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Mojtaba Rafiean, Hadi Rezai Rad,
Volume 4, Issue 3 (9-2017)
Volume 4, Issue 3 (9-2017)
Abstract
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.
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.
Bohlole Alijani, Meysam Toulabi Nejad, Fariba Sayadi,
Volume 4, Issue 3 (9-2017)
Volume 4, Issue 3 (9-2017)
Abstract
Urban climate is strongly influenced by the processes of urban work and life. Expansion of cities and consequently increased human constructions causes to changes in urban climate. The rising temperature of cities rather than the surroundings is one of the effects linked to direct human intervention.
Building heating, air pollution and the use of inappropriate materials in the flooring streets (like asphalt streets due to dark colors in energy-absorption) are effective in phenomenon of urban heat islands that makes unfavorable environment for citizens. Paying attention to the urban surfaces like sidewalk, streets and rooftops has a great role in decreasing effect of this phenomenon. Due to growing urbanization and subsequently cities development, urban heat islands have taken a growing trend in big cities.
In general, the urban heat-island is a result of urbanity features, air pollution, human warmth, presence of impervious surfaces in the city, thermal properties of materials and geometry of urban areas. Heat island phenomenon is a result of many factors that are summarized below: (1) urban Geometry (morphometry) (2) thermal properties of materials which increase the sensible heat storage in the urban texture (3) released human heat as a result of fuel combustion and animal metabolism (4) urban greenhouse gases, leading to an increase in long wave radiation, atmospheric contamination and therefore warmer atmosphere (5) reduction of evaporation levels in cities, which means that energy will be released more as tangible rather than latent heat (6) reduction of turbulence and heat transfer through the streets.
This study aimed to simulate and calculate the maximum amount of heat island (UHI max) according to the conditions of urban geometry in the region of Kucheh bagh in Tabriz that is a pioneer study in Iran.
The study area is located in Kuche bagh region at the intersection of the streets of Ghods and Farvardin in the city of Tabriz.
The Oke’s numerical-theoretical equation was used for this study. First, the geometry of the target area using the radius of 15 meters from the axis of the road was divided into separate blocks. The ratio of street width (W) and height of buildings (H) was calculated in GIS software and at the end, the intensity of UHImax was calculated and simulated using Oke equation.
The urban geometry including building height and street width is calculated using Equation 1.
The theoretical- numerical basis of this equation shows that simulation of H/W ratio is an appropriate ways to describe urban geometry. Increasing the value of this ratio could lead to an increase in urban heat-island through modeling. This model has many advantages compared to other methods used to estimate the urban heat island. So, the selected parameter to calculate urban geometry and the model used to estimate the maximum intensity of heat island is the ratio of H / W and OKE model, respectively. In addition, the average height of buildings located within a radius of 15 meters and an average width of passages were calculated from the equation 2 and 3, respectively.
After calculating the geometry of the study area, the results showed that the blocks E, G and D in terms of height of the buildings have a heterogeneous distribution, but the distribution of blocks C, I and J is illustrative of their standard configuration. Although the blocks E, F and J in terms of street width are less diverse compared to other blocks, but in terms of height of buildings (8.6, 7 and 5 meters) have a different pattern that maximum values of their UHI are 8.3, 7.5 and 6.3 degrees, respectively. Three blocks B, H and I, in addition to their similarity according to street width and height of the buildings, in terms of the ratio of H / W and heat island intensity with the values of 9.6, 9.8 and 10.2 degrees are homogeneous.
It was also found that the greatest difference between the H / W ratio is related to block A (0.54) and block H (1.98); this difference has caused that greatest difference between the maximum intensity of UHI would calculated between the two blocks equal to 5.2 degree.
Misconfiguration causes that energy leaving from city surface deal with the problem due to narrow passages and high buildings. Therefore, consideration appropriate width of passages and streets and height of buildings are necessary to ease heat leaving and reduce intensity of UHI.
These simulations showed that high buildings and narrow streets intensify the heat islands. While in the presence of short buildings and wide streets, the UHI max is lowered. When the ratio H / W in the studied urban area is between 0.54 to 0.81, UHI max remains between 5 to 6.6 C˚, when this ratio increases to 1.01 to 1.98, UHI max will be between 7.5 and 10.2 C˚. The result also revealed that block A and H with 5 and 10.2 C˚ have the minimum and maximum value of UHI intensity, respectively. So can be concluded that block A and H have the most standard and non-standard urban configuration in the region. The estimates from regression model showed that the street width (91.6%) is more effective than the height of the buildings (6.6%) in changing UHI max.
Building heating, air pollution and the use of inappropriate materials in the flooring streets (like asphalt streets due to dark colors in energy-absorption) are effective in phenomenon of urban heat islands that makes unfavorable environment for citizens. Paying attention to the urban surfaces like sidewalk, streets and rooftops has a great role in decreasing effect of this phenomenon. Due to growing urbanization and subsequently cities development, urban heat islands have taken a growing trend in big cities.
In general, the urban heat-island is a result of urbanity features, air pollution, human warmth, presence of impervious surfaces in the city, thermal properties of materials and geometry of urban areas. Heat island phenomenon is a result of many factors that are summarized below: (1) urban Geometry (morphometry) (2) thermal properties of materials which increase the sensible heat storage in the urban texture (3) released human heat as a result of fuel combustion and animal metabolism (4) urban greenhouse gases, leading to an increase in long wave radiation, atmospheric contamination and therefore warmer atmosphere (5) reduction of evaporation levels in cities, which means that energy will be released more as tangible rather than latent heat (6) reduction of turbulence and heat transfer through the streets.
This study aimed to simulate and calculate the maximum amount of heat island (UHI max) according to the conditions of urban geometry in the region of Kucheh bagh in Tabriz that is a pioneer study in Iran.
The study area is located in Kuche bagh region at the intersection of the streets of Ghods and Farvardin in the city of Tabriz.
The Oke’s numerical-theoretical equation was used for this study. First, the geometry of the target area using the radius of 15 meters from the axis of the road was divided into separate blocks. The ratio of street width (W) and height of buildings (H) was calculated in GIS software and at the end, the intensity of UHImax was calculated and simulated using Oke equation.
The urban geometry including building height and street width is calculated using Equation 1.
The theoretical- numerical basis of this equation shows that simulation of H/W ratio is an appropriate ways to describe urban geometry. Increasing the value of this ratio could lead to an increase in urban heat-island through modeling. This model has many advantages compared to other methods used to estimate the urban heat island. So, the selected parameter to calculate urban geometry and the model used to estimate the maximum intensity of heat island is the ratio of H / W and OKE model, respectively. In addition, the average height of buildings located within a radius of 15 meters and an average width of passages were calculated from the equation 2 and 3, respectively.
After calculating the geometry of the study area, the results showed that the blocks E, G and D in terms of height of the buildings have a heterogeneous distribution, but the distribution of blocks C, I and J is illustrative of their standard configuration. Although the blocks E, F and J in terms of street width are less diverse compared to other blocks, but in terms of height of buildings (8.6, 7 and 5 meters) have a different pattern that maximum values of their UHI are 8.3, 7.5 and 6.3 degrees, respectively. Three blocks B, H and I, in addition to their similarity according to street width and height of the buildings, in terms of the ratio of H / W and heat island intensity with the values of 9.6, 9.8 and 10.2 degrees are homogeneous.
It was also found that the greatest difference between the H / W ratio is related to block A (0.54) and block H (1.98); this difference has caused that greatest difference between the maximum intensity of UHI would calculated between the two blocks equal to 5.2 degree.
Misconfiguration causes that energy leaving from city surface deal with the problem due to narrow passages and high buildings. Therefore, consideration appropriate width of passages and streets and height of buildings are necessary to ease heat leaving and reduce intensity of UHI.
These simulations showed that high buildings and narrow streets intensify the heat islands. While in the presence of short buildings and wide streets, the UHI max is lowered. When the ratio H / W in the studied urban area is between 0.54 to 0.81, UHI max remains between 5 to 6.6 C˚, when this ratio increases to 1.01 to 1.98, UHI max will be between 7.5 and 10.2 C˚. The result also revealed that block A and H with 5 and 10.2 C˚ have the minimum and maximum value of UHI intensity, respectively. So can be concluded that block A and H have the most standard and non-standard urban configuration in the region. The estimates from regression model showed that the street width (91.6%) is more effective than the height of the buildings (6.6%) in changing UHI max.
Zahra Taghizade, Ahmad Mazidi,
Volume 6, Issue 3 (9-2019)
Volume 6, Issue 3 (9-2019)
Abstract
Abstract
Urban heat island (UHI) is one of the environmental phenomenon which has made difficult environmental conditions for citizen. This study aims to evaluate the spatial and locational variability of Esfahan urban heat island according to the role of land use. Thus an area about 190.2 square kilometers (km2) in Esfahan, as the microclimate, was studied. In order to analyze the relationship between land use and land cover changes on Esfahan urban heat island, the images of Landsat 7 (TM and ETM +) and Landsat 8 (OLI / TIRS) on 20 July 1989, 17 August 2005, 18 August 2014 have been used. The results show that the urban areas has experienced 31% changes in positive direction; while the agricultural sector and green space havehad a reduction of 25% in their area. The analysis of the intensity of heat island show that heated cores are related topoor and barren lands with about 37/33 and 36/5. Although the most area of thermal classwere related to warm thermal class in 1989 and 2005, the average thermal classes were about 63/8%in 2014. Moreover, the locational variation distribution of Esfahan heat island shows that the locationof the heat island has gradually changed. For example in 2014 it included small parts in the south of the city, military zones and barren lands in the south, some parts in the north west and north east areas and small areas in the east of Esfahan. This means that urban development isn’t the main factor of the surface temperature increase and urban heat development, but rather the type of land use has influenced the decreasing or increasing of air temperature.
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Valiollah Sheikhy, Hossein Malakooti, Sarmad Ghader,
Volume 7, Issue 4 (2-2021)
Volume 7, Issue 4 (2-2021)
Abstract
Abstract
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 Tehran'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.
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 Tehran'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.
Mohammad Javad Barati, Manuchehr Farajzadeh Asl, Reza Borna,
Volume 8, Issue 1 (5-2021)
Volume 8, Issue 1 (5-2021)
Abstract
Evaluation of SADFAT model performance in daily forecast of Land Surface Temperature in the city of Tehran
Abstract
The high spatial and temporal limitations of TIR images for use in urban climatology have been identified as a current scientific challenge. Therefore, the use of Data Fusion Algorithms in Remote Sensing has been considered. In the old methods, two bands of one sensor were used for Data Fusion. In these methods, a panchromatic band was used to increase spatial accuracy, so only spatial resolution was increased. To solve this problem, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) was used to integrate the images of two Landsat and Modis gauges to increase the spatial and temporal resolution of the reflection. but, this algorithm is designed for pixels and unmixing areas that are the same in Modis and Landsat pixels. The use of this model was not suitable for urban areas with a different of landuse. Therefore, the Enhanced STARFM model (ESTARFM) was developed. The ESTARFM model was improved in 2014 to predict thermal radiation and LST, taking into account the annual temperature cycle and the unevenness of the earth's surface, and the SADFAT model was introduced.
In this study, the performance of SADFAT model in the use of OLI spatial resolution and MODIS temporal resolution in LST forecast in urban areas was examined. The metropolis of Tehran has different surface covers and multiple microclimates. So if the algorithm works successfully, This model can be used in other cities to improve urban heat island studies. The inputs for the algorithm are thermal radiance of Modis and Landsat images, the red and near infrared band of Landsat for daily production of LST in 2017 in the city of Tehran. The algorithm uses two pairs of Modis and Landsat images at the same time and sets of Modis images at the time of prediction and then calculate the conversion coefficient for relating the thermal radiance change of a mixed pixel at the coarse resolution to that of a fine resolution. In this way, LST is generated in areas with a variety of landuse.
All the estimated pixels were compared to the base image pixels in that range to evaluate the results of the model. The comparison results for the autumn days with the average correlation coefficient of 0.86 and RMSE equal to 0.122, showed that the model has the highest accuracy in this season and in other seasons with the average correlation coefficient of 0.76 and RMSE about 0.4, has provided good accuracy.
Visual interpretation of the results of SADFAT showed that this model is able to accurately predict the LST of the land cover in different surface coatings and even in areas where one or more urban land uses are mixed in one MODIS pixel.
However, the borders are well separated and the features are not combined. Although the boundaries are clearly defined, in some land uses, the predicted LST is somewhat higher than the observational image.
Landsat and Modis satellites pass through an area with a small time difference, so they are suitable for combining with each other. But in predicting reflectance with the SADFAT algorithm, there are systematic and variable errors that we need to be aware of in order to increase the output accuracy. One of the systematic and unavoidable errors is the instability of the Terra and Aqua satellites passing through at any point, ie at each satellite pass, the location of the study area in Swath and the size of the pixel changes. Due to the distance of the study area from the vertical center of measurement on the ground (Nadir), the amount of this error varies on different days and should be checked for each day. The preventable error is the sudden change in one or more images used (16 days of the same pass time interval for Landsat) is high for estimating surface reflectance with spatial and temporal resolution. These changes may be due to human factors such as air pollution or natural factors. Natural factors such as clouds and dust storms are the main sources of error in using the SADFAT model because they are sudden and temporary and cover a wide area. The occurrence of these two factors has a great impact on reflectance. Therefore, a sudden change in these factors, in one or more images, causes a large error in the calculations.
The study also found minor spatial errors in the prediction, so that even on days when the results were better, points were observed where the values in the predicted LST images did not match exactly with the OLI sensor. The reason for this may be due to changes in vegetation. Although there are some systematic and variable errors in the images and the implementation of the algorithm The results of this study showed that the performance of this model is reliable for predicting the daily LST with a spatial resolution of 30 meters in Tehran.
This method is able to support urban planning activities related to climate change in cities, so it is recommended that its performance be examined separately for different land cover in the city and the efficiency of this algorithm be evaluated with other sensors such as Copernicus Sentinels.
Key words: Spatial and Temporal Data Fusion, SADFAT, Heat island, LST, Urban climatology
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Mahmoud Ahmadi, Zahra Alibakhshi,
Volume 8, Issue 2 (9-2021)
Volume 8, Issue 2 (9-2021)
Abstract
Evaluation of hot spots changes in Tehran city and satellite based on land use and its role in urban heat hazards
Expanded abstract
Problem statement:
Urbanization and human activities affect the urban climate and clearly affect the air temperature close to the surface. In Tehran and its satellite, factors such as climatic region, season, time of day and wind regimes, topography, urban environments, population density, residents' activity, vegetation structure and urban physical form play an important role in the formation of urban heat islands. The purpose of this research is to determine the type of spatial distribution of heat islands of Tehran metropolis and satellite cities using land use and land cover. Replacing natural land cover with impervious surfaces due to urban development has negative environmental, social and economic impacts, in addition to beneficial aspects. Most of the albedo belong to the built areas and the bare land and the smallest of the Albedo belong to the aquatic areas and vegetation. In this research, the hypothesis is whether the suburbs may have higher temperatures than urban areas depending on the type of land use? In fact, it is examined the spatial distribution of the heat island of Tehran and its satellites, in which the use of land and land cover are analyzed as a factor contributing to the creation, intensification or reduction of the thermal island.
Methodology:
Extraction and preparation of imagery data through the Landsat 7 Satellite ETM + sensor over the years 2001-2015 and selection of June as the hottest month of the study area. These images were extracted from Route 164 and Row 35 of the USGS. An assessment was carried out through the accuracy of ground surface temperature data by Landsat satellite images and obtained temperatures from the weather stations in the area based on the Taylor diagram. In order to investigate the spatial structure of the cells obtained in each map, each containing surface temperature and heat island extraction, it used the methods of world spatial autocorrelation (high and low clustering, spatial correlation) and local (Cluster and Outlier analysis, hot spot analysis). The high and low clustering statistics show how the concentration of high or low values in the region. In the next step, the results of analysis of Anselin Local Moran and hot spots were compared in map format. Hot spots were analyzed in all studied regions and in all 7 cities. The area of hot spots was investigated over the course of 15 years and the results were presented in table and diagram form.
Land use was surveyed for every 7 county. In the last section were studied, the relationship between hot spots in each city and type and land use changes over 15 years.
Surface spatial analysis of the surface temperature of the area showed that the cells follow a cluster pattern and their trend towards clustering. Any kind of land cover and land use will create special features in a place that can be increased or decreased with a specific microclimate.
Explaining and results:
After selecting the years 2001, 2005, 2010, and 2015 as the sample and survey of the temperature of each land use in that year, it was determined that artifact, pasture, bare lands, forest, aquatic areas, agriculture and green spaces were respectively have the highest to the lowest temperature in the area. On the other hand, in the area of heat island in a region are Rabat Karim, Ray, Islamshahr, Tehran, Shahriar, Karaj and Shemiranat, respectively.
In spite of the reduction of aquatic areas and even bare lands, because of the large impact of green space or agricultural land was reduced the extent of heat islands during the statistical period, and on the contrary, the reduction of green space and agricultural land in places where even their forest areas have grown, has increased the levels of heat islands. This suggests that the dispersion and extent of green spaces has a more effective role in reducing the heat island compared with the creation of limited forest and planted surfaces in one place. Hence, in Tehran despite the significant growth of artifacts, due to the increasing growth of green space, the heat islands has been reduced compare with the Ray, Robatkarim and Islamshahr cities, which are exactly on its suburbs.
Keywords: Heat Island, hot spots, land use, Tehran, satellite cities.
, Dr Fatemeh Tabib Mahmoudi,
Volume 9, Issue 3 (12-2022)
Volume 9, Issue 3 (12-2022)
Abstract
Investigation of the effects of Covid-19 pandemic on UHI in residential, industrial and green spaces of Tehran
Abstract
Rapid urbanization in recent decades has been a major driver of ecosystems and environmental degradation, including changes in agricultural land use and forests. Urbanization is rapidly transforming ecosystems into buildings that increase heat storage capacity. Loss of vegetation and increase in built-up areas may ultimately affect climate variability and lead to the creation of urban heat islands. The occurrence of natural disasters such as flood, earthquake … is one of the most effecting factors on the changes in intensity of urban heat islands. So far, a lot of research has been done on how it is affected by various types of natural disasters such as floods, earthquakes, droughts and tsunamis.
Two major environmental challenges for many cities are preventing flooding after heavy rains and minimizing urban temperature rise due to the effects of heat islands. There is a close relationship between these two phenomena, because with increasing air temperature, the intensity of precipitation increases. Drought is also a phenomenon that is affected by rainfall, temperature, evapotranspiration, water and soil conditions. One of the major differences between drought and other natural disasters is that they occur over a longer period of time and gradually than others that occur suddenly. Another natural disaster is the tsunami, which increases the area of water by turning wetlands into lakes, thereby increasing the index of normal water differences, which has a strong negative relationship with surface temperature. Ecosystems in urban areas play a role in reducing the impact of urban heat islands. This is because plants and trees regulate the temperature of their foliage by evaporation and transpiration, which leads to a decrease in air temperature.
Applying the locked down of the Covid-19 pandemic since the spring of 2020 has led to the global restoration of climatic elements such as air quality and temperature. In this study, the effects of Covid-19 locked down on the intensity of urban heat islands due to the limitations in industrial activities such as factories and power plants and the application of new laws to reduce traffic in Tehran were investigated. In this regard, the Landsat-8 satellite taken from a part of Tehran city has been used.
Materials and Methods
In order to investigate the effects of locked down in the spring of 2020 on the intensity of urban heat islands; the status of UHI maps in Tehran during the same period of locked down in three years before and one year after has been studied. The proposed method in this paper consists of two main steps. The first step is to generate UHI maps using land surface temperature (LST), normalized difference vegetation index (NDVI) and land use / land cover map analysis. In the second step, in order to analyze the behavioral changes in the intensity of urban heat islands during locked down and compare it with previous and subsequent years, changes in the intensity of UHIs are monitored.
UHI maps consist of three classes of high, medium and low intensities urban heat islands, which are based on performing the rule based analysis on land surface temperature characteristics and normal vegetation difference index derived from Landsat-8 satellite images as well as land use / land cover map. LULC maps are produced by support vector machine classification method consisting of three classes of soil, building and vegetation. In order to calculate the spectral features used in the rule based analysis, atmospheric and radiometric corrections must first be made on the red, near-infrared, and thermal spectral bands of the image captured by the Landsat-8 satellite. Then, vegetation spectral indices including NDVI and PV indices are generated.
Disscussion of Results
The capability of the proposed algorithm in this paper is first evaluated in the whole area covered by satellite images taken from the city of Tehran, and then in three areas including residential, industrial and green spaces. The data used in this article are images taken by the OLI sensor of Landsat-8 satellite in the spring of 2017-2021.
In the first step of the proposed method, maps of urban heat islands are generated based on multi-temporal satellite images of Landsat-8 taken in the years 2017to 2021 in the MATLAB programming software. Then, by comparing pairs of UHI maps in each of the residential, industrial and green space study areas, the trend of changes in the intensity of UHI is analyzed and the effects of locked down application in 2020 are evaluated.
The results of changes detection in urban heat islands in the period under consideration in this study showed that the percentage of areas that are in the class of high UHI in 2020 due to locked down of pandemic Covid-19 compared to the average of three years before that is 55.71%, has a decrease of 17.61%. The percentage of areas in the class of medium UHI intensity in 2020 due to locked down compared to the average of three years ago, which is 39%, increased by 4.8%, and in 2021 this amount again has decreased to less than the average. Also, the percentage of low intensity UHI class in 1399 compared to the average of three years ago, which is 5.3%, has increased by 12.8%.
Conclusion
In this study, the effect of locked down application due to the Covid-19 virus pandemic, which was applied in Iran in the spring of 2020 is investigated on the intensity of urban heat islands in a part of Tehran city and three selected areas with residential, industrial and green space. Detection of changes in the intensity of urban heat islands was done based on the post-classification method and on the UHI classification maps related to the years 2017 to 2021. In order to produce UHI maps, in addition to the land surface temperature, the amount of vegetation index and the type of land use / land cover class were also used in the form of a set of classification rules.
Comparing the results of the study areas of residential, industrial and green spaces, it is important to note that the rate of reduction of the area of UHI with high intensity in the residential area is 5.25% more than the industrial area and 6.1% more than the green space. However, the reduction of locked down restrictions in 2021 had the greatest effect on the return of the area of the high UHI class and caused the area of this class to increase by 23% compared to 2020. These results indicate the fact that restrictions on the activities of industrial units such as factories and power plants and the application of new laws to reduce traffic, despite the same weather conditions in an area have been able to significantly reduce the severity of urban heat islands.
Keywords: Urban Heat Islands, Land Surface Temperature, Vegetation Index, Change Detection, Covid-19
Mr Abolghasem Firoozi, Dr Akram Bemani, Dr Malihe Erfani,
Volume 10, Issue 1 (5-2023)
Volume 10, Issue 1 (5-2023)
Abstract
Introduction:
The growth rate of urbanization during the recent decades of metropolises has had many destructive effects on the urban environment, among which we can mention the change of temperature of surfaces and local climates. The increase in the urban population, the rapid growth of industrialization and the increase in the concentration of pollutants in the lowest level of the atmosphere have affected the severity of the city's heat islands. Land surface temperature (LST) is a key variable to control the relationship between radiant, latent and sensible heat flux. Analyzing and understanding the dynamics of LST and identifying the relationship between it and changes of human origin is necessary for modeling and predicting environmental changes. The heat of urban surfaces is affected by various characteristics of urban surfaces such as color, surface roughness, humidity level, possibility of chemical compounds, etc. In addition, the changes between LST in a city and its surrounding area are due to surface changes, heat capacity and topography. Since the surface temperature regulates the temperature of the lower layers of the atmosphere, it can be considered as a weather indicator and an important factor in the urban environment. Changes in land use by changing the features of the surface cover such as the shape of the constructed areas, the amount of heat absorption, building materials, surface albedo and the amount of vegetation lead to changes in the temperature of the earth's surface. Barren lands with soil cover, on the contrary, increase the surface temperature of the earth. Climatic characteristics at the time of satellite image imaging also play a role in the extent and intensity of urban cold islands, so that satellite imaging in the middle of hot summer days shows urban cold islands better. The innovation of the research is in the large area of the investigated area, which includes eight urban areas, in order to examine the pattern of temperature changes on a wider level.
Materials and methods
Considering the rapid development of urban and industrial areas in the Ardakan-Yazd plain in recent decades, this study aims to investigate changes in the surface temperature pattern using Landsat 7 and 8 satellite images for both winter and summer seasons. It was done in 2002 and 2019. In addition, the relationship between land use/land cover and surface temperature was also investigated. Geometrical correction of satellite images was done using topographic map 1/25000 of Mapping Organization and atmospheric correction using FLAASH method in ENVI software. Algorithms used to obtain land surface temperature for Landsat 7 images were single-window method and for Landsat 8 images, the Landsat Science Office model was used. Land use/land cover layers related to the years 2002 and 2019 were used, and central statistical profiles and LST distribution were extracted for pasture, agricultural land, blown sands, industrial areas, rock outcrops and cities. In addition to examining temperature changes in different uses, it is also possible to compare over time.
Results and discussion
The results of this study showed that the area of cold islands and thermal islands in winter and summer of 2002 is not much different, so that in winter 10.8 percent and in summer 10.4 percent of the area were cold islands and thermal islands in winter 9.02. It was 8.5% of the region in summer, while this difference is huge in 2019. Thus, 9.4% of the area in winter and 12.1% in summer are covered by cold islands, and thermal islands are 8.3% in winter and 1.6% in summer. Changing land use and increasing the size of urban and industrial areas and reducing agricultural land is one of the main reasons for the increase in cold islands. The survey of land use/land cover changes between these years showed that the extent of urban areas increased from 22,045 to 23,714 hectares, and industrial areas also grew by about two times, from 4,615 in 2002 to 8,187 hectares in 2019. However, during this period, the area of agricultural land has decreased from 1161 hectares in 2002 to 793 hectares in 2019. Also, the results show that the percentage of heat islands is higher in winter than in summer. The main reason for this can be the much less vegetation covers in the winter than in the summer, because the vegetation cover acts as a moderator of the earth's surface temperature. Cold islands are formed in the built-up areas in the winter and summer. From 2002 to 2019, the extent of cold islands decreased in winter and increased in summer, while the extent of thermal islands decreased in winter and summer. Also, the results of the validation section of the single-window method and the model of the Landsat Science Office in calculating LST showed that for both summer and winter seasons, Landsat 8 has a higher accuracy than Landsat 7, and the LST estimation model is based on the exclusive method of this The Landsat series of satellites (Landsat Office of Science model) has a higher efficiency than the single-window method.
Conclusion
The results showed that cities play an important role in changes in the temperature pattern of the earth's surface, and the phenomenon of urban cold islands is not exclusive to big cities in hot, dry and semi-arid regions, but also occurs in medium-sized cities. The temperature variability of eight cities located in the Ardakan-Yazd plain with the land use/cover of the suburbs also showed that the cities are colder than the suburbs in both winter and summer seasons. This study showed the role of vegetation in hot and dry areas in reducing LST and also provided evidence for the change in the degraded state of pastures in this area.
Keywords: Urban climate, Land use, Land surface temperature, surface urban cool island (SUCI), surface urban heat islands (SUHI)
Roshanak Afrakhteh, Abdolrasoul Salman Mahini, Mahdi Motagh, Hamidreza Kamyab,
Volume 10, Issue 3 (9-2023)
Volume 10, Issue 3 (9-2023)
Abstract
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.
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.
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