations found in each of the land covers studied are similar to those found in other studies (Zi-Qi et al., 2017;Magidi and Ahmed, 2020). It further reveals that impervious surfaces had a greater surface temperature than non-impervious /water surfaces. ...
... The results indicate that trees, grasslands, and water bodies can effectively abate surface temperatures, which is consistent with previous studies. Armson et al. (2012) show that grass can reduce the maximum surface temperature by up to 24°C, and Zhao et al. (2017) report that green land and water bodies play a significant role in mitigating the UHI. This is so because urban green spaces have usually high thermal inertia and capacity, low thermal conductivity and radiance, and absorb less heat than impervious surfaces (Du et al. 2016). ...
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As urban green spaces have significant cooling effects on the urban heat island (UHI), a precise understanding of these effects is necessary to devise precise greenspace strategies for abating the UHI. This paper explores the impacts of different greenspace (trees, grass, and water) patterns on the UHI in Beijing’s Olympic Area, using different grid cell sizes and spatial statistical models. Greenspace pattern metrics include percent cover, mean patch size (MPS), mean patch shape index (MSI), edge density (ED), and largest percent index (LPI). The results show that different greenspace metrics have varying effects on surface temperature. The spatial error model (SEM) turns out to be a good choice for estimating the relationship between Land Surface Temperature (LST) and the greenspace metrics. The regression coefficients of these metrics vary with grid cell size. Tree and grass edge densities have opposite effects, which suggest that trees should be planted in smaller clusters, whereas grass should be planted in larger and continuous patches in order to reach maximum LST cooling. The optimal grid cell size is in the [120–240 m] range. These findings can help urban planners mitigate the UHI in a city with limited green space availability.
... In this research, there is a link between how the changing land qualities are correlated to human activities and to which extent these changes occur. Land qualities refer to the ability of productiveness of land [30,31,32,33,34,35,36]. ...
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Urbanization has become a hot issue in context of environmental and socio-political scenarios which is being addressed at every forum internationally. The classification results of Islamabad showed that the total area of Islamabad was about 899.56 sq.km. The area wise percentages of various landuse features remained very diverse in the period from 2000 to 2020. The area of barren land was highest in the year 2000 that was around 63 % of total LULC. Barren land faced major shift and the area reduced from 63 % to 40% in 2020. The built-up area has increased rapidly over the course of 20 years which was 4% in 2000 and increased up to 36% in 2020, which is alarming for any country. This study reveals that the settlement area has increased by 54 percent between 2000 and 2020. The geological map of study site is showing that Islamabad is located on the fault lines which are dangerous and the earthquake may hit this area any time which leads to huge disaster in coming future therefore this region is unsuitable for megastructures even for setting up small localities. The drainage network is showing that most of water channels were found in SE direction clearly narrating that the trend of population was also in the same direction. This resemblance signifies that people rush toward water channels to manage their daily routine in a better way. Slope map is showing that there were gentle slopes in southern parts of study site while steep slopes were observed in extreme north. Steep slopes are considered unsuitable for living therefore about 99% of urban settlements were found toward gentle slopes. Hazard map is showing that the NE of study site is dangerous for human settlements because this area was found prone to earthquakes. Luckily most of urban settlements were not found on this site and it is recommended the land authority must not approve any new settlement/mega project on this area. It is recommended that government must take strict action on emergency footings to demarcate urban lands so that the agricultural lands must remain intact to save flora and fauna of the city and to get sustainable agricultural developments.
... To overcome this constraint, spatiotemporal fusion methods of remote sensing data have been developed to synthesize high spatial and temporal resolution images for monitoring the dynamic changes of land surface by fusing coarse resolution images and fine resolution images [9,10]. In the past decade, the synthetic high spatiotemporal resolution images have been widely used in vegetation phenology monitoring [9,11], urban surface temperatures [12][13][14], urbanization [15], crop yield estimating [16,17], and monitoring sudden and short-term change events (e.g., flood) [18]. ...
... With respect to the combined analysis of the different land covers and LST estimated by Landsat, several cities, such as Shenyang, China [101] and Kuala Lumpur metropolitan area, Malaysia [102], show patterns similar to Wuhan, with built-up land displaying the highest temperatures among the land-use types, the lowest being recorded in the water area. As we all know, the specific heat capacity of water is higher than that of soil, which has a certain regulating effect on temperature. ...
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Rapid urbanization greatly alters land surface vegetation cover and heat distribution, leading to the development of the urban heat island (UHI) effect and seriously affecting the healthy development of cities and the comfort of living. As an indicator of urban health and livability, monitoring the distribution of land surface temperature (LST) and discovering its main impacting factors are receiving increasing attention in the effort to develop cities more sustainably. In this study, we analyzed the spatial distribution patterns of LST of the city of Wuhan, China, from 2013 to 2019. We detected hot and cold poles in four seasons through clustering and outlier analysis (based on Anselin local Moran’s I) of LST. Furthermore, we introduced the geographical detector model to quantify the impact of six physical and socio-economic factors, including the digital elevation model (DEM), index-based built-up index (IBI), modified normalized difference water index (MNDWI), normalized difference vegetation index (NDVI), population, and Gross Domestic Product (GDP) on the LST distribution of Wuhan. Finally, to identify the influence of land cover on temperature, the LST of croplands, woodlands, grasslands, and built-up areas was analyzed. The results showed that low temperatures are mainly distributed over water and woodland areas, followed by grasslands; high temperatures are mainly concentrated over built-up areas. The maximum temperature difference between land covers occurs in spring and summer, while this difference can be ignored in winter. MNDWI, IBI, and NDVI are the key driving factors of the thermal values change in Wuhan, especially of their interaction. We found that the temperature of water area and urban green space (woodlands and grasslands) tends to be 5.4 °C and 2.6 °C lower than that of built-up areas. Our research results can contribute to the urban planning and urban greening of Wuhan and promote the healthy and sustainable development of the city.
... In all land types, surface temperature follows the rule of building > roads > bare land > agricultural land > green space > water bodies. The expansion of urban impervious surfaces has an important impact on the generation of urban heat island effects (Zhao et al. 2017). At the same time, the expansion of impervious surfaces also has a serious impact on the aggravation of PM2.5pollution. ...
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Lake surface water temperature (LSWT) is an important factor affecting a lake’s ecological environment. In recent decades, LSWT worldwide has shown an increasing trend in the context of global climate change. This rising trend has been more evident in urban lakes. With the rapid development of urbanization, urban lakes are affected not only by climate warming but also by human activities. Among these factors, due to the increase in impervious surface coverage (ISC), the impact of thermal runoff pollution caused by precipitation events on urban lakes cannot be ignored. Therefore, this study used the Dianchi Lake watershed as a study area, and the surface water temperature of Dianchi Lake, the precipitation data, and the land use data were collected and analyzed. Based on these data, the influence of precipitation events on the surface water temperature of Dianchi Lake was analyzed. The research results show that under the background of different ISC levels and different growth rates of impervious surface area (ISA), precipitation events have different effects on the LSWT. When ISC is low and the growth rate of ISA is slow, the annual precipitation is negatively correlated with the annual average surface water temperature of Dianchi Lake (r = − 0.183). When ISC is high and the growth rate of ISA is fast, the annual precipitation is positively correlated with the average annual surface water temperature of Dianchi Lake (r = 0.65). With the increase in ISC, the correlation between seasonal precipitation and the average surface water temperature in Dianchi Lake changed from negative to positive in spring and autumn. Under the action of impervious surfaces, precipitation events have a warming effect on the surface water temperature of the lake, and this effect will be intensified with the increase in ISC.
... Luke Howard introduced the phenomenon of UHI in 1833, and it is mainly caused by the land surface manipulations generated by the impervious surfaces (Voogt and Oke 2003;Kolokotroni et al. 2006). Numerous studies have been carried out on UHI phenomenon which verified its close relationship with land use land cover (LULC) and LST (Sabet Sarvestani et al. 2011;Stewart and Oke 2012;Asgarian et al. 2015;Bokaie et al. 2016;Santamouris et al. 2017;Zhao et al. 2017;Bozorgi et al. 2018;He 2018;He et al. 2019). ...
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The present study aims at investigating the impact of land cover features in enhancing or mitigating Land Surface Temperature (LST) in a semi-arid tropical metropolitan city of Bengaluru, India. Spatial distribution of LST and land cover types of the area were examined in the circumferential direction, and the contribution of land cover classes on LST was studied over 28 years. Urban growth and LST were modelled using Landsat and MODIS data for the years 1989, 2001, 2005 and 2017 based on the concentric ring approach. The study provides an efficient methodology for modelling and parameterisation of LST and urban growth by fitting an inverse S-curve into urban density (UD) and mean LST data. In addition, multiple linear regression models which could effectively predict the LST distribution based on land cover types were developed for both day and night time. Based on the analysis of remotely sensed data for LST, it is observed that over the years, urban core area has increased circumferentially from 5 to 10 km, and the urban growth has spread towards outskirts beyond 15 km from the city centre. As urban expansion occurs, the area under the study experiences an expansive cooling effect during day time; at night, an expansive heating effect is experienced in accordance with the growth in UD in the suburban area and outskirts. The regression models that were developed have relatively high accuracy with R² value of more than 0.94 and could explain the relationship between LST and land cover types. The study also revealed that there exists a negative correlation between urban, vegetation, water body and LST during day time while a positive correlation is observed during night. Thus, this study could assist urban planners and policymakers in understanding the scientific basis for urban heating effect and predict LST for the future development for implementing green infrastructure. The proposed methodology could be applied to other urban areas for quantifying the distribution of LST and different land cover types and their interrelationships.
... LST can change significantly in a vast homogeneous land surface or even inside a relatively small heterogeneous urban area [14,24,25]. Different types of LULC response differently in TIR band and consequently LST largely varies in an urban environment [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44]. The LULC types are mainly changed by land conversion process [10]. ...
... LST can change significantly in a vast homogeneous land surface or even inside a relatively small heterogeneous urban area [14,24,25]. Different types of LULC response differently in TIR band and consequently LST largely varies in an urban environment [26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44]. The LULC types are mainly changed by land conversion process [10]. ...
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Land surface temperature (LST) and its relationship with normalized difference vegetation index (NDVI) are significantly considered in environmental study. The aim of this study was to retrieve the LST of Raipur City of tropical India and to explore its seasonal relationships with NDVI. Landsat images of four specific seasons for three particular years with fourteen years time interval were analyzed. The result showed a gradual rising (3.63 °C during 1991–2004 and 1.54 °C during 2004–2018) of LST during the whole period of study. The mean LST value of three particular years was the lowest (27.21 °C) on green vegetation and the highest (29.81 °C) on bare land and built-up areas. The spatial distribution of NDVI and LST reflects an inverse relationship. The best (− 0.63) and the least (− 0.17) correlation were noticed in the post-monsoon and winter seasons, respectively, whereas a moderate (− 0.45) correlation were found both in the monsoon and pre-monsoon seasons. This LST-NDVI correlation was strong negative (− 0.51) on vegetation surface, moderate positive on water bodies (0.45), and weak positive on the built-up area and bare land (0.14). In summary, the LST is greatly controlled by surface characteristics. This study can be used as a reference for land use and environmental planning in a tropical city.
... All of the transect profiles demonstrate a pattern of lower temperatures near water and vegetation surfaces, increasing sharply along built-up and barren land sections. The temperature variations found in each of the land covers studied are similar to those found in other studies (Zi-Qi et al., 2017;Magidi and Ahmed, 2020). It further reveals that impervious surfaces had a greater surface temperature than non-impervious /water surfaces. ...
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The land surfaces of hot-humid tropical urban areas are exposed to significant levels of solar radiation. Increased heat gain adds to different land surface temperature profiles in cities, resulting in different thermal discomfort thresholds. Using multi-temporal (1986, 2001, and 2017) landsat data, this study examined the impact of land use change on urban temperature profiles in Umuahia, Nigeria. The findings revealed that over time, built-up regions grow in surface area and temperature at the expense of other land use. The transfer matrix, showed that approximately 59.88 percent of vegetation and 8.23 percent of bareland were respectively changed into built up during the course of 31 years. The highest annual mean temperature in built-up regions was 21.50°C in 1986, 22.20°C in 2001, and 26.01°C in 2017. Transect profiles across the landuses reveals that surface Temperature rises slowly around water/vegetation and quickly over built-up and bare land area. The study observed drastic changes in land cover with a corresponding increase in surface temperature for the period between 1986 and 2017 with consistent decrease in water bodies and bare land in the study area. Overall, the spatio-temporal distribution of surface temperature in densely built up areas was higher than the adjacent rural surroundings, which is evidence of Urban Heat Island. The impact of landuse change on urban surface temperature profiles could provide detailed data to planners and decision makers in evaluating thermal comfort levels and other risk considerations in the study area.
... All of the transect profiles demonstrate a pattern of lower temperatures near water and vegetation surfaces, increasing sharply along built-up and barren land sections. The temperature variations found in each of the land covers studied are similar to those found in other studies (Zi-Qi et al., 2017;Magidi and Ahmed, 2020). It further reveals that impervious surfaces had a greater surface temperature than non-impervious /water surfaces. ...
... Though it is known that both composition and configuration of LULC influence LST over space and time; however, researchers have made significant efforts to quantify the impact of composition. For example, Zhao et al. [12] analyzed the profile and concentric zonal relationship using Landsat images in Shenyang, China. For profile zonation, they created a circle with a radius of 14 km from the city center and divided the circle into eight equal zones for analyzing association between LULC composition and LST. ...
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The spatial composition and configuration of land use land cover (LULC) in the urban landscape impact the land surface temperature (LST). In this study, we assessed such impacts at the neighbourhood level of the City of Edmonton. In doing so, we employed Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensors (TIRS) satellite images to derive LULC and LST maps, respectively. We used three classification methods, such as ISODATA, random forest, and indices-based, for mapping LULC classes including built-up, water, and green. We obtained the highest overall accuracy of 98.53 and 97.90% with a kappa value of 0.96 and 0.92 in the indices-based method for the 2018 and 2015 LULC maps, respectively. Besides, we estimated the LST map from the brightness temperature using a single-channel algorithm. Our analysis showed that the highest contributors to LST were the industrial (303.51 K in 2018 and 295.99 K in 2015) and residential (303.47 K in 2018 and 296.56 K in 2015) neighbourhoods, and the lowest contributor was the riverine/creek (298.77 K in 2018 and 292.89 K in 2015) during the 2018 late summer and 2015 early spring seasons. We also found that the residential neighbourhoods exhibited higher LST in comparison with the industrial with the same LULC composition. The result was also supported by our surface albedo analysis, where industrial and residential neighbourhoods were giving higher and lower albedo values, respectively. This indicated that the rooftop materials played further role in impacting the LST. In addition, our spatial autocorrelation (local Moran’s I) and proximity (near distance) analyses revealed that the structural configurations would additionally play an important role in contributing to the LST in the neighbourhoods. For example, the cluster pattern with a small gap of minimum 2.4 m between structures in the residential neighbourhoods were showing higher LST in compared with the sparse pattern, with large gaps between structures in the industrial areas. The wide passages for wind flow through the large gaps would be responsible for cooling the LST in the industrial neighbourhoods. The outcomes of this study would help planners in planning and designing urban neighbourhoods, and policymakers and stakeholders in developing strategies to balance surface energy and mitigate local warming.
... Land use/land cover heterogeneity (e.g. impervious surface and pervious surface; water, vegetation, agricultural land, building and road) contributes to LST spatiotemporal variation [87,88]. Urban built environment modification such as the increase of buildings and roads and the degradation of water bodies, trees and agricultural lands, enhances UHIs [89]. ...
Article
This paper reviews urban heat (UrHT) challenges following the SBAR (situation, background, assessment and recommendation) framework. The results indicate that heatwaves become more frequent, lasting and intense, especially after 1990s. Above 1960s level, heatwaves across China doubled in both magnitude and frequency by 2018. Jianghuai and Southern China underwent the largest magnitude and most widespread increases. Under 1.5°C warming limit, the average heatwave days and duration across China will increase by 10.8 days and 3.9 days. Drought–heatwave co–occurrence is increasingly frequent at 7–11%/decade (from 1961 to 2018) and the co–occurrence leads to more intense heatwaves. UHIs are a common issue for almost all Chinese cities and UHIs have been aggravating annually. Daytime UHIs peak in summer, indicating the synergies with heatwaves. The synergies are prominent in southeastern cities for strong summer daytime UHIs in eastern cities and intense heatwaves in southern regions. UrHTs have not been recognised and there are no dedicated/mandatory plans. Mega–challenges of climate change, rapid urbanisation, carbon– and labour–intensive economic growth and demographic changes can potentially lock China into UrHT challenges. Addressing UrHT challenges is urgent in China not only for environmental, ecosystem, social and health consequences, but also for economic impacts relevant to labour, capital, and goods or services. Efforts are suggested in technical improvement, policy formulation, social participation, economic investment and co-benefit approach recognition. Overall, this paper provides a comprehensive understanding of heat–related challenges in China and can guide the creation of cool cities and communities in practice.
... During the changing stage, large areas of dilapidated houses were demolished in the city to make way for new construction. At the same time, about half of the developed infrastructure and population was still concentrated in the core area, resulting in a high population density in the city center [78,79]. The construction of the new building in the old area made the heating ...
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Research on the impact of anthropogenic heat discharge in a thermal environment is significant in climate change research. Central heating is more common in the winter in Northeast China as an anthropogenic heat. This study investigates the impact of central heating on the thermal environment in Shenyang, Changchun, and Harbin based on multi-temporal land surface temperature retrieval from remote sensing. An equivalent heat island index method was proposed to overcome the problem of the method based on a single-phase image, which cannot evaluate all the central heating season changes. The method improves the comprehensiveness of a thermal environment evaluation by considering the long-term heat accumulation. The results indicated a significant increase in equivalent heat island areas at night with 22.1%, 17.3%, and 19.5% over Shenyang, Changchun, and Harbin. The increase was significantly positively correlated with the central heating supply (with an R-value of 0.89 for Shenyang, 0.93 for Changchun, and 0.86 for Harbin; p < 0.05). The impact of central heating has a more significant effect than the air temperature. The results provide a reference for future studies of urban thermal environment changes.
... Impervious surfaces are typically associated with anthropogenic urban land uses, and these surfaces prevent water infiltration into the soil and absorb heat from sunshine during the day before releasing it slowly at night [3,4]; examples of impervious surfaces include rooftops, parking lots, roads, driveways, and sidewalks [5]. Previous studies have shown that impervious surfaces have a significant impact on the structure and function of terrestrial ecosystems, biogeochemical cycles, and urban environments and can result in high surface runoff [6,7], air pollution [8][9][10], the transport of aquatic pollutants [11], water quality degradation [12], and urban heat island effects [13,14]. Therefore, impervious surfaces can be considered a key indicator of the urban environment [15]. ...
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Different urban growth patterns have various impact degrees on the urban ecosystem and environment. Impervious surface, a typical artificial construction can be used to reflect urban development. Therefore, this study estimated the spatiotemporal dynamics and expansion patterns of impervious surface area (ISA) in the Guangdong-Hong Kong-Macau (GHM) Bay Area since the establishment of the “Pearl River Delta economic zone” in 1994. Landsat time-series images were used to map the distribution of the ISA based on the combinational biophysical composition index (CBCI) and the bidirectional temporal filtering method (BTFM). The results indicated that the ISA in the GHM Bay Area drastically expanded from 569.23 km2 in 1994 to 10,200.53 km2 in 2016. In addition, the aggregation index (AI) value of the high-density area showed a decreasing trend from 1994 to 2004. However, the value of each landscape metric rapidly increased after 2004. Moreover, the mean ratio of the major axis to the minor axis of standard deviational ellipses from 1994 to 2004 was higher than that from 2005 to 2016. The results of landscape metrics and standard deviational ellipses indicated that the ISA growth pattern changed from edge expanding and leapfrogging to infilling and consolidation, with a turning point in 2004. Moreover, the principal sprawl orientation of the ISA was northwest to southeast before 2004. After 2004, the expansion direction of the ISA was less obvious due to the development pattern of infilling and consolidation. The rapid increase of GDP and population are the driving forces of urban expansion. However, topography and ecological protection policies as the limiting factors, which caused the infilling of the inner city and redevelopment of old urban areas.
... Urbanization has been changed a variety of physical and biological characteristics of the urban landscape, including vegetation cover, water bodies, soil properties, and micro (Kuang et al., 2015;Guha et al., 2018). Understanding the effect of urbanization on the urban environment is essential because sustainable urban expansion can only be realized if the link between urbanization and its environmental impact is well understood (Zhao et al., 2017). This work aims to measure and evaluate the spatio-temporal patterns of urbanization and urban biophysical components in Abha city from this viewpoint. ...
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Rapid urban land use and land cover changes have become a major environmental issue because of their ecological effects, including loss of green space and urban heat islands. Effective monitoring and management techniques are required. The Saudi Arabian twin city of Abha-Khamis Mushyet was selected as a case study for this research. As a result, the current study aimed to statistically and spatially investigate the relationship between land surface temperature (LST) and land use land cover based urban biophysical parameters such as normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), and normalized difference water index (NDWI). This study used random forest (RF) to classify LULC in 1990, 2000, and 2018. We also validated the LULC maps in a novel way. Using mono window algorithm techniques, we extracted LST for three periods. The dynamics of LULC, LST, and biophysical parameters were investigated using standard statistical graphs such as the heat map and the Sankey diagram. The correlation coefficient and the global bivariate Moran’ I approach were used to determine the association between LST and urban biophysical parameters. The relationship was then established in greater detail by categorizing the entire pixel into percentile classes and employing parallel coordinate plots. Finally, the association was built using GeoDA software and a conditional map. The LULC maps revealed a 334.4 percent increase in urban areas between 1990 and 2018. The built-up region is the largest stable LULC, with an 83.6 percent transitional probability matrix between 1990 and 2018. While 17.9%, 21.8%, 12.4%, and 10.5% of agricultural land, scrubland, exposed rocks, and water bodies were converted to built-up areas, respectively. The LST has increased rapidly over time because of LULC changes. The link between LST and urban biophysical parameters revealed that NDBI had a positive relationship, whereas NDWI and NDVI had a negative relationship. As a result, this study could be very important because it could help decision makers figure out how to lessen the effects of urban heat islands because of changes in LULC.
... The firs group focused on spatial patterns and temporal variations of SUHI (He et al., 2019;Hou et al., 2021;Liu et al., 2021;Madanian et al., 2018;Sun et al., 2021). The second group studied on drivers of SUHI, such as social-economic factors (Zhang et al., 2017), land use/land cover factors (Cai et al., 2018;Yang et al., 2021a;Yang et al., 2020a;Zhao et al., 2017) and urban morphology factors (Yang et al., 2021c). The third type is investigated SUHI mitigation strategies, such as contributions of urban greening (Yang et al., 2020b) and urban ventilation to SUHI (Yang et al., 2019a;Yang et al., 2021b). ...
Article
An improved understanding of urban heat island (UHI) is important in urban ecological environment studies. In this study, we investigated footprint (FP) and surface UHI (SUHI) intensity from 2003 to 2020 in 141 China cities using a Gaussian model and multi-source remote sensing data. Results showed that annual daytime FP in 79% cities was larger than nighttime FP. There were more cities having larger daytime FP than nighttime FP in summer compared to other seasons. With the increase of city size, FP showed an increasing tendency in median or small cities, while plateaued in large cities. We also found that high SUHI intensity occurred in cities with warm climate in daytime but occurred in cities with cold climate in nighttime. Moreover, SUHI intensity in daytime was higher than that of nighttime in all climatic zones in summer, while in other seasons, SUHI intensity in daytime was higher than that of nighttime only in warm climatic zones. Finally, 96% cities showed higher SUHI intensity in daytime than that of nighttime in summer, while proportion of such cities was only 45% in winter. Findings of this study are helpful for mitigating UHI effect in cities with different urbanized levels or climatic background.
... It can be observed that the average LST values for the dates studied were higher in the areas dominated by pasture (47.69 ± 1.47 • C), herbaceous Caatinga (47.28 ± 1.27 • C), and shrub Caatinga (46.07 ± 1.44 • C), even higher than those observed in areas with urban infrastructure (45.80 ± 1.52 • C) (Figure 8). According to Zhao et al. [110,111], sites with different land cover types may have an LST increase gradient along the urban to rural profile. The high LST in the pasture, herbaceous, and shrub Caatinga areas is related to the lower percentage of ground covering by vegetation (see Figure 6), which results in drier exposed soil, with higher albedos and lower evaporative cooling flux rates, a factor that increases LST. ...
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Caatinga biome, located in the Brazilian semi-arid region, is the most populous semi-arid region in the world, causing intensification in land degradation and loss of biodiversity over time. The main objective of this paper is to determine and analyze the changes in land cover and use, over time, on the biophysical parameters in the Caatinga biome in the semi-arid region of Brazil using remote sensing. Landsat-8 images were used, along with the Surface Energy Balance Algorithm for Land (SEBAL) in the Google Earth Engine platform, from 2013 to 2019, through spatiotemporal modeling of vegetation indices, i.e., leaf area index (LAI) and vegetation cover (VC). Moreover, land surface temperature (LST) and actual evapotranspiration (ETa) in Petrolina, the semi-arid region of Brazil, was used. The principal component analysis was used to select descriptive variables and multiple regression analysis to predict ETa. The results indicated significant effects of land use and land cover changes on energy balances over time. In 2013, 70.2% of the study area was composed of Caatinga, while the lowest percentages were identified in 2015 (67.8%) and 2017 (68.7%). Rainfall records in 2013 ranged from 270 to 480 mm, with values higher than 410 mm in 46.5% of the study area, concentrated in the northern part of the municipality. On the other hand, in 2017 the lowest annual rainfall values (from 200 to 340 mm) occurred. Low vegetation cover rate was observed by LAI and VC values, with a range of 0 to 25% vegetation cover in 52.3% of the area, which exposes the effects of the dry season on vegetation. The highest LST was mainly found in urban areas and/or exposed soil. In 2013, 40.5% of the region’s area had LST between 48.0 and 52.0 °C, raising ETa rates (~4.7 mm day−1). Our model has shown good outcomes in terms of accuracy and concordance (coefficient of determination = 0.98, root mean square error = 0.498, and Lin’s concordance correlation coefficient = 0.907). The significant increase in agricultural areas has resulted in the progressive reduction of the Caatinga biome. Therefore, mitigation and sustainable planning is vital to decrease the impacts of anthropic actions.
... Some localized hot and cool plots are intermixed in metropolises, and their difference in T a and LST is even greater than the urban-rural difference (Buyantuyev & Wu, 2010). Thus, on a neighborhood level, the imperative is threefold: to study the response range (radius) of a UHI based on urban features (Zhao et al. , 2017); to quantify the relationship between UHII and original or derivative indicators in urban planning; and further to discern a set of practical metrics to understand key drivers underlying the UHI effect. ...
Article
Urban heat islands (UHI) have strong spatiotemporal variations. Despite surface UHIs (SUHIs) with fine spatial distribution, UHIs have many advantages, more continuous temporal patterns, more relevant to people's feelings, and better thermal interaction between adjacent areas. Furthermore, the incomplete overlap between SUHI and UHI patterns within a city likely causes inconsistent research results and even influences urban-planning decisions. This paper conducted a six-day gridding field measurement of UHI intensity (UHII) in Guangzhou in autumn to comprehend its spatiotemporal pattern, to determine its response radius with urban-construction indicators and the accordingly magnitude of their interaction, and to recommend critical urban-construction indicators on a block scale. Results show that the hourly UHII at a given period cannot reflect the daily UHII. The optimal interpretation radius of UHII was up to 100 m, thus implying strong interactions within these buffer zones. The larger the block scale, the more consideration should be given to the influence of configuration of the internal landscape on UHII; the smaller the block scale, the more consideration should be on the surrounding landscape. Based on regression analysis, concrete coverage and tree coverage were the most vital urban-construction indicators on the block scale, followed by sky view factor, site coverage, and waterbody coverage.
... (1) In the study of the RHSs, international research hotspots initially focused on the conceptual and technical complexity [4], and later, the scholars conducted in-depth studies on the factors and principles of urban development [5][6][7][8]. In China, scholars emphasized the ecological and social views of scientific development of human settlements [9], then there was interdisciplinary and integrated development [10][11][12]; the research content covers index system, various reviews and theories [13][14][15], and studies range in size [12,[16][17][18], data material diversification [19][20][21][22], diversification of research methods [23][24][25][26][27][28][29][30][31][32], and so on; (2) Research on PHSs may favor the impact of the development of the Internet and information society [33][34][35][36]; some scholars have explored the pseudo space and behavior based on imagery tags [37], then the concept of PHSs and the theoretical framework of coupling coordination of "three states" human settlements are given [38], research data from traditional data to big data; the theoretical framework of coupling coordination among PHSs and RHSs is explored [39], which may lay a theoretical foundation for further perfecting the principle of action among PHSs and RHSs. ...
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Urban pseudo and reality human settlements (PHSs and RHSs) are important components of the human–land relationship regional system. To explore the coupling and coordination relationship and principle among them is an important approach to high-quality coordinated urban development. Based on the three-dimensional development of human settlements, the theoretical system of a “three states” dynamic frame diagram is constructed. The spatio-temporal heterogeneity and driving principle of coupling coordination among PHSs and RHSs in 34 prefecture-level cities in northeast China from 2011 to 2019 were explained by using the coupling coordination degree, spatial trend surface analysis and geographic detector techniques, and the evolution principle of spatio-temporal coordination was revealed. The results show that: (1) in the temporal dimension, the coupling coordination degree among PHSs and RHSs in the three provinces shows a smooth growth from “slight disadjustment” to “near disadjustment”. (2) With Shenyang, Dalian, Changchun and Harbin as the center, the coordination degree shows a circular pattern decreasing from the transition area to peripheral area. In the direction of south and north, the spatial evolution trend shows a gradual change from a “—” shape to “U” shape. There is spatio-temporal variation of the trend surface from an inverted “U” shape to “—” shape in the east–west direction. (3) The socioeconomic situation is an important driving factor, and the tool system is a new driving system for the coupling and coordinated development of urban PHSs and RHSs.
... For about three decades, from 1982 to 2009, the global leaf area index (LAI) trend was estimated at 0.068 ± 0.045 m 2 m −2 yr −1 (mean ± standard deviation, 1 sigma) [1]. This generally increasing trend since 1982 would have shifted urban energy balance and regulated land-atmosphere interactions, including sensible heat fluxes, evapotranspiration, carbon dioxide (CO 2 ) exchange between land and atmosphere, and other trace gases and aerosols [2][3][4]. Furthermore, the changes in LAI since the 1980s are likely to have affected land-surface boundary conditions and influenced the surface albedo, roughness, and even the dynamics of the terrestrial water cycle [5]. The modulated energy and water cycles caused by changes in LAI might partly have yielded changes in the terrestrial carbon balance [6,7] and atmospheric chemistry through emissions and deposition of CO 2 and other trace gases and aerosols [8]. ...
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China’s contribution to global greening is regulated by increasing atmospheric CO2 concentrations, climate change, and land use. Based on TRENDY project data, this study identified that the shifts in China’s contribution to the global leaf area index (LAI) trend strongly reduced during the warming hiatus, translating from 13.42 ± 26.45% during 1982–1998 into 7.91 ± 25.45% during 1999–2012. First, significant negative sensitivities of LAI to enhanced vapor pressure deficit (VPD), when only considering the climate effect derived from TRENDY models in China, were found to have shifted substantially after the late 1990s. However, globally, LAI had positive rather than negative responses to enhanced VPD. These opposing shifts in the response of LAI to enhanced VPD reduced the national contribution to global vegetation greening. Second, shifts in land-use change and their effects on the LAI trends in the two periods in China were accompanied by major changes in land cover and land management intensity, including forestry. Consequently, the contribution of land use in China reduced by −47.68% during the warming hiatus period, as compared with the warming period. Such a shift in the impact of land-use change on LAI simulated by ecosystem models might result from the models’ lack of consideration of conserving and expanding forests with the goal of mitigating climate change for China. Our results highlight the need for ecosystem models to reproduce the enhanced negative impact on global LAI and consider the shifts in man-made adaptation policies (e.g., forest management) to improve terrestrial ecosystem models in the future.
... Whether the form is reasonable or not will impact production and living, the environment, and the traffic of the city [8]. The correlation between urban form and commuting efficiency [9,10], surface temperature [11][12][13], air quality [14][15][16], and local urban and climate environment [17][18][19][20] has been widely studied. The international debate on urban form has gained an even higher importance since the 2020 pandemic outbreak, particularly with respect to emphasizing the right to city access, the development of the green city, and the proximity of resources that will change the urban form [21][22][23][24]. ...
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Enhancing urban vitality is a key goal for both the government and ordinary urban residents, and creating this vitality is emphasized in China’s urban development strategy. Enhancing urban vitality through the rational design of urban forms is a leading topic of Western urban research. An urban growth pattern (UGP) reflects the dual characteristics of a static pattern and the dynamic evolution of the external urban form. It affects urban vitality by influencing the spatial allocation of internal structural elements and patterns in the adjacent location. The cellular automata (CA) mode can effectively simulate the aggregation process of urban growth (infilling expansion or edge expansion). However, it does not simulate the diffusion of urban growth, specifically the evolution of outlying expansion. In addition, CA focuses on learning, simulating, and building knowledge about geographic processes, but does not spatially optimize collaborative land use against multiple objectives or model multi-scale land use. As such, this paper applies a coupling model called the “promoting urban vitality model,” based on cellular automata (CA) and genetic algorithm (GA) (abbreviated as UV-CAGA). UV-CAGA optimally allocates cells with different UGPs, creating a city form that promotes urban vitality. Wuhan, the largest city in Central China, was selected as a case study to simulate and optimize its urban morphology for 2025. The main findings were as follows. (1) The urban vitality of the optimized urban form scheme was 4.8% higher than the simulated natural expansion scheme. (2) Compared to 2015, after optimization, the simulated sizes of the newly increased outlying, edge, and infilling areas in 2025 were 6.51 km2, 102.69 km2, and 23.48 km2, respectively; these increases accounted for 4.90%, 77.32%, and 17.68%, respectively, of the newly increased construction land area. This indicated that Wuhan is expected to have a very compact urban form. (3) The infilling expansion type resulted in the highest average urban vitality level (0.215); the edge expansion type had the second highest level (0.206); outlying growth achieved the lowest vitality level (0.199). The UV-CAGA model proposed in this paper improves on existing geographical process simulation and spatial optimization models. The study successfully couples the “bottom-up” CA model and “top-down” genetic algorithm to generate dynamic urban form optimization simulations. This significantly improves upon traditional CA models, which do not simulate the “diffusion” process. At the same time, the spatial optimization framework of the genetic algorithm in the model also provides insights related to other effects related to urban form optimization, such as urban environmental security, commuting, and air pollution. The integration of related research is expected to enrich and improve urban planning tools and improve the topic’s scientific foundation.
... The relationship between urban landscape patterns, such as land use type and blue-green space [20], and surface temperature has received extensive attention worldwide. At present, researchers in the field of LST studies are focusing on the following aspects: (1) The impact of the two-dimensional urban landscape pattern on LST, analyzing the impact of different land use types on LST from the perspective of land use type and land use type transformation [1, 21]; (2) Statistical analysis of the mathematical relationship between surface factors and LST using indicators, such as normalized difference vegetation index, normalized difference moisture index, normalized difference built-up index, and building density (BD) [3,22,23]; (3) Correlation between the three-dimensional structure and LST based on indicators, such as building height (BH), floor area ratio (FAR), and sky view factor (SVF) [24][25][26]. It is worth noting that the process of urbanization involves a contradiction between population concentration and limited supply of construction land, which further leads to the rapid expansion of cities in the two-dimensional direction and the continuous increase of BH [27,28]. ...
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This study investigated the relationship between urban form and land surface temperature (LST) using the Multi-access Geographically Weighted Regression (MGWR) model. A case study on Nanjing City was conducted using building data, point-of-interest (POI) data, land use data, remote sensing data, and elevation data. The results show that the MGWR model can reveal the influence of altitude, urban green space, road, building height (BH), building density (BD) and POI on LST, with a superior fitting effect over the geographically weighted regression model. LST in Nanjing exhibits a significant spatial differentiation, and the distribution of LST hotspots is spatially consistent with the level of urban construction. In terms of the two-dimensional landscape pattern, LST decreases with altitude and increases with POI. In terms of the three-dimensional structure, building height has a positive correlation with LST. POI, urban roads, and urban buildings positively affect LST, while urban green space and altitude negatively affect LST. The results of this study were verified against existing findings. The LST of areas with high-rise and super high-rise buildings is lower than that of areas with mid-rise building, which can be attributed to the large number of shadow areas formed by high-rise and super high-rise buildings. A similar phenomenon was also observed between areas with medium- and high-density buildings. These findings provide a reference for urban architecture planning and can help to develop urban heat island adaptation strategies based on local conditions.
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The land surface temperature (LST) has been increased worldwide from time to time with the rapid increase of impervious surfaces such as built-up areas, parking lots, and asphalt and concrete roads. Several studies have examined the impacts of spatial dynamics of land use land cover (LULC) on the spatial variability of LST. However, there have not been systematic reviews conducted about the relationship between LULC and LST. Therefore, this study was conducted to investigate the relationship between LULC and LST with the main objective of synthesizing the relationship between LULC and LST using remote sensing data. An extensive literature search was conducted from the most familiar electronic databases such as Science Direct, Scopus, Web of Science, and Google Scholar between 27/08/2021 and 28/08/2021. The studies that are focussed on the relationship between LULC and LST and/or the impacts of LULC change on the LST using remote sensing were included for the analysis. Besides, papers conducted over the last 5 years (January/2016 to August/2021) were selected in this systematic study since this study focused on the most recent studies. In this systematic review, 100 studies were included for the study analysis. Based on the analysis of this study, built-up land has the first highest LST from the thirteen LULC types. Besides, bare land has a higher LST next to built-up land. On the other side, snow cover has the lowest LST among the LULC types. Lastly, waterbodies have a lower LST compared to vegetation cover.
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High-resolution automatic weather station data and remote sensing data were used to analyze the canopy urban heat island (UHI) effect in Kunming, a central city on a low-latitude plateau in China, and its relationship with land cover types, anthropogenic heat, and meteorological conditions. Results showed that the UHI intensity exponentially decreased with the distance from downtown regions. Furthermore, a forward stepwise regression algorithm was used to assess the contribution of spatial factors (e.g., land cover types and population density) and temporal factors (e.g., meteorological factors) to UHI changes. The contributions of spatial factors to the patterns of nighttime (daytime) UHI intensity reached 61.5% (32.6%). By contrast, the contributions of temporal factors to the temporal changes of nighttime (daytime) UHI varied considerably throughout the seasons, characterized by a maximum in autumn 79.70% (28.00%) and a minimum in spring 57.90% (12.00%). The temporal and spatial factors jointly determined the spatiotemporal patterns of the UHI effects. The temporal variation of spatial factors likewise affected the temporal variation of UHI intensity, whereas temporal factors did not change the spatial distribution of UHI intensity but aggravated its heterogeneity.
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Cities are becoming hotter due to global climate change and urban heat island intensification. This has resulted in an increased number of hospitalizations and deaths during heatwaves. Previous studies have reported a positive correlation between moderate to high sky view factor (SVF) values and land surface temperature (LST), but extremely low SVF situations that occur in urbanized residential areas have not been fully studied. This study investigated the relationship between SVF and summertime LST for urbanized residential areas ranging from very open to very closed considering external factors. Similar to previous studies, the results showed that low-rise detached housing was associated with a higher SVF and a higher LST than high-rise multifamily housing because the ground surface received more direct solar radiation. However, when the SVF was extremely low (less than 0.2) because of being surrounded by high-rise high-density flat-type apartments, this relationship was reversed due to the higher anthropogenic heat, lower ventilation performance, lower green infrastructure, and decreased longwave radiation even though daytime. This has major implications for the health and well-being of residents in high-density urban residential areas as they will receive a higher terrestrial radiation load than previously thought, a dangerous situation in the event of heatwaves. (200 Words)
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Urban ventilation conditions, which are significantly influenced by urban space forms, are essential for healthy and livable urban environments. In urban design practice, morphological indicators related to urban ventilation could be improved for the effective assessment of ventilation quality and timely adjustment of the design. This study attempted to explore local spatial morphological indicators related to ventilation performance. Considering the sky view factor (SVF) as a quantitative parameter of urban local spaces, this study investigated the correlation between the SVF and outdoor ventilation performance. The SVF was calculated using single and multipoint methods, and a wind velocity ratio <V*> was adopted as the spatial ventilation performance indicator. In addition, local spaces were defined using three different partition methods (GSP1, GSP2, and CSP), as the urban space is continuous and complex. A typical urban center space in Nanjing, China, was adopted as the research subject. The wind flows were calculated using the computational fluid dynamics (CFD) method for two wind directions. The results showed a positive correlation between the SVF and ventilation indices. Different partitioning methods had a considerable impact on the correlation, as the value of R² between SVF and <V*> could reach around 0.3 in GSP1 and CSP but only 0.05 in GSP2.
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Obtaining the time and location of sunlight reflection within a street environment is important to improve the comfort and safety of public spaces. This paper proposes a method to estimate the spatiotemporal distribution of sunlight reflection within a street based on street-view data. First, the solar path on the panorama is plotted to obtain the potential orientation of the radiation source and the reflection on building facades. Next, the continuous time–date distribution of direct and reflected sunlight at a certain site is obtained by the panoramic projection transformation approach. The accumulative duration and time-series parameters of the reflected scenario are estimated by a matrix operation. By mapping the time-specific duration of the sunlight reflection scene, we can obtain the spatiotemporal distribution within the street network. We applied the proposed method to a case study in a high-rise business district and quantified the range, duration, and intensity of reflected sunlight in the surrounding area. Seasonal changes, city morphology, and facade orientation were found to be the factors affecting the distribution tendency of reflected sunlight. The risk detection, visualization, and quantification analysis results on sunlight reflection can provide a reference for city management, urban planning, and environmental evaluation.
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Although many prior efforts found that road networks significantly affect landscape fragmentation, the spatially heterogeneous effects of road networks on urban ecoenvironments remain poorly understood. A new remote-sensing-based ecological index (RSEI) is proposed to calculate the ecoenvironmental quality, and a local model (geographically weighted regression, GWR) was applied to explore the spatial variations in the relationship between kernel density of roads (KDR) and ecoenvironmental quality and understand the coupling mechanism of road networks and ecoenvironments. The average effect of KDR on the variables of normalized difference vegetation index (NDVI), land surface moisture (LSM), and RSEI was negative, while it was positively associated with the soil index (SI), normalized differential build-up and bare soil index (NDBSI), index-based built-up index (IBI), and land surface temperature (LST). This study shows that rivers and the landscape pattern along rivers exacerbate the impact of road networks on urban ecoenvironments. Moreover, spatial variation in the relationship between road network and ecoenvironment is mainly controlled by the relationship of the road network with vegetation and bare soil. This research can help in better understanding the diversified relationships between road networks and ecoenvironments and offers guidance for urban planners to avoid or mitigate the negative impacts of roads on urban ecoenvironments.
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The Yangtze River Delta (YRD) urban agglomeration is one of the six major urban agglomerations in the world and is also one of the world's three most frequent high-temperature areas. The rapid urbanization process significantly affects meteorological air temperatures and relative humidity in urban and rural areas. Therefore, this study investigates the spatiotemporal evolution of heat waves (HWs) based on the wet-bulb temperature (TW) and urbanization effects. The results indicated that (1) the annual maximum TW exhibiting a significant decreasing trend is distributed in the Yangtze River valley and Lake Taihu Basin. The number of HW days showed a significant decrease during both 1980–1999 and 2000–2009. (2) The HW characteristics showed an insignificant decreasing trend in the Yangtze River valley, while it showed an insignificant increasing trend in the YRD. Further, the HW duration, intensity, and peak values showed significant upward trends in the coastal areas of the YRD urban agglomeration. (3) The proportion of HW duration increased from 28% in 1962–1990 to 55% in 1991–2019. The contribution of urbanization to the duration of the high-temperature HWs was 47.8%. Moreover, the impact of urbanization on HWs increased after 1990. (4) Although urban areas had a higher probability of extreme temperatures during 1991–2019, decreases in relative humidity have caused a decreasing trend of HW occurrence. Further, urban with low RH on the HW in urban areas were lower than those in rural and suburban areas. This research provides important evidence and recommendations for assessing the impact of heat waves related to urbanization.
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Basic education is about improving the quality of life of a country’s population and promote social cohesions, and it is also an important factor in shaping a country and region’s person-to-person relationship. This study analyzes the spatial morphological patterns, aggregation characteristics, and distribution inequality among kindergarten, elementary, and junior high schools within districts in Shanghai, using point of interest data, kernel density estimation, Ripley’s K-function, location quotient, and grid analysis to investigate the effect on the distribution of schools using construction land growth data. The findings were as follows. (1) There was little difference in the spatial distribution characteristics of the three school types. They all exhibited the spatial distribution characteristics of core area clustering and the coexistence of multiple circadian layers, in which both the agglomeration size and the aggregation intensity showed the order of kindergarten > elementary school > junior high schools. The spatial distribution characteristics of the three types of schools are highly positively correlated with the population distribution. (2) Spatially, low-level schools were adjacent to high-level schools, and the structure of the three school types showed an uneven distribution overall. The aggregation characteristics of the seven inner districts within Shanghai were relatively balanced, while Pudong District showed the phenomenon of being “high in the southeast and low in the northeast,” and the suburban areas showed an uneven distribution of core district aggregation overall. (3) The longer the construction land growth cycle, the greater the density of school points, and the more consistent the distribution of school points with the direction of construction land expansion.
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The concept of Local Climate Zone (LCZ) classification scheme is an effective tool for quantifying the Urban Heat Island (UHI) effect. However, previous studies of the thermal environment using LCZs mainly focused on the meso‑ to-micro-scale or a single time, and the changes in the regional thermal environment were less considered. Thus, we selected the Xi'an urban spatial agglomeration and used remote sensing images from 2008, 2013, and 2019 to determine the spatial and temporal variations in the thermal environment for statistical analysis and contrast. The results confirmed that: (1) The proportions of low-rise types decreased significantly, and the land use for agglomerations and compacts shows an upward trend. (2) The built-up LCZs had higher land surface temperatures (LST), LCZ 10 (heavy industry) was the highest in all years. (3) The LCZs with LCZ A (dense trees), and LCZ G (water) were associated with slightly lower LSTs, and they helped to cool the city. (4) The LSTs tended to increase from natural to urban areas, before gradually decreasing with distance from the city center to rural areas. These findings may provide reference values for quantitative studies of LCZ classification maps and analyzing dynamic changes in urban surface thermal environments, thereby facilitating the UHI analysis and climate-adapted urban planning.
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Now little attention has been paid to using field measurements to explore the coupling relationships between urban forms and the outdoor environment in China. A cold northern city of China was chosen as the research city. The coupling relationships of 30 points between the urban morphology and Outdoor Environment Performance (OEP) at the pedestrian level were analyzed at different scales. Then five different function blocks of education, commerce, industry, residence and parks were selected as the ENVI-met simulation models. The results indicate that an area of 500m range represents the suitable range to analyze the OEP, block size, building density, building height and green space are closely related. The simulation results indicate that air temperature, relative humidity and wind environment differ in land-use categories and urban forms. Urban forms significantly affect human health and comfort at the pedestrian level. This study proposes appropriate urban arrangements and building layouts to lessen the effects of UHIs in various land-use categories. This study contributes to architectural design and urban planning at the 500m range of the city block. It provides a scientific basis for formulating construction planning principles and revising urban construction codes.
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The study examines the farmers' satisfaction and happiness after the land sale for urban expansion of Gurugram, Haryana, India, under the "Development Plan 2031AD for Gurgaon-Manesar Urban Complex". Data was collected through a self-administrated survey questionnaire from 177 male farmers (168 valid) of sixteen villages whose land has been acquired or sold for urban expansion using convenience-cum-judgmental sampling technique. The data analysis was carried out using factor analysis and PLS-SEM (Partial Least Squares - Structural Equation Modeling). The study reveals that income source and spending pattern after the land sale, i.e., occupation and rental income, rise in spending on children education, household items, gadgets, car/sport utility vehicle, shopping, leisure, and social events positively and significantly affect farmers' satisfaction. The urbanization facets, i.e., urbanized lifestyle, nuclear families, basic facilities such as water, electricity, roads, and transportation, significantly affect farmers' satisfaction. The results also show that improved living status and compensation and its utilization for future financial enrichment after the land sale substantially influence farmers' satisfaction. On the contrary, social facilities, i.e., educational institutions, banking, hospitals, recreational facilities, and social costs, i.e., pollution and health concerns have no significant impact on farmers' satisfaction. Notably, this study finds that after the land sale, farmers' satisfaction significantly affects their happiness.
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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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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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