National Geographical Organization Scientific- Research Quarterly of Geographical Data (SEPEHR) 2588-3860 33 130 2024 08 22 A synthesis approach of vegetation indices for monitoring high-risk forest regions using Prisma satellite imaging - A part of the forest areas of northern Iran A synthesis approach of vegetation indices for monitoring high-risk forest regions using Prisma satellite imaging - A part of the forest areas of northern Iran 7 27 711283 10.22131/sepehr.2024.2008022.2998 FA Samaneh Bagheri Remote Sensing and GIS PhD students, Department of Remote Sensing and GIS, University of Tabriz, Tabriz-Iran. 0000-0003-3889-6685 Mahmoud Soorghali Remote Sensing and GIS PhD students, Department of Remote Sensing and GIS, University of Tabriz 0009-0007-4374-2767 Hassan Emami Associate professor, Department of geomatics, University of Tabriz, Tabriz-Iran, 0000-0002-0171-6487 Journal Article 2023 08 05 Extended Abstract 1-Introduction Monitoring vegetation changes is crucial for environmental planning and management, and satellite images offer various methods for detecting these changes, each with its own advantages and disadvantages. The use of various plant indices from remote sensing (RS) systems is utilized to evaluate changes and create thematic maps for monitoring diverse plant cover. Today, RS indices are widely used in research projects in specialized fields, such as vegetation health, stress assessment, plant development rate, and plant greenness, to evaluate vegetation health, stress types, and plant illnesses. Hyperspectral imagery, particularly from the red and near-infrared bands in the electromagnetic spectrum (690-740 nm), has been widely used to derive vegetation indices. This project intends to monitor the forest risk regions of a segment of northern Iran's forests in 2020 using a combination of various indices produced by RS data and a geographic information system (GIS). Prisma hyperspectral images were used to assess the health of forests in Northern Iran's Rudsar, Ramsar, and Tonkabon forests, focusing on water stress, insufficient growth, plant pests, diseases, and greenness. Forest areas are divided into five risk-acceptance regions using RS indices, and the data is analyzed using various GIS weighting methods to determine the remaining dangerous forest regions. 2- Methodology The study utilized twelve plant indices from three categories (greenness, growth, leaf pigments, and leaf surface moisture) and four other individual vegetation indices using various techniques. Based on this, the study selected sixteen forest risk-taking maps from five classes with varying risk-taking potential, weighted the layers using hierarchical analysis, and generated a final map based on the obtained weights. When the average results of combined and individual indices were compared with the classification map, it was discovered that the combined indices were more accurate than the individual indices. Existing composite indices are categorized into three broad groups: plant greenness, leaf pigment, and productivity of water or light usage in the vegetation canopy. The three primary characteristics each possess multiple indices that can be combined to provide crucial insights into forest health. 3- Results and discussion The study reveals that when combined with appropriate indices, combined indices can provide high accuracy in the risk assessment of forest areas in the north of the country. In contrast, an incorrect combination can result in low-accuracy outcomes. The study found that the combined indices had a 11% error in two high-risk forest areas, while individual indices had a nearly double error of 21%. The use of composite indices significantly reduces the inaccuracy of calculating forest risk regions by 50% and enhances the accuracy of monitoring these areas. Furthermore, when the combined indices were examined independently, the findings revealed that the combination of the VCN and VCNW indices yielded the maximum accuracy. These compounds are highly effective in assessing the health of vegetation, assessing plant stress, and determining plant water content. On the other hand, the combined indexes from RC were less accurate than the previous combination, with the highest accuracy levels being SIPI, NDII, NDWI, and WBI. These synthetic substances are utilized in the fields of plant health and stress assessment. The accuracy of SIPI, NDII, NDWI, WBI1, PRI1, and RGRI is significantly reduced when combined with the NC index. The combination's low accuracy may be due to the NDVI index's limitations, as it is primarily used to detect vegetation presence or absence and does not detect plant health or stress. The study presents the first results from research on plant stress in northern Iranian forests using Prisma hyperspectral data. Hyperspectral data is chosen for its superior spatial, spectral, and radiometric resolution, making it ideal for studying dynamic ecosystems in the current research region. Hyperspectral RS allows for non-destructive monitoring of leaf pigments like chlorophyll, carotenoids, and anthocyanin content, crucial indicators of vegetation health. Therefore, the recommendation is to employ a combination of indices with diverse approaches in hyperspectral images instead of using individual indices for monitoring vegetation usage. 4- Conclusion: Forest health monitoring is a crucial aspect of forest management programs, and utilizing RS techniques and data can be highly beneficial in this field. The study compared the accuracy of combined indices and individual indices using the classification map, revealing that combined indices were more precise. In addition, the results showed that in almost two high-risk classes of the forest area, the combined indicators have an error of 11% and the individual indicators have an error of almost twice their error, 21%. Therefore, composite indices significantly reduce forest risk area estimation errors by 50% and improve accuracy. Therefore, it's recommended to use a combination of indices with different approaches in hyperspectral images instead of individual indices for monitoring vegetation usage. Extended Abstract 1-Introduction Monitoring vegetation changes is crucial for environmental planning and management, and satellite images offer various methods for detecting these changes, each with its own advantages and disadvantages. The use of various plant indices from remote sensing (RS) systems is utilized to evaluate changes and create thematic maps for monitoring diverse plant cover. Today, RS indices are widely used in research projects in specialized fields, such as vegetation health, stress assessment, plant development rate, and plant greenness, to evaluate vegetation health, stress types, and plant illnesses. Hyperspectral imagery, particularly from the red and near-infrared bands in the electromagnetic spectrum (690-740 nm), has been widely used to derive vegetation indices. This project intends to monitor the forest risk regions of a segment of northern Iran's forests in 2020 using a combination of various indices produced by RS data and a geographic information system (GIS). Prisma hyperspectral images were used to assess the health of forests in Northern Iran's Rudsar, Ramsar, and Tonkabon forests, focusing on water stress, insufficient growth, plant pests, diseases, and greenness. Forest areas are divided into five risk-acceptance regions using RS indices, and the data is analyzed using various GIS weighting methods to determine the remaining dangerous forest regions. 2- Methodology The study utilized twelve plant indices from three categories (greenness, growth, leaf pigments, and leaf surface moisture) and four other individual vegetation indices using various techniques. Based on this, the study selected sixteen forest risk-taking maps from five classes with varying risk-taking potential, weighted the layers using hierarchical analysis, and generated a final map based on the obtained weights. When the average results of combined and individual indices were compared with the classification map, it was discovered that the combined indices were more accurate than the individual indices. Existing composite indices are categorized into three broad groups: plant greenness, leaf pigment, and productivity of water or light usage in the vegetation canopy. The three primary characteristics each possess multiple indices that can be combined to provide crucial insights into forest health. 3- Results and discussion The study reveals that when combined with appropriate indices, combined indices can provide high accuracy in the risk assessment of forest areas in the north of the country. In contrast, an incorrect combination can result in low-accuracy outcomes. The study found that the combined indices had a 11% error in two high-risk forest areas, while individual indices had a nearly double error of 21%. The use of composite indices significantly reduces the inaccuracy of calculating forest risk regions by 50% and enhances the accuracy of monitoring these areas. Furthermore, when the combined indices were examined independently, the findings revealed that the combination of the VCN and VCNW indices yielded the maximum accuracy. These compounds are highly effective in assessing the health of vegetation, assessing plant stress, and determining plant water content. On the other hand, the combined indexes from RC were less accurate than the previous combination, with the highest accuracy levels being SIPI, NDII, NDWI, and WBI. These synthetic substances are utilized in the fields of plant health and stress assessment. The accuracy of SIPI, NDII, NDWI, WBI1, PRI1, and RGRI is significantly reduced when combined with the NC index. The combination's low accuracy may be due to the NDVI index's limitations, as it is primarily used to detect vegetation presence or absence and does not detect plant health or stress. The study presents the first results from research on plant stress in northern Iranian forests using Prisma hyperspectral data. Hyperspectral data is chosen for its superior spatial, spectral, and radiometric resolution, making it ideal for studying dynamic ecosystems in the current research region. Hyperspectral RS allows for non-destructive monitoring of leaf pigments like chlorophyll, carotenoids, and anthocyanin content, crucial indicators of vegetation health. Therefore, the recommendation is to employ a combination of indices with diverse approaches in hyperspectral images instead of using individual indices for monitoring vegetation usage. 4- Conclusion: Forest health monitoring is a crucial aspect of forest management programs, and utilizing RS techniques and data can be highly beneficial in this field. The study compared the accuracy of combined indices and individual indices using the classification map, revealing that combined indices were more precise. In addition, the results showed that in almost two high-risk classes of the forest area, the combined indicators have an error of 11% and the individual indicators have an error of almost twice their error, 21%. Therefore, composite indices significantly reduce forest risk area estimation errors by 50% and improve accuracy. Therefore, it's recommended to use a combination of indices with different approaches in hyperspectral images instead of individual indices for monitoring vegetation usage.

https://www.sepehr.org/article_711283_ca5f70194b699bb36a245bb47a964894.pdf

National Geographical Organization Scientific- Research Quarterly of Geographical Data (SEPEHR) 2588-3860 33 130 2024 08 22 Optimum location of construction of edible oil refining plant in Iran under conditions of uncertainty with D-AHP combined method Optimum location of construction of edible oil refining plant in Iran under conditions of uncertainty with D-AHP combined method 29 46 709171 10.22131/sepehr.2023.2005002.2986 FA Hamed Asghari Department of Crisis Management, Faculty of Passive Defense, Malik Ashtar University of Technology, Tehran, Iran 0009-0001-3707-4834 Mohammad Reza Fallah Ghanbari Master's degree, Health in Disasters and Emergencies Department, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences(SBMU), Tehran, Iran 0000-0002-4134-2010 Journal Article 2023 07 30 Extwnded Abstract Abstract Introduction: How to invest and choose the right place to build a factory is one of the issues that is of vital importance for factories / companies or organizations due to its effects on factors such as performance, profitability, competitiveness, survival and various criteria such as social, economic, environmental, quality and Quantities and other goals are always noticeable to investors and managers. Materials & Methods: Since decision-making in this field is strategic and as a result, the incomplete information of experts in conditions of uncertainty may reduce the success of future exploitation; Therefore, researchers have introduced different methods to choose the right place; D number theory as an extension of Dempster-Shafer theory in locating, while solving the deficiencies in Dempster-Shafer theory, takes into account the lack of expert information in forecasting. In this research, due to the significant amount of demand and sensitivity in the correct direction of capital resources, considering the high amount of capital required and the great importance in choosing the right place in the geography of Iran to achieve success, and that investing in this industry has always been attractive, while choosing criteria with The importance of investigating the selection of a suitable location for the construction of an edible oil refinery in thirty-one provinces of the country with the combined method of Analytical Hierarchy Process and D-Number Theory (D-AHP), due to its ability to analyze data under conditions of uncertainty that can provide a more realistic estimate , has been investigated. Results & Discussion: the factors affecting the research problem of this research in the form of a combined method (D-AHP) and based on the consensus of the opinions of ten experts and experts have been helped with the help of brainstorming, which include: access to Raw materials, provincial demand, fixed capital costs such as land, etc. and the production capacities (factories) in the region and the frequency of consumption in the neighborhood of the province and the potential threat to the industry in case of a favorable focus are based on the behavior of consumers and political and social factors. Based on the hierarchical structure, the paired relations of D numbers for the criteria, sub-criteria (1 to 17) and options at different levels of investigation and weights have been calculated with this method, and the criteria of access to raw materials (crude oil) and provincial demand are the most important criteria. Finally, the important weights and ranks of places (provinces) in relation to the overall goal have been calculated and prioritized. Important criteria include: access to primary oil raw materials (distance from ports), fixed capital costs such as land, etc., the amount of demand in the provinces, the amount of previously created production capacities, the frequency of consumption in the neighborhood of the provinces, the lifespan of the industry in The future and political and social factors have been investigated and evaluated for 31 provinces of the country with the combined method (D-AHP) and with the consensus opinion of ten experts in the field of Iranian oil industry. Conclusion: Therefore, the suitable place for investment in the future according to the importance coefficient of the criteria and sub-criteria and in the order of priority are as follows: provinces; Tehran (first priority), Semnan (second priority), Alborz (third priority), Central (fourth priority), Mazandaran (fifth priority), Isfahan (sixth priority), Qom (seventh priority), Fars (eighth priority), Lorestan (priority 9th), South Khorasan (10th priority), Khuzestan (11th priority), Kahkiloyeh and Boyar Ahmad (12th priority), Zanjan (13th priority), Hormozgan (14th priority), Kerman (15th priority), Yazd (16th priority), Chaharmahal and Bakhtiari (17th priority), Bushehr (18th priority), Qazvin (19th priority), East Azerbaijan (20th priority), Razavi Khorasan (21st priority), Hamadan (22nd priority), West Azerbaijan (23rd priority) ), Gilan (24th priority), Kurdistan (25th priority), North Khorasan (26th priority), Ardabil (27th priority), Sistan and Baluchistan (28th priority), Ilam (27th priority) 9th), Kermanshah (30th priority), Golestan (31st priority). Finally, the important weights and ranks of the places (provinces) have been calculated and prioritized in relation to the overall goal, which will facilitate optimal decision-making and appropriate selection for new investment and prevent waste in the consumption of capital resources and strategic planning in the long term and prevent It helps and prevents the crisis of reduction of national gross product and reduction of capacity or closure of factories, which will lead to unemployment of many employees and activists in this field and social consequences. And it shows the rational policy making to reach the desired situation. Extwnded Abstract Abstract Introduction: How to invest and choose the right place to build a factory is one of the issues that is of vital importance for factories / companies or organizations due to its effects on factors such as performance, profitability, competitiveness, survival and various criteria such as social, economic, environmental, quality and Quantities and other goals are always noticeable to investors and managers. Materials & Methods: Since decision-making in this field is strategic and as a result, the incomplete information of experts in conditions of uncertainty may reduce the success of future exploitation; Therefore, researchers have introduced different methods to choose the right place; D number theory as an extension of Dempster-Shafer theory in locating, while solving the deficiencies in Dempster-Shafer theory, takes into account the lack of expert information in forecasting. In this research, due to the significant amount of demand and sensitivity in the correct direction of capital resources, considering the high amount of capital required and the great importance in choosing the right place in the geography of Iran to achieve success, and that investing in this industry has always been attractive, while choosing criteria with The importance of investigating the selection of a suitable location for the construction of an edible oil refinery in thirty-one provinces of the country with the combined method of Analytical Hierarchy Process and D-Number Theory (D-AHP), due to its ability to analyze data under conditions of uncertainty that can provide a more realistic estimate , has been investigated. Results & Discussion: the factors affecting the research problem of this research in the form of a combined method (D-AHP) and based on the consensus of the opinions of ten experts and experts have been helped with the help of brainstorming, which include: access to Raw materials, provincial demand, fixed capital costs such as land, etc. and the production capacities (factories) in the region and the frequency of consumption in the neighborhood of the province and the potential threat to the industry in case of a favorable focus are based on the behavior of consumers and political and social factors. Based on the hierarchical structure, the paired relations of D numbers for the criteria, sub-criteria (1 to 17) and options at different levels of investigation and weights have been calculated with this method, and the criteria of access to raw materials (crude oil) and provincial demand are the most important criteria. Finally, the important weights and ranks of places (provinces) in relation to the overall goal have been calculated and prioritized. Important criteria include: access to primary oil raw materials (distance from ports), fixed capital costs such as land, etc., the amount of demand in the provinces, the amount of previously created production capacities, the frequency of consumption in the neighborhood of the provinces, the lifespan of the industry in The future and political and social factors have been investigated and evaluated for 31 provinces of the country with the combined method (D-AHP) and with the consensus opinion of ten experts in the field of Iranian oil industry. Conclusion: Therefore, the suitable place for investment in the future according to the importance coefficient of the criteria and sub-criteria and in the order of priority are as follows: provinces; Tehran (first priority), Semnan (second priority), Alborz (third priority), Central (fourth priority), Mazandaran (fifth priority), Isfahan (sixth priority), Qom (seventh priority), Fars (eighth priority), Lorestan (priority 9th), South Khorasan (10th priority), Khuzestan (11th priority), Kahkiloyeh and Boyar Ahmad (12th priority), Zanjan (13th priority), Hormozgan (14th priority), Kerman (15th priority), Yazd (16th priority), Chaharmahal and Bakhtiari (17th priority), Bushehr (18th priority), Qazvin (19th priority), East Azerbaijan (20th priority), Razavi Khorasan (21st priority), Hamadan (22nd priority), West Azerbaijan (23rd priority) ), Gilan (24th priority), Kurdistan (25th priority), North Khorasan (26th priority), Ardabil (27th priority), Sistan and Baluchistan (28th priority), Ilam (27th priority) 9th), Kermanshah (30th priority), Golestan (31st priority). Finally, the important weights and ranks of the places (provinces) have been calculated and prioritized in relation to the overall goal, which will facilitate optimal decision-making and appropriate selection for new investment and prevent waste in the consumption of capital resources and strategic planning in the long term and prevent It helps and prevents the crisis of reduction of national gross product and reduction of capacity or closure of factories, which will lead to unemployment of many employees and activists in this field and social consequences. And it shows the rational policy making to reach the desired situation.

https://www.sepehr.org/article_709171_65035eb0db3465a759a0034ecec45df8.pdf

National Geographical Organization Scientific- Research Quarterly of Geographical Data (SEPEHR) 2588-3860 33 130 2024 08 22 Comparison of efficiency of random forest and support vector machine methods for mineral potential mapping of copper deposits, - Case study: Dahaj-Bazman Comparison of efficiency of random forest and support vector machine methods for mineral potential mapping of copper deposits, - Case study: Dahaj-Bazman 47 64 712672 10.22131/sepehr.2024.2012344.3023 FA Mohammad Karimi Associate Professor, Faculty of Geodesy and Geomatic, K.N.Toosi University of Technology 0000-0001-5016-9814 Parastoo Pilehforooshha Assistant Professor, Faculty of Geodesy and Geomatic, K.N.Toosi University of Technology 0000-0001-8615-0330 Ali Safari Master of Science in GIS, Faculty of Geodesy and Geomatic, K.N.Toosi University of Technology 0009-0009-4578-2556 Journal Article 2023 09 26 Extended Abstract Introduction: The exploration and preparation of the potential map of mineral reserves requires the use of various methods and techniques, based on the geological and mining knowledge of the investigated area, and the use of predictive models of mineral potential (Bonham-Carter, 1994; Carranza et al., 2008a). According to the investigations, the common models of map integration that are used in the discovery of mineral reserves in the initial exploration stage include index overlap model, fuzzy operators, weighted indicators and smart methods such as random forests and artificial networks. Determining the values of weights and scores that show the relative importance of the effective factors is the primary requirement in combining the maps and preparing the mineral potential map (Agterberg, 1992; Brown et al., 2000). The purpose of this research is to prepare a potential map of copper deposits in Dehj-Bazman region using two methods of random forest and support vector machine. In addition, in order to compare the potential map of porphyry copper reserves resulting from the random forest method, the support vector machine method and the knowledge-based methods of index overlap and fuzzy logic were used. Materials & Methods: The area studied in this research is a part of the magmatic belt of Kerman region, known as the Dehj-Sardouye belt. The information layers controlling mineralization in Dehj-Bazman area include rock units, structures, alterations, geochemistry, geophysics and copper deposits. In practical applications of machine learning algorithms, mineral potential mapping is essentially a bimodal classification problem, such that each undiscovered area is classified as prospective or non-prospective according to some combination of mapping criteria (Zuo, 2011). The final results are a set of predictive maps that show target areas with high ore formation potential. In order to model, training was done. Before training the random forest model, the input data set and the target variable should be prepared and then the model should be trained. The target variables for entering the random forest model and support vector machine were determined as deposit points (values of 1) and non-deposit points (values of 0). Then the genetic algorithm was used to adjust the parameters. Evaluation of the predictive performance of random forest model and support vector machine can be described by the ambiguity matrix. In this matrix, there are four components, which are defined as: (1) a deposit sample that is correctly classified as a deposit (TP); (2) a deposit sample incorrectly classified as a non-deposit sample (FN), (3) a non-deposit sample correctly classified as a non-deposit sample (TN), and (4) a non-deposit sample that is wrongly classified as a deposit sample (FP) (Liu et al., 2005; Tien Bui et al., 2016): (8) (9) (10) (11) (12) After training and evaluating different models, the best model was obtained by adjusting different parameters and it was used to integrate factor maps in order to predict areas with high potential of porphyry copper deposits. Also, knowledge-based methods of fuzzy logic and index overlap were used to combine factor maps to compare with the results of intelligent methods. Results & Discussion: At this stage, the desired information layers were collected and prepared in the GIS environment, and then factor maps were prepared. Accuracy, sensitivity, specificity, predicted positive value, predicted negative value, kappa index and OOB error were used to evaluate the performance of random forest model and support vector machine. Also, the importance of the predictor variables in the random forest model was evaluated through the mean decrease in accuracy and the mean decrease in node impurity or the Gini impurity index (Breiman, 2001). According to the results, the most important predictor in the random forest model is the geochemical map, while the structures factor has the least impact in predicting the preparation of the mineral potential map with the final random forest model. In the potential maps of porphyry copper deposits obtained from two methods of random forest and support vector machine, the target areas cover 14% of the studied area, in which there are 92% and 87% of known deposits, respectively. Finally, the efficiency of machine learning methods and knowledge-based methods were compared. In order to produce porphyry copper potential map with knowledge-based methods, the judgment of expert experts was used to assign weights to each criterion map. For this purpose, weights of 0.3, 0.25, 0.25, 0.1, 0.1 were assigned to produce maps of alteration factor, geochemistry, geology, geophysics and structures respectively. In the potential map obtained from the method of index overlap and fuzzy logic (fuzzy sum), the areas predicted as copper mines cover 16 and 17 percent of the studied area, respectively, in which 83 and 79 percent of the existing mines are located. Conclusion: This research was conducted with the aim of evaluating and comparing the effectiveness of random forest method and support vector machine method and knowledge-based methods to prepare porphyry copper potential map of Dehaj-Bozman region of Kerman province. Based on the results, the random forest model works well in the field of porphyry copper potential map preparation with geochemical, geophysical, geological, alteration and structures datasets. In addition, the random forest algorithm can estimate the importance of factor maps. The results of this research show that the geochemical factor map is the most important and the structure factor map is the least important in predicting the data-driven model of random forests. This estimate of importance is consistent with geological knowledge about porphyry copper mineralization in Dehj-Buzman region. In order to produce porphyry copper potential map with knowledge-based methods, the judgment of expert experts was used to assign weights to each criterion map. According to the obtained results, the performance of the random forest model is better than the vector machine model, and also, the performance of the support vector machine model is better than the knowledge-based methods. Extended Abstract Introduction: The exploration and preparation of the potential map of mineral reserves requires the use of various methods and techniques, based on the geological and mining knowledge of the investigated area, and the use of predictive models of mineral potential (Bonham-Carter, 1994; Carranza et al., 2008a). According to the investigations, the common models of map integration that are used in the discovery of mineral reserves in the initial exploration stage include index overlap model, fuzzy operators, weighted indicators and smart methods such as random forests and artificial networks. Determining the values of weights and scores that show the relative importance of the effective factors is the primary requirement in combining the maps and preparing the mineral potential map (Agterberg, 1992; Brown et al., 2000). The purpose of this research is to prepare a potential map of copper deposits in Dehj-Bazman region using two methods of random forest and support vector machine. In addition, in order to compare the potential map of porphyry copper reserves resulting from the random forest method, the support vector machine method and the knowledge-based methods of index overlap and fuzzy logic were used. Materials & Methods: The area studied in this research is a part of the magmatic belt of Kerman region, known as the Dehj-Sardouye belt. The information layers controlling mineralization in Dehj-Bazman area include rock units, structures, alterations, geochemistry, geophysics and copper deposits. In practical applications of machine learning algorithms, mineral potential mapping is essentially a bimodal classification problem, such that each undiscovered area is classified as prospective or non-prospective according to some combination of mapping criteria (Zuo, 2011). The final results are a set of predictive maps that show target areas with high ore formation potential. In order to model, training was done. Before training the random forest model, the input data set and the target variable should be prepared and then the model should be trained. The target variables for entering the random forest model and support vector machine were determined as deposit points (values of 1) and non-deposit points (values of 0). Then the genetic algorithm was used to adjust the parameters. Evaluation of the predictive performance of random forest model and support vector machine can be described by the ambiguity matrix. In this matrix, there are four components, which are defined as: (1) a deposit sample that is correctly classified as a deposit (TP); (2) a deposit sample incorrectly classified as a non-deposit sample (FN), (3) a non-deposit sample correctly classified as a non-deposit sample (TN), and (4) a non-deposit sample that is wrongly classified as a deposit sample (FP) (Liu et al., 2005; Tien Bui et al., 2016): (8) (9) (10) (11) (12) After training and evaluating different models, the best model was obtained by adjusting different parameters and it was used to integrate factor maps in order to predict areas with high potential of porphyry copper deposits. Also, knowledge-based methods of fuzzy logic and index overlap were used to combine factor maps to compare with the results of intelligent methods. Results & Discussion: At this stage, the desired information layers were collected and prepared in the GIS environment, and then factor maps were prepared. Accuracy, sensitivity, specificity, predicted positive value, predicted negative value, kappa index and OOB error were used to evaluate the performance of random forest model and support vector machine. Also, the importance of the predictor variables in the random forest model was evaluated through the mean decrease in accuracy and the mean decrease in node impurity or the Gini impurity index (Breiman, 2001). According to the results, the most important predictor in the random forest model is the geochemical map, while the structures factor has the least impact in predicting the preparation of the mineral potential map with the final random forest model. In the potential maps of porphyry copper deposits obtained from two methods of random forest and support vector machine, the target areas cover 14% of the studied area, in which there are 92% and 87% of known deposits, respectively. Finally, the efficiency of machine learning methods and knowledge-based methods were compared. In order to produce porphyry copper potential map with knowledge-based methods, the judgment of expert experts was used to assign weights to each criterion map. For this purpose, weights of 0.3, 0.25, 0.25, 0.1, 0.1 were assigned to produce maps of alteration factor, geochemistry, geology, geophysics and structures respectively. In the potential map obtained from the method of index overlap and fuzzy logic (fuzzy sum), the areas predicted as copper mines cover 16 and 17 percent of the studied area, respectively, in which 83 and 79 percent of the existing mines are located. Conclusion: This research was conducted with the aim of evaluating and comparing the effectiveness of random forest method and support vector machine method and knowledge-based methods to prepare porphyry copper potential map of Dehaj-Bozman region of Kerman province. Based on the results, the random forest model works well in the field of porphyry copper potential map preparation with geochemical, geophysical, geological, alteration and structures datasets. In addition, the random forest algorithm can estimate the importance of factor maps. The results of this research show that the geochemical factor map is the most important and the structure factor map is the least important in predicting the data-driven model of random forests. This estimate of importance is consistent with geological knowledge about porphyry copper mineralization in Dehj-Buzman region. In order to produce porphyry copper potential map with knowledge-based methods, the judgment of expert experts was used to assign weights to each criterion map. According to the obtained results, the performance of the random forest model is better than the vector machine model, and also, the performance of the support vector machine model is better than the knowledge-based methods.

https://www.sepehr.org/article_712672_39b8cbacac4a0e66f1a0b1ce37e5c920.pdf

National Geographical Organization Scientific- Research Quarterly of Geographical Data (SEPEHR) 2588-3860 33 130 2024 08 22 Identifying the active tectonic areas of the eastern Caspian coast using radar remote sensing Identifying the active tectonic areas of the eastern Caspian coast using radar remote sensing 65 78 712673 10.22131/sepehr.2024.2009124.3010 FA Mohamad Fathollahzadeh Phd Student, Geomorphology, Department of physical geography, Faculty of geography, University of Tehran 0000-0002-1726-0939 Mojtaba Yamani Professor of Geomorphology, Department of physical geography, Faculty of geography, University of Tehran 0000-0002-2042-7365 Abolghasem Goorabi Associate professor, Geomorphology, Department of physical geography, Faculty of geography, University of Tehran 0000-0002-2787-8687 Mehran Maghsoudi Associate professor of Geomorphology, Department of physical geography, Faculty of geography, University of Tehran Mehrnoosh Ghadimi Assistant professor of geomorphology,Department of physical geography, Faculty of geography, University of Tehran 0000-0002-3700-9506 Journal Article 2023 09 01 Extended Abstract Introduction: The landforms created by tectonic processes are studied by morphotectonics, in other words, morphotectonics is the science of applying geomorphic principles in solving tectonic problems. Quantitative landscape measurements are usually based on the calculation of geomorphic indices, using topographic maps, satellite images aerial photographs, and field visits. Coastal deltas are part of landforms and landscapes that, due to the proximity of two environments, land, and water, leave visible effects against tectonic activities, such as changing the pattern and location of deltas due to the change in the course of coastal rivers, the formation of unbalanced coastal terraces in parts of the coast, and the emergence of cut beaches in the form of seawalls. One of the methods of identifying and measuring land changes is using radar remote sensing. The principles of this technique were first described by Graham in 1974 (Pacheco et al., 2006). Interferometry using radar images with an artificial window or SAR is a precise method based on the use of at least two radar images of the same area, which measures the height displacement changes in wide areas and during different time intervals with a significant accuracy of millimeters (Dong et al., 2018). The coastal areas of northern Iran are of great importance due to the high population density and the ability to grow and develop economically and agriculturally, so monitoring geomorphic changes in the direction of sustainable development of these areas is particularly important. In this research, the eastern coast of the Caspian Sea from Gomishan to Joibar is investigated in terms of subsidence and uplift using radar remote sensing techniques to determine the active tectonic zones of the coast in terms of temporal and spatial changes. Materials and Methods: The Eastern Caspian Plain is the border between the Caspian Sea and West Gorgan and includes the cities of Gomishan, Bandare Turkman, Bandare Gaz, Gulugah, Khazarabad, and Joybar. The absolute height of the Caspian Plain along the coastline is determined according to the sea level, based on the hydrographic data of the Baku station, since 1850, the Caspian sea level has varied between -25.4 and -29.4 (Abdolhi Kakrodi, 2012). The history of seismic activity in North Alborz shows that cities like Rasht, Lahijan, Amol, and Gorgan, have been destroyed many times due to destructive earthquakes (Aqhanbati, 2013). The Alborz fault is an active fault that is stretched in a clockwise direction in the southern Caspian basin. In this research, according to the desired goals and radar remote sensing techniques, a series of Sentinel-1 radar images with a suitable time and space difference (maximum 30 days and maximum 150 meters respectively) including 61 images in time from 2014 to 2021 were prepared and processed. Results: The results obtained from the SBAS model indicate that the eastern part of the Caspian coast is more affected by the uplift and this trend continues up to Gorgan Bay. The Gorgan city has an uplift between 20 and 40 mm/year, which is reversed towards the coastal area, and subsidence of 10 to 52 mm/year occurs, which decreases as it approaches the coast and reaches 10 mm /year. Discussion, Conclusion: According to the results obtained from radar interferometry, the eastern coast of the Caspian Sea is more affected by uplifting. The Gorgan city has an uplift between 20 and 40 mm/year, which is reversed towards the coastal area, and subsidence of 10 to 52 mm/year occurs, which decreases as it approaches the coast and reaches 10 mm/year. To verify the results obtained, the data of the Gorgan geodynamic station was used, which shows subsidence of about 90 to 100 mm in a 6-year period, which is consistent with the values obtained from radar interferometry Based on comments Shahpasandzadeh (2013) and the reports of Nazari et al (2021), active tectonics caused by the Caspian fault that indicates the horizontal geodynamic displacement diagram of Gorgan, the small area towards the north and east during this time, which is observed in the form of numerous branches with a thrust (reverse) mechanism and a right-slip component with a slope to the south in Golestan province. Considering that the main feature of the coast of the Caspian Sea is the Surface rivers and the use of groundwater is very little and also the extraction of gas, oil, and mining resources, which is another factor in the occurrence of land subsidence, does not exist in this area, and there isn’t also huge and heavy structure in the study area that affects the subsidence of the surface; so displacement in the study area is the result of active tectonics. Extended Abstract Introduction: The landforms created by tectonic processes are studied by morphotectonics, in other words, morphotectonics is the science of applying geomorphic principles in solving tectonic problems. Quantitative landscape measurements are usually based on the calculation of geomorphic indices, using topographic maps, satellite images aerial photographs, and field visits. Coastal deltas are part of landforms and landscapes that, due to the proximity of two environments, land, and water, leave visible effects against tectonic activities, such as changing the pattern and location of deltas due to the change in the course of coastal rivers, the formation of unbalanced coastal terraces in parts of the coast, and the emergence of cut beaches in the form of seawalls. One of the methods of identifying and measuring land changes is using radar remote sensing. The principles of this technique were first described by Graham in 1974 (Pacheco et al., 2006). Interferometry using radar images with an artificial window or SAR is a precise method based on the use of at least two radar images of the same area, which measures the height displacement changes in wide areas and during different time intervals with a significant accuracy of millimeters (Dong et al., 2018). The coastal areas of northern Iran are of great importance due to the high population density and the ability to grow and develop economically and agriculturally, so monitoring geomorphic changes in the direction of sustainable development of these areas is particularly important. In this research, the eastern coast of the Caspian Sea from Gomishan to Joibar is investigated in terms of subsidence and uplift using radar remote sensing techniques to determine the active tectonic zones of the coast in terms of temporal and spatial changes. Materials and Methods: The Eastern Caspian Plain is the border between the Caspian Sea and West Gorgan and includes the cities of Gomishan, Bandare Turkman, Bandare Gaz, Gulugah, Khazarabad, and Joybar. The absolute height of the Caspian Plain along the coastline is determined according to the sea level, based on the hydrographic data of the Baku station, since 1850, the Caspian sea level has varied between -25.4 and -29.4 (Abdolhi Kakrodi, 2012). The history of seismic activity in North Alborz shows that cities like Rasht, Lahijan, Amol, and Gorgan, have been destroyed many times due to destructive earthquakes (Aqhanbati, 2013). The Alborz fault is an active fault that is stretched in a clockwise direction in the southern Caspian basin. In this research, according to the desired goals and radar remote sensing techniques, a series of Sentinel-1 radar images with a suitable time and space difference (maximum 30 days and maximum 150 meters respectively) including 61 images in time from 2014 to 2021 were prepared and processed. Results: The results obtained from the SBAS model indicate that the eastern part of the Caspian coast is more affected by the uplift and this trend continues up to Gorgan Bay. The Gorgan city has an uplift between 20 and 40 mm/year, which is reversed towards the coastal area, and subsidence of 10 to 52 mm/year occurs, which decreases as it approaches the coast and reaches 10 mm /year. Discussion, Conclusion: According to the results obtained from radar interferometry, the eastern coast of the Caspian Sea is more affected by uplifting. The Gorgan city has an uplift between 20 and 40 mm/year, which is reversed towards the coastal area, and subsidence of 10 to 52 mm/year occurs, which decreases as it approaches the coast and reaches 10 mm/year. To verify the results obtained, the data of the Gorgan geodynamic station was used, which shows subsidence of about 90 to 100 mm in a 6-year period, which is consistent with the values obtained from radar interferometry Based on comments Shahpasandzadeh (2013) and the reports of Nazari et al (2021), active tectonics caused by the Caspian fault that indicates the horizontal geodynamic displacement diagram of Gorgan, the small area towards the north and east during this time, which is observed in the form of numerous branches with a thrust (reverse) mechanism and a right-slip component with a slope to the south in Golestan province. Considering that the main feature of the coast of the Caspian Sea is the Surface rivers and the use of groundwater is very little and also the extraction of gas, oil, and mining resources, which is another factor in the occurrence of land subsidence, does not exist in this area, and there isn’t also huge and heavy structure in the study area that affects the subsidence of the surface; so displacement in the study area is the result of active tectonics.

https://www.sepehr.org/article_712673_749495845ff6609bd3e603b79af64bac.pdf

National Geographical Organization Scientific- Research Quarterly of Geographical Data (SEPEHR) 2588-3860 33 130 2024 08 22 Use of UAVs and Lidar to identify the spatiotemporal elevation changes of AlamKooh Glacier Use of UAVs and Lidar to identify the spatiotemporal elevation changes of AlamKooh Glacier 79 98 711386 10.22131/sepehr.2024.2008881.3009 FA Sara Sheshangosht Ph.D Student, Department of remote sensing and GIS, Science and Research Branch, Islamic Azad University, Tehran, Iran 0000-0001-9563-2681 Hossein Agamohammadi Assistant Professor , Department of remote sensing and GIS, Science and Research Branch, Islamic Azad University, Tehran, Iran 0000-0002-9497-6295 Nematollah Karimi Associate Professor, Research center for water resource studies, Water Research Institute, Tehran, Iran 0000-0002-7896-4390 Zahra Azizi Assistant Professor , Department of remote sensing and GIS, Science and Research Branch, Islamic Azad University, Tehran, Iran 0000-0001-8572-7134 Mohammad Hassan Vahidnia Assistant Professor . Department of remote sensing and GIS, Faculty of Earth Science, Shahid Beheshti University, Tehran, Iran 0000-0001-7922-715X Journal Article 2023 09 07 Extended Abstract Introduction Glaciers and their short-term and long-term elevation changes are among the most critical environmental hazard indices for monitoring climate change and evaluating geomorphology, perpetually posing risks to climbers, environmentalists, and tourists. The Alamkooh glacier’s snout is known as one of the most dynamic parts of glaciers in Takht-e-Soliman height due to the yearly advance and retreat of glacier movement causing substantial volumes of various glacial deposits to collapse into their downstream areas. Nowadays, the advancements of satellite imagery, aerial photos, and Unmanned Automated vehicles (UAV) pave the path for accurately extracting and evaluating these changes. Therefore, the objectives of this research are: (a) evaluating the use of new and cost-effective technologies (UAVs) in comparison to satellite imagery for monitoring glacier changes, (b) identifying spatiotemporal glacier elevation changes, and (c) evaluation of the elevation change rate of the Alamkooh glacier snout from 2010 to 2020 using high spatial resolution remotely sensing data. In this context, the elevation changes of the snout of Alamkooh Glacier, as the hazardous activist part of this glacier, were assessed using Digital elevation models (DEMs) differences of 2010, 2018, and 2020. Materials and Methods Alamkooh Glacier is located on the northern hillside of Alamkooh Summit in the Takht-e-Soliman region. The snout of this glacier is situated in a steep valley known as Lizbonak and its high activity changes the shape and morphology of this area. In this paper, spatial and temporal elevation changes of Alamkooh Snout were identified and evaluated using DEMs subtraction derived from aerial laser scanning (LiDAR) data in 2010, and from images captured by UAV in 2018 and 2020. Before elevation change analysis, the DEMs obtained through UAVs in 2018 and 2020 were carried out using approximately 40 and 20 ground control points, respectively. The resulting outputs displayed a reliable accuracy of around 15 cm for these DEMs. In addition, for assessing elevation changes precisely, the all of extracted DEMs were preprocessed and orthorectified and then subsequently subtracted pairwise. Then after, the accuracy of elevation changes was appraised based on non-glacial area elevation change. The outcomes of elevation change in this region signify a high level of accuracy in the 10-year time span. According to the results, the average and standard division elevation change of non-glacial area was ±0.05 cm and 0.34 cm respectively. Moreover, the average error assessment on the non-glacial area indicates that within eight years from 2010 to 2018 the average error was ±0.16 cm, and within two years it was ±0.11 cm from 2018 to 2020. Result and discussion Results of DEMs pairwise differences show significant elevation changes in this part of Alamkooh Glacier from 2010 to 2020. The average and the maximum elevation change rates in this period are -0.8 (m/yr.) and -2.31(m/yr.) respectively. The major elevation changes in the snout of Alamkooh happened in the initial period from 2010 to 2018 where the yearly and the maximum mean elevation change rates were -1.03 (m/yr.) and –2.77 (m/yr.) respectively. On the contrary, the elevation changes from 2018 to 2020 were lower than the first period whereas the yearly mean elevation change was about +0.1 (m/yr.) and the maximum elevation change rate was -1.85 (m/yr.). The positive rate of elevation change from 2018 to 2020 is due to debris and ice cubes flowing from upstream and accumulation downstream. Moreover, the Spatial analysis of elevation changes results show a heterogeneous distribution whereas the most significant elevation change in the snout of Alamkooh glacier has occurred predominantly across and along the largest existing valley rather than being evenly spread out across the entire area. The elevation change domain in this valley is between +1.3±0.05 to -23.05±0.05 and the average elevation change of in ten years from 2010 to 2020 is about -8.01 ± 0.05 meters. These changes mostly were negative with decreasing and eroding rates. In contrast, the elevation changes in other valleys only occurred at the exit area of the glacier and just the entrance of the snout area, and the margins did not show a considerable change. When considering all valleys in the snout of Alamkooh the elevation changes distribution across the snout varies between +0.45 to -13.2 (m) with an average of -7.8 (m) which is less than alongside changes at the main valley. Conclusion The results show elevation changes in the Almakooh snout do not have constant rate and largely fluctuate in different years and regions. The maximum elevation changes occurred from 2010 to 2018 and along with the main steepest valley. The main valley plays a vital role in elevation change analysis and flowing debris down. This area is also known as the depletion area of the Alamkooh glacier and its drastic elevation changes are caused due to ice and snow melt. The tremendous historical flood of the SardAbrood River occurred in June 2011 was created and affected by elevation changes in this area. Therefore, the tongue of Alamkooh Glacier is considered one of the most dangerous areas regarding natural hazards, and morphological change studies require precaution regarding approaching or visiting this area. This research also confirms that using time-series of remote sensing data such as UAV and Lidar images is very helpful and cost-effective data for identifying, extracting, and monitoring the spatiotemporal changes of glaciers, debris flow directions, and natural hazards. Extended Abstract Introduction Glaciers and their short-term and long-term elevation changes are among the most critical environmental hazard indices for monitoring climate change and evaluating geomorphology, perpetually posing risks to climbers, environmentalists, and tourists. The Alamkooh glacier’s snout is known as one of the most dynamic parts of glaciers in Takht-e-Soliman height due to the yearly advance and retreat of glacier movement causing substantial volumes of various glacial deposits to collapse into their downstream areas. Nowadays, the advancements of satellite imagery, aerial photos, and Unmanned Automated vehicles (UAV) pave the path for accurately extracting and evaluating these changes. Therefore, the objectives of this research are: (a) evaluating the use of new and cost-effective technologies (UAVs) in comparison to satellite imagery for monitoring glacier changes, (b) identifying spatiotemporal glacier elevation changes, and (c) evaluation of the elevation change rate of the Alamkooh glacier snout from 2010 to 2020 using high spatial resolution remotely sensing data. In this context, the elevation changes of the snout of Alamkooh Glacier, as the hazardous activist part of this glacier, were assessed using Digital elevation models (DEMs) differences of 2010, 2018, and 2020. Materials and Methods Alamkooh Glacier is located on the northern hillside of Alamkooh Summit in the Takht-e-Soliman region. The snout of this glacier is situated in a steep valley known as Lizbonak and its high activity changes the shape and morphology of this area. In this paper, spatial and temporal elevation changes of Alamkooh Snout were identified and evaluated using DEMs subtraction derived from aerial laser scanning (LiDAR) data in 2010, and from images captured by UAV in 2018 and 2020. Before elevation change analysis, the DEMs obtained through UAVs in 2018 and 2020 were carried out using approximately 40 and 20 ground control points, respectively. The resulting outputs displayed a reliable accuracy of around 15 cm for these DEMs. In addition, for assessing elevation changes precisely, the all of extracted DEMs were preprocessed and orthorectified and then subsequently subtracted pairwise. Then after, the accuracy of elevation changes was appraised based on non-glacial area elevation change. The outcomes of elevation change in this region signify a high level of accuracy in the 10-year time span. According to the results, the average and standard division elevation change of non-glacial area was ±0.05 cm and 0.34 cm respectively. Moreover, the average error assessment on the non-glacial area indicates that within eight years from 2010 to 2018 the average error was ±0.16 cm, and within two years it was ±0.11 cm from 2018 to 2020. Result and discussion Results of DEMs pairwise differences show significant elevation changes in this part of Alamkooh Glacier from 2010 to 2020. The average and the maximum elevation change rates in this period are -0.8 (m/yr.) and -2.31(m/yr.) respectively. The major elevation changes in the snout of Alamkooh happened in the initial period from 2010 to 2018 where the yearly and the maximum mean elevation change rates were -1.03 (m/yr.) and –2.77 (m/yr.) respectively. On the contrary, the elevation changes from 2018 to 2020 were lower than the first period whereas the yearly mean elevation change was about +0.1 (m/yr.) and the maximum elevation change rate was -1.85 (m/yr.). The positive rate of elevation change from 2018 to 2020 is due to debris and ice cubes flowing from upstream and accumulation downstream. Moreover, the Spatial analysis of elevation changes results show a heterogeneous distribution whereas the most significant elevation change in the snout of Alamkooh glacier has occurred predominantly across and along the largest existing valley rather than being evenly spread out across the entire area. The elevation change domain in this valley is between +1.3±0.05 to -23.05±0.05 and the average elevation change of in ten years from 2010 to 2020 is about -8.01 ± 0.05 meters. These changes mostly were negative with decreasing and eroding rates. In contrast, the elevation changes in other valleys only occurred at the exit area of the glacier and just the entrance of the snout area, and the margins did not show a considerable change. When considering all valleys in the snout of Alamkooh the elevation changes distribution across the snout varies between +0.45 to -13.2 (m) with an average of -7.8 (m) which is less than alongside changes at the main valley. Conclusion The results show elevation changes in the Almakooh snout do not have constant rate and largely fluctuate in different years and regions. The maximum elevation changes occurred from 2010 to 2018 and along with the main steepest valley. The main valley plays a vital role in elevation change analysis and flowing debris down. This area is also known as the depletion area of the Alamkooh glacier and its drastic elevation changes are caused due to ice and snow melt. The tremendous historical flood of the SardAbrood River occurred in June 2011 was created and affected by elevation changes in this area. Therefore, the tongue of Alamkooh Glacier is considered one of the most dangerous areas regarding natural hazards, and morphological change studies require precaution regarding approaching or visiting this area. This research also confirms that using time-series of remote sensing data such as UAV and Lidar images is very helpful and cost-effective data for identifying, extracting, and monitoring the spatiotemporal changes of glaciers, debris flow directions, and natural hazards.

https://www.sepehr.org/article_711386_5be4e352939fe8396bbbc3b01ec01289.pdf

National Geographical Organization Scientific- Research Quarterly of Geographical Data (SEPEHR) 2588-3860 33 130 2024 08 22 Efficiency of machine learning models in estimation of local geoid height with GPS/Leveling measurements Efficiency of machine learning models in estimation of local geoid height with GPS/Leveling measurements 99 117 706725 10.22131/sepehr.2023.1999012.2969 FA Seyyed Reza Ghaffari-Razin Assistant professor , Department of Geo-science Engineering, Arak University of Technology, Arak, Iran 0000-0002-5579-5889 Navid Hooshangi Assistant professor, Department of Geoscience Engineering, Arak University of Technology 0000-0001-9929-8908 Journal Article 2023 03 25 Extended Abstract Introduction In geodesy, three levels are considered: the physical surface of the earth on which mapping measurements are made, the ellipsoidal reference surface (geometric datum) which is the basis of mathematical calculations, the geoid physical surface (physical datum) which is the basis for measuring heights. Satellite positioning systems measure the height of points relative to the ellipsoid surface. The geoid is one of the equipotential surfaces of the earth's gravity field, which approximates the mean sea level (MSL) by least squares. Geoid is very important in geodesy as a representative of the physical space or the space of observations made on the earth and also as the base level of elevations. The separation between the geoid and the geocentric reference ellipse is called geoid height (N). Although there is only one equipotential surface called geoid, various methods are used to determine it. These methods include: geometric method, geoid determination by satellite method, Gravimetric methods and geoid determination using GPS/leveling. Materials and Methods In this paper, the aim is to estimate the height of the local geoid using machine learning models. To do this, artificial neural network (ANN), adaptive neuro-fuzzy inference model (ANFIS), support vector regression (SVR) and general regression neural network (GRNN) models are used. The geodetic coordinates of 26 GPS stations in the north-west of Iran along with their orthometric height (H0) and normal height (h) were obtained from the national cartographic center of Iran. In all stations, the difference of orthometric height and normal height is considered as geoid height (N). Therefore, the geodetic longitude and latitude of the GPS stations are considered as the input of the machine learning models, and the corresponding geoid height was considered as the output. In order to test the results of machine learning models, two modes of 4 and 7 test stations are considered. Also, the output of the models is compared with the local geoid model IRG2016 presented by Saadat et al. for the Iranian region and also the global geoid model EGM2008. Results and Discussion Due to the availability of a complete set of observations of GPS stations along with orthometric height obtained from leveling in the north-west region of Iran, the study and evaluation of the models proposed in the paper has been carried out in this region. Observations of 26 GPS stations of North-west of Iran were prepared from the national cartographic center (https://www.ncc.gov.ir/). Two modes are considered for training and testing of ANN, ANFIS, SVR and GRNN models. In the first case, the number of training stations is 22 and the number of test stations is 4. But in the second case, by increasing the number of test stations to 7 stations, the error evaluation of the models has been done. It should be noted that the distribution of training and test stations is completely random. After the training step of machine learning models and choosing the optimal structure, the test step is performed in two different modes (4 and 7 stations). At this step, the value of the geoid height in the test stations is estimated and compared with the value obtained from the difference of orthometric height and normal height as a basis. Two statistical indices of relative error in percentage and RMSE in centimeters were calculated for all models and presented in Table (1) for the first case. Table 1. Relative error (%) of ANN, ANFIS, SVR, GRNN and IRG2016 models in the test stations considered for the first case According to the results of Table (1) and comparing the relative error values of all models in the test stations, it shows that the ANFIS model was more accurate than other models. After ANFIS model, IRG2016 model has higher accuracy than ANN, SVR and GRNN models. It should be noted that the IRG2016 local model uses the observations of all Iranian plateau stations to model the local geoid, and therefore it is expected that this model will be more accurate in the study area than other models. Conclusion The evaluations show that in the case of 22 training stations and 4 test stations, the RMSE of ANN, ANFIS, SVR, GRNN and IRG2016 models in the test step are 37.32, 19.83, 49.34, 53.82 and 29.65 cm, respectively. However, in the case of 19 training stations and 7 test stations, the error values of the models are 36.63, 58.31, 39.64, 41.29 and 24.68 cm, respectively. Comparison of RMSE shows that ANN model with less number of training stations provides higher accuracy than ANFIS, SVR and GRNN models. The results of this paper show that by using ANN and ANFIS models, geoid height can be estimated and used with high accuracy locally in civil and surveying applications. Extended Abstract Introduction In geodesy, three levels are considered: the physical surface of the earth on which mapping measurements are made, the ellipsoidal reference surface (geometric datum) which is the basis of mathematical calculations, the geoid physical surface (physical datum) which is the basis for measuring heights. Satellite positioning systems measure the height of points relative to the ellipsoid surface. The geoid is one of the equipotential surfaces of the earth's gravity field, which approximates the mean sea level (MSL) by least squares. Geoid is very important in geodesy as a representative of the physical space or the space of observations made on the earth and also as the base level of elevations. The separation between the geoid and the geocentric reference ellipse is called geoid height (N). Although there is only one equipotential surface called geoid, various methods are used to determine it. These methods include: geometric method, geoid determination by satellite method, Gravimetric methods and geoid determination using GPS/leveling. Materials and Methods In this paper, the aim is to estimate the height of the local geoid using machine learning models. To do this, artificial neural network (ANN), adaptive neuro-fuzzy inference model (ANFIS), support vector regression (SVR) and general regression neural network (GRNN) models are used. The geodetic coordinates of 26 GPS stations in the north-west of Iran along with their orthometric height (H0) and normal height (h) were obtained from the national cartographic center of Iran. In all stations, the difference of orthometric height and normal height is considered as geoid height (N). Therefore, the geodetic longitude and latitude of the GPS stations are considered as the input of the machine learning models, and the corresponding geoid height was considered as the output. In order to test the results of machine learning models, two modes of 4 and 7 test stations are considered. Also, the output of the models is compared with the local geoid model IRG2016 presented by Saadat et al. for the Iranian region and also the global geoid model EGM2008. Results and Discussion Due to the availability of a complete set of observations of GPS stations along with orthometric height obtained from leveling in the north-west region of Iran, the study and evaluation of the models proposed in the paper has been carried out in this region. Observations of 26 GPS stations of North-west of Iran were prepared from the national cartographic center (https://www.ncc.gov.ir/). Two modes are considered for training and testing of ANN, ANFIS, SVR and GRNN models. In the first case, the number of training stations is 22 and the number of test stations is 4. But in the second case, by increasing the number of test stations to 7 stations, the error evaluation of the models has been done. It should be noted that the distribution of training and test stations is completely random. After the training step of machine learning models and choosing the optimal structure, the test step is performed in two different modes (4 and 7 stations). At this step, the value of the geoid height in the test stations is estimated and compared with the value obtained from the difference of orthometric height and normal height as a basis. Two statistical indices of relative error in percentage and RMSE in centimeters were calculated for all models and presented in Table (1) for the first case. Table 1. Relative error (%) of ANN, ANFIS, SVR, GRNN and IRG2016 models in the test stations considered for the first case According to the results of Table (1) and comparing the relative error values of all models in the test stations, it shows that the ANFIS model was more accurate than other models. After ANFIS model, IRG2016 model has higher accuracy than ANN, SVR and GRNN models. It should be noted that the IRG2016 local model uses the observations of all Iranian plateau stations to model the local geoid, and therefore it is expected that this model will be more accurate in the study area than other models. Conclusion The evaluations show that in the case of 22 training stations and 4 test stations, the RMSE of ANN, ANFIS, SVR, GRNN and IRG2016 models in the test step are 37.32, 19.83, 49.34, 53.82 and 29.65 cm, respectively. However, in the case of 19 training stations and 7 test stations, the error values of the models are 36.63, 58.31, 39.64, 41.29 and 24.68 cm, respectively. Comparison of RMSE shows that ANN model with less number of training stations provides higher accuracy than ANFIS, SVR and GRNN models. The results of this paper show that by using ANN and ANFIS models, geoid height can be estimated and used with high accuracy locally in civil and surveying applications.

https://www.sepehr.org/article_706725_58615fb86ae72bc98a525f5db5692151.pdf

National Geographical Organization Scientific- Research Quarterly of Geographical Data (SEPEHR) 2588-3860 33 130 2024 08 22 Spatial analysis of poverty and social gap in metropolitan areas of Iran - Case study: District 11 of Isfahan Municipality Spatial analysis of poverty and social gap in metropolitan areas of Iran - Case study: District 11 of Isfahan Municipality 119 138 711242 10.22131/sepehr.2024.2003479.2981 FA Ali Sadeghi Assistant professor, Department of geography and urban planning, Faculty of geography and planning, University of Isfahan, Isfahan, Iran 0000-0002-2320-7720 Amir Reza Khavarian-Garmsir Assistant professor, Department of geography and urban planning, Faculty of geography and planning, University of Isfahan, Isfahan, Iran 0000-0001-8609-1748 Maryam Zareei Master of geography and urban planning, Isfahan University, Isfahan, Iran Journal Article 2023 07 24 Extended Abstract Introduction: Cities have many challenges, but it can be said that the problem that threatens them is weak. The existence of poverty in cities leads to the occurrence of social and economic issues and causes the stability and development of these cities to be created with problems. For example, poverty can lead to unemployment, homelessness, crime, and increased disease rates. Therefore, eliminating poverty in cities plays a very important role in creating healthy and sustainable societies. Due to population growth and the influx of Afghan immigrants in recent years, some neighborhoods in District 11 of Isfahan municipality have experienced poverty due to inequality and unfair distribution of services and facilities. In order to organize the current situation and overcome the existing conditions, the spatial distribution of poverty spots must first be identified and then, with regular planning, this problem can be solved to prevent the consequences of poverty at the regional level. The aim of the current research is to analyze the spatial distribution of urban poverty indicators in the 11th district of the municipality and the social gap among the residents of this neighborhood. Materials & Methods: The research was applied in terms of purpose and descriptive-analytical in terms of method. Based on the data of the statistical block of the 11th district of Isfahan municipality, hotspot analysis and Moran's spatial autocorrelation were performed in the GIS environment. Excel software was used for urban poverty indicators. SPS software is used for the factor analysis of the defined indicators. Results & Discussion: The results showed that weak in the 11th region of Isfahan municipality has a cluster distribution pattern and spatial autocorrelation. According to the zoning, the parts of the center, east, northeast, and parts of the southeast and south. The west is surrounded by poor and very poor blocks, and in the north, northwest and west parts of region 11, there are very prosperous and prosperous blocks. However, in district 11 of Isfahan municipality, we see a class divide. On the other hand, I can say that having poor space in the 11th district of Isfahan city follows the characteristic pattern, in such a way that as we approach from the south to the north and from the east to the west, the poverty be decreases. Conclusion: Some social and cultural values can perpetuate poverty and social inequality, and people in poverty may have different beliefs, attitudes, and behaviors that exacerbate their economic problems. In addition to individual and social factors, institutional factors such as housing policies, zoning laws, and land use regulations can also play a role in the spatial distribution of poverty and social inequality in urban areas. For example, discriminatory housing policies can lead to the concentration of low-income individuals in specific areas, while deprivation zoning policies can limit their access to affordable housing and employment opportunities. Today, poverty exists in various dimensions of human life and has brought with it problems and challenges. Therefore, in order to reduce poverty and implement human and sustainable development, it is essential to identify scientific and specialized methods, the geography of poverty-stricken areas, and important indicators in this field. The successful implementation of strategies and policies to reduce poverty requires the identification of all factors and needs of residents in the geographical area affected by this problem, so that programs can be developed to reduce poverty and improve conditions. This research contributes to the development of knowledge in the field of poverty and urban social planning. Its results can provide the necessary information to make decisions in addressing the urban poor problem. Finally, the following recommendations are proposed to improve the current conditions in District 11 of Isfahan city: Implementing neighborhood-based projects to achieve sustainable urban redevelopment with people's participation. Establishing neighborhood development offices to identify the specific problems of each neighborhood and provide solutions. Conducting research on poverty with the support and participation of organizations such as the Imam Khomeini Relief Committee and municipal authorities to align their results and find the best solution to address urban poverty. Considering that the main reason for the migration of native residents of District 11 is the presence of Afghan immigrants in this area, and as a result, many social problems have arisen, it is essential to address this issue with appropriate policies; otherwise, we will face more serious problems between native residents and Afghan immigrants in the future. Providing facilities and loans for renovation and reconstruction in the area, especially in the central, eastern, and northeast parts. Creating social justice for the use of facilities. Improving environmental conditions in District 11 of Isfahan, especially in the Sajjad Square neighborhood, which has an unfavorable situation. Municipal officials can address the environmental problems of this area by creating parks and green spaces, paving the streets, removing environmental pollution, collecting garbage, and organizing the vacant lands. Creating a space for the education of working children, supporting them, and providing suitable employment opportunities for them. Improving the physical condition of the area through redevelopment programs, and more. Extended Abstract Introduction: Cities have many challenges, but it can be said that the problem that threatens them is weak. The existence of poverty in cities leads to the occurrence of social and economic issues and causes the stability and development of these cities to be created with problems. For example, poverty can lead to unemployment, homelessness, crime, and increased disease rates. Therefore, eliminating poverty in cities plays a very important role in creating healthy and sustainable societies. Due to population growth and the influx of Afghan immigrants in recent years, some neighborhoods in District 11 of Isfahan municipality have experienced poverty due to inequality and unfair distribution of services and facilities. In order to organize the current situation and overcome the existing conditions, the spatial distribution of poverty spots must first be identified and then, with regular planning, this problem can be solved to prevent the consequences of poverty at the regional level. The aim of the current research is to analyze the spatial distribution of urban poverty indicators in the 11th district of the municipality and the social gap among the residents of this neighborhood. Materials & Methods: The research was applied in terms of purpose and descriptive-analytical in terms of method. Based on the data of the statistical block of the 11th district of Isfahan municipality, hotspot analysis and Moran's spatial autocorrelation were performed in the GIS environment. Excel software was used for urban poverty indicators. SPS software is used for the factor analysis of the defined indicators. Results & Discussion: The results showed that weak in the 11th region of Isfahan municipality has a cluster distribution pattern and spatial autocorrelation. According to the zoning, the parts of the center, east, northeast, and parts of the southeast and south. The west is surrounded by poor and very poor blocks, and in the north, northwest and west parts of region 11, there are very prosperous and prosperous blocks. However, in district 11 of Isfahan municipality, we see a class divide. On the other hand, I can say that having poor space in the 11th district of Isfahan city follows the characteristic pattern, in such a way that as we approach from the south to the north and from the east to the west, the poverty be decreases. Conclusion: Some social and cultural values can perpetuate poverty and social inequality, and people in poverty may have different beliefs, attitudes, and behaviors that exacerbate their economic problems. In addition to individual and social factors, institutional factors such as housing policies, zoning laws, and land use regulations can also play a role in the spatial distribution of poverty and social inequality in urban areas. For example, discriminatory housing policies can lead to the concentration of low-income individuals in specific areas, while deprivation zoning policies can limit their access to affordable housing and employment opportunities. Today, poverty exists in various dimensions of human life and has brought with it problems and challenges. Therefore, in order to reduce poverty and implement human and sustainable development, it is essential to identify scientific and specialized methods, the geography of poverty-stricken areas, and important indicators in this field. The successful implementation of strategies and policies to reduce poverty requires the identification of all factors and needs of residents in the geographical area affected by this problem, so that programs can be developed to reduce poverty and improve conditions. This research contributes to the development of knowledge in the field of poverty and urban social planning. Its results can provide the necessary information to make decisions in addressing the urban poor problem. Finally, the following recommendations are proposed to improve the current conditions in District 11 of Isfahan city: Implementing neighborhood-based projects to achieve sustainable urban redevelopment with people's participation. Establishing neighborhood development offices to identify the specific problems of each neighborhood and provide solutions. Conducting research on poverty with the support and participation of organizations such as the Imam Khomeini Relief Committee and municipal authorities to align their results and find the best solution to address urban poverty. Considering that the main reason for the migration of native residents of District 11 is the presence of Afghan immigrants in this area, and as a result, many social problems have arisen, it is essential to address this issue with appropriate policies; otherwise, we will face more serious problems between native residents and Afghan immigrants in the future. Providing facilities and loans for renovation and reconstruction in the area, especially in the central, eastern, and northeast parts. Creating social justice for the use of facilities. Improving environmental conditions in District 11 of Isfahan, especially in the Sajjad Square neighborhood, which has an unfavorable situation. Municipal officials can address the environmental problems of this area by creating parks and green spaces, paving the streets, removing environmental pollution, collecting garbage, and organizing the vacant lands. Creating a space for the education of working children, supporting them, and providing suitable employment opportunities for them. Improving the physical condition of the area through redevelopment programs, and more.

https://www.sepehr.org/article_711242_7993961550f686e634a6a39ac02da2ed.pdf

National Geographical Organization Scientific- Research Quarterly of Geographical Data (SEPEHR) 2588-3860 33 130 2024 08 22 Analysis the effects of drought on water level changes in The Bakhtegan, Tashk and Maharlo lakes Analysis the effects of drought on water level changes in The Bakhtegan, Tashk and Maharlo lakes 139 159 712674 10.22131/sepehr.2024.2004008.2983 FA Seyed Hossein Mirmousavi Associate professor of climatology, Zanjan University 0000-0002-6262-5474 Journal Article 2023 06 05 Extended Abstract Introduction In hydrological drought, water scarcity spreads through the hydrological cycle and can subsequently reduce groundwater levels, surface water and lake levels, and this means that hydrological drought dominates those areas, leading to long-term effects. In addition, due to climate changes and rainfall and temperature anomalies, droughts have increased in frequency and severity in many regions of the world. The predicted changes for the coming years show that climate variables will not have uniform changes in all regions and regional changes in the amount of precipitation may lead to the creation of hydrological patterns much different from the current conditions. The present study was also carried out with the aim of spatial analysis of drought effects on water level changes in the catchment area of Bakhtegan, Tashk and Maharlo lakes. In this research, an attempt has been made to identify temporal and spatial patterns of changes in the level of this lakes by using satellite images and spatial analysis models. Materials and Methods In the present study, Landsat 5(TM), 7(ETM+) and Landsat 8(OLI) satellite images with a resolution of 30 meters have been used in the period of 2000-2021 to investigate water level changes. Due to the fact that the water level of the studied lakes changes drastically with the rainfall of different months, therefore, it is difficult to determine the amount of water cover for a year without considering the fact that a part of this cover is seasonal and when the rainfall decreases, a part of the lake Dry may not provide accurate results. Based on this, in the present study, one image was used for each month for each year studied to evaluate the changes in the water level of the lakes in all months of the year. Conclusion and Discussion The investigation of the changes in the water level of Maharlo Lake shows that in the drought of 2108 and 2017, the permanent water level of the lake has decreased to 1.8 square kilometers. Meanwhile, in the severe and very severe drought of 2005 and 2004, the permanent water level reached 170.4 square kilometers. Examining the changes in the area of Tashek Lake in 15 years of drought shows that the area of the waterless part of this lake has increased more than the seasonal and permanent water. The highest amount in this field was in 2021 with a very severe drought, which shows that this lake has more critical conditions in terms of permanent dryness than Maharlo Lake. This lake has been in a terrible state for 5 years. Comparing the changes in the area of Bakhtegan lake in different years shows that this lake has a more critical situation than its neighboring lakes (Maharlo and Tashk), so that in a significant number of years (12 years) the lake lacked permanent water and only With monthly or seasonal rains, some water has been temporarily collected on its surface, but it has a short shelf life between 2 to 6 months (November to May). Results The results of the evaluation and analysis of the role of drought in the water level changes of the Bakhtegan, Tashk and Maharlo catchment lakes showed that the area of these lakes has decreased significantly during the studied period, so that over time the area of the water area has decreased. It has been permanently reduced and added to the dry and waterless area. The maximum decrease in the water level of all three investigated lakes occurred during a 6-year drought between 2008 and 2013, in such a way that the area of the part with permanent water was greatly reduced and the area of the dry part of the lakes was increased. Extended Abstract Introduction In hydrological drought, water scarcity spreads through the hydrological cycle and can subsequently reduce groundwater levels, surface water and lake levels, and this means that hydrological drought dominates those areas, leading to long-term effects. In addition, due to climate changes and rainfall and temperature anomalies, droughts have increased in frequency and severity in many regions of the world. The predicted changes for the coming years show that climate variables will not have uniform changes in all regions and regional changes in the amount of precipitation may lead to the creation of hydrological patterns much different from the current conditions. The present study was also carried out with the aim of spatial analysis of drought effects on water level changes in the catchment area of Bakhtegan, Tashk and Maharlo lakes. In this research, an attempt has been made to identify temporal and spatial patterns of changes in the level of this lakes by using satellite images and spatial analysis models. Materials and Methods In the present study, Landsat 5(TM), 7(ETM+) and Landsat 8(OLI) satellite images with a resolution of 30 meters have been used in the period of 2000-2021 to investigate water level changes. Due to the fact that the water level of the studied lakes changes drastically with the rainfall of different months, therefore, it is difficult to determine the amount of water cover for a year without considering the fact that a part of this cover is seasonal and when the rainfall decreases, a part of the lake Dry may not provide accurate results. Based on this, in the present study, one image was used for each month for each year studied to evaluate the changes in the water level of the lakes in all months of the year. Conclusion and Discussion The investigation of the changes in the water level of Maharlo Lake shows that in the drought of 2108 and 2017, the permanent water level of the lake has decreased to 1.8 square kilometers. Meanwhile, in the severe and very severe drought of 2005 and 2004, the permanent water level reached 170.4 square kilometers. Examining the changes in the area of Tashek Lake in 15 years of drought shows that the area of the waterless part of this lake has increased more than the seasonal and permanent water. The highest amount in this field was in 2021 with a very severe drought, which shows that this lake has more critical conditions in terms of permanent dryness than Maharlo Lake. This lake has been in a terrible state for 5 years. Comparing the changes in the area of Bakhtegan lake in different years shows that this lake has a more critical situation than its neighboring lakes (Maharlo and Tashk), so that in a significant number of years (12 years) the lake lacked permanent water and only With monthly or seasonal rains, some water has been temporarily collected on its surface, but it has a short shelf life between 2 to 6 months (November to May). Results The results of the evaluation and analysis of the role of drought in the water level changes of the Bakhtegan, Tashk and Maharlo catchment lakes showed that the area of these lakes has decreased significantly during the studied period, so that over time the area of the water area has decreased. It has been permanently reduced and added to the dry and waterless area. The maximum decrease in the water level of all three investigated lakes occurred during a 6-year drought between 2008 and 2013, in such a way that the area of the part with permanent water was greatly reduced and the area of the dry part of the lakes was increased.

https://www.sepehr.org/article_712674_6813e433e0a3ed19599163edf83be542.pdf

National Geographical Organization Scientific- Research Quarterly of Geographical Data (SEPEHR) 2588-3860 33 130 2024 08 22 Monitoring the dynamic changes of Miangaran wetland sub-basin using Google Earth Engine system Monitoring the dynamic changes of Miangaran wetland sub-basin using Google Earth Engine system 161 178 705975 10.22131/sepehr.2023.1988493.2950 FA Heshmat Karami Department of Geography, School of Human Sciences, Hormozgan University, Bandar Abbas, Iran 0009-0001-4155-7265 Zahra Sayadi Senior expert in remote sensing and geographic information system (GIS), water and soil studies, faculty of human sciences, department of geography, Hormozgan University 0009-0007-0844-4211 Journal Article 2023 01 27 Extended Abstract Introduction Environmental changes are one of the most critical challenges to achieving sustainable development. Wetlands are part of the earth's structure and as one of the important ecosystems consisting of water, vegetation, soil and microorganisms. Monitoring, management and assistance in decision-making and policy-making of surface water changes can be done according to the availability of satellite data. The availability of Landsat data helps a lot in preparing a high-quality map to show the land surface changes. Although remote sensing is superior to traditional methods in terms of time, speed, and cost, these methods require the use of powerful and practical systems that include complex analysis. The use of data and images on the web is a solution that can be used to solve the mentioned problem, which studies can be done with high accuracy and speed without the need for a strong hardware and software system. The Google Earth Engine system creates suitable conditions for processing satellite images for environmental monitoring and analysis. The purpose of this research is to monitor the dynamic changes in the Miangaran wetland sub-basin in the period (2013-2022). Materials & Methods Miangaran wetland with an average area of 2500 hectares is located at a distance of one and a half kilometers from Izeh city, in the northeast of Khuzestan province. Time series analysis is one of the most common operations in remote sensing that helps to understand and model seasonal patterns as well as monitor changes. In this research, 421 images from the ee.ImageCollection ("LANDSAT/LC08/C02/T1_L2") data set were used for the period from 2013 to 2022. The construction of a harmonic model was used in this research due to its flexibility in cyclic calculation with simple and repeatable forms. The normalized differential water index is an index for drawing and monitoring content changes in surface waters. Also, the Normalized Difference Vegetation Index (NDVI) is one of the most common remote sensing indices. Harmonic time series of water body and vegetation cover were extracted using NDWI and NDVI indices in Google Earth Engine platform, and Mann-Kendall's non-parametric test was performed using time series data output with XLSTAT extension in Excel software. Finally, global water data was used to confirm and complete the results of time series analysis. Results, discussion and conclusion The results of the harmonic time series of the water body showed a decreasing and negative trend and more changes in the sub-basin. Kendall's statistical test confirmed the decreasing and negative trend of the water body. Accordingly, since the calculated p-value (<0.0001) is lower than the alpha significance level (0.05), the null hypothesis should be rejected and its alternative hypothesis, the existence of a trend in the time series, should be accepted. The value of Kendall's tau also confirmed a negative value (-0.245) and a decrease. Due to the negative sen's slope statistic for the water area (-0.002), changes are more in the Miangaran Wetland sub-basin. The results of the Mann-Kendall test for the observed vegetation data showed the absence of a trend in the harmonic time series. Since the calculated p-value (0.064) is higher than the significance level of alpha (0.05), the null hypothesis (absence of trend) cannot be rejected. The risk of rejecting the null hypothesis (while true) is 43.6%. Kendall's tau statistic showed a negative value (-0.060) and a non-significant decrease. Therefore, accepting the null hypothesis (absence of trend) indicates that vegetation changes in the harmonic time series were not significantly different from each other. Also, the negative sen's slope statistic for vegetation (-0.026) indicates more changes in the sub-basin of Miangaran Wetland. By comparing with the results and analysis of other researches, it seems that human intervention and change of land use can be the cause of the lack of trend in the Miangaran Wetland sub-basin. Also, according to the negative value of Man-Kendall's vegetation cover which showed a non-significant decreasing trend, it seems that climate change and drought have also played a major role in the changes under the Miangaran wetland basin. The study of the global water data also showed that the water occurrence in terms of space-time is decreasing and the intensity of the change of water occurrence is critical under the basin of Miangaran wetland. The marginal parts of Miangaran Wetland show seasonal water loss, most of these changes occur during the period. This research confirmed the use of harmonic time series in monitoring wetland dynamic changes. Finally, the allocation of water rights, the establishment of laws and the determination of the limit of the ecological bed, and the use of Google Earth Engine capabilities to monitor environmental changes (use, temperature, precipitation, evaporation, etc.) of the Miangaran Wetland sub-basin were suggested. Extended Abstract Introduction Environmental changes are one of the most critical challenges to achieving sustainable development. Wetlands are part of the earth's structure and as one of the important ecosystems consisting of water, vegetation, soil and microorganisms. Monitoring, management and assistance in decision-making and policy-making of surface water changes can be done according to the availability of satellite data. The availability of Landsat data helps a lot in preparing a high-quality map to show the land surface changes. Although remote sensing is superior to traditional methods in terms of time, speed, and cost, these methods require the use of powerful and practical systems that include complex analysis. The use of data and images on the web is a solution that can be used to solve the mentioned problem, which studies can be done with high accuracy and speed without the need for a strong hardware and software system. The Google Earth Engine system creates suitable conditions for processing satellite images for environmental monitoring and analysis. The purpose of this research is to monitor the dynamic changes in the Miangaran wetland sub-basin in the period (2013-2022). Materials & Methods Miangaran wetland with an average area of 2500 hectares is located at a distance of one and a half kilometers from Izeh city, in the northeast of Khuzestan province. Time series analysis is one of the most common operations in remote sensing that helps to understand and model seasonal patterns as well as monitor changes. In this research, 421 images from the ee.ImageCollection ("LANDSAT/LC08/C02/T1_L2") data set were used for the period from 2013 to 2022. The construction of a harmonic model was used in this research due to its flexibility in cyclic calculation with simple and repeatable forms. The normalized differential water index is an index for drawing and monitoring content changes in surface waters. Also, the Normalized Difference Vegetation Index (NDVI) is one of the most common remote sensing indices. Harmonic time series of water body and vegetation cover were extracted using NDWI and NDVI indices in Google Earth Engine platform, and Mann-Kendall's non-parametric test was performed using time series data output with XLSTAT extension in Excel software. Finally, global water data was used to confirm and complete the results of time series analysis. Results, discussion and conclusion The results of the harmonic time series of the water body showed a decreasing and negative trend and more changes in the sub-basin. Kendall's statistical test confirmed the decreasing and negative trend of the water body. Accordingly, since the calculated p-value (<0.0001) is lower than the alpha significance level (0.05), the null hypothesis should be rejected and its alternative hypothesis, the existence of a trend in the time series, should be accepted. The value of Kendall's tau also confirmed a negative value (-0.245) and a decrease. Due to the negative sen's slope statistic for the water area (-0.002), changes are more in the Miangaran Wetland sub-basin. The results of the Mann-Kendall test for the observed vegetation data showed the absence of a trend in the harmonic time series. Since the calculated p-value (0.064) is higher than the significance level of alpha (0.05), the null hypothesis (absence of trend) cannot be rejected. The risk of rejecting the null hypothesis (while true) is 43.6%. Kendall's tau statistic showed a negative value (-0.060) and a non-significant decrease. Therefore, accepting the null hypothesis (absence of trend) indicates that vegetation changes in the harmonic time series were not significantly different from each other. Also, the negative sen's slope statistic for vegetation (-0.026) indicates more changes in the sub-basin of Miangaran Wetland. By comparing with the results and analysis of other researches, it seems that human intervention and change of land use can be the cause of the lack of trend in the Miangaran Wetland sub-basin. Also, according to the negative value of Man-Kendall's vegetation cover which showed a non-significant decreasing trend, it seems that climate change and drought have also played a major role in the changes under the Miangaran wetland basin. The study of the global water data also showed that the water occurrence in terms of space-time is decreasing and the intensity of the change of water occurrence is critical under the basin of Miangaran wetland. The marginal parts of Miangaran Wetland show seasonal water loss, most of these changes occur during the period. This research confirmed the use of harmonic time series in monitoring wetland dynamic changes. Finally, the allocation of water rights, the establishment of laws and the determination of the limit of the ecological bed, and the use of Google Earth Engine capabilities to monitor environmental changes (use, temperature, precipitation, evaporation, etc.) of the Miangaran Wetland sub-basin were suggested.

https://www.sepehr.org/article_705975_5041ad5e2980457688656c002004303f.pdf

National Geographical Organization Scientific- Research Quarterly of Geographical Data (SEPEHR) 2588-3860 33 130 2024 08 22 The application of remote sensing inmodeling the effects of distance from the road on the accumulation of heavy metals in leaves and soil of quercus branti species - Case study: Kakareza Forest, Khorramabad, Lorestan The application of remote sensing inmodeling the effects of distance from the road on the accumulation of heavy metals in leaves and soil of quercus branti species - Case study: Kakareza Forest, Khorramabad, Lorestan 179 196 711234 10.22131/sepehr.2024.2005530.2990 FA Nastaran Nazariani Postdoctoral Researcher in Forestry, Faculty of Natural Resources, Sari University of Agricultural Sciences, Mazandaran, Sari, Iran 0000-0003-3891-5153 Asghar Fallah Professor, Department of forestry, Faculty of natural resources, Sari University of agricultural sciences and natural resources ,Sari, Iran 0000-0002-3300-3975 Hava Hasanvand PhD student in forestry, Faculty of natural resources, Sari University of agricultural sciences and natural resources, Sari, Iran. 0009-0002-3286-288X Hassan Akbari Associate Professor department of forestry,Faculty of natural resources, Sari University of agricultural sciences and natural resources, Sari, Iran 0000-0002-5482-5521 Journal Article 2023 07 03 Extended Abstract Introduction The traditional method of chemical analysis has high accuracy and precision. However, it is time-consuming and laborious, and it is not possible to obtain continuous information about the pollutant status over a large area. Therefore, there is an urgent need for a reliable and environmentally friendly method to quickly identify and investigate the distribution of heavy metals in soil and thus identify suspected contaminated areas (Scheuber & Köhl, 2003:33). Remote sensing is one of the ways that can provide a cost-effective and quick solution to investigate the distribution of heavy metals on a large scale using spectroscopic techniques (Bi et al., 2009:16). Habibi et al. (2023:4) also measured and evaluated the concentration of heavy metals in the aerial parts and soil of the tree species of Bandar Abbas city and also identified the species that has the highest potential for absorbing heavy metals. The results showed that the pattern of heavy metals in soil and leaves of tree species was Mn>Zn>Pb>Cd. (Nikolaevich, 2023:30) they addressed the modeling of heavy metal pollution in Central Russia based on satellite images and machine learning. Al, Fe, and Sb contamination were predicted for 3000 and 12100 grid nodes in an area of 500 km2 for the Central Russian region for 2019 and 2020. Estimating the amount of this pollution requires time and high cost. Considering the traffic on the Aleshtar -Khorramabad highway near Kakareza forests and the effect of heavy metal concentration in the soil and leaves of the oak species which can be caused by natural and human pollution, the accumulation of heavy metals in the species Iranian oak is a serious threat to this forest. Therefore, it is necessary to study and discuss pollutants and their effects on the environmental cycle. In this regard, considering the cost and time-consuming nature of traditional methods and since remote sensing methods are a suitable complement to traditional methods; the aim of the present research is to use remote sensing techniques and spectral analyses to evaluate and model the accumulation of heavy metals in Iranian oak species. Materials and Methods The present study is located on the road of Aleshtar -Khorramabad, 20 kilometers northwest of Khorramabad. For this purpose, five transects were created at distances adjacent to the road, 500 and 1000 meters on both sides of the road, and 10 x 10 m sample pieces were planted. Inside the sample plots, 30 soil samples were randomly collected and 30 leaf samples were collected from trees in all directions of the crown. To extract heavy metals from soil samples and plant samples, the acid digestion method was used and the physical characteristics of the soil were measured using standard methods. After preparing the samples, the concentration of Pb, Cu, and zinc heavy metals in soil and leaves was measured and the index of biological concentration of heavy metals from soil to leaves was calculated. Then the relationship between the concentration of heavy elements measured and the reflectance in different bands or band ratios at the corresponding sampling points was obtained. Non-parametric methods and generalized multiple linear regression models were used in order to model quantitative variables and spectral values corresponding to sample parts in satellite data. ArcGIS software was used to implement sample parts on the image, ENVI software was used for image processing, and STATISTICA software was used for modeling. Results and Discussion Cu and Pb in Iranian oak leaves had significant differences at different distances at the 0.05 level, but Cu did not have significant differences at different distances at the 0.05 level. Cu and Pb did not have significant differences in different soil intervals at the 0.05 level, but Cu had significant differences in different soil intervals at the 0.05 level. The bioconcentration factor was obtained as (0.2, 0.5, 0.2) mg/kg. The study of modeling of non-parametric methods using Sentinel-2 satellite data showed that the highest explanatory coefficient values (0.85, 0.88, and 0.97) were obtained for the three metals Cu, Pb, and Cu, respectively. The artificial neural network (ANN) algorithm obtained the highest accuracy. Also, according to the results of the random forest algorithm, for the three mentioned metals, PSRI, HMSSI, and PSRI indices are the most important in modeling. Based on the findings, the concentration values of Cu and zinc were significantly different at different distances, but the Cu values were not significantly different at different distances. In this regard, Mansour concluded in 2014 that there is a significant difference between the concentration of Cu and zinc in the leaves of the species, which can be attributed to traffic density and human activities, and the high amount of zinc metal in this study is the wear of car tires؛ and stated that the concentration of Cu is caused by the production of greenhouse gases and the use of vehicles using Cu gasoline. Based on the findings, the values of Cu and zinc concentrations at different distances did not have significant differences, but the Cu values had significant differences at different distances. Sources of input of Cu element to the soil are urban, industrial, and agricultural waste, fertilizers, and chemicals that add it to the soil through liquid, solid, or mineral fertilizers. These findings are with the results of some researchers including Wu and colleagues (2010:38), Botsou et al. (2016:17) are consistent. Based on the findings obtained from the calculation of the bioconcentration index and their comparison with the classification proposed by Ma et al. (2001:25) for Iranian oak species plants in relation to Cu, zinc, and Cu metals from soil to leaves, it acts as an accumulating plant. In accordance with the results of this research, in the study of Khodakarmi et al. (2009:15), Iranian oak was included in the category of superabsorbent plants in relation to the accumulation of Cu pollutants, which has a high capacity in terms of root absorption. Also, Madejón et al. (2006:25) stated that oak leaves are more resistant than olive leaves. The concentrations of elements in leaves and fruits decrease with time and the risk of toxicity in the food web is reduced. The review and comparison of five algorithms showed that (ANN) the highest explanatory coefficient values (0.85, 0.88, and 0.97) were obtained for three metals, Cu, Zn, and Cu, respectively. Considering the importance of the PSRI synthetic band in increasing the accuracy of modeling with satellite images and the influence of the visible and near-infrared bands, the amount of reflection measured by the spectroscopic method showed that with the increase in the concentration of heavy elements, the amount of reflection in the visible and infrared range decreases (Liu et al., 2011:24). Conclusion The results showed that Sentinel-2 images along with artificial intelligence techniques have a relatively good ability to model the level of biological pollution index in the region. In line with the obtained results, it is suggested that the Iranian oak species is used to reduce pollution on highways because it accumulates heavy metals. Extended Abstract Introduction The traditional method of chemical analysis has high accuracy and precision. However, it is time-consuming and laborious, and it is not possible to obtain continuous information about the pollutant status over a large area. Therefore, there is an urgent need for a reliable and environmentally friendly method to quickly identify and investigate the distribution of heavy metals in soil and thus identify suspected contaminated areas (Scheuber & Köhl, 2003:33). Remote sensing is one of the ways that can provide a cost-effective and quick solution to investigate the distribution of heavy metals on a large scale using spectroscopic techniques (Bi et al., 2009:16). Habibi et al. (2023:4) also measured and evaluated the concentration of heavy metals in the aerial parts and soil of the tree species of Bandar Abbas city and also identified the species that has the highest potential for absorbing heavy metals. The results showed that the pattern of heavy metals in soil and leaves of tree species was Mn>Zn>Pb>Cd. (Nikolaevich, 2023:30) they addressed the modeling of heavy metal pollution in Central Russia based on satellite images and machine learning. Al, Fe, and Sb contamination were predicted for 3000 and 12100 grid nodes in an area of 500 km2 for the Central Russian region for 2019 and 2020. Estimating the amount of this pollution requires time and high cost. Considering the traffic on the Aleshtar -Khorramabad highway near Kakareza forests and the effect of heavy metal concentration in the soil and leaves of the oak species which can be caused by natural and human pollution, the accumulation of heavy metals in the species Iranian oak is a serious threat to this forest. Therefore, it is necessary to study and discuss pollutants and their effects on the environmental cycle. In this regard, considering the cost and time-consuming nature of traditional methods and since remote sensing methods are a suitable complement to traditional methods; the aim of the present research is to use remote sensing techniques and spectral analyses to evaluate and model the accumulation of heavy metals in Iranian oak species. Materials and Methods The present study is located on the road of Aleshtar -Khorramabad, 20 kilometers northwest of Khorramabad. For this purpose, five transects were created at distances adjacent to the road, 500 and 1000 meters on both sides of the road, and 10 x 10 m sample pieces were planted. Inside the sample plots, 30 soil samples were randomly collected and 30 leaf samples were collected from trees in all directions of the crown. To extract heavy metals from soil samples and plant samples, the acid digestion method was used and the physical characteristics of the soil were measured using standard methods. After preparing the samples, the concentration of Pb, Cu, and zinc heavy metals in soil and leaves was measured and the index of biological concentration of heavy metals from soil to leaves was calculated. Then the relationship between the concentration of heavy elements measured and the reflectance in different bands or band ratios at the corresponding sampling points was obtained. Non-parametric methods and generalized multiple linear regression models were used in order to model quantitative variables and spectral values corresponding to sample parts in satellite data. ArcGIS software was used to implement sample parts on the image, ENVI software was used for image processing, and STATISTICA software was used for modeling. Results and Discussion Cu and Pb in Iranian oak leaves had significant differences at different distances at the 0.05 level, but Cu did not have significant differences at different distances at the 0.05 level. Cu and Pb did not have significant differences in different soil intervals at the 0.05 level, but Cu had significant differences in different soil intervals at the 0.05 level. The bioconcentration factor was obtained as (0.2, 0.5, 0.2) mg/kg. The study of modeling of non-parametric methods using Sentinel-2 satellite data showed that the highest explanatory coefficient values (0.85, 0.88, and 0.97) were obtained for the three metals Cu, Pb, and Cu, respectively. The artificial neural network (ANN) algorithm obtained the highest accuracy. Also, according to the results of the random forest algorithm, for the three mentioned metals, PSRI, HMSSI, and PSRI indices are the most important in modeling. Based on the findings, the concentration values of Cu and zinc were significantly different at different distances, but the Cu values were not significantly different at different distances. In this regard, Mansour concluded in 2014 that there is a significant difference between the concentration of Cu and zinc in the leaves of the species, which can be attributed to traffic density and human activities, and the high amount of zinc metal in this study is the wear of car tires؛ and stated that the concentration of Cu is caused by the production of greenhouse gases and the use of vehicles using Cu gasoline. Based on the findings, the values of Cu and zinc concentrations at different distances did not have significant differences, but the Cu values had significant differences at different distances. Sources of input of Cu element to the soil are urban, industrial, and agricultural waste, fertilizers, and chemicals that add it to the soil through liquid, solid, or mineral fertilizers. These findings are with the results of some researchers including Wu and colleagues (2010:38), Botsou et al. (2016:17) are consistent. Based on the findings obtained from the calculation of the bioconcentration index and their comparison with the classification proposed by Ma et al. (2001:25) for Iranian oak species plants in relation to Cu, zinc, and Cu metals from soil to leaves, it acts as an accumulating plant. In accordance with the results of this research, in the study of Khodakarmi et al. (2009:15), Iranian oak was included in the category of superabsorbent plants in relation to the accumulation of Cu pollutants, which has a high capacity in terms of root absorption. Also, Madejón et al. (2006:25) stated that oak leaves are more resistant than olive leaves. The concentrations of elements in leaves and fruits decrease with time and the risk of toxicity in the food web is reduced. The review and comparison of five algorithms showed that (ANN) the highest explanatory coefficient values (0.85, 0.88, and 0.97) were obtained for three metals, Cu, Zn, and Cu, respectively. Considering the importance of the PSRI synthetic band in increasing the accuracy of modeling with satellite images and the influence of the visible and near-infrared bands, the amount of reflection measured by the spectroscopic method showed that with the increase in the concentration of heavy elements, the amount of reflection in the visible and infrared range decreases (Liu et al., 2011:24). Conclusion The results showed that Sentinel-2 images along with artificial intelligence techniques have a relatively good ability to model the level of biological pollution index in the region. In line with the obtained results, it is suggested that the Iranian oak species is used to reduce pollution on highways because it accumulates heavy metals.

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