نشریه کاربرد سنجش از دور و سیستم اطلاعات جغرافیایی در علوم محیطی
پیاپی 4 (پاییز 1401)

Digital Elevation Model (DEM) has been used in various earth science processes as one of the main sources of studies. The average slope factor of an area is one of the basic and important physical characteristics, and the calculation and determination of this factor are necessary in all plans related to earth sciences. In many experimental relationships, determining the concentration time and the average slope of the study area is the main factor and plays a decisive role in the estimations. Therefore, failure to accurately estimate and determine the average slope value will lead to incorrect estimates of the major and fundamental factors of an area. Therefore, in this research, in order to evaluate slope algorithms using a digital height model, two areas were first considered based on special conditions and characteristics. Then 30 and 90 meters of SRTM were prepared for each of these areas (DEM). By using coding in a Python environment, the slope algorithms such as neighborhood, quadratic, maximum slope, and maximum slope were calculated and the time library and psutil library were used to calculate the time of the algorithm and the memory consumption of the system. using a number of default points; Slope algorithms were spatially compared. The obtained results showed that the downhill slope algorithm obtained higher maximum values ​​and the maximum slope algorithm obtained lower values ​​compared to other algorithms.

Studying the changes and destruction of resources in previous years, and possibility-evaluation and prediction of these changes in subsequent years, could be a significant step in the planning and efficient use of resources, and controlling and containment of the unprincipled changes in future. This study aims to assessment and predicting changes in land use and land cover in the city of Ahvaz. In doing so, Remote Sensing Data, including Landsat TM satellite images of the years 1997, the ETM+ of 2009 and OLI image of 2021 have been used. After accomplishing the needed preprocessing, satellite images were processed and classified through choosing the appropriate training areas. For the Change Assessment, post-classification comparison method was used, and tables and maps of changes were prepared. General changes in the 24-year period is such that the space of built-up areas, with 6 square kilometers rate of growth per year, have been increased from 100/27 square kilometers to 230/33 square kilometers. The extension of the countryside had been associated with the destruction of Barren lands and agricultural, and the agricultural usage had been increased from 951/52 square kilometer in 1997 to 957/26 in 2021. Modeling has been performed through using CA-Markov models. These results are demonstrating the appropriate ability of CA-Markov model in modeling and predicting changes. Thereafter, the land cover map for 2033 was simulated with CA-Markov model.Studying the changes and destruction of resources in previous years, and possibility-evaluation and prediction of these changes in subsequent years, could be a significant step in the planning and efficient use of resources, and controlling and containment of the unprincipled changes in future. This study aims to assessment and predicting changes in land use and land cover in the city of Ahvaz. In doing so, Remote Sensing Data, including Landsat TM satellite images of the years 1997, the ETM+ of 2009 and OLI image of 2021 have been used. After accomplishing the needed preprocessing, satellite images were processed and classified through choosing the appropriate training areas. For the Change Assessment, post-classification comparison method was used, and tables and maps of changes were prepared. General changes in the 24-year period is such that the space of built-up areas, with 6 square kilometers rate of growth per year, have been increased from 100/27 square kilometers to 230/33 square kilometers. The extension of the countryside had been associated with the destruction of Barren lands and agricultural, and the agricultural usage had been increased from 951/52 square kilometer in 1997 to 957/26 in 2021. Modeling has been performed through using CA-Markov models. These results are demonstrating the appropriate ability of CA-Markov model in modeling and predicting changes. Thereafter, the land cover map for 2033 was simulated with CA-Markov model.

Human society and the natural environment have been affected by the phenomenon of urbanization and urban development. The physical growth of cities can have many environmental, economic and social consequences. Changing land use and land cover in cities causes many problems. The purpose of this research is to identify the urban growth and its effect on the thermal pattern using RS. The data used in this research are the images of satellites and Landsat 5 & 8, TM and OLI, TIRS sensors from 1990 to 2019, which were classified using the object-oriented classification technique and then to predict the expansion of Tabriz city for 2029. Markov model and automatic cells (CA-Markov) have been used, Split Window and Mono Window methods have also been used to extract the surface temperature. The results of the research showed that the air temperature of the residential areas has increased by about 1 degree and the highest temperature calculated for the residential areas is in the southwestern parts, which is more than 29 degrees Celsius, as well as the vegetation cover in 1990, 2000, 2010 and 2019 was 81, 44, 61 and 43 square kilometers, respectively, and urban lands were 54, 81, 100 and 128 square kilometers, respectively. which clearly shows the reduction of vegetation and the increase of residential areas, the analysis of the results of the expansion pattern of Tabriz city also showed that the most expansion was in the southern, southwestern and eastern parts.Human society and the natural environment have been affected by the phenomenon of urbanization and urban development. The physical growth of cities can have many environmental, economic and social consequences. Changing land use and land cover in cities causes many problems. The purpose of this research is to identify the urban growth and its effect on the thermal pattern using RS. The data used in this research are the images of satellites and Landsat 5 & 8, TM and OLI, TIRS sensors from 1990 to 2019, which were classified using the object-oriented classification technique and then to predict the expansion of Tabriz city for 2029. Markov model and automatic cells (CA-Markov) have been used, Split Window and Mono Window methods have also been used to extract the surface temperature. The results of the research showed that the air temperature of the residential areas has increased by about 1 degree and the highest temperature calculated for the residential areas is in the southwestern parts, which is more than 29 degrees Celsius, as well as the vegetation cover in 1990, 2000, 2010 and 2019 was 81, 44, 61 and 43 square kilometers, respectively, and urban lands were 54, 81, 100 and 128 square kilometers, respectively. which clearly shows the reduction of vegetation and the increase of residential areas, the analysis of the results of the expansion pattern of Tabriz city also showed that the most expansion was in the southern, southwestern and eastern parts.

The aim of the current research is to locate construction waste disposal sites in Ilam city. Identifying the optimal sites for the disposal of construction waste is one of the basic steps in the field of urban waste management. In the present study, the process of locating construction waste disposal sites in Ilam city was carried out in four basic steps. In the first step, variables affecting the location of construction waste disposal sites were prepared in the form of thematic layers in the framework of the Geographical Information System (GIS) and were spatially evaluated. In the second step, the subject layers were dimensioned and their values were revalued using fuzzy functions. In the third step, the weight of each of the research criteria was calculated using the Analytical Hierarchy Process (AHP) model. In the last step, the subject layers were combined with each other based on the coefficients obtained from the AHP model and the land suitability layer was obtained in order to dispose of construction waste. The results show that the three variables of distance from the city, slope and distance from the main roads, with weights of 0.292, 0.208 and 0.145 respectively, are the most important variables affecting the optimal location of construction waste in Ilam city. are counted Also, two areas located in the southwest and northwest of Ilam city were proposed to create construction waste disposal sites.

In this research, the landslide susceptibility mapping in the area of Mahabad to Sardasht road has been done using different algorithms. The research method is based on the comparison of support vector machine, random forest, and logistic regression algorithms. In this regard, various environmental data and criteria have been used. First, based on the determination of the sample points, the three algorithms have been implemented in order to prepare the landslide susceptibility map, and then comparison and validation of the results of the used models have been addressed. The landslide zoning results have indicated that in general, the southern parts of the region have a higher susceptibility than the northern parts due to the influence of factors such as dense fault structures, higher slope, and higher density of waterways, and based on the support vector machine, it is 71.04%. According to random forest, 53.44% and according to logistic regression, 77.39% of the total area has medium to high landslide susceptibility. The accuracy assessment of the algorithms based on the ROC curve has determined that the support vector machine, random forest, and logistic regression have obtained accuracy values of 0.76, 0.87, and 0.84, respectively, and from this point of view, the random forest algorithm provided the best accuracy. Moreover, the Precision-Recall is equal to 0.809, 0.873, and 0.844, respectively, which indicates the higher accuracy of the random forest algorithm than the other two algorithms in the field of landslide susceptibility mapping in the Mahabad-Sardasht road.In this research, the landslide susceptibility mapping in the area of Mahabad to Sardasht road has been done using different algorithms. The research method is based on the comparison of support vector machine, random forest, and logistic regression algorithms. In this regard, various environmental data and criteria have been used. First, based on the determination of the sample points, the three algorithms have been implemented in order to prepare the landslide susceptibility map, and then comparison and validation of the results of the used models have been addressed. The landslide zoning results have indicated that in general, the southern parts of the region have a higher susceptibility than the northern parts due to the influence of factors such as dense fault structures, higher slope, and higher density of waterways, and based on the support vector machine, it is 71.04%. According to random forest, 53.44% and according to logistic regression, 77.39% of the total area has medium to high landslide susceptibility. The accuracy assessment of the algorithms based on the ROC curve has determined that the support vector machine, random forest, and logistic regression have obtained accuracy values of 0.76, 0.87, and 0.84, respectively, and from this point of view, the random forest algorithm provided the best accuracy. Moreover, the Precision-Recall is equal to 0.809, 0.873, and 0.844, respectively, which indicates the higher accuracy of the random forest algorithm than the other two algorithms in the field of landslide susceptibility mapping in the Mahabad-Sardasht road.

The life and development of human societies requires energy production and consumption, the need to pay attention to the environment and the fatalities of fossil fuels for energy production has focused on the use of energy from renewable energy sources. New energies are important because they are a good alternative to fossil fuels. Fossil fuels cause environmental pollution and air pollution. Geothermal plants, on the other hand, also have environmental impacts on vegetation around the power plants. In this research, various vegetation indices have been used to investigate the potential damaging effects of Meshkinshahr geothermal power plant on vegetation around the site of the power plant. Investigations using Landsat 7 and 8 satellite images and ETM + and OLI sensors from the target area and extracting the SAVI index in two periods of 2000 and 2017 showed that the vegetation index of the areas around the site of the power plant Geothermal has dropped to 6.68 square kilometers. Residential, soil and water areas also increased 53.69, 14.26 and 0/255% respectively. Comparison of two classified images showed that the lagoon class in 2000 had an area equal to 11.18 km2, down from 95.91 km2 in 2017. In addition, the class of residential areas in 2000 has an area of 95.74 km2, in 2017, it is up to 120.41 km2, the soil class ranges from 219.59 km2 to 405.82 km2 and the water class ranges from 0.43 to 0.69 km2 has risen.The life and development of human societies requires energy production and consumption, the need to pay attention to the environment and the fatalities of fossil fuels for energy production has focused on the use of energy from renewable energy sources. New energies are important because they are a good alternative to fossil fuels. Fossil fuels cause environmental pollution and air pollution. Geothermal plants, on the other hand, also have environmental impacts on vegetation around the power plants. In this research, various vegetation indices have been used to investigate the potential damaging effects of Meshkinshahr geothermal power plant on vegetation around the site of the power plant. Investigations using Landsat 7 and 8 satellite images and ETM + and OLI sensors from the target area and extracting the SAVI index in two periods of 2000 and 2017 showed that the vegetation index of the areas around the site of the power plant Geothermal has dropped to 6.68 square kilometers. Residential, soil and water areas also increased 53.69, 14.26 and 0/255% respectively. Comparison of two classified images showed that the lagoon class in 2000 had an area equal to 11.18 km2, down from 95.91 km2 in 2017. In addition, the class of residential areas in 2000 has an area of 95.74 km2, in 2017, it is up to 120.41 km2, the soil class ranges from 219.59 km2 to 405.82 km2 and the water class ranges from 0.43 to 0.69 km2 has risen.