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Nazanin Salimi , Marzban Faramarzi, Dr Mohsen Tavakoli, Dr Hasan Fathizad,
Volume 10, Issue 3 (9-2023)
Volume 10, Issue 3 (9-2023)
Abstract
In recent years, groundwater discharge is more than recharge, resulting in a drop-down in groundwater levels. Rangeland and forest are considered the main recharge areas of groundwater, while the most uses of these resources are done in agricultural areas. The main goal of this research is to use machine learning algorithms including random forest and Shannon's entropy function to model groundwater resources in a semi-arid rangeland in western Iran. Therefore, the layers of slope degree, slope aspect, elevation, distance from the fault, the shape of the slope, distance from the waterway, distance from the road, rainfall, lithology, and land use were prepared. After determining the weight of the parameters using Shannon's entropy function and then determining their classes, the final map of the areas with the potential of groundwater resources was modeled from the combination of the weight of the parameters and their classes. In addition, R 3.5.1 software and the randomForest package were used to run the random forest (RF) model. In this research, k-fold cross-validation was used to validate the models. Moreover, the statistical indices of MAE, RMSE, and R2 were used to evaluate the efficiency of the RF model and Shannon's entropy for finding the potential of underground water resources. The results showed that the RF model with accuracy (RMSE: 3.41, MAE: 2.85, R² = 0.825) has higher accuracy than Shannon's entropy model with accuracy (R² = 0.727, RMSE: 4.36, MAE: 3.34). The findings of the random forest model showed that most of the studied area has medium potential (26954.2 ha) and a very small area (205.61 ha) has no groundwater potential. On the other hand, the results of Shannon's entropy model showed that most of the studied area has medium potential (24633.05 ha) and a very small area (1502.1 ha) has no groundwater potential.
Nabi Mohamadi, Behrouz Sari Saraf, Hashen Rostamzadeh,
Volume 10, Issue 3 (9-2023)
Volume 10, Issue 3 (9-2023)
Abstract
Nowadays, due to global warming, drought and the occurrence of cold periods and heat stress, the study of climatic variables is very important. Therefore, in this research, the long-term forecast of temperature changes in northwest Iran in the base period (1985-2014) and three periods of the near future (2021-2050), the medium future (2051-2080) and the distant future (2100- 2081) was paid. For this purpose, 2 extreme temperature indices including Warm spells duration index (WSDI) and cold spells duration index (CSDI) and Maan-Kendall trend test were used to check the changes. To predict the changes of the profiles in the future period after evaluating 7 general circulation models (GCMs) from the sixth report model series (CMIP6) from two optimal models under three socio-economic forcing scenarios including SSP1-2.6, SSP3-7.0 and SSP5-8.5 was used. The spatial distribution of the trend of changes in the Warm spells duration index (WSDI) in the base period showed that its maximum core is located in the south and southwest of the region, and its amount decreases by moving towards the north and northeast. Spatial changes of the Cold spells duration index (CSDI) are characterized by its maximum cores in the western regions and around Lake Urmia and minimum cores in the central and northern regions of the study area. According to the results, the average Warm spells duration index (WSDI) and of the Cold spells duration index (CSDI) are equal to 5.53 and 3.80 days per year, respectively, and the maximum and minimum Warm spells duration index (WSDI) are 1.8 and 2.7 days, respectively Piranshahr and Parsabad stations and the maximum and minimum and the Cold spells duration index (CSDI) are also 5.7 and 1.32 days corresponding to Zarineh and Marivan stations. Examining the trend of changes also showed that in most stations, the WSDI index has an increasing trend, and this trend has become significant in some stations, but the CSDI index has a decreasing trend and is not significant in any of the stations. The evaluation of different models with different error measurement indices also showed that MRI-ESM2-0 and MPI-ESM1-2-L models have the best performance in simulating temperature extreme in the studied area. The distribution of changes in the future period also showed that the WSDI will increase in most stations and based on all three scenarios, especially the SSP5-8.5 scenario, but the CSDI trend will decrease in most stations and based on the SSP3-7.0 and SSP5-8.5 scenarios will be significant.
Hayedeh Ara, Zahra Gohari, Hadi Memarian,
Volume 10, Issue 3 (9-2023)
Volume 10, Issue 3 (9-2023)
Abstract
Introduction
Desertification is one of the major environmental, socio-economic problems in many countries of the world (Breckle, et.al., 2001). Desertification is actually called land degradation in dry, semi-arid and semi-humid areas, the effects of human activities being one of the most important factors (David and Nicholas, 1994). Sand areas are one of the desert landforms, whose progress and development can threaten infrastructure facilities. The timely and correct identification of the changes in the earth's surface creates a basis for a better understanding of the connections and interactions between humans and natural phenomena for better management of resources. To identify land cover changes, it is possible to use multi-temporal data and quantitative analysis of these data at different times (Lu, et.al., 2004), therefore, one of the accurate management tools that causes the application of management based on current knowledge, these studies Monitoring is done using the mentioned data. The use of satellite data and ground information in such studies has caused many temporal and spatial changes of phenomena to be well depicted, which can be beneficial in better understanding and interaction with the environment and ultimately its sustainable management and development. To obtain and extract basic information, the best tool is to use telemetry technologies, which by using satellite data, in addition to reducing costs, increases accuracy and speed, and its importance is increasing day by day in the direction of sustainable development (Alavi Panah, 1385). Since field studies in the field of spatial changes of sandy areas of this city are difficult and expensive to repeat, facilities such as simulating these areas with expert algorithms and artificial intelligence can be used to investigate and monitor critical areas at regular intervals. Accurate and economically appropriate. Therefore, in this research, with the aim of investigating the effectiveness of these models in the periodic changes of the sandy plains of Ferkhes plain, two algorithms, perceptron neural network and random forest, were chosen, and the reason for choosing these models is the ability to model according to the existing uncertainties, interference Fewer users and insensitivity of the model to how the data is distributed.
Materials and Methods
The progress and development of the sandy areas of the Fern Plain depends on three factors, climatic, environmental and human. Therefore, the input variables to the expert and artificial intelligence models were chosen to cover these three factors. Therefore, factors such as drought, the number of dusty days, as well as vegetation index were entered into the model as dynamic variables, and environmental factors such as soil, elevation and altitude, geology, slope and direction were entered into the model as static variables. The statistical period investigated for the changes of wind erosion zones was considered to be 15 years from 2000 to 2015, based on this time base, qualitatively homogeneous and reconstructed meteorological data and images A satellite was selected and processed in 5-year periods (2000, 2005, 2010 and 2015). Modeling of the changes of sandy areas was done using two algorithms of perceptron neural network and random forest in MATLAB software environment. To choose the best neural network structure, a large number of neural networks with different structures were designed and evaluated. These neural networks were built and implemented by changing adjustable parameters (including transfer function, learning rule, number of middle layer, number of neurons of middle layer, number of patterns). One of the most common types of neural networks is multilayer perceptron (MLP). This network consists of an input layer, one or more hidden layers and an output. MLP can be trained by a back propagation algorithm. Typically, MLP is organized as a set of interconnected layers of input, hidden, and output artificial. The accuracy of these networks was checked by the statistical criteria calculated in the test stage, and finally the network that had the closest result to the reality was selected as the main network. The main active function used in this research is sigmoid, which is a logistic function. Then by comparing the network output and the actual output, the error value is calculated, this error is returned in the form of back propagation (BP) in the network to reset the connecting weights of the nodes (Chang and Liao, 2012). Other evaluation indices MSE, RMSE and R were used as network performance criteria in training and validation. The selection of Fern plain as a study area is due to the high potential of this area in the advancement of sand areas, for this purpose, 8 effective factors in the development of these areas were investigated. These factors were entered into the model in the form of three dynamic indices and five static indices.
Results and Discussion
In evaluating the results of modeling algorithms, dynamic variables in all periods were introduced as the most important factors in the occurrence of wind erosion and the advancement of sand areas. The diagram of the importance of predictor variables is presented in Figure 7. The results show that the vegetation cover index ranks first in all periods, the drought index ranks second in 2000 and 2015, and the dust days index ranks third in these two years. Meanwhile, in 2005 and 2010, the dust index and drought index ranked second and third respectively. Among the static variables used in this research, the height digital model variable was ranked fourth in 2000 and 2010, and in 2005 and 2015, geological and soil variables were important. In almost all studied periods, the direction factor is less important than other factors, which can be removed from the set of variables required for modeling to predict sand areas.
Desertification is one of the major environmental, socio-economic problems in many countries of the world (Breckle, et.al., 2001). Desertification is actually called land degradation in dry, semi-arid and semi-humid areas, the effects of human activities being one of the most important factors (David and Nicholas, 1994). Sand areas are one of the desert landforms, whose progress and development can threaten infrastructure facilities. The timely and correct identification of the changes in the earth's surface creates a basis for a better understanding of the connections and interactions between humans and natural phenomena for better management of resources. To identify land cover changes, it is possible to use multi-temporal data and quantitative analysis of these data at different times (Lu, et.al., 2004), therefore, one of the accurate management tools that causes the application of management based on current knowledge, these studies Monitoring is done using the mentioned data. The use of satellite data and ground information in such studies has caused many temporal and spatial changes of phenomena to be well depicted, which can be beneficial in better understanding and interaction with the environment and ultimately its sustainable management and development. To obtain and extract basic information, the best tool is to use telemetry technologies, which by using satellite data, in addition to reducing costs, increases accuracy and speed, and its importance is increasing day by day in the direction of sustainable development (Alavi Panah, 1385). Since field studies in the field of spatial changes of sandy areas of this city are difficult and expensive to repeat, facilities such as simulating these areas with expert algorithms and artificial intelligence can be used to investigate and monitor critical areas at regular intervals. Accurate and economically appropriate. Therefore, in this research, with the aim of investigating the effectiveness of these models in the periodic changes of the sandy plains of Ferkhes plain, two algorithms, perceptron neural network and random forest, were chosen, and the reason for choosing these models is the ability to model according to the existing uncertainties, interference Fewer users and insensitivity of the model to how the data is distributed.
Materials and Methods
The progress and development of the sandy areas of the Fern Plain depends on three factors, climatic, environmental and human. Therefore, the input variables to the expert and artificial intelligence models were chosen to cover these three factors. Therefore, factors such as drought, the number of dusty days, as well as vegetation index were entered into the model as dynamic variables, and environmental factors such as soil, elevation and altitude, geology, slope and direction were entered into the model as static variables. The statistical period investigated for the changes of wind erosion zones was considered to be 15 years from 2000 to 2015, based on this time base, qualitatively homogeneous and reconstructed meteorological data and images A satellite was selected and processed in 5-year periods (2000, 2005, 2010 and 2015). Modeling of the changes of sandy areas was done using two algorithms of perceptron neural network and random forest in MATLAB software environment. To choose the best neural network structure, a large number of neural networks with different structures were designed and evaluated. These neural networks were built and implemented by changing adjustable parameters (including transfer function, learning rule, number of middle layer, number of neurons of middle layer, number of patterns). One of the most common types of neural networks is multilayer perceptron (MLP). This network consists of an input layer, one or more hidden layers and an output. MLP can be trained by a back propagation algorithm. Typically, MLP is organized as a set of interconnected layers of input, hidden, and output artificial. The accuracy of these networks was checked by the statistical criteria calculated in the test stage, and finally the network that had the closest result to the reality was selected as the main network. The main active function used in this research is sigmoid, which is a logistic function. Then by comparing the network output and the actual output, the error value is calculated, this error is returned in the form of back propagation (BP) in the network to reset the connecting weights of the nodes (Chang and Liao, 2012). Other evaluation indices MSE, RMSE and R were used as network performance criteria in training and validation. The selection of Fern plain as a study area is due to the high potential of this area in the advancement of sand areas, for this purpose, 8 effective factors in the development of these areas were investigated. These factors were entered into the model in the form of three dynamic indices and five static indices.
Results and Discussion
In evaluating the results of modeling algorithms, dynamic variables in all periods were introduced as the most important factors in the occurrence of wind erosion and the advancement of sand areas. The diagram of the importance of predictor variables is presented in Figure 7. The results show that the vegetation cover index ranks first in all periods, the drought index ranks second in 2000 and 2015, and the dust days index ranks third in these two years. Meanwhile, in 2005 and 2010, the dust index and drought index ranked second and third respectively. Among the static variables used in this research, the height digital model variable was ranked fourth in 2000 and 2010, and in 2005 and 2015, geological and soil variables were important. In almost all studied periods, the direction factor is less important than other factors, which can be removed from the set of variables required for modeling to predict sand areas.
Sediqeh Mozaffari Qarah Bolagh, Brhrooz Mozaffari Qarah Bolagh, Mehdi Cheraghi,
Volume 10, Issue 3 (9-2023)
Volume 10, Issue 3 (9-2023)
Abstract
Providing food to achieve food security is considered one of the important goals of development in all countries, reducing food insecurity is considered an important political and social achievement for governments. One of the effects of food insecurity in rural areas is the number of patients and deaths caused by the corona epidemic. In this regard, the current research seeks to answer the following questions: What is the level of food insecurity in the studied villages? And what effect does food insecurity have on the spatial distribution of corona patients and deaths? The type of research is applied and descriptive-analytical in nature. The statistical population of this research is all the villages in the central part of Zanjan city, which were surveyed in full. The method of collecting information is in the form of a library and the method of data analysis is in the form of descriptive statistics and spatial analysis. The findings of the research show that the average food insecurity of the studied villages is equal to 36.08%, the highest level of food insecurity is related to Taham district with 40.76% and the lowest level of food insecurity is related to Mirizat district. In order to analyze the effects of food insecurity on the mortality caused by Corona, geographic weighted regression has been used, based on the results obtained from this spatial analysis tool, the width is equal to 0.172, the remaining squares are equal to 2836, the effective number is equal to 16.86, Sigma is equal to 5.64 and the coefficient of determination, which measures the degree of linear relationship between two variables, has been calculated as equal to 0.72, so it can be said that with the increase in food insecurity, the death rate due to Corona will also increase.
Mohammad Sadegh Ghadam Khair, Reza Borna, Jafar Morshedi, Jebraeel Ghorbanian,
Volume 10, Issue 3 (9-2023)
Volume 10, Issue 3 (9-2023)
Abstract
Introduction
Extensive and massive agriculture, along with other agricultural activities such as animal husbandry, industrial activities in the southern half of the province, has created and intensified extensive changes in the environmental resources and natural structure of the province. This extensive change can show its effects and consequences in the destruction of forest lands, the transformation of rich pastures into poor pastures and barren lands, severe soil erosion, and finally the creation and development of internal centers of dust. and intensify the severity of dust incidents in the province. Dust events have profound and significant effects on agriculture and soil fertility, health and hygiene, disruption and destruction of industries and power plants, and negative effects on the environment, including the deterioration of forests. Airborne particles, which are mainly driven to the region by dust storms, are one of the important components of the atmospheric system. They can not only change the albedo of the energy balance by acting as cloud particle nuclei, or ice nuclei.
Materials and Methods
The study location of this research is Khuzestan province, which is one of the most challenging provinces in the country in terms of environmental hazards. This province, with an area of about 6.5 million hectares, occupies about 4% of the country's area. Dust is one of the major and most important challenges of this province. Its destructive effects can be traced in various dimensions, such as the quality of water resources, the quality and performance of agricultural products, industries and energy transmission networks, and the air quality of cities. Three categories of data have been used in this research. The data of the first category is related to the data of widespread dust days in Khuzestan province. These data were obtained from the dust codes of the current air condition (ww parameter of synoptic stations of the province) during the statistical period of 2000, 2020. The second category of data was actually the remote sensing data of MODIS sensor, which included the Aerosol Optical Depth (AOD) product of MODIS sensor (MOD04 product) and Aerosol Exponential Index (AEA). These two indicators are dimensionless but with different directions. In the AOD index, higher numbers represent more aerosols in the atmosphere and in the AEA index, in addition to the presence of dust in the place, it also provides the size of the aerosol particles. Finally, the third category of data is the reanalysis data related to incoming net shortwave radiation (SNSR), which was taken from the reanalysis data of the European ECMWF database version ERA5 with a spatial resolution of 0.5 arc degrees.
Conclusion
In this research, it was tried to investigate the influence of the dust event in the context of fluctuations and daily changes in the amount of net shortwave radiation received on the earth's surface. The results of the investigation of three cases of widespread dust in the province showed that in these three cases of widespread dust, aerosol particles are generally in the central, southern and western parts of the province (plain and lowland areas of the province) from the type of medium to large particles (index angstrom between 0.5 and 1) and in the eastern and northeastern parts, it was of the type of coarse particles (angstrom index less than 0.5). In the context of the impact of dust events on the amount of shortwave radiation received by the earth's surface, it was seen that in the dust event of July 22, 2010, the Angstrom exponential index indicates the presence of coarse particles in the atmosphere near the earth's surface and the AOD index also indicates the presence of dense dust in the entire area of the province. The received net shortwave radiation (at 12 noon or 09 UTC) was about 194 watts per square meter (about 28 percent) lower than the average for the same month. This drop rate was less in the other two dust waves, whose AOD and Angstrom index values indicated finer and less concentrated dust. In the dust wave of June 19, 2012, the amount of net shortwave radiation received was only 5% (25 W/m2 at 12 noon or 09 UTC) less than the long-term average, and this drop in the dust event of May 12, 2018 was equal to 28 W/m square (about 4% drop compared to the average of the same month).
Extensive and massive agriculture, along with other agricultural activities such as animal husbandry, industrial activities in the southern half of the province, has created and intensified extensive changes in the environmental resources and natural structure of the province. This extensive change can show its effects and consequences in the destruction of forest lands, the transformation of rich pastures into poor pastures and barren lands, severe soil erosion, and finally the creation and development of internal centers of dust. and intensify the severity of dust incidents in the province. Dust events have profound and significant effects on agriculture and soil fertility, health and hygiene, disruption and destruction of industries and power plants, and negative effects on the environment, including the deterioration of forests. Airborne particles, which are mainly driven to the region by dust storms, are one of the important components of the atmospheric system. They can not only change the albedo of the energy balance by acting as cloud particle nuclei, or ice nuclei.
Materials and Methods
The study location of this research is Khuzestan province, which is one of the most challenging provinces in the country in terms of environmental hazards. This province, with an area of about 6.5 million hectares, occupies about 4% of the country's area. Dust is one of the major and most important challenges of this province. Its destructive effects can be traced in various dimensions, such as the quality of water resources, the quality and performance of agricultural products, industries and energy transmission networks, and the air quality of cities. Three categories of data have been used in this research. The data of the first category is related to the data of widespread dust days in Khuzestan province. These data were obtained from the dust codes of the current air condition (ww parameter of synoptic stations of the province) during the statistical period of 2000, 2020. The second category of data was actually the remote sensing data of MODIS sensor, which included the Aerosol Optical Depth (AOD) product of MODIS sensor (MOD04 product) and Aerosol Exponential Index (AEA). These two indicators are dimensionless but with different directions. In the AOD index, higher numbers represent more aerosols in the atmosphere and in the AEA index, in addition to the presence of dust in the place, it also provides the size of the aerosol particles. Finally, the third category of data is the reanalysis data related to incoming net shortwave radiation (SNSR), which was taken from the reanalysis data of the European ECMWF database version ERA5 with a spatial resolution of 0.5 arc degrees.
Conclusion
In this research, it was tried to investigate the influence of the dust event in the context of fluctuations and daily changes in the amount of net shortwave radiation received on the earth's surface. The results of the investigation of three cases of widespread dust in the province showed that in these three cases of widespread dust, aerosol particles are generally in the central, southern and western parts of the province (plain and lowland areas of the province) from the type of medium to large particles (index angstrom between 0.5 and 1) and in the eastern and northeastern parts, it was of the type of coarse particles (angstrom index less than 0.5). In the context of the impact of dust events on the amount of shortwave radiation received by the earth's surface, it was seen that in the dust event of July 22, 2010, the Angstrom exponential index indicates the presence of coarse particles in the atmosphere near the earth's surface and the AOD index also indicates the presence of dense dust in the entire area of the province. The received net shortwave radiation (at 12 noon or 09 UTC) was about 194 watts per square meter (about 28 percent) lower than the average for the same month. This drop rate was less in the other two dust waves, whose AOD and Angstrom index values indicated finer and less concentrated dust. In the dust wave of June 19, 2012, the amount of net shortwave radiation received was only 5% (25 W/m2 at 12 noon or 09 UTC) less than the long-term average, and this drop in the dust event of May 12, 2018 was equal to 28 W/m square (about 4% drop compared to the average of the same month).
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.
Parastou Darouei , Parviz Zeaiean, Farhad Azizpour, Vahid Riahi,
Volume 10, Issue 3 (9-2023)
Volume 10, Issue 3 (9-2023)
Abstract
Introduction
Agricultural activities, as a foundation of growth and development and part of the rural development process, guarantee the economic life of many villages in the country. However, in recent years, other products' water scarcity and resource limitations have affected these activities. This issue has severely challenged the sustainability and life of rural settlements.
In this regard, organizing and developing an optimal cropping pattern is necessary to achieve the goals of sustainable agricultural and rural development in Iran. To achieve this goal, the cultivation of crops must be commensurate with the capabilities of production resources, especially water resources.
Therefore, determining the appropriate spatial distribution of agricultural lands for the cultivation of various crops is one of the primary foundations for implementing optimal cropping pattern. Accordingly, the present study seeks to identify suitable spatial zoning for wheat and barley cultivation as the main crops in agricultural lands in traditional Lenjanat regions, which are exposed to a growing water crisis.
Data and Methodology
According to the main purpose of the research, the data obtained from spatial distribution maps of current cropping patterns and spatial distribution of suitable lands for crop cultivation.
This study prepared the suitability maps of the major agricultural products at a distance of 10 km on both sides of Zayandeh Rud River in Lenjanat region using multi-criteria decision-making methods.
Thus, the agronomic-ecological needs of the two major crops in the area (wheat and barley) were determined, and a standard map for each crop was classified using ArcGIS software. Then, the digital layers were combined by allocating the weight obtained from the Analytical Hierarchy Process and the Simple Additive Weighting method. Finally, talent assessment and land zoning was performed in four categories from unsuitable to very suitable for cultivating wheat and barley crops. Using the analytical hierarchy process method and experts' opinions led to high accuracy results.
Results and Discussion
The results of the land suitability map showed that 90.6% of the agricultural lands in the study area are very suitable and relatively suitable for the cultivation of the wheat crop. The northern and eastern regions, located in Falavarjan county and the north part of Mobarakeh county, are the most suitable areas for wheat cultivation. As we move from the north and east to the west of the study area, the capability areas for wheat cultivation decrease. Limiting factors in these areas are unsuitable soil texture, low temperature, shallow soil, high slope, low rainfall and drainage.
As for barley cultivation, a large part of the area, equal to 30635.3 hectares (more than 91%), is very suitable and relatively suitable. In these areas, in the northern and eastern parts of Lenjanat, unsuitable soil texture, shallow soil, high slope and low drainage are the most critical limiting factors for barley cultivation.
A comparison of "spatial distribution of land suitability" with "spatial distribution of cropping pattern" shows that the crops in this study (wheat and barley) have been cultivated in a suitable area in terms of the ecological potential of lands.
Conclusion
The results of this evaluation can be used in the spatial distribution of the optimal cropping pattern to select a suitable cultivation site for these two crops and other existing and alternative crops.
Wheat and barley are the major crops usually used in planning optimal cropping patterns, regardless of the economic issues. Considering suitable spatial distribution for wheat and barley, they should be distributed in such a way with the slightest difference compared to the current cropping pattern. On the other hand, a large area of the Lenjanat region is suitable for cultivating wheat and barley. In addition, an agricultural unit may have different capacities for other crops, so it is necessary to pay attention to the ecological potential of other crops. Wheat and barley should be cultivated in lands which are unsuitable or semi-suitable for other crops.
Accordingly, it is necessary to provide spatial zoning of existing and alternative crops in the Lenjanat area with fewer water requirements and higher economic benefits to be introduced in the optimal cropping pattern.
In this study, only agronomic-ecological criteria and needs with available data were examined due to data limitations in assessing crop suitability. Therefore, completing land suitability maps by considering more evaluation criteria such as evapotranspiration and the amount of water available is recommended.
Also, to have a "spatial distribution of the optimal cropping pattern", paying attention to the ecological potential of the lands, also considering other criteria and priorities such as natural, socio-cultural, economic and political criteria is necessary. So, we can develop a cropping pattern that provides a basis for desirable space dynamics.
Agricultural activities, as a foundation of growth and development and part of the rural development process, guarantee the economic life of many villages in the country. However, in recent years, other products' water scarcity and resource limitations have affected these activities. This issue has severely challenged the sustainability and life of rural settlements.
In this regard, organizing and developing an optimal cropping pattern is necessary to achieve the goals of sustainable agricultural and rural development in Iran. To achieve this goal, the cultivation of crops must be commensurate with the capabilities of production resources, especially water resources.
Therefore, determining the appropriate spatial distribution of agricultural lands for the cultivation of various crops is one of the primary foundations for implementing optimal cropping pattern. Accordingly, the present study seeks to identify suitable spatial zoning for wheat and barley cultivation as the main crops in agricultural lands in traditional Lenjanat regions, which are exposed to a growing water crisis.
Data and Methodology
According to the main purpose of the research, the data obtained from spatial distribution maps of current cropping patterns and spatial distribution of suitable lands for crop cultivation.
This study prepared the suitability maps of the major agricultural products at a distance of 10 km on both sides of Zayandeh Rud River in Lenjanat region using multi-criteria decision-making methods.
Thus, the agronomic-ecological needs of the two major crops in the area (wheat and barley) were determined, and a standard map for each crop was classified using ArcGIS software. Then, the digital layers were combined by allocating the weight obtained from the Analytical Hierarchy Process and the Simple Additive Weighting method. Finally, talent assessment and land zoning was performed in four categories from unsuitable to very suitable for cultivating wheat and barley crops. Using the analytical hierarchy process method and experts' opinions led to high accuracy results.
Results and Discussion
The results of the land suitability map showed that 90.6% of the agricultural lands in the study area are very suitable and relatively suitable for the cultivation of the wheat crop. The northern and eastern regions, located in Falavarjan county and the north part of Mobarakeh county, are the most suitable areas for wheat cultivation. As we move from the north and east to the west of the study area, the capability areas for wheat cultivation decrease. Limiting factors in these areas are unsuitable soil texture, low temperature, shallow soil, high slope, low rainfall and drainage.
As for barley cultivation, a large part of the area, equal to 30635.3 hectares (more than 91%), is very suitable and relatively suitable. In these areas, in the northern and eastern parts of Lenjanat, unsuitable soil texture, shallow soil, high slope and low drainage are the most critical limiting factors for barley cultivation.
A comparison of "spatial distribution of land suitability" with "spatial distribution of cropping pattern" shows that the crops in this study (wheat and barley) have been cultivated in a suitable area in terms of the ecological potential of lands.
Conclusion
The results of this evaluation can be used in the spatial distribution of the optimal cropping pattern to select a suitable cultivation site for these two crops and other existing and alternative crops.
Wheat and barley are the major crops usually used in planning optimal cropping patterns, regardless of the economic issues. Considering suitable spatial distribution for wheat and barley, they should be distributed in such a way with the slightest difference compared to the current cropping pattern. On the other hand, a large area of the Lenjanat region is suitable for cultivating wheat and barley. In addition, an agricultural unit may have different capacities for other crops, so it is necessary to pay attention to the ecological potential of other crops. Wheat and barley should be cultivated in lands which are unsuitable or semi-suitable for other crops.
Accordingly, it is necessary to provide spatial zoning of existing and alternative crops in the Lenjanat area with fewer water requirements and higher economic benefits to be introduced in the optimal cropping pattern.
In this study, only agronomic-ecological criteria and needs with available data were examined due to data limitations in assessing crop suitability. Therefore, completing land suitability maps by considering more evaluation criteria such as evapotranspiration and the amount of water available is recommended.
Also, to have a "spatial distribution of the optimal cropping pattern", paying attention to the ecological potential of the lands, also considering other criteria and priorities such as natural, socio-cultural, economic and political criteria is necessary. So, we can develop a cropping pattern that provides a basis for desirable space dynamics.
Zynab Dolatshahi, Mehry Akbari, Bohloul Alijani, Darioush Yarahmadi, Meysam Toulabi Nejad,
Volume 10, Issue 3 (9-2023)
Volume 10, Issue 3 (9-2023)
Abstract
This study was aimed at examining the types of inversion and their severity using the thermodynamic indices of the atmosphere such as SI, LI, KI and TT at Bandar Abbas Station for 2010-2020. In this study, Radioosvand data at the Bandar Abbas Station was obtained and used from the University of Wioming for the last 11 years (3.5 local) during the last 11 years (2010 to 2020). The results of the analysis showed that the average number of inversion phenomenon in Bandar Abbas was 501 cases per year, as in some days several types of inversion were observed at different altitude. Of these inversion, about 31.6 % are related to radiation temperature inversion, 4.3 % front, and another 64.1 % for subsidence inversion. Due to the air session underneath, the share of subsidence inversions is more than other types of inversion. In the meantime, the most severe inversion of subsidence was 1354 and the weakest inversions were with 29 cases and fronts. In general, the long -term average intensity coefficient of inversion of Bandar Abbas station with a coefficient of 0.062 indicates that the intensity of the city's inversion is mostly extremely severe, which can be very destructive effects both environmentally and physical health in the city's residents. Bandar Abbas follow. The correlation between the inversion elements also showed that by reducing the thickness of the inversion layer, the intensity of temperature inversion also increased.
Javad Sadidi, Hassan Ahmadi, . Ramin Rezae Shahabi, Amir Pishva, Omid Kheyri, Godratallah Nooraie,
Volume 10, Issue 3 (9-2023)
Volume 10, Issue 3 (9-2023)
Abstract
The pervasiveness of the concept of vulnerability in various dimensions has led to the emergence of the theory of vulnerability in the spatial sciences. According to the theory of vulnerability, in any given space, there is a coefficient of vulnerability, while the levels and amplitude of safety are not evenly distributed on the surface of that space. Residential use is one of the most important and main uses in the urban land use system, and safety management and attention to its defense requirements are very important due to the high population density in large cities. The present study is in the field of assessing the vulnerability of residential uses against external threats with a passive urban defense approach in District 10 of Tehran, which was conducted in the form of spatial studies and by implementing an analytical model in three steps. First, the principles and requirements of passive defense were identified and classified into three groups of structural, demographic and spatial parameters, and using the questionnaire and expert survey tools, the priorities of passive defense principles in relation to residential spaces were determined. Then, based on the network analysis process, the weight of each criterion was determined and the weight of the ANP model was applied to the spatial layers of the region in ArcGIS software. The results of the model showed that in terms of structural indicators, more than 78% of residential units in the region are in the group of structures with high vulnerability and in terms of demographic indicators, in 88% of residential units in case of external threats, the level of vulnerability is high. In terms of spatial indicators, more than 92% of residential spaces are adjacent to several incompatible uses and have the highest vulnerability. In general, the results of overlapping layers showed that more than 86% of residential units in the area are located in vulnerable zones and the vulnerability of residential units in these zones is very high.
Hossein Hataminejad, Alireza Sadeghi,
Volume 10, Issue 3 (9-2023)
Volume 10, Issue 3 (9-2023)
Abstract
Measuring urban resilience can help develop appropriate strategies and policies for cities facing unexpected shocks and their consequences. Since urban resilience is a complex concept and difficult to operationalize, developing a technique or method to actualize this concept is a major milestone in understanding the factors and interactions that help create and maintain resilience. Tehran's metropolis has a high concentration of industries, government organizations, services, and facilities, which makes its management very complicated when a natural disaster occurs. Previous conditions or inherent socio-economic characteristics show that Tehran is not immune from flood forces. In fact, it is important to measure resilience against urban disasters for areas located on rivers in Tehran due to its inherent characteristics and spatial-temporal changes of floods in the region. This research focuses on measuring the resilience of the areas located on the rivers of Tehran. The measurement approach is based on creating a composite index based on six dimensions of social, economic, institutional, infrastructure, social capital, and environmental resilience against floods. This research has been done by developing a mixed multi-criteria decision-making method. The AHP model has been used for prioritizing the selected indicators and the TOPSIS model has been used to rank the areas located on the rivers of Tehran city based on their resilience levels. The results show that region 22 is the most resilient region, while regions 4, 5, and 14 have the lowest resilience levels. The findings of this research can help urban planning organizations such as Tehran Research Planning Center to integrate disaster resilience in urban planning and change from reactive plans to preventive urban adaptive strategies such as risk-sensitive urban land use planning.
Tajdin Karami, Ali Shamaei, Fateme Mohebi,
Volume 10, Issue 4 (12-2023)
Volume 10, Issue 4 (12-2023)
Abstract
Abstract
Ecological resilience is a concept that implies the reversibility of ecological structures and functions against the shocks experienced. The northern zone of Tehran, as the most important ecological support of this city, has undergone many land-use changes in recent decades. The present study has analyzed the role of land-use change in the ecological resilience of green infrastructure (as one of the pillars of ecological structure) in District 1 of Tehran Municipality. This study is an applied one in terms of purpose and is considered a descriptive-analytical one in terms of the method used. In this study Landsat satellite data (1976-2021) were used to detect the changes of interest, and landscape metrics were used to analyze the ecological resilience conditions. Based on the results of this study period, the Number of Patches (NP) has significantly increased and the Class Area (CA) has decreased during the period covered by this study. These changes indicate the fragmentation process and loss of structural cohesion of the green patches. The measurement results for the connectivity metrics (ENN and GYRATE) also showed a small connectivity between the green patches in the area. In addition, the results for CONTAG (Contagion Landscape metrics) measure indicated that, due to low connectivity, the transmission rate is low. Therefore, it can be said that the green infrastructure of the region has lost its structural cohesion in the face of land-use change, and as a result, the expected ecological functions and services have also failed. According to the results, the green infrastructure of the study area is vulnerable to land-use changes and their ecological resilience has been significantly reduced.
Majid Ramezani Mehrian,
Volume 10, Issue 4 (12-2023)
Volume 10, Issue 4 (12-2023)
Abstract
Population growth and urbanization are two primary factors in increasing the risk of flooding in urban areas. Along with the increasing urbanization in many cities, changes in land use have led to an increase in the volume of surface runoff and a change in the flood regimes of rivers. Therefore, urban flooding is one of the risks that directly and indirectly have harmful effects. It has entered various cities in Iran. Since resilience thought provides a comprehensive understanding of the conditions by combining different components, it can be fruitful in creating urban flood risk management tools. To be able to effectively use the concept of resilience in the process of decision-making and management of urban floods, it is necessary to measure and evaluate the city's resilience against flood risk. Despite this, the measurement of resilience in urban environments against floods faces a serious challenge due to the lack of transparency in the field of methodological approaches. Therefore, this study aims to clarify the approaches and methods with a systematic review and meta-analysis of the studies conducted in the field of assessing the resilience of urban environments against floods. According to the findings of the research, the methods of assessing the resilience of urban environments against floods are divided into three categories: quantitative, semi-quantitative, and qualitative. Qualitative methods have less diversity than quantitative methods and often include interviewing methods and theoretical conceptual frameworks. The majority of evaluation methods in this field are quantitative and semi-quantitative methods, which can be placed in two widely used categories, i.e. simulation-based methods and indexing-based methods. In the simulation-based approach, hydrological modeling and flood simulation are generally used. Methods based on indexing have been developed in different ways, but they generally follow the same principles and can be used to analyze the resilience of other types of risks in geographic areas.
Mehran Maghsoudi, Elham Heidary,
Volume 10, Issue 4 (12-2023)
Volume 10, Issue 4 (12-2023)
Abstract
Geological diversity has created a new branch of the tourism industry called geotourism , where geological and geomorphological features are explored . The main focus of geotourism on geological elements includes two items, form and process . There is a set of geological forms and processes in places , which are called geosites . This has given rise to a new branch of tourism called geotourism , which examines places that have the ability to attract tourists and management aspects that can help the local community for economic development. In the first stage , it is very important to know the abilities and characteristics of the studied area . Scientific, tourism and educational evaluation of geosites in the region is the basis for optimal exploitation and sustainable development. In recent years, more attention has been paid to the Garmsar region, which has led to the development of geotourism. The impact of tourists and mines that have been created by humans, the Tastkan caves that have changed the strength of the salt caves, and also the role of natural factors, have all led to the environment's reaction
Dr Ali Zangiabadi, Mr Fazllollah Karimi Ghotbabadi,
Volume 10, Issue 4 (12-2023)
Volume 10, Issue 4 (12-2023)
Abstract
Economic resilience to natural disasters, which is actually how economic capacities affect disasters, is one of the issues that must be considered in any society. It is noteworthy that the type of attitude towards economic resilience and how to analyze it on the one hand, plays a key role in how to recognize the current situation resilience and its causes, and on the other hand also affects policies and measures to reduce risk and how to deal with it. The purpose of this study is to rank the economic resilience of new urban Habitations in the Isfahan Metropolitan against earthquake risk. Due to the studied components and the nature of the subject, the approach of this research is "descriptive-analytical". The statistical population of this study includes 6 new urban Habitations of Shahin shahr, Majlesi, Sepahan shahr, Fooladshahr, Baharestan and Shahid Keshvari. This research is applied in terms of purpose and in the research literature section, information has been collected through the library method. According to the results obtained from ASI in this study, the new urban Habitations of Baharestan, Majlesi, Fooladshahr, Shahid Keshvari, Sepahan Shahr and Shahin Shahr have the first to sixth ranks in terms of economic resilience to earthquake risk, respectively. In order to reduce the adverse effects of earthquake risk, pay attention to the economic capacity of the studied Habitations and reduce the economic risk factors in each community, economic resilience should be considered to avoid financial losses caused by these possible accidents.
Understanding the mechanism of the atmospheric anomalies governing the summer dust in northeast Iran
Ms. Tahmineh Chehreara, Miss Somayeh Hajivand Paydari,
Volume 10, Issue 4 (12-2023)
Volume 10, Issue 4 (12-2023)
Abstract
Identification of dust centers and, of course, the behavior of this phenomenon in different regions creates one of the problems of the last few decades, which is investigated as a hazard. To this end, statistics from 15 meteorological stations in the northeastern region of Iran, including North Khorasan, Razavi Khorasan, and South Khorasan provinces, were used over a 17-year period (2016-2000). To clarify the mechanisms governing dusty days, the meridional and zonal wind components and geopotential height were obtained by referring to the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR). HYSPLIT model and MODIS AOD values were used to track and identify dust centers. The results showed that during the warm season, due to the establishment of a strong quasi-stationary blocking system in the lower levels of the atmosphere, negative vorticity increased in the maximum air descent area, ultimately leading to the dominance of a northern flow for the region. Anomalies in geopotential height and vorticity were identified, and three dominant abnormal patterns were found in the occurrence of maximum dust storms in the region. An increase in geopotential height of more than 5 to 10 geopotential meters and an increase in negative vorticity are considered major conditions. By examining the tracking model and using satellite data, five main centers that affect over 90% of the region's dust storms were identified, among which Turkmenistan has a significant role with two separate centers and one common center with Uzbekistan in the occurrence of summer dust storms in northeastern Iran.
Gholam Hassan Jafari, Zeinab Karimi,
Volume 10, Issue 4 (12-2023)
Volume 10, Issue 4 (12-2023)
Abstract
Abstract
In geosciences, morphotectonic indicators are used to investigate the effectiveness of land surfaces from neotectonic activities. In this article, the results of morphotectonic indices by tectonic zones of Iran, according to the energy released from the earthquake of 1900-2009 and the position of the basins relative to the types of faults (young seismic faults), Quaternary and pre-Quaternary) were analyzed. For this purpose, 110 years old Iran seismic data was extracted from the geodatabase, and during the programming process in MATLAB, it was converted from point-vector to surface-raster. In addition the results of the evaluation of morphotectonic indices of 142 basins of different zones were used; 8 inactive basins, 40 semi- active basins, and 94 active basins. Inactive basins are located in Alborz, Zagros, and Central Iran. . The results indicate that the amount of energy released can't examine a significant role in evaluating the morphotectonic indices of the basins. Basin’s location in the area of Quaternary faults and young seismic is of great value in the tectonically active basin. The lie of semi-active basins adjacent to active basins, or the lie of inactive basins adjacent to semi-active and active basins; and it should be borne in mind that the thresholds used to estimate the tectonic activity status of basins cannot be used as a definite and mathematical criterion in estimating the tectonic status of basins.
Arastoo Yari, Mehdi Feyzolahpour, Neda Kanani,
Volume 10, Issue 4 (12-2023)
Volume 10, Issue 4 (12-2023)
Abstract
Earth surface temperature provides important information on the role of land use and land cover on energy balance processes. Therefore, the purpose of this research is to evaluate the LST patterns due to changes in land use (LULC). The studied area is located in Talesh region with an area of 300.6 square kilometers. For this purpose, Landsat images were downloaded in dry and wet seasons from 1365 to 1401. Four user classes were identified by maximum likelihood classification (MLC) and support vector machine (SVM) in 36-year intervals. The Kappa coefficient values for the SVM model were equal to 0.7802 and for the MLC model it was equal to 0.5328. NDVI, NDSI, and NDWI spectral indices were calculated for vegetation, barren soil, and water and were compared with LST in the above years. Changes in land use during the years 1365 to 1401 were an important factor in changes in the temperature of the earth's surface, which averaged from 13.7 degrees Celsius to 39.5 degrees Celsius in the wet season and -0.37 to 41.07 degrees Celsius in the dry season has been variable. Water areas and vegetation have the lowest and barren soil have the highest LST values. The highest negative correlation of -0.74 belongs to the NDVI index in 1365 and the highest positive correlation of 0.79 belongs to the NDSI index in 1365. The area of the forest area has decreased by 20.3% and agricultural land has increased by 217% in 36 years. Barren lands have changed the most and decreased from 2.68 square kilometers to 12 square kilometers. In general, LST has increased due to the increase of human activities such as the expansion of agricultural land and deforestation in the studied period.
Mohammad Reza Rigi, Atefeh Alie Anvari, Farhad Zolfaghari, Khaled Salimi,
Volume 10, Issue 4 (12-2023)
Volume 10, Issue 4 (12-2023)
Abstract
Introduction: Nowadays, climate change and global warming caused by increasing concentrations of greenhouse gases, especially carbon dioxide, is one of the major challenges facing sustainable development. Carbon accumulation in plant biomass and soils is the simplest and economically way to reduce levels of this atmospheric gas. No research has been done on the assessment of the potential of soil carbon deposition and accumulation in the Capparis decidua and Prosopis cineraria species.
Data and Methodology: The objective of this study was to evaluate the soil carbon accumulation in Capparis decidua and Prosopis cineraria plant species in Keshtegan of Saravan, Iran. Therefore, in order to determine the amount of carbon stored in the soil, soil sampling was done by random-systematic method. One hundered-meter transects were randomly selected in the study areas and sampling points were dug at similar intervals along the transect for sampling.Soil samples were taken from depth of 0 to 30 centimeters under the canopy of Capparis decidua and Prosopis cineraria and bare soil as control (20 samples from each area). Soil organic carbon, soil bulk density, pH, salinity and content of clay, silt and sand were recorded.
Description and Interpretation of Results: The analysis of the data showed that there is a significant difference between the investigated treatments in terms of the amount of clay, organic carbon and carbon accumulation. The average comparison results showed that there is a significant difference between the soil covered by plant species and the soil of the control area. The amount of soil carbon accumulated in the area covered by Capparis decidua (1.32 tons per hectare) was significantly higher than that in area covered by Prosopis cineraria (0.75 tons per hectare) and the control area (0.25 tons per hectare). It shows the positive effect of two plant species on the amount of soil carbon accumulation. The average amount of organic carbon in the area with the Capparis decidua, Prosopis cineraria and the control area was 0.75, 0.31 and 0.1 tons per hectare, respectively.Soil organic matter and sand percentage under the canopy of both plant species were higher than the control. In terms of other characteristics, no significant difference was observed in the three regions. According to the results, it can be stated that the presence of plant canopy can increases the amount of carbon accumulation in the soil and led to global warming mitigation.
Dr Sara Kiani, Dr Morad Kavyani, Dr Amirali Tavasoli,
Volume 10, Issue 4 (12-2023)
Volume 10, Issue 4 (12-2023)
Abstract
The Namak Lake is situated between three provinces: Isfahan, Qom, and Semnan. However, the functioning of Namak Lake and its susceptibility to environmental, ecological, economic, and social influences not only affect the immediate surroundings but also impact other provinces. Naturally, a crisis in this lake can have negative effects on human communities and the residents of the surrounding areas in terms of environmental, economic, and social aspects. Therefore, the aim of this research is to identify the temporal-spatial changes in the salinity of Namak Lake and, subsequently, to investigate and analyze the effects of these changes on the environmental security of the surrounding regions. To achieve this goal, salt zones were identified using soil salinity indices, including the Normalized Difference Salinity Index (NDSI), Salinity Index 1 (SI1), Salinity Index 2 (SI2), and Brightness Index (BI), over a 30-year period (1992-2021) with five-year intervals. Then, using the maximum likelihood method, the salt zones were classified into four land cover types, including water zone, moist zone, salt zone, and other uses. The results of this study indicate that due to the reduction in water inflow into the lake as a result of dam construction in the upstream basin and the effects of climate change, the water zone, or seasonal lake, of Namak Lake has disappeared and the salt zone has expanded in this area. The most significant changes in the lake are related to the northwestern part of the lake, where major rivers such as Jajrood, Shur, Qarechai, and Qamaroud flow into this part of the lake, contributing to its drainage. Therefore, dam construction on these rivers has led to a downward trend in water flow into the lake. Furthermore, the results suggest that due to the absence of settlements and human communities near Namak Lake and the natural and climatic conditions of the region, it is not expected that environmental incidents that could have security and political implications will occur in the short term.
Mrs Ziba Yousefi, Dr Hossein Jahantigh, Dr Farhad Zolfaghari,
Volume 10, Issue 4 (12-2023)
Volume 10, Issue 4 (12-2023)
Abstract
Investigation and monitoring of desertification in arid and semi-arid regions is a major concern for societies and governments due to its increasing rate. It is essential to identify areas at risk of desertification to manage and control this phenomenon in the shortest possible time and at minimum cost. The objective of this study is to create a map of desertification intensity in the MoradAbad plain of Saravan using the Albedo-NDVI model, which is based on remote sensing. Two Albedo and NDVI indicators were extracted from Landsat 8 satellite images in Erdas Imaging software after necessary corrections. A linear regression was formed between the two indicators by selecting 200 pixels corresponding to each indicator. Based on the slope coefficient of the line obtained from linear regression, the equation for determining the intensity of desertification was obtained. A map of the intensity of desertification was prepared based on Jenks’ natural refractive index. To evaluate the accuracy of the model, a clutter matrix was formed between 100 corresponding points. The results of linear regression between NDVI and Albedo indices showed that these two indices have a high negative correlation with each other (R = -0.85). The results of the desertification severity classification based on this model showed that 35% of the area is in the very severe class and only 5% of the area is without degradation. The model’s accuracy value was obtained with a kappa coefficient equal to 0.58, indicating good accuracy of the model.
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