University of Isfahan Geography and Environmental Planning 2008-5362 33 4 2022 12 22 Comparison of Food Security Situations of Rural Households in Heris and Bostanabad with Emphasis on Agricultural Indicators Using FGIS Comparison of Food Security Situations of Rural Households in Heris and Bostanabad with Emphasis on Agricultural Indicators Using FGIS 1 26 26722 10.22108/gep.2022.128816.1430 FA Mohammed Mohsenzadeh Harris Ph.D. Candidate in Geography and Rural Planning, University of Tabriz,Tabriz,Iran Hossein Karimzadeh Assistant Professor, Department of Geography and Rural Planning, University of Tabriz,Tabriz,Iran Mohsen Aghayarihir Assistant pProfessor, Department of Geography and Rural Planning, University of Tabriz,Tabriz,Iran Journal Article 2021 07 11 Abstract Since food security is an indicator of health at the household and individual levels and food insecurity can be a predictor of developmental, health, and nutritional problems, it is necessary to determine its related factors in any society. It is one of the phenomena affecting all economic, social, and political dimensions. The concept of food security is very broad and is defined by the interaction of a range of biological, economic, social, agricultural, and physical factors. Understanding the food security situation of each region can make planning more efficient with more promising results. The purpose of the current study was to investigate the zone of food security in the rural areas of the two cities of Heris and Bostanabad. This was an applied research in terms of purpose and a descriptive-analytical research in terms of methodology. In this study, 22 agricultural indices derived from the results of the 2014 General Agricultural Census were used and the outputs were classified into very good, good, medium, poor, and very poor categories. The information was gained through Fuzzy Logic and Shanon Entropy methods. The research findings indicated that out of 293 rural areas studied, 8 (2.73%), 22 (7.51%), 50 (17.06%), 110 (37.54%), and 103 (35.15%) villages were in very good, good, moderate, poor, and very poor conditions, respectively. Overall, from the agricultural perspective, food security was in a dangerous situation in 72.70% of the villages in the study areas and required the officials’ special attention, especially in the area of sustainable development of agricultural sector. Introduction Food security is recognized as one of the main challenges of the 21st century and with the ever-increasing world population, the need for food is increasing rapidly. Most of the people of the third world countries live in rural areas and improving their food security is very important. In this regard, different countries are trying to increase the food security of their societies by offering various solutions. One of the aspects of human security is food security. By expressing food security in relation to human security, we can realize the importance of the agricultural sector in providing adequate food for the society. East Azerbaijan Province is at the higher level of food security in comparison with other provinces of the country; still, we are far from global indicators. On the other hand, it seems that Heris and Bostanabad cities in this province are in lower levels of food security due to their geographical locations, climate changes, drought conditions, and specially, weak economic relations in comparison with the central cities of the province. It seems that there is a hope for the future of food security in Heris and Bostanabad cities with regard to the existing potentials for agricultural development in these regions. Therefore, the purpose of the current study was to compare food security situations in the rural areas of Heris and Bostanabad cities with emphasis on agricultural indicators. Methodology The purpose of this research was to investigate the zone of food security in the rural areas of the two cities of Heris and Bostanabad in East Azerbaijan Province. Thus, this study was an applied research in terms of the purpose and a descriptive-analytical research in terms of methodology. 22 agricultural indices derived from the results of the 2014 General Agricultural Census were utilized to assess food security situations in the studied rural areas. Shanon Entropy and Fuzzy Logic methods were applied to analyze the data. Discussion The results showed that 8 villages with 5574 households and a population of 18595 people were in very good conditions; 22 villages with 4020 households and a population of 13985 people were in good conditions; 50 villages with 11454 households and a population of 39064 people were in average conditions; 110 villages with 7604 households and a population of 25825 people were in poor conditions; and 103 villages with 2255 households and a population of 7191 people in very poor conditions. The linear regression analysis revealed that all the 22 subscales significantly predicted food security at the alpha level of 0.05. Among these, greenhouse and livestock areas with the beta levels of 0.407 and 0.025 had the highest and lowest shares of food security, respectively. Furthermore, the results of the Mann–Whitney U test showed no significant differences in the villages of these two cities in terms of the indicators of number of oil planters, spice crop planters, fibrous crop planters, greenhouse crop planters, skillworm breeding, grain growers, gardens and orchards, agricultural lands, and greenhouse area. The results of this study are in line with those obtained by Tanhayi et al. (1394), Rahimi Moghaddam et al.(1394), Pakravan et al. (1399), Sheybani et al. (1399), Cauchi et al. (2021), Nicholson et al. (2021), and Amolegbe (2021) in terms of widespread risk of hunger and food insecurity in rural areas. Conclusion The research findings indicated that 89.76% of the studied villages in Heris and Bostanabad cities were in a moderate to low status in terms of food security. The government and the private sector must pay special attention to sustainable development of agriculture, industry, and so on. Also, the indices of greenhouse area and number of livestock had the highest and lowest shares in determining food security from agricultural dimension in these areas, respectively. According to the climate conditions of the studied regions in terms of limited fresh water resources, climate diversity, short agricultural season, etc., the optimal use of resources, e.g., by cultivating plants in greenhouse, which needs less water and land, is one of the country’s priorities. However, it requires more workers. By training workers, we can harvest more crops in the limited area of greenhouse. It is possible to plant and harvest crops in the greenhouse environment during all the seasons of a year. Also, when the crops are uncultivable in the open air, it is possible for the plants to grow in the greenhouse in the off-season. This way, we can produce greenhouse products with best qualities, market them, and increase food security. In the research geographical domain, the villages of Bostanabad City were in more appropriate situations in comparison with those of Heris City due to their proximities to Tabriz-Tehran communication road with more pastures and livestock, higher tourism capabilities, , and potential agricultural lands, besides having a shorter distance to Tabriz Province. In general, food security situation in the studied cities were not good due to the country’s macro-policies, economical situation, inflation, drought, water scarcity, etc. and needed special attention. Keywords: food security, agriculture, Fuzzy Logic, Heris and Bostanabad cities References - Abdul Kadir, M. Kh. (2013). Food security modelling using two stage hybrid model and fuzzy logic risk assessment. PhD Thesis, University of Warwick. - Amolegbe, K. B., Upton, J., Bageant, E., & Blom, S. (2021). Food price volatility and household food security: Evidence from Nigeria. Journal of Food Policy, 102, 102061. - ASCE Standard (2001). Environmental and water resources institute, american society of civil engineers. Standard guidelines for artificial recharge of groundwater. ASCE Standards, EWRI/ASCE 34-01, 2001, p. 106. - Calicioglu, O., Flammini, A., Bracco, S., Bellù, L., & Sims, R. (2019). The future challenges of food and agriculture: An integrated analysis of trends and solutions. Sustainability, 11(1), 222. - Cauchia, J. P., Bambrick, H., Correa-Velez, I., & Moncada, S. (2021). White flour, white sugar, white rice, white salt’: Barriers to achieving food and nutrition security in Kiribati. Journal of Food Policy, 101, 102075. - Chakraborty, S., & Newton, A., S. (2011). Climate change, plant diseases and food security: An overview. Journal of Plant Pathology, 60(1), 2–14. - De Cock, N., D’Haese, M., Vink, N., Van Rooyen, C. J., Staelens, L., Schönfeldt, H. C., & D’Haese, L. (2013). Food security in rural areas of Limpopo province, South Africa. Journal of Food Security, 5(2), 269-282. - Dobbie, S. L. (2016). The potential of agent-based modelling as a tool to unravel the complexity of household food security: A case study of rural Southern Malawi. PhD Thesis, University of Southampton. - FAO IFAD & WFP (2015). The state of food insecurity in the world 2015; Meeting the 2015 international hunger targets: taking stock of uneven progress. Rome, Italy: FAO. - FAO IFAD UNICEF WFP & WHO (2020). The State of Food Security and Nutrition in the World 2020: Transforming food systems for affordable healthy diets. Rome, Italy: Food & Agriculture Organization (FAO). - Fisher, B., Naidoo, R., Guernier, J., Johnson, K., Mullins, D., Robinson, D., & Allison, E. H. (2017). Integrating fisheries and agricultural programs for food security. Journal of Agriculture & Food Security, 6(1), 1–7. - Funk, C. C., & Brown, M. E. (2009). Declining global per capita agricultural production and warming oceans threaten food security. Journal of Food Security, 1(3), 271–289. - Hussain, A., Rasul, G., Mahapatra, B., & Tuladhar, S. (2016). Household food security in the face of climate change in the Hindu-Kush Himalayan region. Journal of Food Security, 8(5), 921–937. - Iese, V., Holland, E., Wairiu, M., Havea, R., Patolo, S., Nishi, M., … & Waqainabete, L. (2018). Facing food security risks: The rise and rise of the sweet potato in the Pacific Islands. Journal of Global Food Security, 18, 48–56. - Islam, M. T., Hossain, M., M. Clarke, M., L. & Akanda, M., A., M. (2012). Adaptation to Climate Change: Biodiversity. Bangladesh: Food Security, Environmental Management and Rural Resilience. - Khumalo, N. Z., & Sibanda, M. (2019). Does urban and peri-urban agriculture contribute to household food security? An assessment of the food security status of households in Tongaat, eThekwini Municipality. Sustainability, 11(4), 1082. - Khush, G. S., Lee, S., Cho, J. I., & Jeon, J. S. (2012). Biofortification of crops for reducing malnutrition. Journal of Plant Biotechnology Reports, 6(3), 195-202. - Mao, Y., Zhao, N., & Yang, X. (2013). Food security and farm land protection in China. Series on Chinese Economics Research, 2. - Mechiche-Alami, A., Yagoubi, J., & Nicholas, K. A. (2021). Agricultural land acquisitions unlikely to address the food security needs of African countries. Journal of World Development, 141, 105384. - Nébié, E. K. I., Ba, D., & Giannini, A. (2021). Food security and climate shocks in Senegal: Who and where are the most vulnerable households?. Journal of Global Food Security, 29, 100513 - Nicholson, C. F., Stephens, E. C., Kopainsky, B., Thornton, P. K., Jones, A. D., Parsons, D., & Garrett, J. (2021). Food security outcomes in agricultural systems models: Case examples and priority information needs. Journal of Agricultural Systems, 188, 103028 - Pozza, L. E., & Field, D. J. (2020). The science of soil security and food security. Journal of Soil Security, 1, 100002. - Quandt, A. (2021). Agroforestry trees for improved food security on farms impacted by wildlife crop raiding in Kenya. Journal of Trees, Forests and People, 4, 100069. - Simon, G. (2012). Food security: Definition, four dimensions, history. University of Roma. Faculty of Economics. - Sinyolo, S., Mudhara, M., & Wale, E. (2014). Water security and rural household food security: Empirical evidence from the Mzinyathi district in South Africa. Journal of Food Security, 6(4), 483–499. - Smith, P. (2013). Delivering food security without increasing pressure on land. Journal of Global Food Security, 2(1) 18-23. - Tadesse, W., Halila, H., Jamal, M., El-Hanafi, S., Assefa, S., Oweis, T., & Baum, M. (2017). Role of Sustainable wheat production to ensure food security in the Cwana Region. Journal of Experimental Biology and Agricultural Sciences, 5, 15-32. - Tincani, L. S. (2012). Resilient livelihoods: Adaptation, food security and wild foods in rural burkina faso. PhD Thesis, University of London. Table 1: Different theoretical approaches to food security Table 2: A number of domestic and foreign studies in the field of food security Fig. 1: Research conceptual model (source: authors, 2021) Table 3: Agricultural indicators affecting food security situation Fig. 2: Research steps (authors, 2021) Fig. 2: Political and geographical locations of the study areas Table 4: Values obtained from Shannon entropy calculation Fig. 1: Weights obtained from Shannon entropy calculation (source: research findings, 2021) Fig. 3: Fuzzy layers of the studied indicators (source: research findings, 2021) Fig. 4: Fuzzy layers of the studied indices (source: research findings, 2021) Fig. 5: Fuzzy layers of the studied indices (source: research findings, 2021) Fig. 6: Final zoning of food security situations in Harris and Bostanabad counties Table 5: Calculation of the share of each indicator in determining food security in the study areas Fig. 2: Contribution of each indicator to determining food security in the study areas (source: research findings, 2021) Table 6: Number of villages located in the food security categories Table 7: Areas occupied by the classes of food security status Table 8: Comparison of food security indicators in Harris and Bostanabad villages Abstract Since food security is an indicator of health at the household and individual levels and food insecurity can be a predictor of developmental, health, and nutritional problems, it is necessary to determine its related factors in any society. It is one of the phenomena affecting all economic, social, and political dimensions. The concept of food security is very broad and is defined by the interaction of a range of biological, economic, social, agricultural, and physical factors. Understanding the food security situation of each region can make planning more efficient with more promising results. The purpose of the current study was to investigate the zone of food security in the rural areas of the two cities of Heris and Bostanabad. This was an applied research in terms of purpose and a descriptive-analytical research in terms of methodology. In this study, 22 agricultural indices derived from the results of the 2014 General Agricultural Census were used and the outputs were classified into very good, good, medium, poor, and very poor categories. The information was gained through Fuzzy Logic and Shanon Entropy methods. The research findings indicated that out of 293 rural areas studied, 8 (2.73%), 22 (7.51%), 50 (17.06%), 110 (37.54%), and 103 (35.15%) villages were in very good, good, moderate, poor, and very poor conditions, respectively. Overall, from the agricultural perspective, food security was in a dangerous situation in 72.70% of the villages in the study areas and required the officials’ special attention, especially in the area of sustainable development of agricultural sector. Introduction Food security is recognized as one of the main challenges of the 21st century and with the ever-increasing world population, the need for food is increasing rapidly. Most of the people of the third world countries live in rural areas and improving their food security is very important. In this regard, different countries are trying to increase the food security of their societies by offering various solutions. One of the aspects of human security is food security. By expressing food security in relation to human security, we can realize the importance of the agricultural sector in providing adequate food for the society. East Azerbaijan Province is at the higher level of food security in comparison with other provinces of the country; still, we are far from global indicators. On the other hand, it seems that Heris and Bostanabad cities in this province are in lower levels of food security due to their geographical locations, climate changes, drought conditions, and specially, weak economic relations in comparison with the central cities of the province. It seems that there is a hope for the future of food security in Heris and Bostanabad cities with regard to the existing potentials for agricultural development in these regions. Therefore, the purpose of the current study was to compare food security situations in the rural areas of Heris and Bostanabad cities with emphasis on agricultural indicators. Methodology The purpose of this research was to investigate the zone of food security in the rural areas of the two cities of Heris and Bostanabad in East Azerbaijan Province. Thus, this study was an applied research in terms of the purpose and a descriptive-analytical research in terms of methodology. 22 agricultural indices derived from the results of the 2014 General Agricultural Census were utilized to assess food security situations in the studied rural areas. Shanon Entropy and Fuzzy Logic methods were applied to analyze the data. Discussion The results showed that 8 villages with 5574 households and a population of 18595 people were in very good conditions; 22 villages with 4020 households and a population of 13985 people were in good conditions; 50 villages with 11454 households and a population of 39064 people were in average conditions; 110 villages with 7604 households and a population of 25825 people were in poor conditions; and 103 villages with 2255 households and a population of 7191 people in very poor conditions. The linear regression analysis revealed that all the 22 subscales significantly predicted food security at the alpha level of 0.05. Among these, greenhouse and livestock areas with the beta levels of 0.407 and 0.025 had the highest and lowest shares of food security, respectively. Furthermore, the results of the Mann–Whitney U test showed no significant differences in the villages of these two cities in terms of the indicators of number of oil planters, spice crop planters, fibrous crop planters, greenhouse crop planters, skillworm breeding, grain growers, gardens and orchards, agricultural lands, and greenhouse area. The results of this study are in line with those obtained by Tanhayi et al. (1394), Rahimi Moghaddam et al.(1394), Pakravan et al. (1399), Sheybani et al. (1399), Cauchi et al. (2021), Nicholson et al. (2021), and Amolegbe (2021) in terms of widespread risk of hunger and food insecurity in rural areas. Conclusion The research findings indicated that 89.76% of the studied villages in Heris and Bostanabad cities were in a moderate to low status in terms of food security. The government and the private sector must pay special attention to sustainable development of agriculture, industry, and so on. Also, the indices of greenhouse area and number of livestock had the highest and lowest shares in determining food security from agricultural dimension in these areas, respectively. According to the climate conditions of the studied regions in terms of limited fresh water resources, climate diversity, short agricultural season, etc., the optimal use of resources, e.g., by cultivating plants in greenhouse, which needs less water and land, is one of the country’s priorities. However, it requires more workers. By training workers, we can harvest more crops in the limited area of greenhouse. It is possible to plant and harvest crops in the greenhouse environment during all the seasons of a year. Also, when the crops are uncultivable in the open air, it is possible for the plants to grow in the greenhouse in the off-season. This way, we can produce greenhouse products with best qualities, market them, and increase food security. In the research geographical domain, the villages of Bostanabad City were in more appropriate situations in comparison with those of Heris City due to their proximities to Tabriz-Tehran communication road with more pastures and livestock, higher tourism capabilities, , and potential agricultural lands, besides having a shorter distance to Tabriz Province. In general, food security situation in the studied cities were not good due to the country’s macro-policies, economical situation, inflation, drought, water scarcity, etc. and needed special attention. Keywords: food security, agriculture, Fuzzy Logic, Heris and Bostanabad cities References - Abdul Kadir, M. Kh. (2013). Food security modelling using two stage hybrid model and fuzzy logic risk assessment. PhD Thesis, University of Warwick. - Amolegbe, K. B., Upton, J., Bageant, E., & Blom, S. (2021). Food price volatility and household food security: Evidence from Nigeria. Journal of Food Policy, 102, 102061. - ASCE Standard (2001). Environmental and water resources institute, american society of civil engineers. Standard guidelines for artificial recharge of groundwater. ASCE Standards, EWRI/ASCE 34-01, 2001, p. 106. - Calicioglu, O., Flammini, A., Bracco, S., Bellù, L., & Sims, R. (2019). The future challenges of food and agriculture: An integrated analysis of trends and solutions. Sustainability, 11(1), 222. - Cauchia, J. P., Bambrick, H., Correa-Velez, I., & Moncada, S. (2021). White flour, white sugar, white rice, white salt’: Barriers to achieving food and nutrition security in Kiribati. Journal of Food Policy, 101, 102075. - Chakraborty, S., & Newton, A., S. (2011). Climate change, plant diseases and food security: An overview. Journal of Plant Pathology, 60(1), 2–14. - De Cock, N., D’Haese, M., Vink, N., Van Rooyen, C. J., Staelens, L., Schönfeldt, H. C., & D’Haese, L. (2013). Food security in rural areas of Limpopo province, South Africa. Journal of Food Security, 5(2), 269-282. - Dobbie, S. L. (2016). The potential of agent-based modelling as a tool to unravel the complexity of household food security: A case study of rural Southern Malawi. PhD Thesis, University of Southampton. - FAO IFAD & WFP (2015). The state of food insecurity in the world 2015; Meeting the 2015 international hunger targets: taking stock of uneven progress. Rome, Italy: FAO. - FAO IFAD UNICEF WFP & WHO (2020). The State of Food Security and Nutrition in the World 2020: Transforming food systems for affordable healthy diets. Rome, Italy: Food & Agriculture Organization (FAO). - Fisher, B., Naidoo, R., Guernier, J., Johnson, K., Mullins, D., Robinson, D., & Allison, E. H. (2017). Integrating fisheries and agricultural programs for food security. Journal of Agriculture & Food Security, 6(1), 1–7. - Funk, C. C., & Brown, M. E. (2009). Declining global per capita agricultural production and warming oceans threaten food security. Journal of Food Security, 1(3), 271–289. - Hussain, A., Rasul, G., Mahapatra, B., & Tuladhar, S. (2016). Household food security in the face of climate change in the Hindu-Kush Himalayan region. Journal of Food Security, 8(5), 921–937. - Iese, V., Holland, E., Wairiu, M., Havea, R., Patolo, S., Nishi, M., … & Waqainabete, L. (2018). Facing food security risks: The rise and rise of the sweet potato in the Pacific Islands. Journal of Global Food Security, 18, 48–56. - Islam, M. T., Hossain, M., M. Clarke, M., L. & Akanda, M., A., M. (2012). Adaptation to Climate Change: Biodiversity. Bangladesh: Food Security, Environmental Management and Rural Resilience. - Khumalo, N. Z., & Sibanda, M. (2019). Does urban and peri-urban agriculture contribute to household food security? An assessment of the food security status of households in Tongaat, eThekwini Municipality. Sustainability, 11(4), 1082. - Khush, G. S., Lee, S., Cho, J. I., & Jeon, J. S. (2012). Biofortification of crops for reducing malnutrition. Journal of Plant Biotechnology Reports, 6(3), 195-202. - Mao, Y., Zhao, N., & Yang, X. (2013). Food security and farm land protection in China. Series on Chinese Economics Research, 2. - Mechiche-Alami, A., Yagoubi, J., & Nicholas, K. A. (2021). Agricultural land acquisitions unlikely to address the food security needs of African countries. Journal of World Development, 141, 105384. - Nébié, E. K. I., Ba, D., & Giannini, A. (2021). Food security and climate shocks in Senegal: Who and where are the most vulnerable households?. Journal of Global Food Security, 29, 100513 - Nicholson, C. F., Stephens, E. C., Kopainsky, B., Thornton, P. K., Jones, A. D., Parsons, D., & Garrett, J. (2021). Food security outcomes in agricultural systems models: Case examples and priority information needs. Journal of Agricultural Systems, 188, 103028 - Pozza, L. E., & Field, D. J. (2020). The science of soil security and food security. Journal of Soil Security, 1, 100002. - Quandt, A. (2021). Agroforestry trees for improved food security on farms impacted by wildlife crop raiding in Kenya. Journal of Trees, Forests and People, 4, 100069. - Simon, G. (2012). Food security: Definition, four dimensions, history. University of Roma. Faculty of Economics. - Sinyolo, S., Mudhara, M., & Wale, E. (2014). Water security and rural household food security: Empirical evidence from the Mzinyathi district in South Africa. Journal of Food Security, 6(4), 483–499. - Smith, P. (2013). Delivering food security without increasing pressure on land. Journal of Global Food Security, 2(1) 18-23. - Tadesse, W., Halila, H., Jamal, M., El-Hanafi, S., Assefa, S., Oweis, T., & Baum, M. (2017). Role of Sustainable wheat production to ensure food security in the Cwana Region. Journal of Experimental Biology and Agricultural Sciences, 5, 15-32. - Tincani, L. S. (2012). Resilient livelihoods: Adaptation, food security and wild foods in rural burkina faso. PhD Thesis, University of London. Table 1: Different theoretical approaches to food security Table 2: A number of domestic and foreign studies in the field of food security Fig. 1: Research conceptual model (source: authors, 2021) Table 3: Agricultural indicators affecting food security situation Fig. 2: Research steps (authors, 2021) Fig. 2: Political and geographical locations of the study areas Table 4: Values obtained from Shannon entropy calculation Fig. 1: Weights obtained from Shannon entropy calculation (source: research findings, 2021) Fig. 3: Fuzzy layers of the studied indicators (source: research findings, 2021) Fig. 4: Fuzzy layers of the studied indices (source: research findings, 2021) Fig. 5: Fuzzy layers of the studied indices (source: research findings, 2021) Fig. 6: Final zoning of food security situations in Harris and Bostanabad counties Table 5: Calculation of the share of each indicator in determining food security in the study areas Fig. 2: Contribution of each indicator to determining food security in the study areas (source: research findings, 2021) Table 6: Number of villages located in the food security categories Table 7: Areas occupied by the classes of food security status Table 8: Comparison of food security indicators in Harris and Bostanabad villages

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University of Isfahan Geography and Environmental Planning 2008-5362 33 4 2022 12 22 Evaluating the Effects of Urban Design Qualities on the Citizens Mental Health and Happiness: A Case Study of Sanandaj City ( Chaharbagh and Soran Neighborhoods) Evaluating the Effects of Urban Design Qualities on the Citizens Mental Health and Happiness: A Case Study of Sanandaj City ( Chaharbagh and Soran Neighborhoods) 27 48 26860 10.22108/gep.2022.131505.1468 FA Firoozeh Karimi Department of Urban Planning and Design, South Tehran Branch, Islamic Azad University, Tehran, Iran; Farzin Charehjoo Department of Urban Planning and Design, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran Kasra Ketabollahi Ph.D Researcher in Urban Planning and Lecturer, Department of Urban Planning and Design, Sanandaj Branch, Islamic Azad University, Sanandaj, Iran. Journal Article 2021 11 17 Extended Abstract Introduction From the beginning of human civilization, man has always been in search of happiness. Aristotle believed that "happiness is the ultimate meaning and purpose of life" (Chen & Zhang, 2018). Happiness and mental health are the most important factors of public health. Assessing the extent to which our place of residence can affect our emotions and overall qualities of life has always been one of the most important theoretical and empirical topics in the various professions of human geography, urban and regional studies, urban design and planning, and assurance. Citizens’ high quality of life in all its objective and mental dimensions is one of the most important concerns of managers and urban planners around the world (Moore et al., 2018 and Ballas, 2013). Designing and planning to improve people’s health in many scientific circles, especially since the early 90s in the 20th century, have been already discussed. However, the researchers’ main problem in this field has been addressing people’’s physical health and neglecting the issue of the impacts of urban spaces on citizens’ mental, emotional, and spiritual health conditions (Pfeiffer & Cloutier, 2016). In general, the spaces of urban neighborhoods should be in a way that they provide citizens with easy access to interactive and green and public environments ensure their healthy physical and mental lives, and make a positive feeling in them. It is clear that having positive face-to-face experiences in the neighborhood can affect the level of citizen happiness (Han et al., 2019). Given that mental health requires extensive studies to determine a specific framework of factors affecting it, this research was innovative in this regard. Also, considering that the outputs of theoretical studies are evaluated in the context of urban spaces, they can be a suitable source for implementation of practical measures and fill the gap between thought and action. Therefore, doing this research was necessary. According to the definitions provided by the World Health Organization, an individual’s happiness and mental health is affected not only by intrapersonal characteristics, but also by social and economic factors related to some other environmental components, such as safety. Existence of environments for leisure time, existence of green spaces and vegetation, vitality of the environment, etc. have all been considered as factors that are related to individuals’ levels of happiness and mental health (Hoisington et al., 2019; Wu et al., 2014;Firdaus, 2017;and Melis et al., 2015). Various studies have shown that citizens’ quality of life is directly related to their living environments. In the meantime, the public spaces of cities are of special importance as a platform that provides the ground for collective life and can strengthen or weaken citizens’ health. In general, the artificial environment is one of the most influential determinants and prerequisites for citizens’ health. In many studies, its impact on various aspects of health has been examined. Among them, citizens' mental health and happiness are the most influential dimensions that are strongly influenced by urban structures and environments (Melis et al., 2015). According to Choe (2012), "Of the main concerns of urban designers and planners in different cities, especially in the old and worn-out contexts of cities, are reduction of citizens’ quality of life and their loss of vitality, as well as reduction of the physical quality of the environment." "It is a concept that seems almost difficult to define. This concept is mainly defined by life satisfaction, mental comfort, and enjoyment for well-being and security (Choe, 2012). Urban neighborhoods and streets have a special role in attracting people to the public space of cities and are very important for the city vitality and its inhabitants. This is while the growing trend of using vehicles has reduced the social role of cities and this issue has led to less human interactions in cities and paying more attention to cars and the need for motor vehicles (Horijani, 1397). Therefore, in the profession of urban design and planning, creating a context to provide spaces that can improve the residents’ positive feelings and emotions and reduce their negative emotions is necessary and undeniable. Accordingly, the effort to create a happy atmosphere for citizens in the society that can provide them with a peace of mind. This should be considered as one of the main tasks of social institutions, such as municipalities, city councils, and urban management systems in general. Due to the importance of the issue and lack of a comprehensive and useful research on it in Sanandaj, this study was done to make a comparison between two different neighborhoods with worn and new structures and different social contexts in Sanandaj City. Methodology The present study was based on a descriptive-analytical method in terms of purpose and data collection was done in the two ways of library and field studies. To achieve the research objective, which was investigation of environmental qualities affecting the citizens’ happiness in Sanandaj City, the necessary framework was identified and extracted by reviewing the existing sources and documents and then a questionnaire was used to collect the background data. To answer the research questions, Kolmogorov-Smirnov tests, one-way analysis of variance, and regression analysis were utilized, as well as SmartPLS software. To test normality of the research variables Kolmogorov-Smirnov test was applied. The results related to the citizens’ different levels of vitality, mental health, and happiness and also differences in urban design qualities in both neighborhoods were used via one-way variance to evaluate the effectiveness of qualities. Urban design was used on the level of the citizens’ mental health. Also, using regression analysis, different levels of each of the urban design qualities in the mentioned neighborhoods were studied through statistical calculations. Finally, SmartPLS software was employed to develop structural equation modeling and provide suggestions. Considering the importance of looking at cities for citizens' happiness and joy, the main purpose of this study was to explain the framework of urban design qualities that affected the citizens’ mental health and happiness. Discussion From the structural equation model of the research, it could be stated that the research hypotheses were confirmed with 95% confidence and the quality components of urban design had a positive and significant effect on the citizens’ mental health. Also, based on beta coefficients, it was clear that the components of perceptual and transportation qualities had the most and least impacts on the citizens’ mental health and happiness, respectively. In general, evaluations of the components of functional quality, transportation quality, ecosystem quality, perceptual quality, visual quality, social environment quality, and total time quality (coefficient of determination: 97%) related to health changes determined the citizens’ mental health and the rest of changes in the citizens’ mental health were related to other factors that were outside the scope of this study. Table 13: Structural equation results (source: authors) Hypotheses B t Hypothesis status Communication mental health Operational Quality 0.49 4.67 Confirmation Positive mental health Transportation Quality 0.14 2.45 Confirmation Positive mental health Ecosystem Quality 0.31 3.84 Confirmation Positive mental health Perceptional Quality 0.77 7.37 Confirmation Positive mental health Visual Quality 0.53 6.78 Confirmation Positive mental health Social Environment Quality 0.50 6.13 Confirmation Positive mental health Time Quality 0.41 4.15 Confirmation Positive mental health Urban Design Quality 0.94 240.52 Confirmation Positive |t|>1.96 significant at P<0.05, |t|>2.58 significant at P<0.01 Conclusion The results of the statistical analyses showed that based on what was asked in the first question, the residents of the two different neighborhoods of Sanandaj located in the two different types of traditional and developed contexts enjoyed life on different levels of satisfaction , mental health, vitality, and happiness, which could be attributed to the differences in the levels of some environmental qualities, such as environmental sensory richness, attractive visual qualities, and existence of different spaces for them to stop and spend time. Leisure time could create a ground for improving the residents’ social interactions in the two neighborhoods. The results obtained in this study are consistent with the results of the studies of Zuniga-Teran et al. (2017), Pfeiffer et al. (2016), Cao (2016), Taheri and Taheri (1398), and Abrun et al. (1397). Regarding the impacts of environmental qualities on the citizens’ levels of mental health, the studied qualities, i.e., functional quality, quality of transportation, quality of ecosystem, perceptual quality, visual quality, quality of social environment, and quality of time, could be mentioned as the factors affecting their health. In addition, it could be stated that the existence of such qualities could be the definitive environmental predictors that ensured part of the citizens’ mental health that was affected by the environment and urban design qualities. These results are consistent with the results obtained by Han et al. (Impacts of Access to Parks and Green Spaces on Mental Health) (Han & Kim, 2019), Wales (Impacts of Sense of Safety and Security on Mental health and Well-being) (Wills, 2014), Zhang et al. (The Effects of Different Environmental Qualities on Mental Health and Happiness) (Chen et al., 2018), Griff et al. (The Effects of the Physical and Social Structures of the Environment on the Health and Mental Stress of Citizens) (Greif & Dodoo, 2015), Taheri et al. (The Effect of Physical Environment on Citizens' Satisfaction with the Environment and Their Happiness) (Taheri and Taheri, 2009), and Abron et al., 1397), thus confirming their results and completing their studies. The research results emphasized the importance of urban design in the citizens’ mental health. Therefore, it is necessary to increase the citizens’ happiness based on the research approach, besides including the related courses of urban design for obtaining the master's degree at the university, the criteria of a happy city based on the results of the present research and other similar researches, as well as the related experiences gained by the relevant organizations in Iran so as to take valuable steps for achieving the goal of Masharaliyeh in the medium and long term. Keywords: mental health, SmartPLS software, quality of urban design, Sanandaj References - AnderssonNordbø, Emma Charlott; Nordh, Helena; Raanaas, Ruth Kjærsti; & Aamodt, Geir. (2018). GIS-derived measures of the built environment determinants of mental health and activity participation in childhood and adolescence: A systematic review, (177), 19-37. https://doi.org/https://doi.org/10.1016/j.landurbplan.2018.04.009 - Argyle, Michael. (2001). The Psychology of Happiness. London: Rutledge. - Ballas, Dimitris. (2013). What makes a “happy city”?, (32), 39-50. https://doi.org/https://doi.org/10.1016/j.cities.2013.04.009 - Cao, Jason. (2016). How does neighborhood design affect life satisfaction? Evidence from Twin Cities. Travel Behaviour and Society, 5(1), 68-76. https://doi.org/DOI:10.1016/j.tbs.2015.07.001 - Chen, Chiu-lin; & Zhang, Heng. (2018). Do You Live Happily? Exploring the Impact of Physical Environment on Residents’ Sense of Happiness (Vol. 112). Presented at the IOP Conference Series Earth and Environmental Science. https://doi.org/DOI: 10.1088/1755-1315/112/1/012012 - Choe, Yonhyok. (2012). Quality of life and subjective well-being : A comparison of Korea and five western European countries ( France , Germany , Italy , Sweden , the United Kingdom ). (pp. 1-19). University College of South Stockholm. - Firdaus, Ghuncha. (2017). Built Environment and Health Outcomes: Identification of Contextual Risk Factors for Mental Well-being of Older Adults. Ageing International, 42(1), 62-77. https://doi.org/10.1007/s12126-016-9276-0 - Garrido-Cumbrera, Marco; Gálvez Ruiz, David; Braçe, Olta; & López Lara, Enrique. (2018). Exploring the association between urban sprawl and mental health. Journal of Transport & Health, (10), 381-390. https://doi.org/https://doi.org/10.1016/j.jth.2018.06.006 - Greif, Meredith; & Dodoo, Nii-Amoo. (2015). How community physical, structural, and social stressors relate to mental health in the urban slums of Accra, Ghana. Health & Place, (33), 57-66. https://doi.org/https://doi.org/10.1016/j.healthplace.2015.02.002 - Han, Min Jee Nikki; & Kim, Mi Jeong. (2019). Green Environments and Happiness Level in Housing Areas toward a Sustainable Life. Sustainability, 11(17), 1-18. https://doi.org/10.3390/su11174768 - Hoisington, Andrew J.; Stearns-Yoder, Kelly A.; Schuldt, Steven J.; Beemer, Cody J.; Maestre, Juan P.; Kinney, Kerry; … Brenner, Lisa A. (2019). Ten questions concerning the built environment and mental health. Building and Environment, (58-69), 155. https://doi.org/https://doi.org/10.1016/j.buildenv.2019.03.036 - Howell, A. (2013). Planning for healthy communities in Nova Scotia: The current state of practice (Unpublished master's thesis). University of Waterloo, Ontario, Canada. - Melis, Giulia; Gelormino, Elena; Marra, Giulia; Ferracin, Elisa; & Costa, Giuseppe. (2015). The Effects of the Urban Built Environment on Mental Health: A Cohort Study in a Large Northern Italian City. International Journal of Environmental Research and Public Health, 12(11), 14898-14915. https://doi.org/10.3390/ijerph121114898 - Moore, T. H. M.; Kesten, J. M.; López-López, J. A.; Ijaz, S.; McAleenan, A.; Richards, A.; … Audrey, S. (2018). The effects of changes to the built environment on the mental health and well-being of adults: Systematic review. Health & Place, 53, 237-257. https://doi.org/10.1016/j.healthplace.2018.07.012 - Ochodo, Charles; Ndetei, D. M.; Moturi, W. N.; & Otieno, J. O. (2014). External Built Residential Environment Characteristics that Affect Mental Health of Adults. Journal of Urban Health : Bulletin of the New York Academy of Medicine, 91(5), 908-927. https://doi.org/10.1007/s11524-013-9852-5 - Pfeiffer, Deirdre; & Cloutier, Scott. (2016a). Planning for Happy Neighborhoods. Journal of the American Planning Association, 82(3), 267-279. https://doi.org/DOI: 10.1080/01944363.2016.1166347 - Rohe, William M. (2009). Urban planning and mental health. Prevention in Human Services, 4(1-2), 79-110. https://doi.org/10.1080/10852358509511162 - Veenhoven, Ruut. (2012). Happiness: Also Known as “Life Satisfaction” and “Subjective Well-Being.” In Handbook of Social Indicators and Quality of Life Research. Springer. - Veenhoven, Ruut. (2014). HAPPINESS. Encyclopedia of Quality of Life and Well-Being Research, Springer, Dordrecht, Netherlands, 2637-2641. https://doi.org/https://doi.org/10.1007/978-94-007-0753-5_1224 - Wills, Eduardo. (2014). Feeling Safe and Subjective Well-being. In In book: Encyclopedia of Quality of Life and Wellbeing Research (pp. 2233-2235). Springer. - Wu, Yu-Tzu; Nash, Paul; Barnes, Linda E.; Minett, Thais; Matthews, Fiona E.; Jones, Andy; & Brayne, Carol. (2014). Assessing environmental features related to mental health: a reliability study of visual streetscape images. BMC public health, 14, 1094. https://doi.org/10.1186/1471-2458-14-1094 - Woodward, A., & Kawachi, I., (2000). Why reduce health inequalities? Journal of Epidemiology and Community Health, 54, 923-929. - Wood, Linda- Tam, Sandra- Macfarlane, Ronald- Fordham, Jan- Campbell, Monica and McKeown, David, 2011, Toronto Public Health. Healthy Toronto by Design. Toronto, Ontario, Canada. - World Health Organization »WHO«, 2010, Why Urban Health Matters, Available at https://www.who.int/world-health-day/2010/media/whd2010background.pdf. - World Health Organization. (2011). Healthy urban planning: Report of a consultation meeting. Retrieved from http:// www.who.int/ kobe_centre/ publications/ urban_ planning 2011 .pdf: - World Health Organization. (2014, October). Health and the city: Urban living in the 21st century visions and best solutions for cities committed to health and well-being. Paper presented at the International Healthy Cities Conference, Athens, Greece. - World Health Organization. Regional Office for the Western Pacific. (2015). Healthy cities: Good health is good politics: Toolkit for local governments to support healthy urban development. Retrieved from https:// iris.wpro.who.int/ bitstream/ handle/ 10665.1/ 11865/WPR_2015_DNH_004_eng.pdf: https://www.who.int/global_health_histories/background/en/ - Zuniga-Teran, Adriana A.; Orr, Barron J.; Gimblett, Randy H.; Chalfoun, Nader V.; Guertin, David P.; & Marsh, Stuart E. (2017). Neighborhood Design, Physical Activity, and Wellbeing: Applying the Walkability Model. International Journal of Environmental Research and Public Health, 14(1). https://doi.org/10.3390/ijerph14010076 Tables and Figures: Fig. 1: Community Health and Ecosystem Model, Source (Wood et al., 2011: 26 and Hancock, 1993: 44) Fig. 2: Evolution of the concept of health in the world (Source: authors) Table 1: A review of previous studies in the field of happiness (Source: authors) Table 2: Summary of definitions related to quality in the environment and urban design (Authors' references based on Pour Mohammadi, 1389, p. 48; Parsi, 1378, pp. 18-27; Kolkohen, 1985, pp. 103-104; Carmona et al., 2012, pp. 211-265; and Lynch et al., 1979, p. 415) Fig. 3: Urban design qualities (Source: authors) Table 2: Introduction of the case study of Chaharbagh and Soran neighborhoods in Baharan Town (Source: authors) Table 3: Details of the respondents to the research questionnaire (Summer of 2020, source: authors) Table 4: Means of mental health component between the two neighborhoods (Source: authors) Table 5: Regression analysis of variance of mental health component between the two neighborhoods (Source: authors) Table 6: Average components of functional quality between the two neighborhoods (Authors) Table 7: Average components of transportation quality between the two neighborhoods (Source: authors) Table 8: Average components of ecological quality between the two neighborhoods (Source: authors) Table 9: Average components of perceptual quality between the two neighborhoods (Source: authors) Table 10: Average components of visual quality between the two neighborhoods (Source: authors) Table 11: Average components of social environment quality between the two neighborhoods (Source: authors) Table 12: Average components of time quality between the two neighborhoods (Source: authors) Chart 1: The main research model with structures in the state of absolute value (|T-Value |)(Source: authors) Table 13: Structural equation results (Source: authors) Extended Abstract Introduction From the beginning of human civilization, man has always been in search of happiness. Aristotle believed that "happiness is the ultimate meaning and purpose of life" (Chen & Zhang, 2018). Happiness and mental health are the most important factors of public health. Assessing the extent to which our place of residence can affect our emotions and overall qualities of life has always been one of the most important theoretical and empirical topics in the various professions of human geography, urban and regional studies, urban design and planning, and assurance. Citizens’ high quality of life in all its objective and mental dimensions is one of the most important concerns of managers and urban planners around the world (Moore et al., 2018 and Ballas, 2013). Designing and planning to improve people’s health in many scientific circles, especially since the early 90s in the 20th century, have been already discussed. However, the researchers’ main problem in this field has been addressing people’’s physical health and neglecting the issue of the impacts of urban spaces on citizens’ mental, emotional, and spiritual health conditions (Pfeiffer & Cloutier, 2016). In general, the spaces of urban neighborhoods should be in a way that they provide citizens with easy access to interactive and green and public environments ensure their healthy physical and mental lives, and make a positive feeling in them. It is clear that having positive face-to-face experiences in the neighborhood can affect the level of citizen happiness (Han et al., 2019). Given that mental health requires extensive studies to determine a specific framework of factors affecting it, this research was innovative in this regard. Also, considering that the outputs of theoretical studies are evaluated in the context of urban spaces, they can be a suitable source for implementation of practical measures and fill the gap between thought and action. Therefore, doing this research was necessary. According to the definitions provided by the World Health Organization, an individual’s happiness and mental health is affected not only by intrapersonal characteristics, but also by social and economic factors related to some other environmental components, such as safety. Existence of environments for leisure time, existence of green spaces and vegetation, vitality of the environment, etc. have all been considered as factors that are related to individuals’ levels of happiness and mental health (Hoisington et al., 2019; Wu et al., 2014;Firdaus, 2017;and Melis et al., 2015). Various studies have shown that citizens’ quality of life is directly related to their living environments. In the meantime, the public spaces of cities are of special importance as a platform that provides the ground for collective life and can strengthen or weaken citizens’ health. In general, the artificial environment is one of the most influential determinants and prerequisites for citizens’ health. In many studies, its impact on various aspects of health has been examined. Among them, citizens' mental health and happiness are the most influential dimensions that are strongly influenced by urban structures and environments (Melis et al., 2015). According to Choe (2012), "Of the main concerns of urban designers and planners in different cities, especially in the old and worn-out contexts of cities, are reduction of citizens’ quality of life and their loss of vitality, as well as reduction of the physical quality of the environment." "It is a concept that seems almost difficult to define. This concept is mainly defined by life satisfaction, mental comfort, and enjoyment for well-being and security (Choe, 2012). Urban neighborhoods and streets have a special role in attracting people to the public space of cities and are very important for the city vitality and its inhabitants. This is while the growing trend of using vehicles has reduced the social role of cities and this issue has led to less human interactions in cities and paying more attention to cars and the need for motor vehicles (Horijani, 1397). Therefore, in the profession of urban design and planning, creating a context to provide spaces that can improve the residents’ positive feelings and emotions and reduce their negative emotions is necessary and undeniable. Accordingly, the effort to create a happy atmosphere for citizens in the society that can provide them with a peace of mind. This should be considered as one of the main tasks of social institutions, such as municipalities, city councils, and urban management systems in general. Due to the importance of the issue and lack of a comprehensive and useful research on it in Sanandaj, this study was done to make a comparison between two different neighborhoods with worn and new structures and different social contexts in Sanandaj City. Methodology The present study was based on a descriptive-analytical method in terms of purpose and data collection was done in the two ways of library and field studies. To achieve the research objective, which was investigation of environmental qualities affecting the citizens’ happiness in Sanandaj City, the necessary framework was identified and extracted by reviewing the existing sources and documents and then a questionnaire was used to collect the background data. To answer the research questions, Kolmogorov-Smirnov tests, one-way analysis of variance, and regression analysis were utilized, as well as SmartPLS software. To test normality of the research variables Kolmogorov-Smirnov test was applied. The results related to the citizens’ different levels of vitality, mental health, and happiness and also differences in urban design qualities in both neighborhoods were used via one-way variance to evaluate the effectiveness of qualities. Urban design was used on the level of the citizens’ mental health. Also, using regression analysis, different levels of each of the urban design qualities in the mentioned neighborhoods were studied through statistical calculations. Finally, SmartPLS software was employed to develop structural equation modeling and provide suggestions. Considering the importance of looking at cities for citizens' happiness and joy, the main purpose of this study was to explain the framework of urban design qualities that affected the citizens’ mental health and happiness. Discussion From the structural equation model of the research, it could be stated that the research hypotheses were confirmed with 95% confidence and the quality components of urban design had a positive and significant effect on the citizens’ mental health. Also, based on beta coefficients, it was clear that the components of perceptual and transportation qualities had the most and least impacts on the citizens’ mental health and happiness, respectively. In general, evaluations of the components of functional quality, transportation quality, ecosystem quality, perceptual quality, visual quality, social environment quality, and total time quality (coefficient of determination: 97%) related to health changes determined the citizens’ mental health and the rest of changes in the citizens’ mental health were related to other factors that were outside the scope of this study. Table 13: Structural equation results (source: authors) Hypotheses B t Hypothesis status Communication mental health Operational Quality 0.49 4.67 Confirmation Positive mental health Transportation Quality 0.14 2.45 Confirmation Positive mental health Ecosystem Quality 0.31 3.84 Confirmation Positive mental health Perceptional Quality 0.77 7.37 Confirmation Positive mental health Visual Quality 0.53 6.78 Confirmation Positive mental health Social Environment Quality 0.50 6.13 Confirmation Positive mental health Time Quality 0.41 4.15 Confirmation Positive mental health Urban Design Quality 0.94 240.52 Confirmation Positive |t|>1.96 significant at P<0.05, |t|>2.58 significant at P<0.01 Conclusion The results of the statistical analyses showed that based on what was asked in the first question, the residents of the two different neighborhoods of Sanandaj located in the two different types of traditional and developed contexts enjoyed life on different levels of satisfaction , mental health, vitality, and happiness, which could be attributed to the differences in the levels of some environmental qualities, such as environmental sensory richness, attractive visual qualities, and existence of different spaces for them to stop and spend time. Leisure time could create a ground for improving the residents’ social interactions in the two neighborhoods. The results obtained in this study are consistent with the results of the studies of Zuniga-Teran et al. (2017), Pfeiffer et al. (2016), Cao (2016), Taheri and Taheri (1398), and Abrun et al. (1397). Regarding the impacts of environmental qualities on the citizens’ levels of mental health, the studied qualities, i.e., functional quality, quality of transportation, quality of ecosystem, perceptual quality, visual quality, quality of social environment, and quality of time, could be mentioned as the factors affecting their health. In addition, it could be stated that the existence of such qualities could be the definitive environmental predictors that ensured part of the citizens’ mental health that was affected by the environment and urban design qualities. These results are consistent with the results obtained by Han et al. (Impacts of Access to Parks and Green Spaces on Mental Health) (Han & Kim, 2019), Wales (Impacts of Sense of Safety and Security on Mental health and Well-being) (Wills, 2014), Zhang et al. (The Effects of Different Environmental Qualities on Mental Health and Happiness) (Chen et al., 2018), Griff et al. (The Effects of the Physical and Social Structures of the Environment on the Health and Mental Stress of Citizens) (Greif & Dodoo, 2015), Taheri et al. (The Effect of Physical Environment on Citizens' Satisfaction with the Environment and Their Happiness) (Taheri and Taheri, 2009), and Abron et al., 1397), thus confirming their results and completing their studies. The research results emphasized the importance of urban design in the citizens’ mental health. Therefore, it is necessary to increase the citizens’ happiness based on the research approach, besides including the related courses of urban design for obtaining the master's degree at the university, the criteria of a happy city based on the results of the present research and other similar researches, as well as the related experiences gained by the relevant organizations in Iran so as to take valuable steps for achieving the goal of Masharaliyeh in the medium and long term. Keywords: mental health, SmartPLS software, quality of urban design, Sanandaj References - AnderssonNordbø, Emma Charlott; Nordh, Helena; Raanaas, Ruth Kjærsti; & Aamodt, Geir. (2018). GIS-derived measures of the built environment determinants of mental health and activity participation in childhood and adolescence: A systematic review, (177), 19-37. https://doi.org/https://doi.org/10.1016/j.landurbplan.2018.04.009 - Argyle, Michael. (2001). The Psychology of Happiness. London: Rutledge. - Ballas, Dimitris. (2013). What makes a “happy city”?, (32), 39-50. https://doi.org/https://doi.org/10.1016/j.cities.2013.04.009 - Cao, Jason. (2016). How does neighborhood design affect life satisfaction? Evidence from Twin Cities. Travel Behaviour and Society, 5(1), 68-76. https://doi.org/DOI:10.1016/j.tbs.2015.07.001 - Chen, Chiu-lin; & Zhang, Heng. (2018). Do You Live Happily? Exploring the Impact of Physical Environment on Residents’ Sense of Happiness (Vol. 112). Presented at the IOP Conference Series Earth and Environmental Science. https://doi.org/DOI: 10.1088/1755-1315/112/1/012012 - Choe, Yonhyok. (2012). Quality of life and subjective well-being : A comparison of Korea and five western European countries ( France , Germany , Italy , Sweden , the United Kingdom ). (pp. 1-19). University College of South Stockholm. - Firdaus, Ghuncha. (2017). Built Environment and Health Outcomes: Identification of Contextual Risk Factors for Mental Well-being of Older Adults. Ageing International, 42(1), 62-77. https://doi.org/10.1007/s12126-016-9276-0 - Garrido-Cumbrera, Marco; Gálvez Ruiz, David; Braçe, Olta; & López Lara, Enrique. (2018). Exploring the association between urban sprawl and mental health. Journal of Transport & Health, (10), 381-390. https://doi.org/https://doi.org/10.1016/j.jth.2018.06.006 - Greif, Meredith; & Dodoo, Nii-Amoo. (2015). How community physical, structural, and social stressors relate to mental health in the urban slums of Accra, Ghana. Health & Place, (33), 57-66. https://doi.org/https://doi.org/10.1016/j.healthplace.2015.02.002 - Han, Min Jee Nikki; & Kim, Mi Jeong. (2019). Green Environments and Happiness Level in Housing Areas toward a Sustainable Life. Sustainability, 11(17), 1-18. https://doi.org/10.3390/su11174768 - Hoisington, Andrew J.; Stearns-Yoder, Kelly A.; Schuldt, Steven J.; Beemer, Cody J.; Maestre, Juan P.; Kinney, Kerry; … Brenner, Lisa A. (2019). Ten questions concerning the built environment and mental health. Building and Environment, (58-69), 155. https://doi.org/https://doi.org/10.1016/j.buildenv.2019.03.036 - Howell, A. (2013). Planning for healthy communities in Nova Scotia: The current state of practice (Unpublished master's thesis). University of Waterloo, Ontario, Canada. - Melis, Giulia; Gelormino, Elena; Marra, Giulia; Ferracin, Elisa; & Costa, Giuseppe. (2015). The Effects of the Urban Built Environment on Mental Health: A Cohort Study in a Large Northern Italian City. International Journal of Environmental Research and Public Health, 12(11), 14898-14915. https://doi.org/10.3390/ijerph121114898 - Moore, T. H. M.; Kesten, J. M.; López-López, J. A.; Ijaz, S.; McAleenan, A.; Richards, A.; … Audrey, S. (2018). The effects of changes to the built environment on the mental health and well-being of adults: Systematic review. Health & Place, 53, 237-257. https://doi.org/10.1016/j.healthplace.2018.07.012 - Ochodo, Charles; Ndetei, D. M.; Moturi, W. N.; & Otieno, J. O. (2014). External Built Residential Environment Characteristics that Affect Mental Health of Adults. Journal of Urban Health : Bulletin of the New York Academy of Medicine, 91(5), 908-927. https://doi.org/10.1007/s11524-013-9852-5 - Pfeiffer, Deirdre; & Cloutier, Scott. (2016a). Planning for Happy Neighborhoods. Journal of the American Planning Association, 82(3), 267-279. https://doi.org/DOI: 10.1080/01944363.2016.1166347 - Rohe, William M. (2009). Urban planning and mental health. Prevention in Human Services, 4(1-2), 79-110. https://doi.org/10.1080/10852358509511162 - Veenhoven, Ruut. (2012). Happiness: Also Known as “Life Satisfaction” and “Subjective Well-Being.” In Handbook of Social Indicators and Quality of Life Research. Springer. - Veenhoven, Ruut. (2014). HAPPINESS. Encyclopedia of Quality of Life and Well-Being Research, Springer, Dordrecht, Netherlands, 2637-2641. https://doi.org/https://doi.org/10.1007/978-94-007-0753-5_1224 - Wills, Eduardo. (2014). Feeling Safe and Subjective Well-being. In In book: Encyclopedia of Quality of Life and Wellbeing Research (pp. 2233-2235). Springer. - Wu, Yu-Tzu; Nash, Paul; Barnes, Linda E.; Minett, Thais; Matthews, Fiona E.; Jones, Andy; & Brayne, Carol. (2014). Assessing environmental features related to mental health: a reliability study of visual streetscape images. BMC public health, 14, 1094. https://doi.org/10.1186/1471-2458-14-1094 - Woodward, A., & Kawachi, I., (2000). Why reduce health inequalities? Journal of Epidemiology and Community Health, 54, 923-929. - Wood, Linda- Tam, Sandra- Macfarlane, Ronald- Fordham, Jan- Campbell, Monica and McKeown, David, 2011, Toronto Public Health. Healthy Toronto by Design. Toronto, Ontario, Canada. - World Health Organization »WHO«, 2010, Why Urban Health Matters, Available at https://www.who.int/world-health-day/2010/media/whd2010background.pdf. - World Health Organization. (2011). Healthy urban planning: Report of a consultation meeting. Retrieved from http:// www.who.int/ kobe_centre/ publications/ urban_ planning 2011 .pdf: - World Health Organization. (2014, October). Health and the city: Urban living in the 21st century visions and best solutions for cities committed to health and well-being. Paper presented at the International Healthy Cities Conference, Athens, Greece. - World Health Organization. Regional Office for the Western Pacific. (2015). Healthy cities: Good health is good politics: Toolkit for local governments to support healthy urban development. Retrieved from https:// iris.wpro.who.int/ bitstream/ handle/ 10665.1/ 11865/WPR_2015_DNH_004_eng.pdf: https://www.who.int/global_health_histories/background/en/ - Zuniga-Teran, Adriana A.; Orr, Barron J.; Gimblett, Randy H.; Chalfoun, Nader V.; Guertin, David P.; & Marsh, Stuart E. (2017). Neighborhood Design, Physical Activity, and Wellbeing: Applying the Walkability Model. International Journal of Environmental Research and Public Health, 14(1). https://doi.org/10.3390/ijerph14010076 Tables and Figures: Fig. 1: Community Health and Ecosystem Model, Source (Wood et al., 2011: 26 and Hancock, 1993: 44) Fig. 2: Evolution of the concept of health in the world (Source: authors) Table 1: A review of previous studies in the field of happiness (Source: authors) Table 2: Summary of definitions related to quality in the environment and urban design (Authors' references based on Pour Mohammadi, 1389, p. 48; Parsi, 1378, pp. 18-27; Kolkohen, 1985, pp. 103-104; Carmona et al., 2012, pp. 211-265; and Lynch et al., 1979, p. 415) Fig. 3: Urban design qualities (Source: authors) Table 2: Introduction of the case study of Chaharbagh and Soran neighborhoods in Baharan Town (Source: authors) Table 3: Details of the respondents to the research questionnaire (Summer of 2020, source: authors) Table 4: Means of mental health component between the two neighborhoods (Source: authors) Table 5: Regression analysis of variance of mental health component between the two neighborhoods (Source: authors) Table 6: Average components of functional quality between the two neighborhoods (Authors) Table 7: Average components of transportation quality between the two neighborhoods (Source: authors) Table 8: Average components of ecological quality between the two neighborhoods (Source: authors) Table 9: Average components of perceptual quality between the two neighborhoods (Source: authors) Table 10: Average components of visual quality between the two neighborhoods (Source: authors) Table 11: Average components of social environment quality between the two neighborhoods (Source: authors) Table 12: Average components of time quality between the two neighborhoods (Source: authors) Chart 1: The main research model with structures in the state of absolute value (|T-Value |)(Source: authors) Table 13: Structural equation results (Source: authors)

https://gep.ui.ac.ir/article_26860_ab6e00891386105d00e9c4313cfc0291.pdf

University of Isfahan Geography and Environmental Planning 2008-5362 33 4 2022 12 22 Monitoring and Estimating the Fire-Affected Areas of the Zagros Mountains Using Landsat Satellite Images Monitoring and Estimating the Fire-Affected Areas of the Zagros Mountains Using Landsat Satellite Images 49 62 26782 10.22108/gep.2022.131560.1470 FA Mousa Abedini Professor in Geomorphology , Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran , Maryam Mohamadzadeh Shishegaran Ph.D. candidate, Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran Ehsan Ghale Ph.D. student, Department of Physical Geography, Faculty of Social Sciences, University of Mohaghegh Ardabili, Ardabil, Iran Journal Article 2021 11 22 Abstract Forests have an extremely important place in the ecosystem in terms of providing social and environmental balances. Fire is the greatest danger to forests. Therefore, estimating fire formation and its behavioral characteristics is very important to deal with this issue. Every year, large areas of forests in the Zagros region in western Iran are burned and destroyed. The extent and distribution of fires, the mountainous terrain, and the difficulty of crossing the predominantly forested areas of the Zagros Mountain Range have hampered managers’ abilities to obtain quantitatively reliable information about the burned areas, levels of damage, as well as their statistics and factual information. Utilizing appropriate and available satellite data and images can provide useful information about pre- and post-forest fire conditions. The aim of this study was to estimate fire affected areas and be aware of the efficiency and capability of Landsat satellite data and NBR and dNBR indices in identifying, evaluating, and mapping the burned forests of Zagros region. For this purpose, after preparing the required images, the fires that occurred in June 2016 in the forests of Zagros were investigated by using remote sensing techniques and the necessary processing. The results showed that NBR and dNBR indices provided good information about the impact of fire and its changes. 13685 hectares of Zagros forests were burned in this fire in 2016. Introduction Due to the location of Iran in the dry belt of the Earth and the high pressure area of the subtropical region, weather conditions are perfect for the occurrence of unexpected events. According to the surveys, our country is among the 10 most accident-prone countries in the world. One of these incidents that happen abundantly in Iran is the phenomenon of fire in forests and pastures. Forests are an important part of the Earth's ecosystem. They are a great resource for various purposes, including a genetic reservoir, water reservoir, carbon source, and a source of energy storage in nature. They play an essential role in improving the environment and keeping it in balance. At the same time, they are an important natural resource for proper development in the social economy; yet, this huge resource has been endangered by fires. Every year, thousands of hectares of forests are burned in different regions. Forest fire with natural or human origin has direct or indirect harmful and destructive effects on human life. In addition to the destruction of the environment and its pollution, it causes the destruction of wood reserves, livestock, agricultural and grazing lands, buildings, and human lives and property, besides having many economic, social, and psychological consequences. Methodology In this research, Landsat 8 Satellite images were used, which were obtained from the USGS (United States Geological Survey) website. Landsat 8 is the 8th satellite of this series. The most important role of Landsat program is monitoring and ensuring that the resources necessary for human livelihood, such as food, water, and forests will continue to exist. Landsat 8 satellite images were used by the OLI sensor to extract the land use map and the TIRS sensor was used to extract the surface temperature of the ground and fire-affected areas. In this study, the fires that occurred in Kermanshah, Ilam, and Kurdistan provinces were selected from among the fires in the forests and pastures of Zagros in June 2019. The fire in Kermanshah Province started on Thursday, June 8, 2019, and because it was not constrained in time, it spread to the borders of Ilam and Kurdistan provinces on June 9, 2019. The data used were case-by-case with descriptive information due to the extent of fires during this time period and limited access to satellite images. First, the areas with fires were identified and then, Landsat 8 Satellite images were prepared on a case-by-case basis before and after these two fires. Discussions According to the main objective of the study --determination of fire-affected areas -- after separation and identification of fire areas through the used indicators, classification and separation of the burnt areas from other areas were done. In this regard, the basic pixel method and the maximum likelihood algorithm were used for classification. The 3 classes of fire-affected areas, residential areas, and other land uses were used in the classification process. Using the results obtained from the classification map (Fig. 6), the extent of the fire areas in the study area was estimated to be 13,685 hectares. Conclusions The changes after the fire and burn severity were analyzed using the NBR index. As a general conclusion, it could be said that according to the accuracy of the classification results in distinguishing the burned areas from other areas with high confidence, the power and capability of Landsat Satellite images and NBR and dNBR indices in separating and distinguishing the burned forest areas could be emphasized. Based on the results of the fire intensity maps obtained from this study, it could be claimed that the spatial and spectral resolutions of Landsat images could be a good enough for preparing correct statistics and information about fire areas, especially for preparing a map of burned areas in the Zagros forests of Iran. According to the results obtained from the maps of ground surface temperatures related to before and after the fire, which indicated an increase of 9◦C in the studied area, and by extracting the fire-affected areas by using the NBR and dNBR indices and simultaneously performing fire classification via the supervised method (maximum likelihood algorithm), as well as aligning these areas, it was possible to estimate the size of the areas affected by fire in the study area (13,685 hectares) with high confidence. Keywords: fire, NBR and dNBR indices, classification, Zagros References -Aboelnour, M., & Engel, B. (2018). Application of remote sensing techniques and Geographic Information Systems to Analyze Land Surface Temperature in Response to Land Use/Land Cover Change in Greater Cairo Region, Egypt. Journal of Geographic Information System, 10, 57-88. - Ardakani, A., Valadanzooj, M., Mansourian, A. (2010). Spatial Analysis of Fire Potential in Iran Different Region by Using RS and GIS. Journal of Environmental Studies, 35(52), 25-34. - Chen, Y., Lara, M.J., & Hu, F.S. (2020). A robust visible near-infrared index for fire severity mapping in Arctic tundra ecosystems. ISPRS Journal of Photogrammetry and Remote Sensing, 159, 101–113. - Darvishi, S., Solaimani, K., Rashidpour, M. (2019). Impact of vegetation indices and urban surface characteristics on land surface temperature changes (Case study: Sanandaj city). Journal of RS and GIS for Natural Resources, 10(1), 17-35. - Gondwe, S.V.C., Muchena, R., & Boys, J. (2018). Detecting land Use and Land Cover and Land Surface Temperature Change in Lilongwe City, Malawi. Journal of Remote Sensing and GIS, 9(2), 17-26. - Hussaina, M., Chen, D., Cheng, A., Wei, H.,& Stenley, D. (2013). Change Detection fromRemotely SensedImages: From Pixel basedto Object-based Approaches.Journal of Photogrammetry and RemoteSensing, 80, 91–106. - Kakehmami, A., Ghorbani, A., AsghariSarasekanrood, S., Ghale, E., Ghafari, S. (2020). Study of the relationship between land use and vegetation changes with the land surface temperature in Namin County. Journal of RS and GIS for Natural Resources, 11(2), 27-48. - Kaufman, Y.J., Ichoku, C., & Giglio, L. (2003). Fire and Smoke Observation from the Earth Observation System MODIS Instrument-Products, Validation, and Operational Use. International Journal of Remote Sensing, 24(8), 1765-1781. - Kurnaz, B., Bayik, C., & Abdikan, S. (2019). Determination of Forest Fire Area by Using Satellite Images: Muğla Case. 3rd International Conference on Advanced Engineering Technologies, 19-21. - Labib, S. M., & Harris, A. (2018). The potentials of Sentinel-2 and LandSat-8 data in green infrastructure extraction, using object based image analysis (OBIA) method. European Journal of Remote Sensing, 51(1), 231-240. - Niu, R., & Zhai, P. (2012). Study on forest fire danger over Northern China during the recent 50 years. Climatic Change, 111(3-4), 723-736. - Novelli, A., Aguilar, M.A., Nemmaoui, A., Auilar, F.J., & Tarantino, E. (2016). Performance evaluation of object based greenhouse detection fromSentinel-2 MSI and Landsat 8 OLI data: A case study from Almería(Spain). International Journal of Applied Earth Observation and Geoinformation, 52, 403–411. - Parks, S.A., Dillon, G.K., & Miller, C. (2014). A New Metric for Quantifying Burn Severity: The Relativized Burn Ratio. Remote Sensing, 6, 1827-1844. - Quintano, C., Fernández-Manso, A., & Fernández-Manso, O. (2018). Combination of Landsat and Sentinel-2 MSI data for initial assessing of burn severity. International Journal Applied Earth Observation Geoinformation, 64, 221–225. - Sobrino, J.A., Jiménez-Muñoz, J.C., & Paolini, L. (2004). Land surface temperature retrieval from LANDSAT TM 5. Journal of Remote Sensing of Environment, 90(4), 434-440. - Veysi, S., Naseri, A., Hamzeh, S., Moradi, P. (2016). Estimation of sugarcane field temperature using Split Window Algorithm and OLI LandSat 8 satellite images. Journal of RS and GIS for Natural Resources, 7(1), 27-40. - Wan, Z., & Dozier, J. (1996). A generalized split-window algorithm for retrieving land-surface temperature from space. Journal of Geoscience and Remote Sensing, 34(4), 892-905. Abstract Forests have an extremely important place in the ecosystem in terms of providing social and environmental balances. Fire is the greatest danger to forests. Therefore, estimating fire formation and its behavioral characteristics is very important to deal with this issue. Every year, large areas of forests in the Zagros region in western Iran are burned and destroyed. The extent and distribution of fires, the mountainous terrain, and the difficulty of crossing the predominantly forested areas of the Zagros Mountain Range have hampered managers’ abilities to obtain quantitatively reliable information about the burned areas, levels of damage, as well as their statistics and factual information. Utilizing appropriate and available satellite data and images can provide useful information about pre- and post-forest fire conditions. The aim of this study was to estimate fire affected areas and be aware of the efficiency and capability of Landsat satellite data and NBR and dNBR indices in identifying, evaluating, and mapping the burned forests of Zagros region. For this purpose, after preparing the required images, the fires that occurred in June 2016 in the forests of Zagros were investigated by using remote sensing techniques and the necessary processing. The results showed that NBR and dNBR indices provided good information about the impact of fire and its changes. 13685 hectares of Zagros forests were burned in this fire in 2016. Introduction Due to the location of Iran in the dry belt of the Earth and the high pressure area of the subtropical region, weather conditions are perfect for the occurrence of unexpected events. According to the surveys, our country is among the 10 most accident-prone countries in the world. One of these incidents that happen abundantly in Iran is the phenomenon of fire in forests and pastures. Forests are an important part of the Earth's ecosystem. They are a great resource for various purposes, including a genetic reservoir, water reservoir, carbon source, and a source of energy storage in nature. They play an essential role in improving the environment and keeping it in balance. At the same time, they are an important natural resource for proper development in the social economy; yet, this huge resource has been endangered by fires. Every year, thousands of hectares of forests are burned in different regions. Forest fire with natural or human origin has direct or indirect harmful and destructive effects on human life. In addition to the destruction of the environment and its pollution, it causes the destruction of wood reserves, livestock, agricultural and grazing lands, buildings, and human lives and property, besides having many economic, social, and psychological consequences. Methodology In this research, Landsat 8 Satellite images were used, which were obtained from the USGS (United States Geological Survey) website. Landsat 8 is the 8th satellite of this series. The most important role of Landsat program is monitoring and ensuring that the resources necessary for human livelihood, such as food, water, and forests will continue to exist. Landsat 8 satellite images were used by the OLI sensor to extract the land use map and the TIRS sensor was used to extract the surface temperature of the ground and fire-affected areas. In this study, the fires that occurred in Kermanshah, Ilam, and Kurdistan provinces were selected from among the fires in the forests and pastures of Zagros in June 2019. The fire in Kermanshah Province started on Thursday, June 8, 2019, and because it was not constrained in time, it spread to the borders of Ilam and Kurdistan provinces on June 9, 2019. The data used were case-by-case with descriptive information due to the extent of fires during this time period and limited access to satellite images. First, the areas with fires were identified and then, Landsat 8 Satellite images were prepared on a case-by-case basis before and after these two fires. Discussions According to the main objective of the study --determination of fire-affected areas -- after separation and identification of fire areas through the used indicators, classification and separation of the burnt areas from other areas were done. In this regard, the basic pixel method and the maximum likelihood algorithm were used for classification. The 3 classes of fire-affected areas, residential areas, and other land uses were used in the classification process. Using the results obtained from the classification map (Fig. 6), the extent of the fire areas in the study area was estimated to be 13,685 hectares. Conclusions The changes after the fire and burn severity were analyzed using the NBR index. As a general conclusion, it could be said that according to the accuracy of the classification results in distinguishing the burned areas from other areas with high confidence, the power and capability of Landsat Satellite images and NBR and dNBR indices in separating and distinguishing the burned forest areas could be emphasized. Based on the results of the fire intensity maps obtained from this study, it could be claimed that the spatial and spectral resolutions of Landsat images could be a good enough for preparing correct statistics and information about fire areas, especially for preparing a map of burned areas in the Zagros forests of Iran. According to the results obtained from the maps of ground surface temperatures related to before and after the fire, which indicated an increase of 9◦C in the studied area, and by extracting the fire-affected areas by using the NBR and dNBR indices and simultaneously performing fire classification via the supervised method (maximum likelihood algorithm), as well as aligning these areas, it was possible to estimate the size of the areas affected by fire in the study area (13,685 hectares) with high confidence. Keywords: fire, NBR and dNBR indices, classification, Zagros References -Aboelnour, M., & Engel, B. (2018). Application of remote sensing techniques and Geographic Information Systems to Analyze Land Surface Temperature in Response to Land Use/Land Cover Change in Greater Cairo Region, Egypt. Journal of Geographic Information System, 10, 57-88. - Ardakani, A., Valadanzooj, M., Mansourian, A. (2010). Spatial Analysis of Fire Potential in Iran Different Region by Using RS and GIS. Journal of Environmental Studies, 35(52), 25-34. - Chen, Y., Lara, M.J., & Hu, F.S. (2020). A robust visible near-infrared index for fire severity mapping in Arctic tundra ecosystems. ISPRS Journal of Photogrammetry and Remote Sensing, 159, 101–113. - Darvishi, S., Solaimani, K., Rashidpour, M. (2019). Impact of vegetation indices and urban surface characteristics on land surface temperature changes (Case study: Sanandaj city). Journal of RS and GIS for Natural Resources, 10(1), 17-35. - Gondwe, S.V.C., Muchena, R., & Boys, J. (2018). Detecting land Use and Land Cover and Land Surface Temperature Change in Lilongwe City, Malawi. Journal of Remote Sensing and GIS, 9(2), 17-26. - Hussaina, M., Chen, D., Cheng, A., Wei, H.,& Stenley, D. (2013). Change Detection fromRemotely SensedImages: From Pixel basedto Object-based Approaches.Journal of Photogrammetry and RemoteSensing, 80, 91–106. - Kakehmami, A., Ghorbani, A., AsghariSarasekanrood, S., Ghale, E., Ghafari, S. (2020). Study of the relationship between land use and vegetation changes with the land surface temperature in Namin County. Journal of RS and GIS for Natural Resources, 11(2), 27-48. - Kaufman, Y.J., Ichoku, C., & Giglio, L. (2003). Fire and Smoke Observation from the Earth Observation System MODIS Instrument-Products, Validation, and Operational Use. International Journal of Remote Sensing, 24(8), 1765-1781. - Kurnaz, B., Bayik, C., & Abdikan, S. (2019). Determination of Forest Fire Area by Using Satellite Images: Muğla Case. 3rd International Conference on Advanced Engineering Technologies, 19-21. - Labib, S. M., & Harris, A. (2018). The potentials of Sentinel-2 and LandSat-8 data in green infrastructure extraction, using object based image analysis (OBIA) method. European Journal of Remote Sensing, 51(1), 231-240. - Niu, R., & Zhai, P. (2012). Study on forest fire danger over Northern China during the recent 50 years. Climatic Change, 111(3-4), 723-736. - Novelli, A., Aguilar, M.A., Nemmaoui, A., Auilar, F.J., & Tarantino, E. (2016). Performance evaluation of object based greenhouse detection fromSentinel-2 MSI and Landsat 8 OLI data: A case study from Almería(Spain). International Journal of Applied Earth Observation and Geoinformation, 52, 403–411. - Parks, S.A., Dillon, G.K., & Miller, C. (2014). A New Metric for Quantifying Burn Severity: The Relativized Burn Ratio. Remote Sensing, 6, 1827-1844. - Quintano, C., Fernández-Manso, A., & Fernández-Manso, O. (2018). Combination of Landsat and Sentinel-2 MSI data for initial assessing of burn severity. International Journal Applied Earth Observation Geoinformation, 64, 221–225. - Sobrino, J.A., Jiménez-Muñoz, J.C., & Paolini, L. (2004). Land surface temperature retrieval from LANDSAT TM 5. Journal of Remote Sensing of Environment, 90(4), 434-440. - Veysi, S., Naseri, A., Hamzeh, S., Moradi, P. (2016). Estimation of sugarcane field temperature using Split Window Algorithm and OLI LandSat 8 satellite images. Journal of RS and GIS for Natural Resources, 7(1), 27-40. - Wan, Z., & Dozier, J. (1996). A generalized split-window algorithm for retrieving land-surface temperature from space. Journal of Geoscience and Remote Sensing, 34(4), 892-905.

https://gep.ui.ac.ir/article_26782_35232998054e17e278d059891135627f.pdf

University of Isfahan Geography and Environmental Planning 2008-5362 33 4 2022 12 22 Mapping of Land Suitability Evaluation for Surface and Drip Irrigation Methods in Sistan Plain: A Case Study of Counties of Sistan and Baluchestan Province Mapping of Land Suitability Evaluation for Surface and Drip Irrigation Methods in Sistan Plain: A Case Study of Counties of Sistan and Baluchestan Province 63 78 26668 10.22108/gep.2022.133003.1502 FA Zohreh Mosleh Assistant Professor, Soil and Water Research Institute, Agricultural Research, Education and Extension Organization, Karaj, Iran Alireza Ziaei Javid Instructor, Soil and Water Research Institute, Agricultural Research, Education and Extension Organization, Karaj, Iran Javad Seyedmohammadi Assistant Professor, Soil and Water Research Institute, Agricultural Research, Education and Extension Organization, Karaj, Iran Mohammad Jamshidi Assistant Professor, Soil and Water Research Institute, Agricultural Research, Education and Extension Organization, Karaj, Iran Journal Article 2022 03 12 Abstract For the proper use of water resources and prevention of soil degradation, it is necessary to select the best irrigation system. The aim of this study was to map the land suitability evaluation for different irrigation methods (surface and drip) in 46000 ha of Sistan plain including Hirmand, Hamon, Nimroz, Zahak, and Zabol counties. For this purpose, 600 pedons with an approximate distance of 700 to 800 m were excavated, described, and sampled. In this study, land suitability evaluation for different irrigation methods based on soil properties and topography was done using the parametric method. Then, land suitability maps for surface and drip irrigation methods were prepared using the Inverse Distance Weighting (IDW) method. The results showed that in the studied area, the conditions for drip irrigation are better than the surface irrigation method. However, in many studied areas, there are restrictions for drip irrigation, and for proper and efficient use of this irrigation system, the restrictions must be removed. Moreover, the results confirmed that in all studied areas the most important limitations are salinity/alkalinity and limitations due to physical soil properties. The silty clay and silty loam textural were the most limitation classes. Due to the fact that changing the physical properties is not easily possible, it seems that soil remediation based on salinity and alkalinity can help to remove or reduce the limitations. Introduction Food security and stability in the world greatly depend on the management of natural resources. Moreover, farm production should be increased using these limited resources for feeding the growing global population. The scarcity of water in arid and semiarid regions such as Iran is a restrictive element for the agricultural sector. Therefore, there is an urgent need to develop initiatives to save water in this particular sector. For proper use of water resources and prevention of soil degradation, it is necessary to select the best irrigation system. In this regard, the aim of this study was to map the land suitability evaluation for different irrigation methods (surface and drip) in 46000 ha of Sistan plain including Hirmand, Hamon, Nimroz, Zahak, and Zabol counties. Methodology The present study was conducted in an area of about 46000 ha in the Sistan plain, northeast of Sistan and Baluchestan Province, Iran. The Sistan plain is located in Hirmand, Hamon, Nimroz, Zahak and Zabol counties. In this study, 600 pedons with an approximate distance of 700 to 800 m were excavated. All the pedons were described according to the field book for describing and sampling soils (Schoeneberger, Wysocki, & Benham, 2012). Then, soil samples were taken from different genetic horizons. Soil samples were air-dried, grounded, and passed through a 2-mm sieve. Then, particle size distribution, pH, Electrical conductivity (EC), calcium carbonate equivalent (CCE), the content of organic carbon, soluble Ca, Mg, Na, and K were determined. In this study, land suitability evaluation for different irrigation methods based on soil properties and topography was done using the parametric method. The capability index for irrigation was determined based on the rating of soil properties. Then, the suitability classes were defined according to the value of the capability index. After that, land suitability maps for surface and drip irrigation methods were prepared using the Inverse Distance Weighting (IDW) method. Discussion The results show that Hirmand County with 916 ha has the highest and Hamoon County with 226 ha has the lowest land area with S1 suitability class for surface irrigation. In Hamoon County, in relation to the ability to use surface irrigation, the majority of the studied lands (approximately 4492 ha) have S2ns class. Zahak County with 929 ha has the highest and Hamoon County with 214 ha has the lowest level of lands with S1 suitability class for drip irrigation. Based on the values of overall accuracy and kappa index, it can be stated that for all counties and both irrigation methods, the predictions were accurate. The overall accuracy of 80% and the kappa index of 0.7 indicate the high accuracy of the predicted maps. Conclusion The results show that in the studied area, the conditions for drip irrigation are better than the surface irrigation method. However, in many studied areas, there are restrictions for drip irrigation, and for proper and efficient use of this irrigation system, the restrictions must be removed. Moreover, results confirm that, in all studied areas, the most important limitations are salinity/alkalinity and limitations due to physical soil properties. The silty clay and silty loam textural are the most limiting classes. As changing the physical properties is not easily possible, it seems that soil remediation based on salinity and alkalinity can help to remove or reduce the limitations. Keywords: Suitability Index, Pedon, Irrigation System. References - Albaji, M., Nasab, B., & Naseri, A. A. (2010). Comparison of different irrigation methods based on the parametric evaluation approach in the plain West of Shush, Iran. Journal of Irrigation and Drainage, 59(1), 547-558. - Albaji, M., Eslamian, S., Naseri, A. A., & Eslamian, F. (2020). Handbook of irrigation system selection for semi-arid regions. CRC Press, Taylor and Francis Group. - Barberis, A., & Minelli, S. (2005). Land evaluation in Shouyang County, Shanxi Province, China. The 25th Professional Master Course. IAO: Florence, Italy. - Bienvenue, J. S., Ngardeta, M., & Mamadou, K. (2003). Land evaluation in the Province of Thies Senegal. The 23rd Professional Master Course. IAO: Florence, Italy. - Braester, C. (1983). Moisture variation at the soil surface and the advance of the wetting front during infiltration at constant flux. Journal of Water Resource Research, 9(1), 687-694. - Cindy, S. K., & Hunt, J. R. (1996). Prediction of wetting front movement during one-dimensional infiltration into soils. Journal of Water Resource Research, 32(1), 55–64. - Congalton, R. (1991). A review of assessing the accuracy of classiﬁcation of remotely sensed data. Journal of Remote Sensing Environmental, 37(1), 35–46. - Dengiz, O. (2006). Comparison of different irrigation methods based on the parametric evaluation approach. Turkish Journal of Agriculture and Forestry, 30(1), 21-29. - FAO. (2017). Water for sustainable food and agriculture. A report produced for the G20 presidency of Germany. Roma: Food and Agriculture Organization of the United Nations. - Jolaini, M., & Mehrabadi, H. R. (2012). Investigation the effect of surface and subsurface drip irrigation methods and irrigation interval on yield quality and quantity of cotton. Journal of Water and Soil, 26(3), 736-742. - Schoeneberger, P. J., Wysocki, D. A., & Benham, E. C. (2012). Field book for describing and sampling soils. Natural Resources Conservation Service. National Soil Survey Center. - Sedigh Kia, M., Nateghi, M. B., Kaviayni, S., & Naghipour, N. (2015). Evaluation and zoning of irrigation methods on Etka orginiziation lands in Dorud, using analytical hierarchy process. Journal of Water Research in Agriculture, 28(4), 749-758. - Soil Survey Staff. (1996). Soil survey laboratory methods manual. Natural Resources Conservation Service (NRCS). - Sys, C., Van Ranst, E., & Debaveye, J. (1991). Land evaluation, principles in land evaluation and crop production calculations. Agricultural Publications. Abstract For the proper use of water resources and prevention of soil degradation, it is necessary to select the best irrigation system. The aim of this study was to map the land suitability evaluation for different irrigation methods (surface and drip) in 46000 ha of Sistan plain including Hirmand, Hamon, Nimroz, Zahak, and Zabol counties. For this purpose, 600 pedons with an approximate distance of 700 to 800 m were excavated, described, and sampled. In this study, land suitability evaluation for different irrigation methods based on soil properties and topography was done using the parametric method. Then, land suitability maps for surface and drip irrigation methods were prepared using the Inverse Distance Weighting (IDW) method. The results showed that in the studied area, the conditions for drip irrigation are better than the surface irrigation method. However, in many studied areas, there are restrictions for drip irrigation, and for proper and efficient use of this irrigation system, the restrictions must be removed. Moreover, the results confirmed that in all studied areas the most important limitations are salinity/alkalinity and limitations due to physical soil properties. The silty clay and silty loam textural were the most limitation classes. Due to the fact that changing the physical properties is not easily possible, it seems that soil remediation based on salinity and alkalinity can help to remove or reduce the limitations. Introduction Food security and stability in the world greatly depend on the management of natural resources. Moreover, farm production should be increased using these limited resources for feeding the growing global population. The scarcity of water in arid and semiarid regions such as Iran is a restrictive element for the agricultural sector. Therefore, there is an urgent need to develop initiatives to save water in this particular sector. For proper use of water resources and prevention of soil degradation, it is necessary to select the best irrigation system. In this regard, the aim of this study was to map the land suitability evaluation for different irrigation methods (surface and drip) in 46000 ha of Sistan plain including Hirmand, Hamon, Nimroz, Zahak, and Zabol counties. Methodology The present study was conducted in an area of about 46000 ha in the Sistan plain, northeast of Sistan and Baluchestan Province, Iran. The Sistan plain is located in Hirmand, Hamon, Nimroz, Zahak and Zabol counties. In this study, 600 pedons with an approximate distance of 700 to 800 m were excavated. All the pedons were described according to the field book for describing and sampling soils (Schoeneberger, Wysocki, & Benham, 2012). Then, soil samples were taken from different genetic horizons. Soil samples were air-dried, grounded, and passed through a 2-mm sieve. Then, particle size distribution, pH, Electrical conductivity (EC), calcium carbonate equivalent (CCE), the content of organic carbon, soluble Ca, Mg, Na, and K were determined. In this study, land suitability evaluation for different irrigation methods based on soil properties and topography was done using the parametric method. The capability index for irrigation was determined based on the rating of soil properties. Then, the suitability classes were defined according to the value of the capability index. After that, land suitability maps for surface and drip irrigation methods were prepared using the Inverse Distance Weighting (IDW) method. Discussion The results show that Hirmand County with 916 ha has the highest and Hamoon County with 226 ha has the lowest land area with S1 suitability class for surface irrigation. In Hamoon County, in relation to the ability to use surface irrigation, the majority of the studied lands (approximately 4492 ha) have S2ns class. Zahak County with 929 ha has the highest and Hamoon County with 214 ha has the lowest level of lands with S1 suitability class for drip irrigation. Based on the values of overall accuracy and kappa index, it can be stated that for all counties and both irrigation methods, the predictions were accurate. The overall accuracy of 80% and the kappa index of 0.7 indicate the high accuracy of the predicted maps. Conclusion The results show that in the studied area, the conditions for drip irrigation are better than the surface irrigation method. However, in many studied areas, there are restrictions for drip irrigation, and for proper and efficient use of this irrigation system, the restrictions must be removed. Moreover, results confirm that, in all studied areas, the most important limitations are salinity/alkalinity and limitations due to physical soil properties. The silty clay and silty loam textural are the most limiting classes. As changing the physical properties is not easily possible, it seems that soil remediation based on salinity and alkalinity can help to remove or reduce the limitations. Keywords: Suitability Index, Pedon, Irrigation System. References - Albaji, M., Nasab, B., & Naseri, A. A. (2010). Comparison of different irrigation methods based on the parametric evaluation approach in the plain West of Shush, Iran. Journal of Irrigation and Drainage, 59(1), 547-558. - Albaji, M., Eslamian, S., Naseri, A. A., & Eslamian, F. (2020). Handbook of irrigation system selection for semi-arid regions. CRC Press, Taylor and Francis Group. - Barberis, A., & Minelli, S. (2005). Land evaluation in Shouyang County, Shanxi Province, China. The 25th Professional Master Course. IAO: Florence, Italy. - Bienvenue, J. S., Ngardeta, M., & Mamadou, K. (2003). Land evaluation in the Province of Thies Senegal. The 23rd Professional Master Course. IAO: Florence, Italy. - Braester, C. (1983). Moisture variation at the soil surface and the advance of the wetting front during infiltration at constant flux. Journal of Water Resource Research, 9(1), 687-694. - Cindy, S. K., & Hunt, J. R. (1996). Prediction of wetting front movement during one-dimensional infiltration into soils. Journal of Water Resource Research, 32(1), 55–64. - Congalton, R. (1991). A review of assessing the accuracy of classiﬁcation of remotely sensed data. Journal of Remote Sensing Environmental, 37(1), 35–46. - Dengiz, O. (2006). Comparison of different irrigation methods based on the parametric evaluation approach. Turkish Journal of Agriculture and Forestry, 30(1), 21-29. - FAO. (2017). Water for sustainable food and agriculture. A report produced for the G20 presidency of Germany. Roma: Food and Agriculture Organization of the United Nations. - Jolaini, M., & Mehrabadi, H. R. (2012). Investigation the effect of surface and subsurface drip irrigation methods and irrigation interval on yield quality and quantity of cotton. Journal of Water and Soil, 26(3), 736-742. - Schoeneberger, P. J., Wysocki, D. A., & Benham, E. C. (2012). Field book for describing and sampling soils. Natural Resources Conservation Service. National Soil Survey Center. - Sedigh Kia, M., Nateghi, M. B., Kaviayni, S., & Naghipour, N. (2015). Evaluation and zoning of irrigation methods on Etka orginiziation lands in Dorud, using analytical hierarchy process. Journal of Water Research in Agriculture, 28(4), 749-758. - Soil Survey Staff. (1996). Soil survey laboratory methods manual. Natural Resources Conservation Service (NRCS). - Sys, C., Van Ranst, E., & Debaveye, J. (1991). Land evaluation, principles in land evaluation and crop production calculations. Agricultural Publications.

https://gep.ui.ac.ir/article_26668_03e5c9c9e290b2ce0e7f1b7f143ee203.pdf

University of Isfahan Geography and Environmental Planning 2008-5362 33 4 2022 12 22 Investigating the Effects of Land Changes on some Pollutants in the Mahshahr Industrial Zone using Remote Sensing and Analysis of Variance (ANOVA) Images Investigating the Effects of Land Changes on some Pollutants in the Mahshahr Industrial Zone using Remote Sensing and Analysis of Variance (ANOVA) Images 79 96 26642 10.22108/gep.2022.133195.1510 FA Sorour Pourmohammadi MA Student of Environment Sciences, Department of Natural Resources, Isfahan University of Technology, Isfahan, Iran Ali Lotfi Assistant Professor of Environment Sciences, Department of Natural Resources, Isfahan University of Technology, Isfahan, Iran Mehrdad Alranaee PhD Candidate of Environment Sciences, Department of Natural Resources, Isfahan University of Technology, Isfahan, Iran Journal Article 2022 04 07 Abstract Air pollution is known as one of the most important environmental problems in cities, especially in industrial cities, which causes various cardiovascular diseases and many deaths every year. Assessing the trend of spatial variations in the distribution of air pollution in areas with heavy industry is essential for the sustainable development of these areas. Considering the importance of Mahshahr County as the largest industrial hub of the country, the present study examines the concentrations of NO2, SO2, O3, and PM10 pollutants in different areas of Mahshahr industrial area, mapping and examining them in relation to different uses. In this study, Sentile 2 satellite images and ENVI 5.3 software and the supervised maximum probability classification method were used to classify land use. Also, using the KRIGING interpolation method and air quality monitoring station information, the concentrations of the mentioned pollutants were seasonally zoned in the region. Analysis of variance (ANOVA) was used to investigate the differences between the concentrations of pollutants in the land uses in different seasons of the year. The results of the study showed that the highest risk use was industrial use and the lowest risk use was agricultural use. Also, the lowest concentration of studied pollutants was related to the Sarbandar station. The results of ANOVA also showed that among the different seasons of 2019-2020, autumn and winter have the most significant differences. The results of this study can help identify air pollution and its relationship with land use changes, as well as control and reduce the concentration of pollutants in the study area. Introduction Air pollution is one of the major environmental and economic problems worldwide, and it is becoming more acute in industrial areas. The World Health Organization (WHO) and the International Agency for Research on Cancer (IARC) have identified air pollution as a carcinogen for humans, and about 90% of people around the world breathe in polluted and toxic air every day. Awareness of the effects of different concentrations of air pollutants on the use of industrial areas is one of the basic requirements in land planning and management and sustainable development. Currently, the use of remote sensing is the best method for preparing land use maps. Given the importance of the impact of pollutants released into the atmosphere and land use, especially in industrial areas, there is a need for accurate and effective cognition in this field and researchers seek to understand the relationship between the spatial distribution of air pollutants and its relationship with land use. The present study aims at investigating the concentrations of O3, PM10, NO2, and SO2 pollutants in the industrial area of Mahshahr city and evaluating the trend of air pollution in relation to spatial and temporal changes and its possible relationship with land use. Methodology In the present study, satellite images of Sentile 2 and ENVI 5.3 software and a supervised maximum probability classification method have been used to classify land use. Also, using the KRIGING interpolation method and air quality monitoring station information, the concentrations of these pollutants were seasonally zoned in the region and analyzed by variance analysis (ANOVA). They were used to investigate the differences between the concentrations of pollutants in land uses in different seasons of the year. Discussion The results of the average concentration of pollutants studied in different seasons of the mentioned years show that the highest concentration of studied pollutants in different seasons of the mentioned years is related to the industrial area (stations 1, 2, 3, 4, and 5 in the region. The lowest concentration of the mentioned pollutants is related to the Sarbandar residential area (station 7). Also, the Mahshahr residential area (station 6) due to being closer to the Mahshahr special economic zone, has a higher concentration of studied pollutants than the Sarbandar area. Estuaries barren areas 1 and 2, in autumn and winter, have the highest concentration of pollution. The results also showed that the highest endangered use is industrial use and the lowest endangered use is agricultural use. Conclusion In general, it can be concluded that the highest concentration of NO2, SO2, O3, and PM10 pollutants is related to the Mahshahr Special Economic Zone station and the lowest concentration of these pollutants is related to the Sarbandar station. Mahshahr Economic Special Zone, due to the existence of several petrochemical industries located in this place and the high production and spread of these pollutants in the atmosphere, also affects other parts of the study area. Among the land uses used in this study, estuaries have higher concentrations of pollutants due to their proximity to Mahshahr Special Economic Zone and being more affected by this area with petrochemical industries. With the distance from these areas, the impact of land uses on pollutants is less and the least impact of pollutants on agricultural land use is less. Also, the results of the analysis of variance show that there is no significant difference between the concentrations of pollutants in different land use classes. The results of this study can help health authorities to identify the most polluted areas, the impact of air pollution on land use and the health of people in the area, as well as the extent of land use changes in the area. The study also helps policymakers in designing and implementing action plans to reduce concentrations of NO2, SO2, O3, and PM10 pollutants. Keywords: Land Use, Air Pollution, Analysis of Variance, Remote Sensing, Maximum Probability, Mahshahr County. References - Abbasspour, M., Javid, A., & Saeidi, S. (2014). The Impact of Urban Parks on PM10 Suspended Particles, Through Using GIS Software. Journal of Environmental Science and Technology, 16(1), 1-12. - Akbari, E., Zangane Asadi, M. A., & Taghavi, E. (2016). Change detection land use and land cover regional neyshabour using Different methods of statistical training theory. Journal of Geographical Planning of Space, 6(20), 35-50. - Albanese, S., De Vivo, B., Lima, A., Frattasio, G., Kříbek, B., Nyambe, I. & Majer, V. (2014). Prioritizing environmental risk at the regional scale by a GIS aided technique. Journal of Geochemical Exploration, 144, 332-344. - Atai, H., & Hashemi Nasab, S. (2011). Identification and analysis of atmospheric mid-level patterns affecting air pollution in Isfahan. Journal of Research and Urban Planning, 2(4), 97-113. - Borge, R., Narros, A., Artíñano, B., Yagüe, C., Gómez-Moreno, F., & Paz, D. (2016). Assessment of microscale spatiotemporal variation of air pollution at an urban hotspot in Madrid (Spain) through an extensive field campaign. Journal of Atmospheric Environment, 140, 432-445. - Chiang, T., Yuan, T., Shie, R., Chen, C., & Chan, C. (2016). Increased incidence of allergic rhinitis, bronchitis and asthma, in children living near a petrochemical complex with SO2 pollution. Environment International Journal, 96, 1-7. - Deligiorgi, D., & Philippopoulos, K. (2018). Spatial interpolation methodologies in urban air pollution modeling. Journal of Advanced Air Pollution, 341, 62-73. - El Baroudy, A. A. (2016). Mapping and evaluating land suitability using a GIS-based model. Catena, 140, 96-140. - Fallah Sourki M., Kavian A., & Omidvar E. (2016). Prioritizitzation of Haraz sub-watersheds in order to soil and water conservation practices based on morphometric and land use characteristics. Journal of Science and Technology of Agriculture and Natural Resources, 20(77), 85-99. - Fan, F., Weng, Q., & Wang, Y. (2007). Land use land cover change in Guangzhou, China, from 1998 to 2003, based on Landsat TM/ETM+ imagery. Sensors, 7, 1323-1342. - Guo, L., Yuan, P., Song, Y., Peng, J., & Wang, L. (2011). Case study and environmental risk assessment of the petrochemical industry. In 2011 International Conference on Remote Sensing, Environment and Transportation Engineering (pp. 5783-5786). IEEE. - Halim, N. D. A., Latif, M. T., Mohamed, A. F., Maulud, K. N. A., Idrus, S., Azhari, A., ... & Sofwan, N. M. (2020). Spatial assessment of land use impact on air quality in mega urban regions, Malaysia. Journal of Sustainable Cities and Society, 63, 102436. - Han, L., Zhao, J., Gao, Y., Gu, Z., Xin, K., Zhang, J, (2020). Spatial distribution characteristics of PM2.5 and PM10 in Xi’an City predicted by land use regression models. Journal of Sustainable Cities and Society, 61, 1-16. - Ismailnejad, M., Eskandari Sani, M., & Barzaman, S. (2015). Evaluation and zoning of urban air pollution in Tabriz. Journal of Regional Planning, 5(19), 173-186. - Jiang, Z., Cheng, H., Zhang, P., & Kang, T. (2021). Influence of urban morphological parameters on the distribution and diffusion of air pollutants: A case study in China. Journal of Environmental Sciences, 105, 163-172. - Kelishadi, R., Moeini, R., & Poursafa, P. (2014). Independent association between air pollutants and vitamin D deficienty in young children in Isfahan, Iran. Paediatrics and International Child Health, 34(1), 50-55. - Khan, J., Kakosimos, K., Raaschou, O., Brandt, J., Jensen, S. S., & Ellermann, T. (2019). Development and performance evaluation of new Air GIS–a GIS based air pollution and human exposure modelling system. Journal of Atmospheric Environment, 198, 102-121. - Khavarian-Garmsir, A. R., & Rezaei, M. R. (2015). Selection of appropriate locations for industrial areas using GIS-fuzzy methods. A case study of Yazd Township, Iran. Journal of Settlements and Spatial Planning, 6(1), 19-25. - Kongtip, P., Singkaew, P., Yoosook, W., Chantanakul, S., & Sujiratat, D. (2013). Health effects of people living close to a petrochemical industrial estate in Thailand. Journal of the Medical Association of Thailand, 96(5), 64-72. - Kuo, Y. C., Lu, S. T., Tzeng, G. H., Lin, Y. C., & Huang, Y. S. (2013). Using fuzzy integral approach to enhance site selection assessment a case study of the optoelectronics industry. Procedia Computer Science, 17, 306-313. - López-Serrano, P., Corral-Rivas, J., Díaz-Varela, R., Álvarez-González, J., & López-Sánchez, C. (2016). Evaluation of radiometric and atmospheric correction algorithms for aboveground forest biomass estimation using landsat 5 TM data. Journal of Remote Sensing, 8(5), 1-19. - Lue, D., Xu, J., Yue, W., Mao, W., Yang, D., & Wang, J. (2020). Response of PM2.5 pollution to land use in China. Journal of Cleaner Production, 244, 1-25. - Masroor, K., Yousefi, S., Fanaei, F., & Raeesi, M. (2020). Spatial modelling of PM2.5 concentrations in Tehran using Kriging and inverse distance weighting (IDW) methods. Journal of Air Pollution and Health, 5(1), 1-9. - Memarbashi, E., Azadi, H., Barati, A.A., Mohajeri, F., Passel, S. V., & Witlox, F. (2017). Land-use suitability in Northeast Iran: application of AHP-GIS hybrid model. ISPRS International Journal of Geo-Information, 6(12), 1-15. - Metia, S., Ha, P., Duc, H. N., & Scorgie, Y. (2020). Urban air pollution estimation using unscented Kalman filtered inverse modeling with scaled monitoring data. Journal of Sustainable Cities and Society, 54, 97-101. - Montero, J. M., & Fernández, G. (2018). Functional kriging prediction of atmospheric particulate matter concentrations in Madrid, Spain: Is the new monitoring system masking potential public health problems? Journal of Cleaner Production, 175, 283-293. - Nadal, M., Cadiach Ricoma, O., Kumar, V., Poblet, P., Mari, M., Schuhmacher, M. & Domingo, J. (2011). Health Risk Map of a Petrochemical Complex through GIS-Fuzzy Integration of Air Pollution Monitoring Data. Human and Ecological Risk Assessment: An International Journal, 17, 873-891. - Noorpoor, A., & Feiz, S. (2014). Determination of the Spatial and Temporal Variation of SO2, NO2 and Particulate Matter Using GIS Techniques and Estimation of Concentration Modeling with LUR Method. Journal of Environmental Studies, 40(3), 723-738. - Qiao, P., Lei, M., Yang, S., Yang, J., Guo, G., & Zhou, X. (2018). Comparing ordinary kriging and inverse distance weighting for soil as pollution in Beijing. Journal of Environmental Science and Pollution Research, 25(16), 597-608. - Sarwar, M. T., & Maqbool, A. (2019). Causes and control measures of urban air pollution in China. Environment and Ecosystem Science (EES), 3(1), 35-36. - Seifi, M., Yunesian, M., & Nadafee, K. (2021). Exposure to ambient air pollution and socio-economic status on intelligence quotient among schoolchildren in a developing country. Journal of Environmental Science and Pollution Research, 29(1), 1-9. - Sharifi Sadeh, M., & Ahmadi Nadoushan, M. (2018). Application of a land use regression (LUR) model to the spatial modelling of air pollutants in Esfahan city. Journal of Environmental Sciences, 16(2), 203-216. - Shi, X., Li, M., Hunter, O., Guetti, B., Andrew A., & Stommel, E. (2019). Estimation of environmental exposure: interpolation, kernel density estimation or snapshotting. Annals of GIS, 25(1), 1-8. - Tehrani, N. A., Mollalo, A., Farhani, F., & Pahlevanzade, N. (2021). Time-Series Analysis of COVID-19 in Iran: A Remote Sensing Perspective. Journal of Geospatial Information and Community Resilience, 21, 277-290. - Vahdat Mohammadi, A., & Rahimi, S. (2013). Impact of urban land use pattern on Tehran air quality. Journal of Research and Urban Planning, 4(14), 123-142. - Vahdat, A., & Alimohammadi, A. (2020). Study of Hourly Variability of Association between Land Use Parameters and CO Pollutants Using LUR Model in Tehran. Iranian Journal of Remote Sensing and GIS, 12(1), 1-18. - Xu, H., Bechle, M. J., Wang, M., Szpiro, A. A., Vedal, S., & Bai, Y. (2019). National PM2.5 and NO2 exposure models for China based on land use regression, satellite measurements, and universal kriging. Journal of Science of the Total Environment, 655, 423-433. - Yang, C. Y., Wang, J. D., Chan, C. C., Hwang, J. S., & Chen, P. C. (2021). Respiratory symptoms of primary school children living in a petrochemical polluted area in Taiwan. Pediatr Pulmonol 25, 299-303. - Yu, H., Russell, A., Mulholland, J., Odman, T., Hu, Y. & Chang, H. (2018). Cross-comparison and evaluation of air pollution field estimation methods. Journal of Atmospheric Environment, 179, 49-60 Abstract Air pollution is known as one of the most important environmental problems in cities, especially in industrial cities, which causes various cardiovascular diseases and many deaths every year. Assessing the trend of spatial variations in the distribution of air pollution in areas with heavy industry is essential for the sustainable development of these areas. Considering the importance of Mahshahr County as the largest industrial hub of the country, the present study examines the concentrations of NO2, SO2, O3, and PM10 pollutants in different areas of Mahshahr industrial area, mapping and examining them in relation to different uses. In this study, Sentile 2 satellite images and ENVI 5.3 software and the supervised maximum probability classification method were used to classify land use. Also, using the KRIGING interpolation method and air quality monitoring station information, the concentrations of the mentioned pollutants were seasonally zoned in the region. Analysis of variance (ANOVA) was used to investigate the differences between the concentrations of pollutants in the land uses in different seasons of the year. The results of the study showed that the highest risk use was industrial use and the lowest risk use was agricultural use. Also, the lowest concentration of studied pollutants was related to the Sarbandar station. The results of ANOVA also showed that among the different seasons of 2019-2020, autumn and winter have the most significant differences. The results of this study can help identify air pollution and its relationship with land use changes, as well as control and reduce the concentration of pollutants in the study area. Introduction Air pollution is one of the major environmental and economic problems worldwide, and it is becoming more acute in industrial areas. The World Health Organization (WHO) and the International Agency for Research on Cancer (IARC) have identified air pollution as a carcinogen for humans, and about 90% of people around the world breathe in polluted and toxic air every day. Awareness of the effects of different concentrations of air pollutants on the use of industrial areas is one of the basic requirements in land planning and management and sustainable development. Currently, the use of remote sensing is the best method for preparing land use maps. Given the importance of the impact of pollutants released into the atmosphere and land use, especially in industrial areas, there is a need for accurate and effective cognition in this field and researchers seek to understand the relationship between the spatial distribution of air pollutants and its relationship with land use. The present study aims at investigating the concentrations of O3, PM10, NO2, and SO2 pollutants in the industrial area of Mahshahr city and evaluating the trend of air pollution in relation to spatial and temporal changes and its possible relationship with land use. Methodology In the present study, satellite images of Sentile 2 and ENVI 5.3 software and a supervised maximum probability classification method have been used to classify land use. Also, using the KRIGING interpolation method and air quality monitoring station information, the concentrations of these pollutants were seasonally zoned in the region and analyzed by variance analysis (ANOVA). They were used to investigate the differences between the concentrations of pollutants in land uses in different seasons of the year. Discussion The results of the average concentration of pollutants studied in different seasons of the mentioned years show that the highest concentration of studied pollutants in different seasons of the mentioned years is related to the industrial area (stations 1, 2, 3, 4, and 5 in the region. The lowest concentration of the mentioned pollutants is related to the Sarbandar residential area (station 7). Also, the Mahshahr residential area (station 6) due to being closer to the Mahshahr special economic zone, has a higher concentration of studied pollutants than the Sarbandar area. Estuaries barren areas 1 and 2, in autumn and winter, have the highest concentration of pollution. The results also showed that the highest endangered use is industrial use and the lowest endangered use is agricultural use. Conclusion In general, it can be concluded that the highest concentration of NO2, SO2, O3, and PM10 pollutants is related to the Mahshahr Special Economic Zone station and the lowest concentration of these pollutants is related to the Sarbandar station. Mahshahr Economic Special Zone, due to the existence of several petrochemical industries located in this place and the high production and spread of these pollutants in the atmosphere, also affects other parts of the study area. Among the land uses used in this study, estuaries have higher concentrations of pollutants due to their proximity to Mahshahr Special Economic Zone and being more affected by this area with petrochemical industries. With the distance from these areas, the impact of land uses on pollutants is less and the least impact of pollutants on agricultural land use is less. Also, the results of the analysis of variance show that there is no significant difference between the concentrations of pollutants in different land use classes. The results of this study can help health authorities to identify the most polluted areas, the impact of air pollution on land use and the health of people in the area, as well as the extent of land use changes in the area. The study also helps policymakers in designing and implementing action plans to reduce concentrations of NO2, SO2, O3, and PM10 pollutants. Keywords: Land Use, Air Pollution, Analysis of Variance, Remote Sensing, Maximum Probability, Mahshahr County. References - Abbasspour, M., Javid, A., & Saeidi, S. (2014). The Impact of Urban Parks on PM10 Suspended Particles, Through Using GIS Software. Journal of Environmental Science and Technology, 16(1), 1-12. - Akbari, E., Zangane Asadi, M. A., & Taghavi, E. (2016). Change detection land use and land cover regional neyshabour using Different methods of statistical training theory. Journal of Geographical Planning of Space, 6(20), 35-50. - Albanese, S., De Vivo, B., Lima, A., Frattasio, G., Kříbek, B., Nyambe, I. & Majer, V. (2014). Prioritizing environmental risk at the regional scale by a GIS aided technique. Journal of Geochemical Exploration, 144, 332-344. - Atai, H., & Hashemi Nasab, S. (2011). Identification and analysis of atmospheric mid-level patterns affecting air pollution in Isfahan. Journal of Research and Urban Planning, 2(4), 97-113. - Borge, R., Narros, A., Artíñano, B., Yagüe, C., Gómez-Moreno, F., & Paz, D. (2016). Assessment of microscale spatiotemporal variation of air pollution at an urban hotspot in Madrid (Spain) through an extensive field campaign. Journal of Atmospheric Environment, 140, 432-445. - Chiang, T., Yuan, T., Shie, R., Chen, C., & Chan, C. (2016). Increased incidence of allergic rhinitis, bronchitis and asthma, in children living near a petrochemical complex with SO2 pollution. Environment International Journal, 96, 1-7. - Deligiorgi, D., & Philippopoulos, K. (2018). Spatial interpolation methodologies in urban air pollution modeling. Journal of Advanced Air Pollution, 341, 62-73. - El Baroudy, A. A. (2016). Mapping and evaluating land suitability using a GIS-based model. Catena, 140, 96-140. - Fallah Sourki M., Kavian A., & Omidvar E. (2016). Prioritizitzation of Haraz sub-watersheds in order to soil and water conservation practices based on morphometric and land use characteristics. Journal of Science and Technology of Agriculture and Natural Resources, 20(77), 85-99. - Fan, F., Weng, Q., & Wang, Y. (2007). Land use land cover change in Guangzhou, China, from 1998 to 2003, based on Landsat TM/ETM+ imagery. Sensors, 7, 1323-1342. - Guo, L., Yuan, P., Song, Y., Peng, J., & Wang, L. (2011). Case study and environmental risk assessment of the petrochemical industry. In 2011 International Conference on Remote Sensing, Environment and Transportation Engineering (pp. 5783-5786). IEEE. - Halim, N. D. A., Latif, M. T., Mohamed, A. F., Maulud, K. N. A., Idrus, S., Azhari, A., ... & Sofwan, N. M. (2020). Spatial assessment of land use impact on air quality in mega urban regions, Malaysia. Journal of Sustainable Cities and Society, 63, 102436. - Han, L., Zhao, J., Gao, Y., Gu, Z., Xin, K., Zhang, J, (2020). Spatial distribution characteristics of PM2.5 and PM10 in Xi’an City predicted by land use regression models. Journal of Sustainable Cities and Society, 61, 1-16. - Ismailnejad, M., Eskandari Sani, M., & Barzaman, S. (2015). Evaluation and zoning of urban air pollution in Tabriz. Journal of Regional Planning, 5(19), 173-186. - Jiang, Z., Cheng, H., Zhang, P., & Kang, T. (2021). Influence of urban morphological parameters on the distribution and diffusion of air pollutants: A case study in China. Journal of Environmental Sciences, 105, 163-172. - Kelishadi, R., Moeini, R., & Poursafa, P. (2014). Independent association between air pollutants and vitamin D deficienty in young children in Isfahan, Iran. Paediatrics and International Child Health, 34(1), 50-55. - Khan, J., Kakosimos, K., Raaschou, O., Brandt, J., Jensen, S. S., & Ellermann, T. (2019). Development and performance evaluation of new Air GIS–a GIS based air pollution and human exposure modelling system. Journal of Atmospheric Environment, 198, 102-121. - Khavarian-Garmsir, A. R., & Rezaei, M. R. (2015). Selection of appropriate locations for industrial areas using GIS-fuzzy methods. A case study of Yazd Township, Iran. Journal of Settlements and Spatial Planning, 6(1), 19-25. - Kongtip, P., Singkaew, P., Yoosook, W., Chantanakul, S., & Sujiratat, D. (2013). Health effects of people living close to a petrochemical industrial estate in Thailand. Journal of the Medical Association of Thailand, 96(5), 64-72. - Kuo, Y. C., Lu, S. T., Tzeng, G. H., Lin, Y. C., & Huang, Y. S. (2013). Using fuzzy integral approach to enhance site selection assessment a case study of the optoelectronics industry. Procedia Computer Science, 17, 306-313. - López-Serrano, P., Corral-Rivas, J., Díaz-Varela, R., Álvarez-González, J., & López-Sánchez, C. (2016). Evaluation of radiometric and atmospheric correction algorithms for aboveground forest biomass estimation using landsat 5 TM data. Journal of Remote Sensing, 8(5), 1-19. - Lue, D., Xu, J., Yue, W., Mao, W., Yang, D., & Wang, J. (2020). Response of PM2.5 pollution to land use in China. Journal of Cleaner Production, 244, 1-25. - Masroor, K., Yousefi, S., Fanaei, F., & Raeesi, M. (2020). Spatial modelling of PM2.5 concentrations in Tehran using Kriging and inverse distance weighting (IDW) methods. Journal of Air Pollution and Health, 5(1), 1-9. - Memarbashi, E., Azadi, H., Barati, A.A., Mohajeri, F., Passel, S. V., & Witlox, F. (2017). Land-use suitability in Northeast Iran: application of AHP-GIS hybrid model. ISPRS International Journal of Geo-Information, 6(12), 1-15. - Metia, S., Ha, P., Duc, H. N., & Scorgie, Y. (2020). Urban air pollution estimation using unscented Kalman filtered inverse modeling with scaled monitoring data. Journal of Sustainable Cities and Society, 54, 97-101. - Montero, J. M., & Fernández, G. (2018). Functional kriging prediction of atmospheric particulate matter concentrations in Madrid, Spain: Is the new monitoring system masking potential public health problems? Journal of Cleaner Production, 175, 283-293. - Nadal, M., Cadiach Ricoma, O., Kumar, V., Poblet, P., Mari, M., Schuhmacher, M. & Domingo, J. (2011). Health Risk Map of a Petrochemical Complex through GIS-Fuzzy Integration of Air Pollution Monitoring Data. Human and Ecological Risk Assessment: An International Journal, 17, 873-891. - Noorpoor, A., & Feiz, S. (2014). Determination of the Spatial and Temporal Variation of SO2, NO2 and Particulate Matter Using GIS Techniques and Estimation of Concentration Modeling with LUR Method. Journal of Environmental Studies, 40(3), 723-738. - Qiao, P., Lei, M., Yang, S., Yang, J., Guo, G., & Zhou, X. (2018). Comparing ordinary kriging and inverse distance weighting for soil as pollution in Beijing. Journal of Environmental Science and Pollution Research, 25(16), 597-608. - Sarwar, M. T., & Maqbool, A. (2019). Causes and control measures of urban air pollution in China. Environment and Ecosystem Science (EES), 3(1), 35-36. - Seifi, M., Yunesian, M., & Nadafee, K. (2021). Exposure to ambient air pollution and socio-economic status on intelligence quotient among schoolchildren in a developing country. Journal of Environmental Science and Pollution Research, 29(1), 1-9. - Sharifi Sadeh, M., & Ahmadi Nadoushan, M. (2018). Application of a land use regression (LUR) model to the spatial modelling of air pollutants in Esfahan city. Journal of Environmental Sciences, 16(2), 203-216. - Shi, X., Li, M., Hunter, O., Guetti, B., Andrew A., & Stommel, E. (2019). Estimation of environmental exposure: interpolation, kernel density estimation or snapshotting. Annals of GIS, 25(1), 1-8. - Tehrani, N. A., Mollalo, A., Farhani, F., & Pahlevanzade, N. (2021). Time-Series Analysis of COVID-19 in Iran: A Remote Sensing Perspective. Journal of Geospatial Information and Community Resilience, 21, 277-290. - Vahdat Mohammadi, A., & Rahimi, S. (2013). Impact of urban land use pattern on Tehran air quality. Journal of Research and Urban Planning, 4(14), 123-142. - Vahdat, A., & Alimohammadi, A. (2020). Study of Hourly Variability of Association between Land Use Parameters and CO Pollutants Using LUR Model in Tehran. Iranian Journal of Remote Sensing and GIS, 12(1), 1-18. - Xu, H., Bechle, M. J., Wang, M., Szpiro, A. A., Vedal, S., & Bai, Y. (2019). National PM2.5 and NO2 exposure models for China based on land use regression, satellite measurements, and universal kriging. Journal of Science of the Total Environment, 655, 423-433. - Yang, C. Y., Wang, J. D., Chan, C. C., Hwang, J. S., & Chen, P. C. (2021). Respiratory symptoms of primary school children living in a petrochemical polluted area in Taiwan. Pediatr Pulmonol 25, 299-303. - Yu, H., Russell, A., Mulholland, J., Odman, T., Hu, Y. & Chang, H. (2018). Cross-comparison and evaluation of air pollution field estimation methods. Journal of Atmospheric Environment, 179, 49-60

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University of Isfahan Geography and Environmental Planning 2008-5362 33 4 2022 12 22 Analysis and Comparison of the Conventional Methods of Georeferencing of Aerial Photos Analysis and Comparison of the Conventional Methods of Georeferencing of Aerial Photos 97 112 26834 10.22108/gep.2022.133652.1524 FA Mohsen Bagheri Bodaghabadi Assistant Professor, Soil and Water Research Institute, Agricultural Research, Education and Extension Organization, Karaj, Iran Fatemeh Ebrahimi Meymand PhD Candidate, Soil and Water Research Institute, Agricultural Research, Education and Extension Organization, Karaj, Iran Journal Article 2022 05 21 Abstract Aerial photos are the basis of many researches, both applied and executive works, related to the earth. Applications of aerial photos, especially for GIS-based analysis, aerial photo georeferencing, and/or geometric correction are essential. Various methods have been proposed for georeferencing. The aim of this study was to achieve a suitable simple method with a low cost and acceptable accuracy. For this purpose, conventional georeferencing methods, including first- to third-order polynominal transformtions, spline transformtion, projective transformtion, and orthorectiﬁcation transformtion were used in ILWIS3.3 and ArcGIS10.7 for two areas (flat area of Shahrekord and rugged area of Chaleshtor). Errors of the different methods were analyzed based on minimum and maximum errors, Mean of Error (ME), standard division, and Root Mean Square Error (RMSE). The results showed that the errors of the varied methods had fewer changes for the flat compared to the rugged area. Yet, with the increasing relief of land surface, the errors increased sharply and showed a significant difference. Based on the results, the most accurate georeferencing method was orthorectiﬁcation method (ME=35 and RMSE=38). However, considering the time and data required for the orthorectiﬁcation method, the most suitable georeferencing method was identified as the spline method, which had acceptable accuracy, but not the problems of the orthorectiﬁcation method, such as marginal information of the aerial photos and digital elevation models. Introduction: The potential of aerial photographs as a tool in the research of land resources has long been recognized. It has been the basis of many applied types of research, including soil survey studies. Despite the informative potential of aerial photographs, so far, their usage has been scarce due to the processing difficulty related to the lack of some key information. Fortunately, a recently introduced technology, such as Geographic Information System (GIS), has shown to be able to overcome the classic photogrammetric limitations. Generally, , these photos must be converted from analog to digital forms to be used as a basis for mapping of soil resources. Thus, they have to georeferenced and then imported into GIS. However, the accuracy of this process is very important due to the nature of aerial photographs and distortion caused by moving the phenomena to their actual positions. Therefore, the research design achieved an acceptable method of georeferencing with high accuracy and low cost, besides not being time-consuming. Materials and Methods: For this purpose, two study areas were selected from the roughness point of view (a flat area and a hilly area). Then, 6 transformation methods were applied for georeferencing of aerial photographs, including first-, second-, and third-order polynomial transformations, projective transformation, spline transformation, and orthorectiﬁcation. Afterwards, 15 ground control points and 15 specific points on the aerial photographs were selected for measuring the error values. They included the points that were clearly and accurately recognizable on both the aerial photographs and Google Earth images, such as roads, buildings, waterways, peaks, etc. The coordinates of all the points were obtained based on the Universal Transverse Mercator (UTM) coordinate system using Google Earth images. The measurement error for each of these points was obtained based on the Euclidean distance between each point on the georeferenced aerial photographs and the Google Earth images. Finally, Descriptive statistics, such as minimum and maximum errors, Mean of Error (ME), standard deviation, and Root Mean Square Error (RMSE) were utilized to compare the errors of the 6 used georeferencing methods and introduce the best one. Georeferencing was done by using ILWIS 3.3 and ArcGIS10.7 and the calculations were performed via Excel. Results and Discussion: The results revealed that the errors of the different methods had fewer changes for the flat compared to the rugged area, but with the increaing relief of land surface, the errors increased sharply and showed a significant difference. Based on the results, orthorectification was the most accurate method of transforming the aerial photographs into the ground coordinate system (MR=35 and RMSE=38). However, this method required many data and was a time-consuming and costly method. Therefore, the most suitable approach to transforming the aerial photographs into the ground coordinate system, especially when there was not enough information and time for orthorectification, could be the spline method, which had acceptable accuracy for both flat and uneven areas. Keywords: geometric correction, distortion, displacement, Geographical Information System (GIS) References: - Bannari A., Karl S., Catherine Ch. and Shahid Kh. (2015). Spatial Variability Mapping of Crop Residue Using Hyperion (EO-1) Hyperspectral Data. Remote Sensing, 7, 8107-8127. - Casson, B., Delacourt, C., Baratoux, D., & Allemand, P. (2003). Seventeen years of the “La Clapiere” landslide evolution analysed from ortho-rectiﬁed aerial photographs. Engineering Geology, 68, 123–139. - Chen, J. J., Huang, C. L., Wu, Y. G., & Wu, P. H. (2021). Influence Factors of 3D Modeling with Aerial Images. International Journal on Computer, Consumer and Control, 10 (2), 1-11. - Chintan . D ., Rahul . J., Srivastava. S., (2015). A Survey on geometric correction of satellite imagery. International journal of computer applications, 116 (12). - Hackeloeer, A., Klasing, K., Krisp, J. M., & Meng, L. (2014). Georeferencing: a review of methods and applications. Annals of GIS, 20(1), 61-69. - Karsli, F., & Dihkan, M. (2010). Determination of geometric deformations in image registration using geometric and radiometric measurements. Scientific Research and Essays 5(3), 260-274. - Lee, H.L., Beng, Y.L., Yin, C.W., & Wai, S.C. (2013). Aerial Images Rectification Using Non-parametric Approach. Journal of Convergence 4(2), 15-21. - Liu D, Zhou G, Huang J, Zhang R, Shu L, Zhou X, Xin CS. (2019). On-Board Georeferencing Using FPGA-Based Optimized Second-Order Polynomial Equation. Remote Sensing, 11(2):124. https://doi.org/10.3390/rs11020124 - McNish, I. G., & Smith, K. P. (2022). Oat crown rust disease severity estimated at many time points using multispectral aerial photos. Phytopathology, 112(3), 682-690. - Pha S.H. and Takeuchi W. (2017). Effect of GCPs in distribution and location on geometric correction of corona satellite image. Coordinate Magzine, XIII(07), 14-19. - Powers, P. S., Chiarle, M., & Savage, W. Z. (1996). A digital photogrammetric method for measuring horizontal surﬁcial movements on the Slumgullion earthﬂow, Hinsdale County, Colorado. Computers and Geosciences, 22, 651–663. - Richards, J. A., & Jia, X. (1999). Remote sensing digital image analysis. An introduction. Berlin: Springer. - Riquelme A., Soldato M. D., Tomás R., Cano M., Bordehore L. J. and Moretti S. 2019. Digital landform reconstruction using old and recent open access digital aerial photos. Geomorphology, 329, 206-223. - Rocchini D., A. Di Rita, 2005, Relief effects on aerial photos geometric correction. Applied Geography, 25, 159–168. - Rossiter, D. G., and Tomislav Hengl. (2004). Technical note: Creating geometrically-correct photo-interpretations, photomosaics, and base maps for a project GIS. Enschede, NL: ITC, Department of Earth System Analysis. - Russ, J. C. (2002). The image processing handbook (3rd ed). Boca Raton, FL: CRC Press. - Sameen M. I., Pradhan B., Aziz O. S. (2018). "Classification of Very High-Resolution Aerial Photos Using Spectral-Spatial Convolutional Neural Networks", Journal of Sensors, 2018, 1-12. - Sempio, J. N. H., Aranas, R. K. D., Lim, B. P., Magallon, B. J., Tupas, M. E. A., and Ventura, I. A. (2019). Assessment of different image transformation methods on diwata-1 smi images using structural similarity measure, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences., XLII-4/W19, 393–400, https://doi.org/10.5194/isprs-archives-XLII-4-W19-393-2019. - Tao, C.V.; Hu, Y. (2001). Use of the Rational Function Model for Image Rectification. Canadian Journal of Remote Sensing, 27(6), 593–602. doi:10.1080/07038992.2001.10854900. - Toutin T. (2004). Review article: Geometric processing of remote sensing images: models, algorithms and methods, International Journal of Remote Sensing, 25(10), 1893-1924 - Wang M., J. Hu, M. zhou, J.M. Li, Z. Zhang. (2013). Geometric Correction Of Airborne Linear Array Image Based On Bias Matrix. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Hannover Workshop, 21 – 24 May, Hannover, Germany. Figures and Tables - Fig. 1: Study areas and locations of the aerial photos of Shahrekord and Chaleshtar - Fig. 2: A 3-dimensional (south to north) view of the aerial photos of the relief areas Shahrekord (top) and Chaleshtar (bottom) - Fig. 3: The original aerial photograph of Shahrekord Area with ground control points (triangles) and error measurement points (circles) - Fig. 4: The original aerial photograph of Chaleshtar Area with ground control points (triangles) and error measurement points (circles) - Table 1: Error calculation by affine (first-order) transformation method for the aerial photos of Shahrekord - Table 2: Measured error statistics for the aerial photos of Shahrekord - Table 3: Measured error statistics for the aerial photos of Chaleshtor - Fig. 5: The effects of the different georeferencing methods on the aerial photos of Shahrekord (Transformations: 1) affine (first order); 2) second order; 3) third order, 4) projective; 5) spline; and 6) orthorectiﬁcation with first order) Abstract Aerial photos are the basis of many researches, both applied and executive works, related to the earth. Applications of aerial photos, especially for GIS-based analysis, aerial photo georeferencing, and/or geometric correction are essential. Various methods have been proposed for georeferencing. The aim of this study was to achieve a suitable simple method with a low cost and acceptable accuracy. For this purpose, conventional georeferencing methods, including first- to third-order polynominal transformtions, spline transformtion, projective transformtion, and orthorectiﬁcation transformtion were used in ILWIS3.3 and ArcGIS10.7 for two areas (flat area of Shahrekord and rugged area of Chaleshtor). Errors of the different methods were analyzed based on minimum and maximum errors, Mean of Error (ME), standard division, and Root Mean Square Error (RMSE). The results showed that the errors of the varied methods had fewer changes for the flat compared to the rugged area. Yet, with the increasing relief of land surface, the errors increased sharply and showed a significant difference. Based on the results, the most accurate georeferencing method was orthorectiﬁcation method (ME=35 and RMSE=38). However, considering the time and data required for the orthorectiﬁcation method, the most suitable georeferencing method was identified as the spline method, which had acceptable accuracy, but not the problems of the orthorectiﬁcation method, such as marginal information of the aerial photos and digital elevation models. Introduction: The potential of aerial photographs as a tool in the research of land resources has long been recognized. It has been the basis of many applied types of research, including soil survey studies. Despite the informative potential of aerial photographs, so far, their usage has been scarce due to the processing difficulty related to the lack of some key information. Fortunately, a recently introduced technology, such as Geographic Information System (GIS), has shown to be able to overcome the classic photogrammetric limitations. Generally, , these photos must be converted from analog to digital forms to be used as a basis for mapping of soil resources. Thus, they have to georeferenced and then imported into GIS. However, the accuracy of this process is very important due to the nature of aerial photographs and distortion caused by moving the phenomena to their actual positions. Therefore, the research design achieved an acceptable method of georeferencing with high accuracy and low cost, besides not being time-consuming. Materials and Methods: For this purpose, two study areas were selected from the roughness point of view (a flat area and a hilly area). Then, 6 transformation methods were applied for georeferencing of aerial photographs, including first-, second-, and third-order polynomial transformations, projective transformation, spline transformation, and orthorectiﬁcation. Afterwards, 15 ground control points and 15 specific points on the aerial photographs were selected for measuring the error values. They included the points that were clearly and accurately recognizable on both the aerial photographs and Google Earth images, such as roads, buildings, waterways, peaks, etc. The coordinates of all the points were obtained based on the Universal Transverse Mercator (UTM) coordinate system using Google Earth images. The measurement error for each of these points was obtained based on the Euclidean distance between each point on the georeferenced aerial photographs and the Google Earth images. Finally, Descriptive statistics, such as minimum and maximum errors, Mean of Error (ME), standard deviation, and Root Mean Square Error (RMSE) were utilized to compare the errors of the 6 used georeferencing methods and introduce the best one. Georeferencing was done by using ILWIS 3.3 and ArcGIS10.7 and the calculations were performed via Excel. Results and Discussion: The results revealed that the errors of the different methods had fewer changes for the flat compared to the rugged area, but with the increaing relief of land surface, the errors increased sharply and showed a significant difference. Based on the results, orthorectification was the most accurate method of transforming the aerial photographs into the ground coordinate system (MR=35 and RMSE=38). However, this method required many data and was a time-consuming and costly method. Therefore, the most suitable approach to transforming the aerial photographs into the ground coordinate system, especially when there was not enough information and time for orthorectification, could be the spline method, which had acceptable accuracy for both flat and uneven areas. Keywords: geometric correction, distortion, displacement, Geographical Information System (GIS) References: - Bannari A., Karl S., Catherine Ch. and Shahid Kh. (2015). Spatial Variability Mapping of Crop Residue Using Hyperion (EO-1) Hyperspectral Data. Remote Sensing, 7, 8107-8127. - Casson, B., Delacourt, C., Baratoux, D., & Allemand, P. (2003). Seventeen years of the “La Clapiere” landslide evolution analysed from ortho-rectiﬁed aerial photographs. Engineering Geology, 68, 123–139. - Chen, J. J., Huang, C. L., Wu, Y. G., & Wu, P. H. (2021). Influence Factors of 3D Modeling with Aerial Images. International Journal on Computer, Consumer and Control, 10 (2), 1-11. - Chintan . D ., Rahul . J., Srivastava. S., (2015). A Survey on geometric correction of satellite imagery. International journal of computer applications, 116 (12). - Hackeloeer, A., Klasing, K., Krisp, J. M., & Meng, L. (2014). Georeferencing: a review of methods and applications. Annals of GIS, 20(1), 61-69. - Karsli, F., & Dihkan, M. (2010). Determination of geometric deformations in image registration using geometric and radiometric measurements. Scientific Research and Essays 5(3), 260-274. - Lee, H.L., Beng, Y.L., Yin, C.W., & Wai, S.C. (2013). Aerial Images Rectification Using Non-parametric Approach. Journal of Convergence 4(2), 15-21. - Liu D, Zhou G, Huang J, Zhang R, Shu L, Zhou X, Xin CS. (2019). On-Board Georeferencing Using FPGA-Based Optimized Second-Order Polynomial Equation. Remote Sensing, 11(2):124. https://doi.org/10.3390/rs11020124 - McNish, I. G., & Smith, K. P. (2022). Oat crown rust disease severity estimated at many time points using multispectral aerial photos. Phytopathology, 112(3), 682-690. - Pha S.H. and Takeuchi W. (2017). Effect of GCPs in distribution and location on geometric correction of corona satellite image. Coordinate Magzine, XIII(07), 14-19. - Powers, P. S., Chiarle, M., & Savage, W. Z. (1996). A digital photogrammetric method for measuring horizontal surﬁcial movements on the Slumgullion earthﬂow, Hinsdale County, Colorado. Computers and Geosciences, 22, 651–663. - Richards, J. A., & Jia, X. (1999). Remote sensing digital image analysis. An introduction. Berlin: Springer. - Riquelme A., Soldato M. D., Tomás R., Cano M., Bordehore L. J. and Moretti S. 2019. Digital landform reconstruction using old and recent open access digital aerial photos. Geomorphology, 329, 206-223. - Rocchini D., A. Di Rita, 2005, Relief effects on aerial photos geometric correction. Applied Geography, 25, 159–168. - Rossiter, D. G., and Tomislav Hengl. (2004). Technical note: Creating geometrically-correct photo-interpretations, photomosaics, and base maps for a project GIS. Enschede, NL: ITC, Department of Earth System Analysis. - Russ, J. C. (2002). The image processing handbook (3rd ed). Boca Raton, FL: CRC Press. - Sameen M. I., Pradhan B., Aziz O. S. (2018). "Classification of Very High-Resolution Aerial Photos Using Spectral-Spatial Convolutional Neural Networks", Journal of Sensors, 2018, 1-12. - Sempio, J. N. H., Aranas, R. K. D., Lim, B. P., Magallon, B. J., Tupas, M. E. A., and Ventura, I. A. (2019). Assessment of different image transformation methods on diwata-1 smi images using structural similarity measure, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences., XLII-4/W19, 393–400, https://doi.org/10.5194/isprs-archives-XLII-4-W19-393-2019. - Tao, C.V.; Hu, Y. (2001). Use of the Rational Function Model for Image Rectification. Canadian Journal of Remote Sensing, 27(6), 593–602. doi:10.1080/07038992.2001.10854900. - Toutin T. (2004). Review article: Geometric processing of remote sensing images: models, algorithms and methods, International Journal of Remote Sensing, 25(10), 1893-1924 - Wang M., J. Hu, M. zhou, J.M. Li, Z. Zhang. (2013). Geometric Correction Of Airborne Linear Array Image Based On Bias Matrix. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Hannover Workshop, 21 – 24 May, Hannover, Germany. Figures and Tables - Fig. 1: Study areas and locations of the aerial photos of Shahrekord and Chaleshtar - Fig. 2: A 3-dimensional (south to north) view of the aerial photos of the relief areas Shahrekord (top) and Chaleshtar (bottom) - Fig. 3: The original aerial photograph of Shahrekord Area with ground control points (triangles) and error measurement points (circles) - Fig. 4: The original aerial photograph of Chaleshtar Area with ground control points (triangles) and error measurement points (circles) - Table 1: Error calculation by affine (first-order) transformation method for the aerial photos of Shahrekord - Table 2: Measured error statistics for the aerial photos of Shahrekord - Table 3: Measured error statistics for the aerial photos of Chaleshtor - Fig. 5: The effects of the different georeferencing methods on the aerial photos of Shahrekord (Transformations: 1) affine (first order); 2) second order; 3) third order, 4) projective; 5) spline; and 6) orthorectiﬁcation with first order)

https://gep.ui.ac.ir/article_26834_9e3461483171e7920efa52a5d7a47d56.pdf

University of Isfahan Geography and Environmental Planning 2008-5362 33 4 2022 12 22 Urban Vulnerability Rating against Earthquake Hazard Using ELECTRE FUZZY Model: A Case Study of Kerman City Urban Vulnerability Rating against Earthquake Hazard Using ELECTRE FUZZY Model: A Case Study of Kerman City 113 134 27003 10.22108/gep.2022.132688.1491 FA Narjes Salari Ph.D. student of Geomorphology, University of Isfahan, Isfahan, Iran Mojgan Entezari Associate Professor, Department of Natural geography, University of Isfahan, Isfahan,Iran , Mostafa Khabazi Assocait professor of geomorphology, shahid bahonar university, kerman. kerman.Iran Journal Article 2022 02 15 Due to the location of Kerman city on the border of mountains and plains, there are several faults in its vicinity, some of which can cause very destructive earthquakes. The city has a very old central core in terms of architecture and is mainly made of brick and mud buildings that have a high level of vulnerability. Based on this, detailed and scientific studies of seismic vulnerability are among the necessities of Kerman urban management. Therefore, this city was chosen as the study area in this study. The information research and analysis methods were database-based methods and ELECTRE FUZZY model. In this study, quality of buildings, household density in a residential unit, width of passages, distance from the faults, distance from medical centers, access to the fire-fighting station, access to green space, access to temporary accommodation centers, and land use were examined. The results indicated that the 4th and 5th urban areas of Kerman City were the most vulnerable areas against earthquakes. The vulnerability zoning of earthquake risk in this city revealed that the areas of high vulnerability were 28 and 23% in Zones 4 and 5, respectively. More than 50% of the area with high vulnerability rating was located in these areas. Also, Urban Areas 1, 3, and 2 were in the next ranks of high vulnerability zone, respectively. Introduction Due to the location of Iran on one of the two seismic belts in the world and existence of many faults in this country, occurrence of earthquakes in the Iranian plateau is normal. Due to the location of Kerman City on the border of mountains and plains, there are several faults in its vicinity, some of which can cause very destructive earthquakes. The city has a very old central core in terms of architecture and is mainly made of brick and mud buildings that have a high level of vulnerability. Considering the importance of the issue of assessing vulnerability of cities to earthquakes, as well as the issues of geography and urban planning, GIS method and spatial and descriptive data of the main components and behavioral elements were utilized to make an appropriate estimate of the risk of earthquake and determine the effect of each vulnerability factor in the mentioned city. Methodology A descriptive-analytical research was done through data analysis method according to database-based methods and by using GIS-based software. The physical factors of urban planning in this study were as follows: 1) quality of building, 2) household density in a residential unit, 3) width of passage, 4) distance from fault, 5) distance from medical center, 6) access to the fire-fighting station, 7) access to green space, 8) access to temporary accommodation center, and 9) land use. Fuzzy model was used for weighting the factors and the weighting factors were placed in a function to identify vulnerability of the city fabric using GIS. Discussion Fuzzyization could be done directly by using algorithms or logic expressions. The method of number Fuzzyization used in this research included the two methods of direct and expert calculations. In the expert calculation method, after determining the amplitude of changes in each parameter, the domains were classified based on the previous studies and then, they were manually calculated according to reality and weighted according to the experts’ opinions. After matching the areas specified in the detailed plan of the city with the comprehensive urban plan map, the boundaries of urban areas were carefully determined and then, each parameter was given tabular information in Arc Map. After assigning the data to each layer, weighting was performed according to the vulnerability spectrum of each parameter. The classifications were based on the previous studies and research. After obtaining the fuzzy data, the ELECTRE method was used to integrate the fuzzy layers with each other and deduce the final map that showed the effect of each parameter in vulnerability against the different real risks of earthquake. Conclusion By investigating and evaluating the seismic vulnerability of Kerman City, analyzing the information collected based on database-based methods, and using the ELECTRE FUZZY model and the mentioned criteria, the high vulnerability of Kerman Regions 4 and 5 were determined. In general, based on the vulnerability zoning of earthquake risk in Kerman, the areas of high vulnerability were obtained to be 28 and 23% in Zones 4 and 5, respectively. In fact, more than 50% of the area with high vulnerability rating was located in these areas. Also, Urban Areas 1, 3, and 2 were in the next ranks of high vulnerability zone, respectively. The percentages of medium vulnerability areas were 13 and 15% in Regions 4 and 5, respectively. Moreover, the percentages of low vulnerability areas were 59 and 62% in Regions 4 and 5, respectively. Keyword: earthquake, fault, old texture of Kerman, Vulnerability References - Antonioni Gigliola, G., &Cozzani, Valerio. (2007). A Methodology for the Quantitative Risk, Triggered by Seismic Events. Journal of Hazardous Materials, As sessment of Major Accidents. - Bazazanlotfi, S., & Rahimi, M. (2017). A Study on Vulnerability of UrbanNeighborhoods to Earthquake (Case Study: Farahzad Neighborhood, Tehran). Journal of Civil Engineering and Materials Application,1(1):1-7. https://doi: 10.15412/J.JCEMA.12010101. - Estrada, M., Zavala, C.,Lazares, F., & Morales, J. (2012). GIS Tool for Calculating Repair Cost of Buldings Due to Earthquakes Effects (CCRE - CISMID). 9th International Conference on Urban Earthquake Engineering/ 4th Asia Conference on Earthquake Engineering, Tokyo Institute of Technology, Tokyo, Japan, pp 1695- 1698. - Sadrykia, M.,Delavar, M.,&Zare, M. (2017). A GIS-Based Fuzzy Decision Making Model for Seismic Vulnerability Assessment in Areas with Incomplete Data, ISPRS Int. J. Geo-Inf. 2017, 6(4): 119, doi: 10.3390/ijgi6040119. - Saafizadeh, M., &Bagheripour, M. H. (2019). Evaluation of peak ground acceleration for the city of Kerman through seismic hazard analysis. Scientia Iranica A (2019) 26(1), 257-272. - Shamsipour, A. A., &Shekhi, M. (2010). Zoning of Sensitive Area and Environment Vulnerable in West of Fars Province using Fazzy and AHP Classification, Physical Geography Research Quarterly, 73(73):53-68. - Wang, X., & Triantaphyllou, E. (2008). Ranking irregularities when evaluating alternatives by using some ELECTRE methods. Omega, Vol. 36, pp. 45 – 63. Due to the location of Kerman city on the border of mountains and plains, there are several faults in its vicinity, some of which can cause very destructive earthquakes. The city has a very old central core in terms of architecture and is mainly made of brick and mud buildings that have a high level of vulnerability. Based on this, detailed and scientific studies of seismic vulnerability are among the necessities of Kerman urban management. Therefore, this city was chosen as the study area in this study. The information research and analysis methods were database-based methods and ELECTRE FUZZY model. In this study, quality of buildings, household density in a residential unit, width of passages, distance from the faults, distance from medical centers, access to the fire-fighting station, access to green space, access to temporary accommodation centers, and land use were examined. The results indicated that the 4th and 5th urban areas of Kerman City were the most vulnerable areas against earthquakes. The vulnerability zoning of earthquake risk in this city revealed that the areas of high vulnerability were 28 and 23% in Zones 4 and 5, respectively. More than 50% of the area with high vulnerability rating was located in these areas. Also, Urban Areas 1, 3, and 2 were in the next ranks of high vulnerability zone, respectively. Introduction Due to the location of Iran on one of the two seismic belts in the world and existence of many faults in this country, occurrence of earthquakes in the Iranian plateau is normal. Due to the location of Kerman City on the border of mountains and plains, there are several faults in its vicinity, some of which can cause very destructive earthquakes. The city has a very old central core in terms of architecture and is mainly made of brick and mud buildings that have a high level of vulnerability. Considering the importance of the issue of assessing vulnerability of cities to earthquakes, as well as the issues of geography and urban planning, GIS method and spatial and descriptive data of the main components and behavioral elements were utilized to make an appropriate estimate of the risk of earthquake and determine the effect of each vulnerability factor in the mentioned city. Methodology A descriptive-analytical research was done through data analysis method according to database-based methods and by using GIS-based software. The physical factors of urban planning in this study were as follows: 1) quality of building, 2) household density in a residential unit, 3) width of passage, 4) distance from fault, 5) distance from medical center, 6) access to the fire-fighting station, 7) access to green space, 8) access to temporary accommodation center, and 9) land use. Fuzzy model was used for weighting the factors and the weighting factors were placed in a function to identify vulnerability of the city fabric using GIS. Discussion Fuzzyization could be done directly by using algorithms or logic expressions. The method of number Fuzzyization used in this research included the two methods of direct and expert calculations. In the expert calculation method, after determining the amplitude of changes in each parameter, the domains were classified based on the previous studies and then, they were manually calculated according to reality and weighted according to the experts’ opinions. After matching the areas specified in the detailed plan of the city with the comprehensive urban plan map, the boundaries of urban areas were carefully determined and then, each parameter was given tabular information in Arc Map. After assigning the data to each layer, weighting was performed according to the vulnerability spectrum of each parameter. The classifications were based on the previous studies and research. After obtaining the fuzzy data, the ELECTRE method was used to integrate the fuzzy layers with each other and deduce the final map that showed the effect of each parameter in vulnerability against the different real risks of earthquake. Conclusion By investigating and evaluating the seismic vulnerability of Kerman City, analyzing the information collected based on database-based methods, and using the ELECTRE FUZZY model and the mentioned criteria, the high vulnerability of Kerman Regions 4 and 5 were determined. In general, based on the vulnerability zoning of earthquake risk in Kerman, the areas of high vulnerability were obtained to be 28 and 23% in Zones 4 and 5, respectively. In fact, more than 50% of the area with high vulnerability rating was located in these areas. Also, Urban Areas 1, 3, and 2 were in the next ranks of high vulnerability zone, respectively. The percentages of medium vulnerability areas were 13 and 15% in Regions 4 and 5, respectively. Moreover, the percentages of low vulnerability areas were 59 and 62% in Regions 4 and 5, respectively. Keyword: earthquake, fault, old texture of Kerman, Vulnerability References - Antonioni Gigliola, G., &Cozzani, Valerio. (2007). A Methodology for the Quantitative Risk, Triggered by Seismic Events. Journal of Hazardous Materials, As sessment of Major Accidents. - Bazazanlotfi, S., & Rahimi, M. (2017). A Study on Vulnerability of UrbanNeighborhoods to Earthquake (Case Study: Farahzad Neighborhood, Tehran). Journal of Civil Engineering and Materials Application,1(1):1-7. https://doi: 10.15412/J.JCEMA.12010101. - Estrada, M., Zavala, C.,Lazares, F., & Morales, J. (2012). GIS Tool for Calculating Repair Cost of Buldings Due to Earthquakes Effects (CCRE - CISMID). 9th International Conference on Urban Earthquake Engineering/ 4th Asia Conference on Earthquake Engineering, Tokyo Institute of Technology, Tokyo, Japan, pp 1695- 1698. - Sadrykia, M.,Delavar, M.,&Zare, M. (2017). A GIS-Based Fuzzy Decision Making Model for Seismic Vulnerability Assessment in Areas with Incomplete Data, ISPRS Int. J. Geo-Inf. 2017, 6(4): 119, doi: 10.3390/ijgi6040119. - Saafizadeh, M., &Bagheripour, M. H. (2019). Evaluation of peak ground acceleration for the city of Kerman through seismic hazard analysis. Scientia Iranica A (2019) 26(1), 257-272. - Shamsipour, A. A., &Shekhi, M. (2010). Zoning of Sensitive Area and Environment Vulnerable in West of Fars Province using Fazzy and AHP Classification, Physical Geography Research Quarterly, 73(73):53-68. - Wang, X., & Triantaphyllou, E. (2008). Ranking irregularities when evaluating alternatives by using some ELECTRE methods. Omega, Vol. 36, pp. 45 – 63.

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