جستجوی مقالات مرتبط با کلیدواژه « Urban heat island » در نشریات گروه « جغرافیا »

Today, in many countries of the world, environmental issues are very important for researchers. In this field, the spatial expansion of urban heat islands is one of the most important environmental issues. Urban heat islands are formed due to the growth of human activities and the dominance of artificial environments on the natural environments. The main purpose of this research was spatial analysis and identification of urban heat islands in Rasht metropolitan. In this regard, four main indicators (Density of land use, Green Infrastructure, Transportation, and Urban density) were selected. Environmental studies, Google Earth software, the master plan of Rasht, and the questionnaire method were used to collect information. The pairwise comparison method was used to investigate the relative weight of the indicators. For data analysis, the Arc Map software used spatial analysis tools such as point density, line density, kernel density, interpolation, Euclidean distance, and Zonal Statistic model. Results showed that the transportation index with a coefficient of 0.44 was more important than other indices in the formation of urban heat islands. The index of the land use density with a comparative weight of 0.341 was the second most important index. The indicators of urban density and natural infrastructure were the third and fourth most important indicators. According to the results obtained from the overlaying of the indicators, the center of urban heat islands was identified in Bagharabad, Chelekhane and Bazar neighborhoods. Spatial analysis showed that the spatial expansion of urban heat island is towards the western neighborhoods of Rasht such as Pirsera and Ziabri. According to the results of the research, in 26% of the Rasht area, the possibility of the formation of urban heat islands was high and very high. Also, in 48 percent of the neighborhoods of Rasht city, in other words, 26 out of 55 neighborhoods of the city the intensity of urban heat islands was high and very high. Therefore, managers and city officials must pay more attention to this issue in planning for the future of Rasht City and adopt policies that reduce the negative effects and consequences of this issue.

This paper is a discussion of urban heat islands (UHIs), which unique residential areas are characterized by dense central cores surrounded by less dense peripheral lands. UHIs experience higher temperatures due to impermeable surfaces and specific land use patterns. These temperature variations have negative environmental and social impacts, leading to increased energy consumption, air pollution, and public health concerns. It emphasizes the need for simpler approaches to comprehend UHI temperature dynamics and explains how urban development patterns contribute to land surface temperature variation. The case study of Guilan Plain illustrates the relationship between development patterns and temperature, utilizing techniques like principal component analysis and generalized additive models.
This paper focuses on mapping land use and land surface temperature in the southwestern region of the Caspian Sea, specifically in the low-lying area of Guilan province. The research utilized satellite data from Landsat sensors for three different time periods: 2002, 2012, and 2021. A spatial unit known as a "city block" was employed through object-based analysis using eCognition software. Thermal bands from Landsat, such as TM band 6, ETM+ band 6, and TIR-1 band 10, were used to retrieve land surface temperature. The radiative transfer equation was used to calculate temperature, accounting for atmospheric and emissivity effects.
The study employed the normalized difference vegetation index (NDVI) method to estimate land surface radiance. The main focus of the study was to identify predictive variables for urban land surface temperature within the context of residential city blocks. These variables were categorized as intrinsic (related to the block's structure) and neighboring (related to adjacent blocks) variables. Intrinsic variables included block area, shape index, perimeter-to-area ratio, and central core index, calculated using Fragstats software. Neighboring variables encompassed metrics like shared boundary length, mother polygon area, number of neighboring blocks, average distance to neighboring block centers, average area of neighboring blocks, average shape index of neighboring blocks, and average central core index of neighboring blocks. Principal Component Analysis (PCA) was employed to select significant variables that captured the majority of data variance. Variables with eigenvalues greater than 1 in each principal component were considered significant contributors. Varimax rotation was applied to the PCA results to ensure accurate variable selection.
The study utilized a Generalized Additive Model (GAM) approach, implemented using the mgcv package in R, to model the relationship between urban land surface temperature and predictor variables. Smoothing parameters were estimated using a restricted maximum likelihood method. Model accuracy and interpretability were assessed using the coefficient of determination (R-squared) and the F-test analysis. the study's results include the generation of land use maps for three different time periods using object-based image analysis. Urban block characteristics were aligned with spectral units through density, shape, and scale coefficients. Over the years, the average block size showed variation, increasing from 61.19 hectares to 62.21 hectares. Urban expansion was observed across the years, with the urban area expanding from 9.5% to 11.1% of the region. Surface temperatures ranged from 22.84 to 26.26°C, with urban temperatures spanning 26.14 to 53.04°C. Independent variables were calculated for intrinsic and neighboring categories, with varying characteristics like block size, shape index, and perimeter-to-area ratio. Principal Component Analysis identified influential parameters, leading to the selection of block size, and shared boundary. the polygon area, and perimeter-to-area ratio as main variables for a generalized additive regression model. This model demonstrated non-linear relationships between these predictors and urban temperature. Block size, shared boundary, and mother polygon area exhibited a positive relationship with temperature, while the perimeter-to-area ratio displayed a negative trend. The model's performance was satisfactory, indicated by an R-squared value of 0.619.
The discussion focuses on the challenges and complexities of predicting urban surface temperature through studies on land use patterns. the current study concentrates on analyzing surface temperature within urban block units and categorizing variables into intrinsic and neighboring factors to enhance the understanding of the relationship between urban surface temperature and spatial distribution. Despite calculating urban surface temperature as a seasonal average across years, notable variations in temperatures were observed across different years. These variations are attributed to environmental conditions, climatic factors, and atmospheric influences that fluctuate over time. Consequently, the study aims to mitigate the impact of dynamic parameters by basing its models on cumulative temperature changes over various years. However, despite its reliability, this approach might lead to biased results when dealing with short-term time-series imagery.
The discussion also delves into the study's approach of focusing on spatial indices of urban units as predictive neighboring parameters. This choice stems from the fact that other units, particularly agricultural ones, experience significant changes over shorter periods, which can disrupt model calibration. Principal Component Analysis highlights the importance of block size as a key predictor of urban surface temperature, emphasizing the shift from polygon area to block size as a spatial scale. The study concludes that both block size and aggregation significantly influence urban temperature patterns. The Generalized Additive Model reveals that block size and mother polygon area exhibit a positive relationship with urban surface temperature, while the perimeter-to-area ratio displays an inverse correlation. This parameter indicates that units with smaller central cores and higher perimeter-to-area ratios experience cooler temperatures due to engagement with neighboring units, especially agricultural ones. In conclusion, the findings suggest that urban blocks function as distinct entities where temperature-related factors are influenced by intrinsic attributes like shape, as well as by the positioning of a unit relative to others.
The conclusion highlights the continuous growth of studies investigating the connection between land use patterns and urban surface temperature. Block size emerges as a central factor in determining urban surface temperature, alongside block dispersion and aggregation, which play crucial roles as predictors in residential areas. Additionally, the study emphasizes the importance of spatial configuration and unit structure in shaping urban temperature patterns. The proposed methodology has the potential to enhance understanding of parameter significance in shaping urban temperature patterns across various regions of Iran.

The purpose of this study is to reveal the physical expansion and spatial changes of heat islands in Minab city during three time periods (1975-1988, 1988-2000, and 2000-2014). For this purpose, TM and OLI images of Landsat satellite were  used. The physical changes of the city were obtained by analyzing the values extracted from the two indices of normalized difference built (NDBI) and normalized difference vegetation (NDVI). To find heat islands of the city, single-channel and separate-window algorithms  and to investigate the contribution of population growth and urban horizontal growth on changes in heat islands, Shannon and Holdren entropy models were  used. The results showed that the maximum amount of physical expansion of the city happened 901.61 hectares, in the years 1988 to 2000 of which 55.6 percent was based on population growth and 44.4 percent was based on the incrae of land per capita. The area of the heat islands was estimated to be 112.96 hectares, indicating the north-south and east-west trend, which has grown more than 4 times compared to the previous period. The share of population factor in this growth was 62.8 hectares and the share of urban sprawl was 50.16 hectares. The Shannon’s entropy value was 0.3, indicating an unrestricted growth in the considered period. In general, in the 39-year period, a 5 fold increase in the size, a 6.34 fold increase in the population and a 5.7 fold increase in the heat islands area were observed in the Minab city. Since no documentary report has been presented regarding the investigation of heat islands in Minab city, the results of the present research can be used to manage the concentration of the city points.

Alteration of urban forms and geometries due to rapid unplanned urban development can result in localized elevation of land surface temperature - a phenomenon known as urban heat island. This study examins the impact of land development patterns on land surface temperatures in a heterogeneous urban environment. As many as 18 Landsat satellite images for 1995, 2008 and 2021 (average cloudless images per year) were used in this sutdy. First, land development patterns were generated from the processing of Landsat satellite images in SAGA GIS using the method of Stewart and Oke. At the second stage, land surface temperatures for 1995, 2008, and 2021 were extracted using the single-channel algorithm. At the third stage, the relationship between mean value of land surface temperatures and the heterogeneous form of Tehran was analyzed. The results showed that among all LCZs, the highest mean value of land surface temperatures belonged to the Heavy Industry LCZ with the average temperatures of 5.32, 48.18, and 51.87°C for 1995, 2008, and 2021, followed by the Bare Soil/Sand LCZ with the average temperatures of 47.25, 49.25 and 52.36°C for the same years. The lowest mean value of land surface temperatures belonged to the Water LCZ with the average temperatures of 20.5, 22.63, and 23.15°C for 1995, 2008, and 2021, respectively. It was also found that the Compact Low-Rise LCZ had higher mean value of land surface temperatures than both Compact High-Rise and Compact Mid-Rise LCZs. The differences observed between the highest and lowest land surface temperatures in the study area are indicative of the significant impacts of urban form on land surface temperatures. The findings can contribute to our understanding of urban environments and help to devise better plans for minimizing the adverse effects of land use and development on these environments.

Urban heat island monitoring with remote sensing data is increasing and one of the most important reasons is to provide more spatial information of urban temperature than terrestrial data. The heat island resulting from this data is called the surface urban heat island. Different methods can show the intensity of Surface urban heat island in a city differently. Furthermore, consider of the temporal and spatial variations in temperature can cause error in calculating urban heat island. The relationship between factors such as vegetation index, land use, altitude and meteorological factors with urban heat island has been investigated and proven in previous researches. In this regard, predicting the land surface temperature in and around the cities to simulate the intensity of the urban heat island in the coming years has been of interest to researchers because reliable predictions of the difference between urban and rural areas are essential for planning about cities. Different cities may affected by different factors depending on the climate in which they are established. Therefore, in the study of the heat island of Tabriz and Urmia, land use is investigated as a determining factor. In addition, temporal variations in the area of Lake Urmia will be studied to assess the relationship between the extent changes of this water body and the intensity of surface urban heat island in Tabriz and Urmia.

In this study, MODIS land surface temperature data (MOD11A1) in tile No. h21, v06 has been used to investigate the urban heat island. This tile covers northwestern Iran. The common time series used in MODIS Terra and Aqua is from 2003 to 2019. Terra and Aqua each monitor the entire earth twice a day. In this study, all four observations have been used in order to evaluate the diurnal variations of the urban heat island. Second MODIS data that used in this study is the land cover type (MCD12Q1). To identify the types of land cover, the FAO Land Cover Classification System has been selected among the existing classification layers, which has been generated by applying the supervised classification method to the MODIS reflectance data. Another MODIS product has been used to study the changes in the area of Lake Urmia. This product provides a time series of the world's lakes extent, depth and reservoir variations. The data were obtained from the detection of water and land pixels using a machine-learning algorithm.Urban area and type of pervasive land cover around the city has been obtained using MODIS land cover type data. The pixels that belonged to a specific land cover in more than 75% of the study period (temporal frequency of land use species) were considered as the representative pixels of that land cover. To determine the urban and rural area, an area equal to the size of the urban extent around it has been selected as rural area. The land covers were examined among the rural pixels. The pixels that cover more than two thirds of the rural area have been identified (spatial frequency of land covers). The intensity of the heat island has been estimated according to each of the dominant land covers. Then, the intensity of surface urban heat island in relation to each of the land covers of rural district has been compared. This process also has been done once for the whole area of the rural pixels.

Evaluations of land cover type in rural area of Urmia and Tabriz cities showed that land cover type of cropland and natural herbaceous has the largest area with more than 75% of land cover frequency. The surface urban heat island in Tabriz has annual cycles. In the warm period of the year, the cropland shows a more intense heat island rather than natural herbaceous and all rural area. In addition, at this time of the year, the estimate of urban heat island in relation to the area of natural herbaceous in most cases indicates the cold heat island in Tabriz. These conditions are inverted during the cold period of the year and the urban heat island in cropland and natural herbaceous shows the cold and heat island, respectively. The intensity of the urban heat island of Urmia in the land cover of cropland and natural herbaceous is well separated and show completely different annual cycles, but its annual variations is the same as in Tabriz. The use of natural herbaceous as a rural land cover in Urmia shows a more severe cold island in the warm period of the year than Tabriz.The urban heat island of Urmia at night shows obvious differences compared to Tabriz. First, the annual in the Urmia heat island cycles are well seen, which indicates the increase of the night heat island in the warm period of the year and its decrease in the cold period. The second major difference is the urban heat island values related to different rural land cover type. The heat island of Urmia, although in smaller numbers, often shows more intensity than Tabriz. It may be due to the smaller size of Urmia city compared to Tabriz and its shorter distance from the lake that cause to more affect by water extent variation of the Urmia Lake. Because of this condition, the daytime urban heat island in Urmia occurred more frequently. In addition, there is a significant difference between urban heat island of rural land covers in Urmia. While this difference is less in Tabriz and less in nighttime than daytime.

Calculation of the surface urban heat island with MODIS data showed that in some cases, especially during the daytime cold island occurs in some parts of the two cities of Tabriz and Urmia. The calculated heat or cold island was determined by selected type of land covers in rural area. In addition, the selected type of land cover in rural area has a great effect on estimating the intensity of the urban heat island. Cropland as a rural area during the night shows more intense heat island than natural herbaceous while during the daytime the opposite condition was happened. The use of all type land covers as rural area shows the intensity of the heat island between cropland and natural herbaceous as rural area.Due to the large effect of heterogeneous surfaces on the measurement of surface temperature during the daytime, measurements at nighttime can provide the intensity of the urban heat island with better accuracy. In this regard, nighttime observations of MODIS land surface temperature, especially in Aqua, which is the closest observation to minimum temperatures, can be useful in monitoring the intensity of the urban heat island and its temporal-spatial changes, especially in warm period of the year.

Rapid urbanization in Tabriz has a significant impact on the urban thermal environment and these changes have affected the climate, environment and quality of life of residents. The aim of this study was to evaluate the urban heat islands (UHI) of Tabriz using spatial autocorrelation methods and its relationship with physical parameters of the surface. NDVI and Split Window algorithms based on TIRS and OLI Sensor of Landsat 8 satellites were used to calculate the vegetation index and land surface temperature; then the relationship between LULC, NDVI and Land Surface Temperature (LST) changes evaluated. Spatial autocorrelation methods Moran's I and Hot Spot were used to identify UHI. The results showed that in the metropolis of Tabriz, there is a significant inverse relationship between α = 0.05 between LST and NDVI. LST minimum and maximum 11.99 to 58.49 Celsius respectively in central and north-west of the city is obtained. Also, the assessment of land surface temperature with LULC has shown that the impervious surface along with the worn-out urban textures are the most important reasons for the intensification of Tabriz urban heat islands. The Moran's I space spatial autocorrelation method showed that the LST of Tabriz has a spatial structure or in other words has a cluster pattern and its value varies between 0.92 and 0.95. The urban heatislands of Tabriz are of the peripheral and triangular type, which increase the intensity and extent of urban heatislands from the center. The largest identified heatisland is located in District 6 of the city. Due to the existence of Tabriz Airport, as well as the existence of barren lands and worn-out urban textures, this area has the maximum land surface temperature (LST).

Urban heat islands in hot and dry climates have adverse effects on the environment and human health. In this study, a method has been proposed to investigate the factors affecting the heat islands of Iran's central plateau climate. In the first step, after applying geometric, radiometric, atmospheric corrections and preparing Landsat 8 satellite images, including OLI-TIRS sensors, Tasseled Cap transformation is created. In the second step, the surface temperature of the earth is extracted using Split window algorithm. In the third step, in order to evaluate the heat islands, the Urban Thermal Field Variance Index is classified into six levels. Finally, using the correlation coefficient between TCT and Urban Thermal Field Variance Index indicators, the relationship of heat islands with the desert, urban areas, vegetation, and humidity is evaluated. In order to evaluate the proposed method, the city of Qom has been studied. The results of the proposed method show that heat islands are inversely related to the amount of vegetation (-0.613), water and humidity (-0.535) and directly related to the amount of soil and desert areas (0.709). Examining the Urban Thermal Field Variance Index, it was shown that the rate of this index in the core of the studied city is less than the outskirts of the city which can be due to the expansion and dispersion of the city, insulation of the roofs of residential houses, increasing the density of vegetation in the suburbs, river crossing through the city center, the presence of barren areas, ring roads, factories and industrial towns in the suburbs cited. The results reveal that the proposed method is an efficient method to analyze the factors affecting the phenomenon of heat islands.

Increasing population growth and consequently the development of urban areas can profoundly affect climate events and thus intensify phenomena such as heat stress. Given the expected effects of this phenomenon on human health, it is very important to provide mitigating operational solutions to control future conditions. Therefore, the present study was conducted with the aim of simulating the effect of urban planning solutions on dynamic processes in the urban environment and at the local scale in Tehran city using the WRF mid-scale numerical model. Simulations were performed using 4 nested domains with a two-way interactive nesting procedure. The study used a simple Single-Layer Urban Canopy Model and a more advanced multi-layered approach called Multi‐layer urban canopy (BEP). The results of the simulations, after comparing the two urban schemes with a sensitivity measurement for different strategies, showed that the surface reflectance change scenario has the greatest impact on the land surface compared to the two scenarios of increasing urban green areas and reducing building density. Due to Tehranchr('39')s specific topographic location and high overall temperature in this region, Tehran is relatively vulnerable to heat stress. Compared to the intensity of 5.5 °C for base mode, applying control measures can reduce the intensity of UHI up to 3 °C when using bright colors with high reflectivity for the ceiling and 1 ° C by replacing impermeable surfaces with natural vegetation in urban areas of Tehran.

The population of Isfahan has increased ten times over the past six decades. The rapid urbanization of the historical city of Isfahan has had a great environmental impact. At the same time, drying of Zayandeh Rood, increasing the air temperature, decreasing rain has brought the city to a critical environmental situation. The emergence of the urban heat island is only one of the consequences of the environmental changes of the past decades. Urban heat island has consequences for the health of citizens and it affects the consumption of water and energy. In this study, MODIS Aqua/LST data was used for night and day from 1379 to 1395. By using this data, the background climate of the metropolis of Isfahan was identified with the distance-azimuth (DA) charts. Then the representative pixel within the city and the representative pixel of the background climate were identified. Based on the time series of LST over these two pixels SUHI index of Isfahan metropolis was calculated. Studies show that the Isfahan metropolitan area is colder than the suburbs during the day and it is warmer than its surroundings during the night. The magnitude of the SUHI is maximal in January and is weaker in the summer. Regarding the temporal and spatial behavior of the SUHI, it seems that the changes made by the city in humidity, albedo, and composition of the atmosphere have played an important role in the emergence of the SUHI. Zayandeh Rood has played a major role in modulating the land surface temperature in the metropolitan area of Isfahan, and its drying has had environmental consequences.

The Urban Heat Island (UHI) describes the temperature difference between urban and rural temperatures. Finding urban heat island mitigation strategies is of great importance, given expected influences on human health and air quality. This study presents numerical simulations over a summer period to investigate the impact of urban heat island control measures on Tehran urban air quality. The WRF-Chem Chemical Mesoscale Model is used to investigate the effect of increasing urban vegetation and highly reflective surfaces on the concentration of primary pollutants (CO, NO) as well as secondary pollutants (O3) in urban canyons. In order to account for the heterogeneity of urban areas, a multi-layered urban canopy model is coupled with WRF-Chem. Using this canopy model at its broad range requires introducing several urban user classes in WRF-Chem. Tehran metropolis is considered to simulate designed experiments in the summer of 2016. The selected reduction measures in the simulations are able to reduce the urban temperature by about 1-3 degrees Celsius and average daily ozone concentration by 5 to 10 percent. The modeling results also presented secondary negative effects on urban air quality, which is strongly related to the reduction of vertical mixing in the urban boundary layer. The simulation results show a 1 to 20% increase in the primary pollutants (NO and CO). Despite the daily average decrease in ozone concentration, highly reflective surfaces due to severe short-wavelength radiation that accelerates photochemical reactions can lead to an increase in the peak ozone concentration by up to 9% at noon hours.

Significant emission of heat from human activities and overheating of synthetic surfaces over natural surfaces leads to urban heat island formation. The average annual temperature in central areas of a large city is at least about 1-3 ° C above the surrounding area. On calm nights, city centers can experience temperatures as high as 12 ° C. In addition to the health problems caused by rising temperatures, the effect of increasing rates of photochemical reactions, which in turn worsens indoor air quality, is also of particular importance (Oke, 1982). Specific measures such as the use of green roofs or facades and materials with high reflectivity are able to reduce the intensity of the urban heat island. The purpose of this study is to use the WRF-Chem model, coupled with urban parameterization schemes, to investigate the dynamical and chemical processes when applying conventional reduction strategies. The study area is the urban area of ​​Tehran as one of the most polluted cities in Iran.

To show the three-dimensional structure of urban areas, the urban parameterization plan was used along with the Noha land surface model. In order to show the heterogeneity of urban land levels and to use urban modeling with its full expansion, the main urban class (1) in the WRF / Chem model was divided into 3 subclasses (31-33). The range of the model for the internal nest was 103 by 79 network cells with a horizontal resolution of 1.33 km. In order to identify morphological features for each class of Tehran urban area, the characteristics of roads and buildings (building height, street width, albedo level, vegetation, etc.) as well as thermodynamic properties and roughness characteristics within the model were updated. The simulation period was July 17 to July 23, 2016, a period of thermal stress in Tehran that could be considered as a special period for future weather conditions in Tehran. The basic mode (control) was simulated along with urban planning strategies such as increasing urban vegetation (park), increasing building surface whiteness (albedo), and changing building density (density) for further study.

During the study of simulation sensitivity, different meteorological and pollution parameters of the simulated air in the default mode were compared with the average observations of the three urban metering stations. The correlation between simulations and observations, except for CO, was greater than 0.5, and the model performed well in reproducing various parameters. Comparing the results of simulations with observational data, it can be stated that the model generally simulates the hourly changes of meteorological variables well, but more or less estimates the concentration of air pollutants during the simulation period (Grossman and Sobert and Clark, 2013). One reason for the comparison method is that simulation outputs are extracted at the beginning of each hour, while measurements are reported as average or average daily (Akbari et al., 2001). To investigate the effect of different reduction strategies, the effect of each strategy on the concentration of different pollutants was simulated. On average, the air temperature decreases by 3.37 degrees Celsius and 1.7 degrees Celsius, respectively, for the albedo and park scenario. In relation to the primary pollutants CO, SO2 or NOx, the positive effect of reduced temperature is reversed. This was particularly the case for a scenario in which the whiteness of the roof and walls of buildings increased (the relative increase in primary contaminants by up to 20%). An increase in ozone concentration of up to 9% for the Albido scenario was found around 1300 hours, which could be due to a further increase in ceiling and wall surface whitening from 0.2 to 0.7 (Takabayashi and Moriyama, 2007), which is 67% higher than the increase in use. It was done by two other studies (Taha, 2008 and 1997-A) and on the other hand, the maximum decrease in air temperature in Tehran urban area was 2.170 C, which is about 0.83 33 2.83 33 C lower than the decrease. The temperature was reported by Taha (2008).

Simulation experiments were designed and studied to evaluate urban thermal island control strategies that could have an adverse effect on urban air quality. The selected measures showed positive and negative effects on the concentration and dispersion of pollutants. Albido's strategies and urban vegetation are able to improve air quality, followed by a daily decrease in the average concentration of ozone. Also, lowering the temperature has a significant effect on the dynamic structure of the urban boundary layer. Reduction of turbulent kinetic energy (TKE) due to lower temperatures leads to a decrease in turbulence mixing rate and a decrease in the height of the mixing layer, which leads to higher surface concentrations of primary contaminants. This case study provides simulation for a city in certain climatic conditions, because for cities with different sizes, locations, population densities, emission conditions, or different meteorological conditions, similar actions may have different effects on air quality. In urban planning, the social effects of parks on increasing the well-being of citizens should also be considered

Surface temperature is considered to be a substantial factor in urban climatology. Italso affects internal air temperature of buildings, energy exchange, and consequently the comfort of city life. An Urban heat island (UHI) is an urban area with a significantly higher air temperature than its surrounding rural areas due to urbanization. Annual average air temperature of an urban area with a populationof almost one million can be one to three degreeshigher than its surrounding rural areas. This phenomenon can affect societies by increasing costs of air conditioning, air pollution, heat-related illnesses, greenhouse gas emissions and decreasing water quality. Today, more than fifty percent of the world’s population live in cities, and thus, urbanization has become a key factor in global warming. Tehran, the capital of Iran and one of the world’smegacities, is selected as the case study area of the present research. A megacity is usually defined as a residential area with a total population of more than ten million. We encountered significant surface heat island (SHI) effect in this area due to rapid urbanization progress and the fact that twenty percent of population in Iran are currently living in Tehran.SHI has been usually monitored and measured by in situ observations acquired from thermometer networks. Recently, observing and monitoring SHIs using thermal remote sensing technology and satellite datahave become possible. Satellite thermal imageries, especially those witha higher resolution, have the advantage of providing a repeatable dense grid of temperature data over an urban area, and even distinctive temperature data for individual buildings.Previous studies of land surface temperatures (LST) and thermal remote sensing of urban and rural areas have been primarily conducted using AVHRR or MODIS imageries.

Recently, most researchers use high resolution satellite imagery to monitor thermal anomalies in urban areas. The present study takes advantage of themost recentsatellite in the Landsat series (Landsat 8) to monitor SHI, and retrieve brightness temperatures and land use/cover types.Landsat 8 carries two kind of sensors: The Operational Land Imager (OLI) sensor has all former Landsat bands in addition of three new bands: a deep blue band for aerosol/coastal investigations (band 1), a shortwave infrared band for cirrus detection (band 9), and a Quality Assessment (AQ) band. The Thermal Infrared Sensor (TIRS) provides two high spatial resolution thirty-meter thermal bands (band 10 and 11). These sensors use corrected signal-to-noise ratio (SNR) radiometric performance quantized over a 12-bit dynamic range. Improved SNR performance results in a better determination of land cover type. Furthermore, Landsat 8 imageries incorporate two valuable thermal imagery bands with 10.9 µm and 12.0 µm wavelength. These two thermal bands improve estimation of SHI by incorporating split-window algorithms, and increase the probability of detectingSHI and urban climatemodification. Therefore, it is necessary to design and use new procedures to simultaneously (a) handle the two new high resolution thermal bands of Landsat 8 imageries and (b) incorporate satellite in situ measurement into precise estimation of SHI.Lately, quantitative algorithms written for urban thermal environment and their dependent factors have been studied. These include the relationship between UHI and land cover types, along with its corresponding regression model. The relation between various vegetation indices and the surface temperature was also modelled in similar works. The present paper employ a quantitative approach to detect the relationship between SHI and common land cover indices. It also seeks to select properland coverindices from indices like Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI), Normalized Difference Bareness Index (NDBaI), Normalized Difference Build-up Index (NDBI), Modified Normalized Difference Water Index (MNDWI), Bare soil Index (BI), Urban Index (UI), Index based Built up Index (IBI) and Enhanced Built up and Bareness Index (EBBI). Tasseled cap transformation (TCT) which is a method used for Landsat 8 imageries, compacts spectral data into a few bands related to thecharacteristics of physical scene with minimal information loss. The three TCT components, Brightness, Greenness and Wetness, are computed and incorporated to predict SHI effect.The main objectives of this research include developing a non-linear and kernel base analysis model for urban thermal environment area using support vector regression (SVR) method, and also comparing the proposed method with linear regression model (LRM) using a linear combination of incorporated land cover indices (features). The primary aim of this paper is to establish a framework for an optimal SHI using proper land cover indices form Landsat 8 imageries. In this regard, three scenarios were developed:  a) incorporating LRM with full feature set without any feature selection; b) incorporating SVR with full feature set without any feature selection; and c) incorporating genetically selected suitable features in SVR method (GA-SVR). Findings of the present study can improve the performance of SHI estimation methods in urban areas using Landsat 8 imageries with (a) an optimal land cover indices/feature space and (b) customized genetically selected SVR parameters.

The present study selects Tehran city as its case study area. It employs a quantitative approach to explore the relationship between land surface temperature and the most common land cover indices. It also seeks to select proper (urban and vegetation) indices by incorporating supervised feature selection procedures and Landsat 8 imageries. In this regards, a genetic algorithm is applied to choose the best indices by employing kernel, support vector regression and linear regression methods. The proposed method revealed that there is a high degree of consistency between affected information and SHI dataset (RMSE=0.9324, NRMSE=0.2695 and R2=0.9315).

Nowadays, studying urban expansion is very importantin developing countries. Rapid growth of cities has devastating environmental impacts,and irreparable economic and social consequences. Moreover, studying urban expansion is of great importance for managers and planners of a society. Land surface temperature (LST) is one of the important parameters in urban-regional planning.Urban heat, which is usually referred to as urban heat island, can affect human health, theecosystem, surrounding air, air pollution, urban planning, and energy management. The phenomenon of urban heat island (UHI) is closely related toland-use changes in urban areas, especially when natural surfaces turn intoimpermeable urban surfaces, and increases heat flux and reduces latent heat.

In this study, a collection of Landsat-5 multi-temporal satellite images received in 1986, 1989, 1993, 1998, 2001, 2008, and Landsat 8 multi-temporal satellite images received in 2013, 2015 and 2017, was used along with night images of the MODIS sensor recieved in 2001, 2008, 2013, 2015, 2017 (on the same day Landsat-5 and Landsat-8 satellite images were received). In order to classify land cover and calculate land surface temperature usingLandsat 5, Landsat 8 and MODIS sensorsatellite images, initial pre-processing (radiometric and geometric corrections)was performed.In order to classifyland cover in the study area, training areas were selected using Google Earth andthen, land cover classification was carried outusing Neural Network Algorithm. Since, classifying urban areas wasthe priority ofthe present study, Normalized Difference Built-up Index (NDBI) was also used.Ultimately, pixelidentified by classification algorithm and NDBI index was allocated tourban areas. A simple relationship suggested by the United States Geological Survey (USGS) was used to estimate land surface temperature from Landsat-5 imageries.Split-window algorithm was also used to estimate land surface temperature from Landsat-8 and MODIS imageries. Since, Landsat-8 and MODIS imageries were collectedwith only afew hours (or less than that)time difference, and their thermal bands’spectral rangeswere close to each other, Landsat-8 thermal bands’emissivity coefficient with a higher spatial resolution (30 m) was used to calculate land surface temperature from MODIS images.

Classifying land cover in Shahr-e Kordusing Landsat-5 and Landsat-8 imageries received in 1986, 1989, 1993, 1998, 2001, 2008, 2013, 2015, and 2017 indicated that in this31-year time period,residential areas were approximately duplicatedand reached from 1004 hectares to 2112 hectares. Analysis of land surface temperature maps using Landsat 5, and Landsat 8 imageries indicated that urban areas and areas with dense vegetation had lower surface temperatures compared to areas with thin vegetation cover. Therefore, land surface temperature of urban areas is lower than the surrounding areas. However, land surface temperature obtained from MODIS imageries indicated that land surface temperature of urban areas is higher at nights. Therefore, urban heat islands in this city occur at nights. Results indicated that with increasingexpansion of urban areas, urban heat islands also intensifyat nights.

Although, Shahr-ekordis a less developed urban area as compared to other Iranian metropolises,expansion of its constructed areas can stillhave negative effects on the environment and climate of the region. The present study investigates urban growth, and itsinfluence on land surface temperature and occurrence of urban heat island. Thermal maps produced in the present study indicated that daytime air temperature of this city was relatively lower than other regions. But this is not the case at nights: compared to other areas,residential areas have a higher temperature at nights. This indicates the existence of a heat island in the city, and possibly have adverse and devastating effects on humidity, reduces precipitation, changes local winds and the climate. Results also indicate that urban expansion have directlyaffected urban heat islands. Thus, urban heat islandshave intensified and expanded during this time period. Therefore, it is concluded that there is a direct relationship between land surface temperature and land use type.

Green spaces in the urban environment can contribute to reduce the Urban Heat Island. In a context of climate change, with the expected increase in temperature, dryness and intensity of heat waves, green areas assume even higher importance as they can create a cooling effect that extends to the surrounding areas. This study analyses the thermal performance of Golmohamady Park (9.2866 ha) with vegetation cover (7.5464 ha) and its influence in the surrounding atmospheric environment space in Esfahan. Measurements of weather parameters (temperature, relative humidity and wind speed) were carried out along a selected path, starting from inside the green area to surrounding streets with different orientations and solar exposure. It was found that the park was cooler than the surrounding areas, either in the sun or in the shade. These differences were higher in hotter days and particularly related to the mean radiant temperature (Tmrt). The highest difference found was of 6.7 C in relation to air temperature. In both cases this difference occurred between the shaded site inside the park and the sunny site in an E-W oriented street in the North of Golmohamady Park. Besides the local weather conditions, particularly the low wind speed, the sun exposure and the urban geometry are the potential factors that explain these differences. The cooling effect of green spaces on the surrounding environment can be enhanced by additional measures related to the urban features of each city.

The simplest definition of urbanization is that urbanization is the process of becoming urban. Urban climate is defined by specific climate conditions which differ from surrounding rural areas. Urban areas, for example, have higher temperatures than surrounding rural areas and weaker winds. Land Surface Temperature is an important phenomenon in global climate change. As the green house gases in the atmosphere increases, the LST will also increase. Energy and water exchanges at the biosphere–atmosphere interface have major influences on the Earth's weather and climate. Numerical models ranging from local to global scales must represent and predict effects of surface fluxes. The urban thermal environment is influenced by the physical characteristics of the land surface and by human socioeconomic activities. The thermal environment can be considered to be the most important indicator for representing the urban environment. Vegetation is another important component of the urban ecosystem that has been the subject of much basic and applied research. Urban vegetation influences the physical environment of cities through selective absorption and reflection of incident radiation and regulation of latent and sensible heat exchange Satellite-borne instruments can provide quantitative physical data at high spatial or temporal resolutions. Visible and near-infrared remote sensing systems have been used extensively to classify phenomena such as city growth, land use /cover changes, vegetation index and population statistics. Finally, we propose a model applying non-parametric regression to estimate future urban climate patterns using predicted Normalized Difference Vegetation Index and Heat Island Intensity.
I conducted all spatial analysis in the UTM Zone 39 Northern Hemisphere projection. The fundamental procedure I used for evaluating change in land surface temperature was to relative temperature for both images, so that the values are temperature difference between the coldest and hottest areas in Tehran metropolitan. subtracting these images from each other results in relative temperature change from 2003 to 2015. Landsat satellite data were used to extract land use/land cover information and their changes for the abovementioned cities. Land surface temperature was retrieved from Landsat thermal images. The relationship between land surface temperature and landuse /land-cover classes, as well as the normalized vegetation index (NDVI) was analyzed.
In this study, LST for Tehran metropolitan was derived using SW algorithm with the use of Landsat 8 Optical Land Imager (OLI) of 30 m resolution and Thermal Infrared Sensor (TIR) data of 100 m resolution. SW algorithm needs spectral radiance and emissivity of two TIR bands as input for deriving LST. The spectral radiance was estimated using TIR bands 10 and 11. Emissivity was derived with the help of land cover threshold technique for which OLI bands 2, 3, 4 and 5 were used. The output revealed that LST was high in the barren regions whereas it was low in the hilly regions because of vegetative cover. As the SW algorithm uses both the TIR bands (10 and 11) and OLI bands 2, 3, 4 and 5, the LST generated using them were more reliable and accurate. NDVI negatively affected LST and Urban Heat Island in vegetation areas in 2003 and 2015 in Tehran metropolitan. This analysis provides an effective tool in evaluating the environmental influences of zoning in urban ecosystems with remote sensing and geographical information systems. This method exhibits a promising performance in UHI forecast. The predicted LST confirms that urban growth has severely influenced UHI pattern through expanding the hot area. Our study confirmed that LST prediction performance is strongly depended on the resolution.
The results reveal that the urban LST is affected mainly by the land surface characteristics and has a close relation to the abundance of vegetation greenness. The spatial distance from the UHI centre is another important factor influencing the LST in some areas. The methodology presented in this paper can be broadly applied in other metropolitans which exhibit a similar dynamic growth. Our findings can represent a useful tool for policy makers and the community awareness of environmental assessment by providing a scientific basis for sustainable urban planning and management. This provides an effective tool in evaluating the vegetation greenness of different zoning in urban ecosystems with remote sensing and geographical information systems. From the perspective of land use planning and urban management, it is recommend that planners and policy makers should pay serious attention to future land use policies that maintain a relevant proportion of public space, green areas, and land surface physical characteristics.

The rapid growth of urbanization formed in the 19th century after the industrial revolution in the developed countries and after that the urban area of many cities extended rapidly. In those cities many different activities such as transportation, industrial and construction activities is much higher than rural environments, while in that environments vegetation and green spaces are lower. Thus, these activities cause the increased level of temperature in the urban environments compared to surrounding areas which this phenomenon is called “urban heat island” (Oke, 1973). This phenomenon is one of the main concerns in the urban environments due to its impact on biological, meteorological, environmental, social and economical aspects. For instance, urban head island causes earlier bloom and the blossom of plants and trees, prolongation of the growing season. Thus, Babol is one of the densely built cities of Mazandaran province, which is confronted with rapid population growth during few last decades. The aim of this research is to investigate the impact of urbanization on heat island and day to day temperature variation in that city.
The study area was conducted in the Babol city, Mazandaran. Due to the lack of meteorological station in the Babol city, three devices with data logger (MIC 98583 USB-Data Logger, Taiwan) were equipped to record temperature and relative humidity in three environments urban, suburban, and green. The temperature and relative humidity were recorded every hour throughout the course of 80 days (July 6 to September 22) in 2015.
The monitoring boxes were placed at a height of about 2.5 m above the ground surface. This study is investigated the day to day temperature variation with respect to the impact of urbanization on temperature variations. For this purpose, the two following integrated methods were used: 1) the day to day temperature variation (DTD); 2) the difference between day to day variability of daily maximum temperature (DTD max) and day to day variability of daily minimum temperature (DTDmin) (Tam et al., 2015).
The day to day temperature variation is based on the following equations: Equation 1: Where Σ is the sum over all n data elements, t is daily temperature, i is the counter that marches through the days in a time period (e.g. a month),| | is the absolute value, and n is the number of days elements.
Equation 2: ΔDTD is the difference between day to day variability of daily Tmax (DTDtmax) and day to day variability of daily Tmin (DTDtmin). A positive value indicates greater day-time day to day temperature variation and a negative value indicates greater night-time day to day temperature variation (Tam et al., 2015). The significant difference for temperature (maximum and minimum) and daily relative humidity in different environments was tested using the one-way analysis of variance (ANOVA); and then the day to day variability of temperature was calculated based on a DTD and ΔDTD equations for all three environments. Analysis of the data was conducted using Excel and R software.
The difference between the mean temperature of urban and suburban environments in our study area is around 1°C and this difference between urban and green environments is around 1.8°C. The mean relative humidity in the urban and green environments are minimum (67%) and maximum (77%), respectively. Day to day temperature variation of daily temperature DTD(tmean) and temperature maximum DTD(tmax) in the urban environment is higher than suburban and green environments, but the day to day temperature variation of daily temperature minimum DTD(tmin) is less than the two other environments. The difference of DTD(tmax) and DTD(tmin) in the urban environment is higher than the two other environments and is nearly zero for the green environment. These values indicate higher variation of the daily temperature in the urban and a very small difference in the daily and nightly temperatures variation for the green environment. The results of this research demonstrate that the heat island not only affects the temperature in Babol city, but also influences its day to day temperature variation.
Based on these results, one could observe the impact of urbanization on climatic parameters in particular temperature and humidity. Moreover, the green environment can play an important role on the climate change of Babol city.

Land Surface Thermal (LST) is a key variable to control the relations between different types of radial, latent, and sensible thermal fluxes in urban areas. For analyzing and understanding dynamics of LST, it is necessary to recognize its relations with changes created by men. Recognizing such relations is a requirement for modeling and predicting environmental changes and also for urban policies. On the other hand, increases occur in vegetation cover is an effective strategy to reduce the effects of urban microclimate. The present study analyzes the trends of surface thermal changes and its spatial correlations with vegetation cover in Tehran metropolitan during 2003 to 2016. Free clouds satellite images of Tehran by Landsat8 (August 2016) and Aster (August 2003) were analyzed using Envi software. Different algorithms of remote sensing were applied to convert LST and Normalized Difference Vegetation Index (NDVI) indicators into spatial patterns. Spatial outcomes of the present study indicate that during a decade the minimum amount of surface thermal and the average amount of LST have decreased 3.67°c and 0.47°c respectively. In contrast, the average amount of NDVI was increased from 0.06 to 0.10. Also, the estimation of spatial correlation between LST and NDVI indicators showed an amount 0.02 reduction in 22 urban regions of Tehran metropolitan during the last decade. Such a reduction indicates that increasing functions of human activities have affected the rates of thermal and energy of urban surfaces. Therefore, to reduce energy consumption in the city, it is evident that different patterns of physical development should be applied for the city.

The simplest definition of urbanization is that urbanization is the process of becoming urban. Urban climate is defined by specific climate conditions which differ from surrounding rural areas. Urban areas, for example, have higher temperatures than surrounding rural areas and weaker winds. Land Surface Temperature is an important phenomenon in global climate change. As the green house gases in the atmosphere increases, the LST will also increase. Energy and water exchanges at the biosphere–atmosphere interface have major influences on the Earth's weather and climate. Numerical models ranging from local to global scales must represent and predict effects of surface fluxes. In this study, LST for Tehran Metropolitan, was derived using SW algorithm with the use of Landsat 8 Optical Land Imager (OLI) of 30 m resolution and Thermal Infrared Sensor (TIR) data of 100 m resolution. SW algorithm needs spectral radiance and emissivity of two TIR bands as input for deriving LST. The spectral radiance was estimated using TIR bands 10 and 11. Emissivity was derived with the help of land cover threshold technique for which OLI bands 2, 3, 4 and 5 were used. The output revealed that LST was high in the barren regions whereas it was low in the hilly regions because of vegetative cover. As the SW algorithm uses both the TIR bands (10 and 11) and OLI bands 2, 3, 4 and 5, the LST generated using them were more reliable and accurate.

Urban heat island phenomenon is generally caused by a reduction in latent heat and a rise in sensible heat in urban areas. Today, this is one of the major problems of the large cities which has attracted the attention of many researchers and experts in various fields. This study investigates heat island in Tehran metropolitan as the most densely populated city of Iran. This paper aims to use satellite imageries to compares the normalized difference vegetation index (NDVI) and impervious surface (ISA) as representative parameters that of surface urban heat island (SUHI) by examining their relationship with land surface temperature indices (LST) and land-use map. For this purpose, LANDSAT 5 TM imageries and Tehran1:2000 land use map and sub-pixels model has been used. The results show that there are a linear and strong relationship between LST and ISA, while the relationship between LST and NDVI is much weaker and in order to quantitative investigation of LST in urban heat island we used thermal remote sensing. Results indicated that ISA indicator is suitable than NDVI. Also, the investigation on percentage of impervious surfaces in each land-use represents that residential land uses has the highest percentage of impervious surfaces because having the surfaces like asphalt and concrete and vegetation is the lowest one.

In this study, using Land sat TM images of band 6 sensors were identified heat island of Mashhad city in 4 years (1371-1390) in hottest months(July and august). The results showed that in the city of Mashhad places such as parks, gardens ( Astan Quds Razavi , malek Abad ) to high vegetation index and humidity have low temperature and areas to low humidity and free of vegetation and open spaces with dominant non-users and users of transport transport of soil and rock, such as 12 urban areas have high land surface temperature and urban heat islands form.

High urban population, irregular urban development, improper green space capitation, and heterogeneous distribution of green spaces in Tehran have made the heat island to increase its intensity and become multi-core; of which one is located above the Velayat urban park. Due to the green space sprawl in this region, and regarding the relationship between the heat island intensity and urban green spaces, the effects of green spaces sprawl on urban heat island pattern are examined in this research. To carry this out, the land use/land cover (LULC) and green space sprawl were investigated using Google, TM and OLI-TIRS imagery data were obtained and processed using ENVI 4.7. The results were entered into the microclimatic model Envi-met with spatial resolution of 20 meters for the region of 3.4 to 2.6 kilometers. Simulations were done with hourly intervals of which a six hour interval was depicted in this research. Results indicate a strong significant correlation between the model outputs and measured data of Meydan-e-Bahman pollution measuring station (related to Department of Environment). The central region of Velayat Park is a usual place for a cold core to form. Furthermore, high buildings located around the park prevent the cold weather (tongue) to penetrate the residential areas. The effects of green space sprawl on the heat island pattern could be seen as an increase in the temperature differences of various land covers in warm seasons and as a decrease in diurnal temperature of different land covers in cold seasons.