Geomorphology
Fariba Esfandyari Darabad; Ghobad Rostami; Raoof Mostafazadeh; Mousa Abedini
Abstract
In the current study, the risk of landslides in the Zamkan Watershed, located in Kermanshah Province, was evaluated. Two machine learning models, Support Vector Machine (SVM), and Logistic Regression, were used to prepare a landslide susceptibility map. Toward this, 13 informational layers including ...
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In the current study, the risk of landslides in the Zamkan Watershed, located in Kermanshah Province, was evaluated. Two machine learning models, Support Vector Machine (SVM), and Logistic Regression, were used to prepare a landslide susceptibility map. Toward this, 13 informational layers including elevation, slope, aspect, Melton ruggedness number, terrain convexity, stream length, valley depth, topographic wetness index, precipitation, geological formations, distance from rivers, distance from roads, and vegetation cover were utilized as independent variables. Approximately 70% of the watershed's landslide pixels were used for model training, and 30% for model validation. Model validation was performed using ROC curves. The results indicated the higher performance and accuracy of the radial basis function (RBF) kernel of the SVM model for generating landslide hazard maps in the study area. The area under the curve (AUC) for the RBF kernel was approximately 0.951 for model training and 0.944 for model testing. The results suggest that slope with a coefficient of 0.28, precipitation with a coefficient of 0.27, lithology with a coefficient of 0.26, and elevation with a coefficient of 0.22 are the main controlling factors for landslides occurrence in the Zamkan Watershed. Both the SVM model and logistic regression confirmed the deterministic effects of selected factors on landslides. About 35% of the study area as classified as highly susceptible to landslides, primarily in the eastern half of the watershed. Factors such as high elevation, steep slopes, heavy precipitation, and the Kazhdomi Formation's composition were identified as key contributors to this susceptibility.
Geomorphology
Mousa Abedini; AmirHesam Pasban
Abstract
Soil erosion is one of the serious environmental threats that can affect the political, social and economic aspects of countries. One of the widely used experimental models for estimating the amount of soil erosion is the modified global soil erosion equation known as the RUSLE model. The purpose of ...
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Soil erosion is one of the serious environmental threats that can affect the political, social and economic aspects of countries. One of the widely used experimental models for estimating the amount of soil erosion is the modified global soil erosion equation known as the RUSLE model. The purpose of this research is to analyze and zonate the amount of soil erosion and its relationship with hydrogeomorphic indicators and vegetation cover of Khiavchai Meshkinshahr watershed in Ardabil province. RUSLE model factors include rain erosion (R), soil erodibility (K), topography (LS), vegetation (C) and protection operations (P). respectively, by using rainfall data, soil texture layer, digital model of height and land use were prepared in the environment of geographic information system (GIS) and after overlapping the layers, the amount of annual soil erosion between 0 and 150.54 tons per hectare per year in The area level was estimated. In the next step, the hydrogeomorphic and vegetation indices that are effective in soil erosion include topographic moisture index (TWI), waterway capacity index (SPI), domain curvature index (Curvatore), section curvature index (Profil Curvatore), surface curvature index (Plan) Curvatore) and Normalized Vegetation Index (NDVI) were created in ArcMap environment and zoning maps were prepared. The results of this research also showed that the topography factor with a correlation coefficient of 0.92% had the greatest impact on the estimation of annual soil erosion by the RUSLE model. In another study, the relationship between hydrogeomorphic indices and vegetation cover with annual soil erosion rate was conducted, and the results showed that normal vegetation cover indices and cross-sectional curvature were the most and least effective with correlation coefficients of 0.57 and 0.05, respectively, compared to other indices.The results of this research confirm the possibility of combining the effective indicators of hydrogeomorphic and vegetation on erosion, as well as the possibility of using other effective indicators and the capabilities of RS and GIS to quantitatively estimate the amounts of soil erosion.
Geomorphology
leila aghayary; Mousa Abedini
Abstract
Therefore, the purpose of this research is to investigate and analyze the most important factors involved in creating the risk of subsidence in the Ardabil plain and to identify the susceptible surfaces that are likely to be involved in subsidence in the near future. The purpose of this research in the ...
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Therefore, the purpose of this research is to investigate and analyze the most important factors involved in creating the risk of subsidence in the Ardabil plain and to identify the susceptible surfaces that are likely to be involved in subsidence in the near future. The purpose of this research in the first stage is to evaluate the subsidence using radar interferometry technique in the Sarscape software environment, using the capabilities of A1 Sentinel images in the time frame of 2016 and 2021, and also in the following, in relation to the zoning of susceptible areas with the algorithm Aras multi-criteria was implemented in Edrisi software environment. The results of the present study showed that between 0 and 22 mm of subsidence has occurred in the studied area, and the highest amount of subsidence is concentrated in the central part and then in the eastern and north-eastern parts. According to the results of subsidence risk zoning; The criteria of water level drop, distance from the river, geology, and land use are the most important factors involved in creating the risk of subsidence in the study area, respectively, with a weighting factor of 0.221, 0.166, 0.152, and 0.147, respectively, and 267/41 and 403/21 square kilometers of the range have a very high probability of danger. Finally, it can be said that the most important factor involved in increasing the amount and potential of subsidence in the Ardabil plain is the excessive use of underground water and the drop in the water level.
hydrogeology
Mousa Abedini; Sajjad Javadi; Raoof Mostafazadeh; AmirHesam Pasban
Abstract
Today, soil erosion is one of the major problems of watersheds and agricultural areas and natural resources, which causes land degradation and decreases soil fertility. Therefore, the purpose of this study is to investigate the relationship between vegetation and geomorphic indices with the values of ...
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Today, soil erosion is one of the major problems of watersheds and agricultural areas and natural resources, which causes land degradation and decreases soil fertility. Therefore, the purpose of this study is to investigate the relationship between vegetation and geomorphic indices with the values of erosion and sediment in the watershed of Koot-e-Tootraghi basin, which was done by using the capabilities of GIS to extract the geomorphic characteristics of the basin. For this purpose, erosion and sedimentation rates were calculated using the modified Psiac model (MPSIAC). Also, in order to extract physiographic and geomorphic features including: TWI topographic moisture layers, SPI current strength, SLOPE slope, domain curvature, profile curvature and sub-basin plan curvature, from the height digital model with a spatial accuracy of 30 meters, as well as other layers used in the MPSIAC model including1:25000 topographic maps, 1:100000 geological maps were used. According to the box diagram, the indices related to curvature have little changes in the studied area. Also, the indices related to curvature have little changes in the studied area. Based on the results, there is a positive and significant correlation of 0.47 (p-value less than 0.01) between the standard index of vegetation cover and topographic humidity index. In addition, there is a significant correlation (0.63) between waterway power index and slope. It was also found that the relationship between the slope and the normalized index of vegetation has an inverse and significant relationship (0.48) (p-value less than 0.01.).
Geomorphology
Mousa Abedini; Ehsan Ghale
Abstract
1-IntroductionDue to increasing land-use changes, mainly for human activities, it is necessary to monitor vegetation changes, evaluate their trends and their environmental impacts for future planning and resource management. With the increase in population and the development of technologies, human beings ...
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1-IntroductionDue to increasing land-use changes, mainly for human activities, it is necessary to monitor vegetation changes, evaluate their trends and their environmental impacts for future planning and resource management. With the increase in population and the development of technologies, human beings are, currently, considered the most important and powerful tool of environmental change in the biosphere. Land use is the type of land use in the current situation, which includes all land uses in various sectors of agriculture, natural resources, and industry. Due to the provision of a wide and integrated view of an area, reproducibility, easy access, high accuracy of information obtained, and high speed of analysis, using satellite data is a good way to prepare a land-use map, especially in large geographical areas. One of the most widely used methods of extracting information from satellite images is classification, which allows users to generate different information. According to the type of classification method of the study area, the characteristics of the educational points get different results to separate the thematic phenomena and extract information more accurately.2-MethodologyMordagh River, which is known as Mordi Chai in the region, originates from the southern slope of Sahand Mountain located in East Azerbaijan and flows south. By connecting the sub-branches, it continues its way to the city of Maragheh, passes through the city of Malekan, and enters Lake Urmia. In the present study, Landsat satellite images, TM, and OLI sensors from 2000 and 2020 were used to identify the area and prepare a land-use map. To prepare for classification and processing on them, the necessary pre-processing was first done on the images. Images were pre-processed in ENVI5.3 software using the FLAASH method. Finally, ENVI5.3 software was used to classify the base pixel and eCognition Developer 64 software was used for object-oriented classification. To evaluate the classification results, the Kappa coefficient and overall accuracy were used to evaluate the classification accuracy of the maps. 3-Results and DiscussionAccording to the obtained results, it is observed that the most area in the study area in 2000 with the method of minimum distance belongs to the use of medium and dense rangeland. The lowest area for the year 2000 is the use of residential areas. In 2020, the highest area of land use is 173.875 square kilometers. The lowest area is related to the use of snow with a rate of 0.199 square kilometers and the use of residential areas, which compared to 2000, has an increase of up to 5.54 square kilometers. In the maximum likelihood method in 2000 and 2020, the highest areas are related to medium rangeland and soil uses, respectively. The lowest area for 2000 is related to vegetation and for 2020 is snow use. In addition, in the support vector machine method, the highest and lowest areas for 2000 are related to medium rangeland and vegetation uses, respectively, and for 2020, medium rangeland and snow uses have the highest and lowest areas, respectively. According to the maps obtained from the object-oriented method, the highest area in 2000 is related to medium rangeland with 156.406 square kilometers and then dense rangeland with 96.514 square kilometers. The lowest area is related to the use of residential areas with 11.141 square kilometers. In 2020, the highest area is related to the use of dense rangeland (126.907 square kilometers). In addition, the lowest area is snow use with an amount of 5.199 square kilometers.4-Conclusions According to the results of this study and other studies, it can be suggested that the object-oriented classification method for land-use change studies is a more appropriate and accurate method than the pixel-based method. One of the most important reasons for achieving high accuracy in the object-oriented classification method is that in this method, in addition to spectral information, information related to texture, shape, position, and content is also used in the classification process. The study of pixel-based classification showed that in selecting educational examples, the more uniform the user is and free of mixed pixels, the more accurate the classification process is. So that the land use classification and vegetation in the pixel-based method had the highest accuracy, which due to the uniform surface of both land use and homogeneous texture, the selection of training samples in these uses with the highest accuracy and have played an important role in improving overall accuracy and kappa coefficient. Based on the results of the extent of different classes related to the land use of the basin studied in 2000 and 2020, we see a decreasing trend of dense rangeland, medium rangeland, and vegetation and increasing land use of residential areas and soil. What is very clear in these maps is the excessive reduction of pastures and their conversion to other uses.Given the growing population and the need for food and economic issues, this transformation is obvious and it cannot be said that this change can be prevented.
