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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.
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.
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.
Saeedeh Koohestani, Bijan Sayyafzadeh, Abdolreza Sarvghad Moghadam, Mahdi Sharifi,
Volume 10, Issue 4 (12-2023)
Volume 10, Issue 4 (12-2023)
Abstract
By increasing the number of process industrial plants because of societies necessity to their productions, a new branch of accidents caused by various occurred process failures and their effect on the societies and environment and economy has been introduced. Beside it, the increase of the accidents because of natural hazards effect on the industrial plants and their huge costs to the societies and governments and high vulnerability of plants and urbanized territory to the branch of the accidents, increased the attention to this type of accidents. However, in many parts of the world still do not pay attention seriously to this issue and by considering them as very low probability accidents, eliminate paying attention and accepting the responsibility of them while the frequency of such accidents is under growing! In this article according to the existing statistics, an evaluation and comparison of consequences of natural hazards that caused NaTech events has been done. The purpose of the NaTech events is process events that triggered because of natural hazards that are known as events with low probability and high consequences that can affect a wide area and cause huge accidents associated with domino effects. After introducing and categorizing NaTech events, a comparison of their distribution and consequences of these events in Iran and the world has been done according existing articles and researches. Researches shows opposite of the natural hazards and their effects on some structures and infrastructures, Natech events has not been paid under attention enough in Iran. While the variety of industrial plants and their structures in Iran is high, their existing condition and repairing and maintenance of them is not proper and according collected statistics in this article, the potential of NaTech events is also high in country. In the first step, to increase the preparedness for NaTech events, review of effective world experiences in this field is recommended. Recognition of past events and categorizing them and codification of data that should be included in safety reports and scenarios evaluation and considering the domino effects and review the recommendations in this field are parts of this step.
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. Habibollah Fasihi, Dr. Taher Parizadi,
Volume 10, Issue 4 (12-2023)
Volume 10, Issue 4 (12-2023)
Abstract
Urban planners consider historical fabric as the beating heart of cities. However, cities and specifically their historical fabrics are constantly under influence of natural and human-induced hazards. This study aimed to assess the vulnerability of Ferdowsi neighborhood as an example of Tehran’s historical fabric. The data was obtained from geographical information system (GIS) files and a survey, as well as historical contexts and documents were analyzed as part of this study. Ten municipal experts were also asked to provide a score of 1 to 5 for each of the 29 indicators compared to standard levels. The mean value of these scores was then used to evaluate the role of each parameter in the vulnerability of this neighborhood. Study findings indicate a high potential for the occurrence of natural and human-induced hazards in the study area. The abundance of unstable multistory buildings and derelict electricity and water networks, storage of flammables in warehouses, and lack of sewage network for half of the buildings were the most significant factors contributing to the vulnerability of this ancient fabric. Problems such as insecurity, drug abuse in public spaces, overcrowding and daytime congestion have also led to rapid relocation of neighborhood residents, who subsequently were replaced by commercial activities and warehouses. In conclusion, numerous problems are contributing to the vulnerability of historical fabric of this neighborhood and such problems are highly likely to be applied to other urban historical fabrics in Tehran.
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.
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.
Dr Mohammad Rahmani, Dr ّfarhan Ahmadi Mirghaed, Dr Sareh Mollaaghajanzadeh,
Volume 11, Issue 1 (5-2024)
Volume 11, Issue 1 (5-2024)
Abstract
This study aimed to assess the habitat quality of the Tajan watershed in northern Iran through land use changes from 1992 to 2052 and to investigate its relationship with landscape metrics, including number of patches (NP), patch density (PD), edge density (ED), largest patch index (LPI), landscape shape index (LSI), and splitting index (SPLIT). Landsat 8 and 4 images were processed to produce land use maps for 1992, 2022, and 2052 using maximum likelihood, cross-combination, and CA-Markov methods in ENVI and TerrSet software. Habitat quality was also assessed using InVEST in three scenarios based on the land use maps. Relationships were analyzed using least squares regression and Spearman's correlation test. The results showed that from 1992 to 2052, forest and agricultural areas had the most decreasing (-82,460 hectares) and increasing (76,392 hectares) changes, respectively. Habitats in the central part of the watershed had higher quality than those in the northern and southern parts. The relationship between habitat quality and land use changes is significant and inverse (-0.95<R<-0.85, P-value <0.01), with indirect and significant NP, PD, ED, and LSI metrics (-0.46<R<-0.35, P-value <0.05), and weak for LPI and SPLIT metrics. This study confirmed that the quality of habitats in the study area has decreased under the influence of land use changes and landscape features in the past 30 years, and the same trend is likely to continue in the next 30 years. The results of this study can be used as a basis for decision-making in the direction of land use development in the region and its environmental protection.
Sahar Afiati, Bohloul Alijani, Sayyed Mohammad Hosseini,
Volume 11, Issue 1 (5-2024)
Volume 11, Issue 1 (5-2024)
Abstract
Cold and frost are one of the climatic hazards that cause damage to various activities every year. Climate change, on the other hand, causes spatial and temporal changes in glaciation. The purpose of this study is to analyze the temporal-spatial changes and predict the future of glaciers in Hamadan province. CanESM2 model was used to predict the minimum daily temperature in the province. Data mining of general circulation models was Downscaling using LARS-WG model. The above parameters were simulated for a period of 30 years (2050-2021) under three scenarios RCP2.6, RCP4.5 and RCP8.5 for selected stations. The results of the monthly minimum temperature survey in the study stations of the province showed that the minimum temperature in the period (2050-2021) in all studied stations according to all three scenarios will increase in all months of the year compared to the base period. The average minimum temperature of the province is equal to 2.5 degrees Celsius, which in the coming decades based on the scenarios of RCP2.6, RCP4.5 and RCP8.5 will reach 6, 6.2 and 6.3 degrees Celsius, respectively, which is the highest The changes are related to Nojeh station and the lowest is related to Hamedan. The spatial distribution of the beginning and end of freezing in the future period indicates that freezing in the northeastern and northern parts of the province starts earlier and ends later than in other parts of the province, while in the southern parts of the province it starts later and ends earlier. The results of examining the changes in the onset of frost in the next decade compared to the base period showed that in all stations studied the onset of frost will decrease between 3 to 11 days.
Dr Mehdi Safari Namivandi,
Volume 11, Issue 1 (5-2024)
Volume 11, Issue 1 (5-2024)
Abstract
Geotourism is one of the important pillars of tourism, which plays an important role in the economic and social development of regions, and this issue is doubly important in border regions. Considering that one of the ways to create security in the border zone is the economic development of the region, therefore, the development of geotourism in the border zone of the country is important. Considering the importance of the issue, in this research, the effects of geotourism development in creating sustainable security in Marivan city have been analyzed. In this research, the 30-meter height digital model of SRTM, the results of interviews and library studies have been used as research data. The most important research tools are ArcGIS, Expert Choice and SPSS. Also, in this research, Comanescu models, AHP and SWOT model were used. According to the intended goals, this research has been carried out in several stages, in the first stage to identify and evaluate geosites, in the second stage to analyze the effects of geotourism development on the goals of sustainable development and regional security, and in the third stage to identify effective factors. The development of geotourism has been discussed. The evaluation results of the identified geosites based on the Comanescu model have shown that the geosites of Marivan city have a high potential for the development of geotourism, which can be paid attention to with economic development, environmental development, infrastructure development and finally, it should be associated with the stable security of the region. Also, the results of the SWOT model have shown that the existence of rare geosites with a weight of 0.08, the lack of long-term development plans with a weight of 0.08, the creation of stable security in the region with a weight of 0.091, and the seasonality of employment with Weight 0.058, respectively, are considered as the most important strengths, weaknesses, opportunities and threats of geotourism development in Marivan city.
Khabat Derafshi,
Volume 11, Issue 1 (5-2024)
Volume 11, Issue 1 (5-2024)
Abstract
Coastal areas are constantly changing physically and ecologically, depending on natural and human factors. The natural causes of coastline changes are assessed in three ways: short-term changes including the effects of up and down currents, long-term changes including climate change, periodic storms and waves, and accidental changes including sudden natural events. Today, coastal tourism is considered as one of the important factors in the development of coastal areas. In this regard, the Caspian Sea, with many tourist attractions such as lush forests, accessible foothills and mountains, historical monuments and appropriate welfare facilities, benefits from the sea and beaches. The coastal area of Babolsar City, due to its many facilities and capabilities to attract tourists, much of which is due to natural and environmental attractions, every year, hosts a large number of tourists who come to this area to take advantage of its facilities and attractions, including the beautiful beach and very beautiful forests. This coastal area because its dynamic nature, is exposed to permanent erosion and variability due to processes such as river, wind, tectonic, wave and tide and marine transgression-regression in the area causes the destruction of coastal facilities and recreational places. Therefore, any planning to change the land use and construction in this coastal area should be considered in terms of the sea water fluctuation impacts on the shoreline position. Coastal environmental degradation as a result of Caspian Sea water level fluctuation are very probable and human behaviors in non-optimal choice of the land use locate intensify these losses. Coastal tourism, as one of the coastal land uses is heavily influenced by fluctuations in sea level in both marine transgression-regression statuses.
Mrs Masumeh Gholami, Dr Ali Ahmadabadi, Dr Ezatollah Ghanavati,
Volume 11, Issue 1 (5-2024)
Volume 11, Issue 1 (5-2024)
Abstract
The process of erosion and sedimentation on river banks is often accelerated by natural events and human activities, which lead to natural hazards. This process has also faced serious risks in Jajrud river, urban and rural settlements, agricultural lands, construction structures around the river. The Jajroud River has important importance and effects in securing agricultural water rights, feeding underground aquifers, providing drinking water in the region and the green and natural spaces of Fashm, Migun, Lavasanat and downstream, which has been subject to many changes in recent years due to direct and indirect human interference. including the expansion of urban and rural construction, river engineering operations and wall construction along the river, encroachment on the river boundary and changing the course of the river channels, the use of non-native vegetation on the banks of the river, and as a result, the eco-hydro geomorphological conditions of the river have become unstable. The phenomenon of erosion and sedimentation in Jajroud river has brought negative consequences such as changing the bed, accumulation and settling of large amounts of sediment and overflowing of canal water and as a result the risk of flooding.
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