Geographic Data
Iraj Teimouri; Akbar Asghari Zamani; Erfan Moharrampour
Abstract
Extended AbstractIntroduction: UHI is a phenomenon whereby urban regions experience warmer temperatures than their rural surroundings. UHI influences well- being and welfare Average energy consumption and consequently, pollution and social equity of cities. Many factors contribute to urban heat island ...
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Extended AbstractIntroduction: UHI is a phenomenon whereby urban regions experience warmer temperatures than their rural surroundings. UHI influences well- being and welfare Average energy consumption and consequently, pollution and social equity of cities. Many factors contribute to urban heat island formation, as time (day and season), synoptic weather (wind, cloud), city form (materials, geometry, greenspace), city function (energy use, water use, pollution), city size (linked to form and function), geographic location (climate, topography, rural surrounds). Due to UHI adverse impacts on urban metabolism, ecological environment, the favourable living condition of cities and overall livability of cities, it has been an important research topic across various field of study and scholars gave more and more attention to it. UHI has been studied for a long time, it was first described by Luke Howard in the 1810s. During the last decade Significant research efforts have been performed to evaluate the urban heat island phenomenon's impact on the urban environment. According the literature review the main goal of this study is; exploring the effect of Urban Morphology on UHI, in the Tabriz city. Materials & MethodThis study is a correlation one. Be. In this research, ArcMap, ENVI and SPSS software have been used to generate data and apply relationships. To conduct this research, Landsat 8 images of OLI sensors at different dates for summer and winter have been used. In this study, to evaluate the UHI and influenced area of the city, the satellite images of land sat 8 OLI/ TIRS (thermal band 10) were used. The land sat 8 OLI/TIRS images that covered Tabriz summer and winter in the year of 2014 to 2019 were provided by USGS.To perform radiometric correction of images from ENVI 5.1 software using FLAASH method. Flash is the first atmospheric correction tool that corrects visible wavelengths and infrared and infrared wavelengths of up to 3 micrometers. In the flash method, the Meta Data file is used to correct the desired bands, which include multispectral bands and thermal bands. For multispectral bands, radiance and reflection operations were performed, but for thermal bands, only radiance operations were performed. In this context, the Lowest and Highest Position, Spatial Autocorrelation, Hot and Cold spots and finally multivariate Regression analysis were used.Results and Discussion The results of this study showed that the high temperature is most widespread in suburban areas especially in north west and south east rather than central parts of the city. According to the research findings, the average temperature of Tabriz in summer for the studied periods is equal to 37.7 ºC. also the average temperature varies in different years and does not show a specific trend. The average temperature of the city during the study period in winter is equal to 11.8 ºC. But according to the finding, the average temperature of the city in summer and winter is low compared to the surrounding areas. The average temperature difference between the city and surrounding areas is 33.7 ºC and 22.5 ºC in winter. Findings related to the autocorrelation pattern of Moran spatial analysis also show that the distribution of UHI in the city of Tabriz is clustered. The present study also showed that urban morphology can affect the intensity of Heat Islands. Based on the findings of regression analysis and calculated F (17.65) and the coefficient of significance obtaind at the level of 0.00, the predictor varizbles can well satisfy the behavior of the research dependent variable in the summer. For winter, the whole model can be generalized according to the calculated F (9.36) and significance coefficient (0.00). on the other hand, the present study showed that the distance from the green space has an effect on the intensity of UHI, so that based on the findings of the study and calculated F(7.596) and significant level(0.00) this can be confirmed.ConclusionThe present study sought to investigate the effect of urban morphology on the intensity of UHI. For this purpose, we used Landsat 8 satellite images and the technique of separate window algorithm to estimate the surface temperature. Spatial statistical analyzes such as Moran and Hot & Cold spots and multivariate linear regression were also used for analysis. In line with previous studies conducted in Iran, this study also showed that the temperature inside the city is cooler than the surrounding temperature and in a way in a city like Tabriz, we are facing cold islands instead of heat islands. The reason can be related to the compactness and high density of buildings in the cities, which requires further research. This study also showed that the surface temperature is affected by urban morphology and distance from green space. The research opens new field for future researches.
Geographic Data
Foroogh Mohammadi Ravari; Ahmad Mazidi; Zahra Behzadi shahrbabak
Abstract
Extended Abstract
Introduction
Replacing natural vegetation cover with impermeable urban surfaces) stone, cement, metal, etc.) has resulted in increased land surface temperature which is considered to be the most important problem of urban areas. Distinct temperature difference between the city and ...
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Extended Abstract
Introduction
Replacing natural vegetation cover with impermeable urban surfaces) stone, cement, metal, etc.) has resulted in increased land surface temperature which is considered to be the most important problem of urban areas. Distinct temperature difference between the city and the surrounding areas is called heat island (Melkpour et al., 2018). Increased land surface temperature and resulting heat islands in urban areas built without proper preplanning (Khakpour et al., 2016) especially in developing countries such as Iran experiencing a rapid growth rate have resulted in widespread environmental problems. Heat islands mainly occur due to the presence of man-made surfaces which prevent the reflection of sunlight and result in temperature increase. In general, urban heat islands result in increased air and land surface temperature and thermal inversion (Gartland, 2012).
Methodology
The present study applies a statistical-analytical research method based upon statistical data received from meteorological stations and extracted from satellite images. Climatic data recorded from 1976 to 2020 in Yazd Meteorological Station were retrieved from the General Meteorological Department of Yazd Province and used to measure temperature changes. Urban climate studies mainly take advantage of long-term patterns and thus, the present study has applied the common Man-Kendall method to measure the trend of temperature changes in warm season (July, August, and September). Also, satellite images collected by Landsat 4-8 in a 33-year period, including four statistical periods with a time interval of 11 years (the average recorded in July, August and September of 1987, 1998, 2009 and 2020), have been used to extract heat islands of Yazd city in warm seasons. These images collected under clear weather conditions were retrieved from the United States Geological Survey website (http://glovis.usgs.gov/) in the WGS-1984 UTM image system. NDVI index was used to investigate the vegetation cover. Main land uses discussed in the present study included barren lands, urban areas, vegetation cover and roads. Sample land uses were collected from Google Earth and visually interpreted in ArcMap. Maximum likelihood algorithm was used for the classification process. Finally, Land Surface Temperature was extracted from satellite images and compared with air temperature trend using the Mann-Kendall test.
Results & Discussion
Results indicate that due to thicker vegetation cover in summer, there has been a negative relationship between the vegetation cover and land surface temperature. In other words, land surface temperature has increased with decreased vegetation cover and vice versa. Types of land use identified in satellite images collected from Yazd city have showed that the city has experienced a widespread physical expansion during the 33-year statistical period regardless of the season under investigation and thus, built-up urban land use class has expanded significantly. As a result, vegetation cover has experienced a negative trend and decreased. Land surface temperature extracted from thermal images of Yazd city has proved parts of northwest and south of the city to be the core of its heat islands. This is due to the presence of barren lands, lack of evapotranspiration mechanisms, high heat absorption capacity and low conduction capacity. Man-Kendall test has found a significant increasing trend for temperature especially in recent years in which the temperature has increased about 2.3 °C. This is most possibly due to the increasing trend of urban population in recent decades, followed by increased residential structures and resulting heat island phenomenon.
Conclusion
In general, classification of urban land use types in Yazd has shown a significant physical expansion of the city during the statistical period. This physical development has occurred in all directions; beginning from the central and northeast-southeast parts, and moving towards northwest-southwest parts. Maximum NDVI was observed in a strip along the central part of Yazd in which vegetation cover is thicker. Green spaces are also observed in some areas of the city. Color spectrum of the LST map has shown relative changes of the ambient temperature in various parts of the city. High and very high temperature (between 41.5 and 50 °C) show the location of the heat islands on LST maps. Also, areas with a deep red color and a temperature above 50 °C have formed hot clusters formed or strengthened between 2009 and 2020 in the west and southwest parts of the city. Satellite images and related graphs have showed that in 2020, Yazd have witnessed a sharp increase in temperature and a heat island. Temperature data of Yazd Meteorological Station and Man-Kendall test have shown a significant increasing trend (about 2.3°C), especially in recent years. These are related to the urban population growth in recent decades, followed by increased urban structures (residential-commercial) and heat island phenomenon.
Remote Sensing (RS)
Narges Arab; Abdolrassoul Salmanmahiny; Alireza Mikaeili Tabrizi; Thomas Houet
Abstract
Extended Abstract Introduction:The Land surface temperature is one of the most important factors in controlling the biological, chemical and physical processes of the earth.Land surface temperature data provide information about the spatial and temporal changes of the Land's surface on a global ...
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Extended Abstract Introduction:The Land surface temperature is one of the most important factors in controlling the biological, chemical and physical processes of the earth.Land surface temperature data provide information about the spatial and temporal changes of the Land's surface on a global scale.Land surface temperature is used as the main parameter in many studies, including energy stock estimation, humidity and evapotranspiration, climate change, urban heat islands and environmental studies. Therefore, it is necessary to measure the temperature of the Land surface in order to plan its use. In general, LST investigation is important and necessary to deal with interdisciplinary issues in earth sciences, urban climatology, environmental changes and human-environment interactions. LST can provide important information about surface physical properties as well as climate, which plays a vital role in many environmental processes. In such a situation, LST maps, which are prepared from satellite images, are a desirable option because they provide a permanent data collection.Materials and methods: Many algorithms have been used by researchers to estimate LST using satellite images, especially thermal bands. In this research, Split window and Mono window algorithms are used from Landsat 8 satellite images to obtain land surface temperature (LST) in Mashhad city.The purpose of this study was to investigate the spatial distribution of the Land surface temperature and also to determine an accurate method for preparing the Land surface temperature map.In the present study, using the Split window algorithm, the land surface temperature (LST) data was used from the TIRS sensor in Landsat 8. Also, in addition to TIRS, OLI sensor data are also needed to estimate LST when using the Split window algorithm. In the first stage, the OLI bands of Landsat 8, bands 3, 4 and 5 are layered on top of each other and the NDVI image is produced using bands 4 and 5. The FVC image is obtained using the NDVI image. FVC is calculated by considering the fraction of vegetation in the area. The split window algorithm uses the FVC image to generate the land surface emissivity (LSE) image. The LSE image measures the internal characteristics of the Land surface, which shows the ability to convert thermal energy into radiant energy. Estimating land surface emissivity (LSE) requires soil and vegetation emissivity for bands 10 and 11. LSE images from bands 10 and 11 are obtained separately and then the average and difference of LSE are calculated. The NDVI image is classified into soil and vegetation and is obtained separately for soil and vegetation. Landsat 8 has two TIRS bands. TB, or brightness temperature, is estimated for bands 10 and 11. The thermal calibration process is done by converting thermal digital numerical values (DN) of thermal bands 10 and 11 of the TIRS meter to spectral radiance of the atmosphere (TOA) and then to TB. Finally, LST is estimated using SW, TB, average LSE, LSE difference and water vapor constant.Results and discussion: The results showed that the temperature of the Land surface calculated by the Mono-window and Split-window method compared to the air temperature calculated in the desired weather station showed a difference of 5.1 and 1.7 degrees Celsius respectively. Therefore, it can be said that the Split window method has higher accuracy and the temperature obtained from it is more consistent with the actual temperature.The regression analysis between the results obtained from these two algorithms for LST shows the value of R2 equal to 0.96, as shown in Figure 8.The close correlation between the LST retrieved using the Split window algorithm, with the LST retrieved from the Mono window algorithm, shows that they can be transferred with a small accuracy error.The difference in LST estimation from Mono-window and Split-window algorithms can be attributed to the spectral bands and atmospheric water vapor content used in LST retrieval. The SW algorithm uses two spectral bands (band 10 and 11) with wavelengths of approximately 11 and 12 μm, while the Mono-window algorithm uses one spectral band (band 10) with a wavelength of approximately 11.5 μm to retrieve LST. In addition, the SW Split window algorithm uses the water vapor content of the atmosphere, which represents the true value of the prevailing conditions at the site. On the other hand, the water vapor content of the atmosphere is not used in the Mono window algorithm. The water vapor content of the atmosphere is a sensitive parameter that affects the climate and the temperature of the Land surface. Since two spectral bands are used in the SW algorithm to determine the emission rate and brightness temperature, and these values are used together with the atmospheric water vapor content in LST retrieval, the SW algorithm is able to record the conditions in the region more accurately. and provide better results compared to the Mono window algorithm.Conclusion: The results showed that the air temperature calculated by the Mono window and Split window method compared to the air temperature calculated in the desired weather station shows a difference of 1.7 and 5.1 degrees centigrade on average, respectively. Therefore, it can be said that the Split window method has a higher accuracy and the obtained temperature is more consistent with the actual temperature. The calculated LST values can differ by up to 5 degrees Celsius with the observed air temperature measurement at the station. In the parts covered with greenery, there are low LST values, while in the southeast with barren lands, non-cultivable lands and urban areas, there are high LST values. The results of this research can provide planners and experts with useful information about the temperature status of different regions where the possibility of building weather stations is impossible, and identifying regions with the potential to create thermal islands and its relationship with land use. and provide protection of natural resources.
Arastou Zarei; Reza Shahhoseini; Ronak Ghanbari
Abstract
Extended Abstract
Introduction
As a key parameter describing physics of land surface processes on local and global scales, land Surface Temperature (LST) is the result of all interactions and energy flows between land surface and the atmosphere. Temperature changes rapidly on temporal ...
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Extended Abstract
Introduction
As a key parameter describing physics of land surface processes on local and global scales, land Surface Temperature (LST) is the result of all interactions and energy flows between land surface and the atmosphere. Temperature changes rapidly on temporal and spatial scales, and thus a complete description of LST require measurements involving spatial and temporal frequencies. Hence, climatological, meteorological, and hydrogeological studies require having access to wide scale information about spatial changes of air temperature. Since the LST product of SLSTR uses linear split-window algorithm, the present study has used nonlinear split-window algorithm to estimate LST in Sentinel-3 images. Linearity of the radiation transfer equation in linear algorithm and some approximations used in split-window algorithms (such as transfer approximation as a linear function of vapor value) result in considerable errors because of which nonlinear algorithm is used in the present study. Using linear split-window algorithm to estimate LST in tropical climates also leads to a high level of error. The present study seeks to estimate LST using a nonlinear split-window algorithm and data retrieved from Sentinel-3 in different seasons of 2018 and 2019. The results are also evaluated using temperature product of MODIS and SLSTR.
Materials & Method
A time series of sentinel-3 images retrieved from 2018 to 2019 was used as research data. Data were collected by Sentinel-3 SLSTR sensors operated by the European Space Agency (ESA). Obviously, images shall be radio-metrically corrected before calculating physical land surface parameters such as temperature, emissivity, reflectance and radiance, albedo, and etc. To reach this goal, it is necessary to omit or minimize the effect of atmosphere, epipolar geometry of sensor, sunlight, topography, and surface characteristics while estimating surface parameters in these images. The current study seeks to estimate LST applying a nonlinear split-window algorithm on Sentinel-3 data collected during different seasons of 2018 and 2019 and to evaluate the results using temperature product of MODIS, SLSTR, and in-situ data. Pearson Correlation Coefficient and Root Mean Square Error (RMSE) were also used as relative and quantitative criteria to evaluate the accuracy of the proposed method and determine the deference between temperature calculated by the proposed method and temperature product of MODIS and SLSTR sensor. Hence, four frames of LST product collected by MODIS, and SLSTR in April, June, and October, 2018 and January, 2019 were used to evaluate the proposed method.
Results & Discussion
The proposed method was also indirectly evaluated using temperature products of MODIS and SLSTR sensor. Applying parameters of mean and root mean square error, the evaluation has shown that the results obtained from the proposed method in the one-year reference period were more similar to the results obtained from MODIS sensor. Comparing nonlinear Split-Window algorithm and MODIS products, RMSE ranged from 1.21 to 2.46 and the highest and lowest accuracy belonged to winter and summer, respectively. Comparing this algorithm with the SLSTR product, RMSE ranged from 0.76 to 2.24 and the highest and lowest accuracy belonged to winter and summer, respectively. Proper performance of the algorithm in winter is due to the relative balance of atmospheric water vapour in this season. Comparing nonlinear modelling of atmospheric water vapour in the non-linear algorithm of a Split-window and the linear algorithm in SLSTR and MODIS products, the small difference between temperature calculated by the algorithm and the products can be justified. However, due to temperature fluctuations in summer, results obtained by the proposed method were not reliable enough compared to both temperature products. Generally, results obtained from the proposed method showed a higher correlation with the temperature product of SLSTR sensor, which is due to the similar spectral bands used in calculating the surface temperature. Relative comparison of the Split-Window and the MODIS product’s nonlinear algorithm showed a coefficient of determination ranging from 0.76 to 0.96, while comparing this algorithm with the SLSTR product showed a determination coefficient of 0.80 to 0.98. Comparing temperature obtained from the nonlinear Split-Window algorithm with SLSTR and MODIS temperature products, the proposed algorithm was relatively stable no matter which season was taken into account.
Conclusion
The present study seeks to estimate Land Surface Temperature using a nonlinear Split-Window algorithm and Sentinel-3 data collected in different seasons. Values obtained from the algorithm were validated using in-situ dataset retrieved from the meteorological station. They were also evaluated using temperature product of MODIS and SLSTR. To increase the accuracy level, temperature product of MODIS and SLSTR were also evaluated and compared with the in-situ dataset and provided good results. Generally, there is a significant difference between temperature values estimated by the NSW algorithm for different seasons especially summer. However, a similar trend was observed in temperature changes reported by SLSTR and MODIS, and the proposed algorithm in different seasons of the study area. Although, the nonlinear Split-Window algorithm showed a higher accuracy in spring and winter, overall results indicated that the proposed method was relatively stable no matter which season was taken into account. It can be concluded that LST estimation with nonlinear Split-window method and Sentinel-3 satellite data has an acceptable level of accuracy and thus, can be used in large scale environmental crises such as climate changes.
Reza Parhizcar isalu; Khalil Valizadeh Kamran; Bakhtiar Faizizadeh
Abstract
Extended Abstract
Introduction
Geothermal energy is one of the major sources of new and environmentally friendly energieswhich, if used correctly and based on environmental parameters, plays an important role in the energy balance of the country and the goals of sustainable development.However, detecting ...
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Extended Abstract
Introduction
Geothermal energy is one of the major sources of new and environmentally friendly energieswhich, if used correctly and based on environmental parameters, plays an important role in the energy balance of the country and the goals of sustainable development.However, detecting and exploring sources of this energy using modern and low cost methods –as a replacement for land surveying methods-can help planners and authorities working in the field of energy. In this regard, thermal remote sensing with a vast coverage of the earth’s surface, and the possibilityof calculating land surface temperature using satellite imagery plays an important role as a new economic tool.Mapping land surface temperature is a key point in achieving geothermal anomalies and different algorithms play an important role in land surface temperature estimation. Therefore, identifying potential sources of geothermal energyusingremotely sensed thermal data is a challenging and yet interesting subject.
Materials and Methods
The present study takes advantage of images received from OLI and TIRS sensors (Landsat 8) to estimate land surface temperature, analyze thermal anomalies, and identify areas with potential geothermal resources in Meshkinshahr.The images were retrieved fromUSGSin Geo TIFF format.Envi 5.3, eCognition 9.1, MATLAB and ArcMap 10.4.1 were used to prepare, process and analyze the images.Moreover, meteorological data received fromMeshkinshahr station was collected from the General Department and Meteorological Center of Ardabil Provincewith the aim of identifying the optimal algorithm for calculation ofland surface temperature. Data wascollected for a one-day period (31/08/2017), i.e. the same day Landsat 8 passed over the areaunder study.
Results and Discussion
The present study sought to identify areas with potential geothermal resources using thermal remote sensing and a combination of surface temperature and thermal anomaly models. In order to calculate thermal anomaly, an observational thermal image is required, which is in fact the same land surface temperature calculated using Split Window and Mono Window algorithmsfor the image received from the satellite thermal band at the moment of collecting images. It should be noted that the land surface temperature calculated with these algorithms was evaluated using statistical data recorded in the temperature monitoring station. Results indicated higher accuracy of Split Window algorithm (3 ° C difference). Since, temperature obtained from this algorithm was more consistent with the actual temperature, its results were used as the observational thermal image.A thermal model was also defined to model factors responsible for heat variation from one pixel to another one. These two images were calculated and subtracted to reach the thermal anomaly image.In order to identify thermal anomalies caused by undergroundfactors heating the earthsurface, other factors responsible for increasing/decreasinglandsurfacetemperature should be normalized in the image. Thus, the effect of parameters such as solar energy, environmental degradation and evaporation on land surface temperature obtained from split window algorithm was investigated and finally, areas with heat anomalies and evidences indicating the presence of geothermal resources around themwere selected as areas with potential geothermal resources.Results indicate that inthe area surroundingSabalanmountains,two regions with 5.5 and 10.05 hectares in the northern and northeastern parts of Moyelvillage, a1.4 hectares area in the southwestern part of Qutursouli Spa, and the southern part of the Qinrjah Spa with an area of 1.1 hectare had potentialgeothermal resources and a high potential for exploration of geothermal resources.
Conclusion
The presence of hot springs, a geothermal power plant and other evidences shows that Ardabil Province and especially Meshkinshahr city has the potential for geothermal energy production as one of the major sources of new and environmentally friendly energies.However, no effective studies have been performed to identify these resources using modern and low-cost methods including thermal remote sensing.Therefore, the present study for the first time took advantage ofGIS and remote sensingto identify areas appropriate for geothermal energy extraction inMeshkinshahr city and concluded that remote sensing studies on Landsat 8 satellite images have a high efficiency for identifying areas with potential geothermal resources. Thus, areas identified in the present study have a strong spatial correlation with the geothermal evidences founded in the region.
Mahdi Sedaghat; Hamid Nazaripour
Abstract
Extended Abstract Introduction Soil moisture is considered to be a key parameter in meteorology, hydrology, and agriculture, and the estimation of its temporal-spatial distribution contributes to understanding the relations between precipitation, evaporation, water cycle, and etc. Soil moisture reduction ...
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Extended Abstract Introduction Soil moisture is considered to be a key parameter in meteorology, hydrology, and agriculture, and the estimation of its temporal-spatial distribution contributes to understanding the relations between precipitation, evaporation, water cycle, and etc. Soil moisture reduction results in the creation of centers susceptible to dust storms. With socio-economic impacts ranging from urban to intercontinental and from a few minutes to several decades, this can challenge regional development. The first estimate of potential dust sources is derived from the soil properties. With the reduction of surface soil moisture and the wind speedcrossing a certain threshold level, wind erosion process can cause the formation of dust storms. Field studies have proved that increasing the moisture content in soil from zero to about 3%, reduces the dust concentrationsignificantly. To understand the climatology of dust and develop related numerical predictive methods, continuous recording of dust storms is essential, which requires effective and continuous monitoring of the variations in surface soil moisture. Remote sensing technology is an effective method for calculating soil moisture. This technology was first used for the estimation of energy flux and surface soil moisture in the 1970s. To extract the surface soil moisture content, some remote sensing methods use surface radiation temperature and some others apply water transfer (soil/vegetation/air) (SVAT) model. Various indices have been developed for soil moisture monitoring, such as soil moisture (SM), soil water index (SWI), Temperature-Vegetation-Dryness Index (TVDI), Soil Moisture Index (SMI) and Perpendicular Soil Moisture Index (PSMI), all of which combine vegetation and surface temperature variables. Materials and Methods Soil moisture is considered to be a significant parameter in the exchange of mass and energy between the Earth surface and the atmosphere. Lack of soil moisture or decreased moisture in soil is considered to be a factoraccelerating the process of dust storm formation. During the previous decades, water stresses on the ecosystem of Hour-al-Azim have transformed this wetland into one of the main dust centers in the southwest Iran. Hour-al-Azim is one of the largest wetlands in southwestern Iran. This wetland is shared between in Iran and Iraq. It is located between N 30° 58´- N31° 50´ and E 47° 20´- 47° 55´. The Iranian part of this wetland encompassed an area of 64,100 ha in the 1970s, while in the 2000s, the area has decreased to only 29,000 ha. The present study aims to monitor the spatial-temporal variability of soil moisture in Hour-al-Azim wetland and to investigate the relation between these changes and dust storms in the southwest Iran. To reach this end, we used 8-day images obtained from the Aqua satellite in the period of 2003 to 2017 and also annual frequency of dust storms with a visibility of less than 1000 m in the period of1987–2017. A database consisting of 189 images of the red band, near-infrared band, and ground surface temperature (LST) was created, which contained 4 images per year (one image per season). The resolution of the red / near-infrared band data and daily LST values were 231.65 and 926.62 meters, respectively. Then, soil adjusted vegetation indices (SAVI) and perpendicular soil moisture index (PSMI) were extracted. SAVI index is used to reduce the effect of background soil on vegetation cover in semi-arid and arid environments with less than 30% vegetation cover.Compared to NDVI, SAVIwith L = 0.5reduces the effect of soil changes on green plants. In the next step, a trapezoidal method was used to calculate the PSMI index. In order to investigate changes in the soil moisture content of the Hour-al-Azim wetland, three time series obtained from regional mean of SAVI, LST and PSMI remote sensing indices and a time series consisting of the number of days with dust storms observed in the 9 stations were evaluated using simple linear regression test. Results and discussion Extracting Soil Adjusted Vegetation Index indicated that in the study period, the highest values of this index was observed with a regional mean of 0.15 on 4/7/2014 and the lowest values was observed with a regional mean of 0.08 on 1/1/2005. Land Surface Temperature survey showed that during the study period, the highest values of this index was observed with a regional mean of 54.42 ° C on 7/4/2010 and the lowest values was observed with a regional mean of 17.28 ° C on 1/1/2007. The regional mean of Perpendicular Soil Moisture Index indicates that despite winter is considered to bethe wettest season of the region, PSMI index with a regional mean of 0.2 has experienced the driest soil moisture conditionsat the beginning of winter (1/1/2016),while it had experienced the wettest soil moisture conditionsin the same season on 1/1/2009 with a regionalaverage of 0.13. Conclusion Finding of the present study indicate an increasing trend in the range of remote sensing indicators. The range of SAVI index is increasing, which means that the density of vegetation in the Wetland is decreasing. Perpendicular Soil Moisture Index values also show an increasing trend, indicating a decrease in soil moisture content. As a result of the decrease in soil moisture, the vegetation density also has decreased and the land surface temperature has increased. Results of statistical tests indicate the role of changes in environmental conditions of Hour-al-Azim wetland in the frequency of dust storms. Using findings of the present study, or studies such as Kim et al. (2017), it is possible to take advantage of soil moisture variations for the prediction of dust generation, its emission, and spread level.
Javad Javdan; Mohammad Hossein Rezaei Moghaddam; Yousef Ebadi
Abstract
Extended Abstract
Introduction
Land surface temperature (LST) is one of the key parameters in environmental studies on local to global scales. Considering the limitations of local meteorological stations, remote sensing has opened a new horizon in collection of suchinformation. Recently, successful ...
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Extended Abstract
Introduction
Land surface temperature (LST) is one of the key parameters in environmental studies on local to global scales. Considering the limitations of local meteorological stations, remote sensing has opened a new horizon in collection of suchinformation. Recently, successful launch of Landsat 8 with two thermal bands has provided a good opportunity for retrieving land surface temperature usingthermal remote sensing technology. Many studies had been performedwith the aim of retrieving land surface temperature, but available evidencesshow a significant calibration uncertainty inThermal Infrared Sensor (TIRS) of Landsat 8 band 11 and thus development of new studies based on onethermal band seems to be necessary. However, calibration documents issued by the United States Geological Survey (USGS) indicated uncertainty ofdata received from Band 11 Thermal Infrared Sensor (TIRS) of Landsat 8 and suggested using Band 10 data as a single spectral band for LST estimation.
Materials & Methods
In this study, mono-window algorithm with its three essential parameters (ground emissivity, atmospheric transmittance and effective mean atmospheric temperature)has been developedunderan automated algorithmin MATLABand was used for Landsat 8 data.Thermal band 10 was used to estimate brightness temperature. Bands 4 and 5 were also used to calculate the NDVI. Retrieval of LST from Landsat 8 TIRS data is performed based on the premise that brightness temperature (Ti)can be computed for any pixel of Band 10 using the mono-window algorithm.Since the observed thermal radiance for Band 10 of Landsat 8 TIRS is stored and transferredasa digital number (DNs) with 16 digits between 0 and 65,535, it is possible toconvertthe DN value into thermal radiance and then convert radiance into brightness temperature.Ground emissivity is calculatedusing land cover patterns received from other bands of Landsat 8, and the other two parameters are estimated based on the local meteorologicaldata. Usually, obtaining an accurate estimate of ground emissivity is very difficult, and the atmospheric water vapor content is considered to be a sensitive parameter in traditional LST retrieval methods.
Results & Discussion
The algorithm has been successfully applied to Tabriz city in north west of Iran with the aim of analyzing spatial distribution of LST. After running the algorithm on the satellite images of the study area on July 18,2016, a lower land surface temperature was observed in green spaces with 1.2°C accuracy as compared to urban areas and wastelands. The lowest temperature in the study area was 20°C and the highest temperature was 53°C and mean temperature was 38.78°C.Results indicate that the algorithm candiscover natural urban heat islands accurately. Moreover, spatial distribution of LST in the region is quite well matched with the land covers. Successful application of the algorithm proves the efficiency of improved mono-window algorithm as a method used for retrieving LST from Landsat 8 data.
Conclusion
Compared to common methods,the proposed algorithm estimates land surface temperature with minimum requirement for user intervention, least possible time and an acceptable accuracy. Itgives researches an opportunity to easily compute LST and apply it in other studies, and thus it is a significant tool.
Mehrdad Hadipour; Hamid Darabi; Aliakbar Davudirad
Abstract
Extended Abstract Introduction With the development of urbanization, a large part of agricultural areas and forests have been replaced by residential areas, industrial centers, and other infrastructures. This is due to human life style and his endeavor to reach sustainable urbanization. A series of changes ...
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Extended Abstract Introduction With the development of urbanization, a large part of agricultural areas and forests have been replaced by residential areas, industrial centers, and other infrastructures. This is due to human life style and his endeavor to reach sustainable urbanization. A series of changes in the reflection of light from different material’s surface, heat storage and heat transfer, have changednatural and artificial landscape orsignificantly affected local climate. Therefore, public concerns about urban sprawl, increasing urban population and quality of urban environmental have motivated planners to seek better perspectives for development of urban areas. Increasing temperature of urban areas is considered to be one of the most important environmental problem in cities. This increasing temperature results in creation of Urban Heat Islands (UHI) in some parts of urban areas, which are significantly warmer than surrounding urban environment. Therefore,a new and successful method of urban planning should be introduced with respect to spatial distribution of land surface temperature (LST) to achieve better urbanization and reduce environmental impacts on cities. Materials & Methods The present study takes advantage of Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+) thematic maps to investigate therelationship between air pollution, and two indexes of NDBI and NDVI with land surface temperature (LST) and Urban Heat Islands (UHI) in urban areas. Satellite imageries of Arak (an industrial city in Iran) has been chosen for the case study. Urban and natural areas and impermeable surfaces such as roads, buildings and other constructions are rapidly developing in this city. In the first step of research methodology, necessary pre-processing programs such as radiometric corrections were performed on the satellite imageries. Then satellite imageries were transformed toatmospheric images to produce NDBI and NDVI indexes. Finally,land surface temperature maps wereproduced using the method of Landsat Project Science Institute in Arc GIS 10.3. To classify satellite images, seven land use classes were identified as poor pastures, averagepastures, rich pastures, bare lands, Lake’s Shore, agricultural lands and residential lands.Then, training images classification method was used to collect samples from the study area and classification was performed using maximum likelihood method for monitoring. In order to analyze LST parameter using NDBI and NDVI indexes, air quality data,and statistical methods like Kolmogorov-Smirnov test, paired t test and Pearson correlation test were used. The results of Kolmogorov-Smirnov test indicated that data used in this study was normally distributed. The results of t test, temperature recorded by synoptic stations in Arak and remotely sensed data indicated that the accuracy of the test is more than 5%. Thus, the difference between residential land use and other urban land uses was not statistically significant. Moreover, results indicate that there is a more than 99 percent correlation between temperature recorded by the synoptic stations in Arak and data collected from satellite imageries. Results of correlation with remotely sensed data indicatedthatthere is a significant correlation between99 percent of results and less than 5 micron particles. Results & Discussion Correlation between air pollution data andremotely sensed data (LST) indicated that LST and less than 5.2 micronparticlesare significantly correlated with 99% accuracy. Urban heat island usually occurs in metropolitan area and its surroundings. Due to climate changes, urban heat islands are constantly developing. This results in increased energy consumption for air conditioning systems. Thus, reducing the effects of urban heat islands has become an important global issue. The present study has successfully explained the effects of urban heat islands and their environmental problems on normal life. Detailed program of related measures and policies should reduce the intensityof urban heat island. Final development of the cities should be based on land surface temperatures in surrounding areas in a way that cities can reach a lower surface temperature as compared to the temperature before urban development. Conclusion Following strategies are suggested for a more comprehensive consideration of urban green spaces in urban planning and future development of cities: Paying attention to architecturalcriteria and urban land use, and alsopaying attention to soil and water management parametersbased on the principles of green architecture, paying attention to standards of anthropogenic temperature rise caused by human activities, and the problem of urban heat islands. Moreover, it is crucially important to prepare the necessary situation for the community to reach a good physical and mental health.
