University of Isfahan Geography and Environmental Planning 2008-5362 34 3 2023 09 23 Relationship between Knowledge and Tourists’ Environmental Behaviors Considering the Intermediate Variable of Environmental Attitudes (Case Study: Darabad, Darakeh, Ahar Fasham, Vardij, and Bam-e Tehran) Relationship between Knowledge and Tourists’ Environmental Behaviors Considering the Intermediate Variable of Environmental Attitudes (Case Study: Darabad, Darakeh, Ahar Fasham, Vardij, and Bam-e Tehran) 1 26 26745 10.22108/gep.2022.130834.1463 FA Mina Nouri - M.Sc., Department of Environment, Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan, Iran Afsaneh Afzali Assistant Professor, Department of Environment, Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan, Iran Sediqheh Kiani Salmi Associate Professor, Department of Geography and Tourism, Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan, Iran Journal Article 2021 10 03 Abstract The most important aspect of nature-oriented tourists’ behaviors is paying attention to environmental sustainability. Much environmental research in the field of tourism has focused on the roles of values, attitudes, beliefs, and norms in shaping environmental behavior. In this study, according to the theory adopted by the majority of psychologists concerning the effect of attitude on behavior, it was used as an intermediate variable for explaining the relationship between knowledge and tourists’ environmental behaviors in the tourist areas of Tehran. This research was applied in terms of purpose and a descriptive survey in terms of data collection method. To conduct it, a researcher-made questionnaire with 75 variables was prepared and adjusted. Its reliability was confirmed by a Cronbach's alpha of 0.939 and its validity was formally confirmed. The statistical population included the tourists referring to 5 mountain tourism areas of Tehran. To estimate the sample size of 302 people from an unknown community, Cochran's formula was used. The study area included 5 mountain tourism areas of Tehran. Structural equation modeling was used to analyze the data. The analytical results of the behavioral models of the nature-oriented tourists showed a positive and significant relationship between attitude and environmental behavior and also between attitude and environmental knowledge with the factor loads of 0.68 and 0.82 at the significant level of less than 0.05, respectively. Also, the tourists’ environmental behaviors were studied based on their educational levels. According to the results, the tourists’ environmental behaviors depended on their levels of education. Thus, it could be stated that the existence of a cognitive analysis system called “tourist environmental knowledge” in combination with an emotional system would lead to the formation of their environmental attitudes, which could act as a basis for improving and optimizing their behaviors in the tourism destinations. Keywords: Environmental Behavior, Place Attachment, Environmental Commitment, Environmental Attitude, Nature-Based Tourists Introduction Environmental education is as a key solution to the community in the world. It makes people sensitive to the events, as well as physical, biological, social, and political changes in the environment. According to previous studies, nature-oriented tourists in the tourism process cause damage to the environment, while the most important goal of tourism is development by preserving natural resources for the next generation. Therefore, the present study examined the tourists’ environmental behaviors and the relationship between them and the independent factors, such as environmental attitude, environmental knowledge, environmental commitment, gender, and education, in order to identify the causes of the tourists’ environmental behaviors in the tourism areas of Tehran, including Darakeh, Darabad, Bam-e Tehran, Ahar Fasham Village, and Vardij Village. Methodology The present study was applied in terms of purpose and descriptive-analytical in nature. Due to the lack of accurate statistics of the tourists and uncertainty of the statistical community, 300 selected tourists aged 10 to 71 years answered the research questionnaire randomly. After having a conversation with the municipalities and Cultural Heritage centers of the regions, as well as Tourism and Handicraft Organization of Tehran Province, no exact statistics on the number of tourists in each region per year were found. Still, it was mentioned that in terms of regional geographical location and tourism history, their highest and lowest numbers were in Darakeh and Vardij regions, respectively. 80, 70, 70, 45, and 35 questionnaires were distributed in Darakeh, Darabad, Bam-e Tehran, Ahar Fasham, and Vardij, respectively. The method of data collection was based on a combination of library and field methods. The data collection tool was a researcher-made questionnaire with a combination of general and special questions. The validity of the research instrument was confirmed by the experts and its reliability was corroborated by using Cronbach's alpha coefficient of 0.939. To analyze the collected data, one-sample t-test, ANOVA analysis of variance, paired t-test, and Tukey post-test were applied in SPSS software. AMOS software was employed for structural equation modeling. Discussion The analytical results obtained from the application of one-sample t-test with the significance level of less than 0.05 and the positive upper and lower edges of the confidence level showed that the tourists had an attachment to the studied tourist sites. The findings of the study of the tourists’ environmental behaviors with respectively unfavorable and somewhat unfavorable situations of 48 and 11% of the studied indicators indicated the ecotourists’ inappropriate behaviors in the environment. Using structural equation modeling, the data analysis revealed that the effects of environmental attitude, environmental commitment, and place attachment on the occurrence of the tourists' environmental behaviors were significant. The factor loads of 0.62, 0.64, and 0.42 with the significance levels of less than 0.05 and appropriateness of the model fit indices confirmed the results. Also, environmental knowledge with the factor load of 0.68 directly and indirectly affected environmental attitude and formation of environmentally friendly behavior with the regression weight of 0.82, respectively. The effect of gender on the occurrence of environmental behavior showed that the effect of this factor was not significant between the men and women. However, the results of the ANOVA test identified a positive and significant relationship between the tourists’ environmental behaviors and their levels of education. Conclusion According to the research results, both environmental attitude and knowledge had positive and significant effects on the tourists’ behaviors. The relationship between environmental attitude and behavior has been discussed in many studies. In fact, by examining most domestic and foreign studies, a positive and significant relationship between environmental attitude and environmental behavior could be observed, which had been rarely denied by other researches. Although many researches in Iran have measured the relationship between environmental knowledge and attitude and their effects on individual’s environmental behavior, no studies have been professionally done on these two important components of tourists in Tehran. Of course, changing attitude alone is not necessary and sufficient, but other variables that affect behavior change should be considered. Researchers have also found that environmental knowledge has a positive and significant effect on environmental attitude. As mentioned in the previous sections of this study, there was a direct relationship between environmental behavior and knowledge as it was observed in the behaviors of the buyers of green goods. However, by studying different researches, we also got this point about environmental knowledge that this variable alone could not lead to a positive intention and behavior in the environment. The fact that environmental knowledge does not lead to environmental behavior by itself may be related to many factors, which can include traditional environmental knowledge in Iranian ethnic groups, traditional environmental education in schools based on knowledge transfer to the students, and lack of seriousness in environmental education. Therefore, environmental education alone cannot change the students' environmental behaviors even in high schools. Environmental knowledge can only motivate environmentalists to change behavior. Of course, the public media have been raising public awareness about the environment for several years and it can be hoped that this small awareness will have indirect effects and cause public concern and attention, thus creating a starting point for environmental action. This study also found that attitude as a mediator could play a positive and significant role between environmental knowledge and environmental behavior. Weber says cognition alone has no incentive to change environmental behavior, but when it is inspired by the emotional system, protective measures are more likely to be taken. Considering previous research, it is possible to understand that environmental attitude plays a role in environmental emotion to some extent. Jane Goodall describes the knowledge-feeling-action sequence as follows: "Only if we understand, we can we care. We will only help if we care. Only if we help, we will be saved." The role of education in improving environmental performance shows that educated people have a high understanding of such issues and problems. Thus, the following suggestions are recommended to improve the tourists’ environmental behaviors: - Making more films, documentaries, serials, and programs about environmental issues - Providing environmental education with the aim of changing environmental attitude and behavior in kindergarten children - Making public programs for the general public with the aim of becoming more familiar with the environment and its role in human life References - Arbuthnott, K. D. (2009). Education for sustainable development beyond attitude change. International Journal of Sustainability in Higher Education, 10, 152-163. - Blok, V., Wesselink, R., Studynka, O., & Kemp, R. (2015). Encouraging sustainability in the workplace: A survey on the pro-environmental behaviour of university employees. Journal of Cleaner Production, 106, 55-67. - Blomquist, G. C., & Whitehead, J. C. (1998). Resource quality information and validity of willingness to pay in contingent valuation. Journal of Resource and Energy Economics, 20(2), 179-196. - Brosdahl, D. J., & Carpenter, J. M. (2010). Consumer knowledge of the environmental impacts of textile and apparel production, concern for the environment, and environmentally friendly consumption behavior. Journal of Textile and Apparel, Technology and Management, 6(4), 1-9. - Budak, D. B., Budak, F., Zaimoglu, Z., Kekec, S., & Sucu, M. Y. (2005). Behaviour and attitudes of students towards environmental issues at faculty of agriculture, Turkey. Journal of Applied Sciences, 5(7), 1224-1227. - Chang, L. C. (2010). The effects of moral emotions and justiﬁcations on visitors’ intention to pick ﬂowers in a forest recreation area in Taiwan. Journal of Sustainable Tourism, 18(1), 137-150. - Denys, P., & Holmes, M. (1998). Animal magnetism: At home with celebrities and their animal companions. Smithmark Publishers. - Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. New York: Harcourt Brace Jovanovich College Publishers. - Erten, S. (2003). Okul Öncesi Öğretmen Adaylarında Çevre Dostu Davranışların Araştırılması. Hacettepe Üniversitesi Eğitim Fakültesi Dergisi, 28(28), 91-100. - Fernandez-Manzanal, R., Serra, L. M., Morales, M. J., Carrasquer, J., Rodríguez-Barreiro, L. M., del Valle, J., & Murillo, M. B. (2015). Environmental behaviours in initial professional development and their relationship with university education. Journal of Cleaner Production, 108, 830-840. - Frick, J., Kaiser, F. G., & Wilson, M. (2004). Environmental knowledge and conservation behavior: Exploring prevalence and structure in a representative sample. Journal of Personality and Individual Differences, 37(8), 1597-1613. - Gao, J., Huang, Z., & Zhang, C. (2017). Tourists' perceptions of responsibility: An application of norm-activation theory. Journal of Sustainable Tourism, 25(2), 276-291. - Goldman, D., Yavetz, B., & Pe'er, S. (2006). Environmental literacy in teacher training in Israel: Environmental behavior of new students. Journal of Environmental Education, 38(1), 3-22. - Gossling, S. (2002). Global environmental consequences of tourism. Global Environmental Change, 12(4), 283-302. - Gössling, S., & Schumacher, K. P. (2011). Implementing carbon neutral destination policies: Issues from the Seychelles. Journal of Sustainable Tourism, 18(3), 377-391. - Hess-Quimbita, G., & Pavel, M. (1996). Assessing an environmental attitude development model: Factors influencing the environmental attitudes of college students. New York. - Hines, J. M., Hungerford, H. R., & Tomera, A. N. (2013). Analysis and Synthesis of research on responsible environmental behavior: A meta-analysis. Journal of Environmental Education, 18(2), 1-8. - Hopper, J. R. (1991). Recycling as altruistic behavior: Normative and behavioral strategies to expand participation in a community recycling program. Journal of Environment and Behavior, 23(2), 195-220. - Huang, X., & Ge, J. (2019). Electric vehicle development in Beijing: An analysis of consumer purchase intention. Journal of Cleaner Production, 216, 361-372. - Hungerford, H. R., & Volk, T. L. (1990). Changing learner behavior through environmental education. Journal of Environmental Education, 21(3), 257-270. - Ilgar, R. (2007). Çevre eğitiminde yaygın eğitimin rolü ve önemi. Ondokuz Mayıs Üniversitesi Eğitim Fakültesi Dergisi, 23, 40-48. - Juvan, E., & Dolnicar, S. (2017). Drivers of pro-environmental tourist behaviours are not universal. Journal of Cleaner Production, 166, 879-890. - Kafyri, A., Hovardas, T., & Poirazidis, K. (2012). determinants of visitor pro-environmental intentions on two small Greek Islands: Is Ecotourism possible at coastal protected areas?. Journal of Environmental Management, 50, 64-76. - Keniger, L. E., Gaston, K. J., Irvine, K. N., & Fuller, R. A. (2013). What are the benefits of interacting with nature?. International Journal of Environmental Research and Public Health, 10(3), 913-935. - Kiatkawsin, K., & Han, H. (2017). Young travelers' intention to behave pro-environmentally: Merging the value-belief-norm theory and the expectancy theory. Journal of Tourism Management, 59, 76-88. - Kollmuss, A., & Agyeman, J. (2002). Mind the gap: Why do people act environmentally and what are the barriers to pro-environmental behavior?. Journal of Environmental Education Research, 8(3), 239-260. - Laroche, M. (2001). Targeting consumers who are willing to pay more for environmentally friendly products. Journal of Consumer Marketing, 18, 203-520. - Laroche, M., Tomiuk, M. A., Bergeron, J., & Barbaro-Forleo, G. (2009). Cultural differences in environmental knowledge, attitudes, and behaviours of canadian consumers. Administrative Sciences / Revue Canadienne des Sciences de l Administration, 19(3), 267-282. - Latif, S. A., Bidin, Y. H., & Awang, Z. (2013). Towards the realization of green cities: The moderating role of the residents’ education level. Journal of Social and Behavioral Sciences, 85, 646-652. - Lee, T. H. (2011). How recreation involvement, place attachment and conservation commitment affect environmentally responsible behavior. Journal of Sustainable Tourism, 19(7), 895-915. - Lee, T. H., Jan, F. H., & Yang, C. C. (2013). Conceptualizing and measuring environmentally responsible behaviors from the perspective of community-based tourists. Journal of Tourism Management, 36, 454-468. - Levine, D. S., & Strube, M. J. (2012). Environmental attitudes, knowledge, intentions and behaviors among college students behaviors among college students. Journal of Social Psychology, 152(3), 308-326. - Li, D., Zhao, L., Ma, S., Shao, S., & Zhang, L. (2019). What inﬂuences an individual’s pro-environmental behavior? A literature review. Journal of Resources, Conservation & Recycling, 146, 28-34. - Lu, J. L., & Wang, C. Y. (2018). Investigating the impacts of air travellers’ environmental knowledge on attitudes toward carbon oﬀsetting and willingness to mitigate the environmental impacts of aviation. Transportation Research Part D: Transport and Environment, 59, 96-107. - Luo, W. B., Zhang, X. H., Zhong, C., Meng, B., & Timothy, D. J. (2017). Research on the influencing factors of tourists’ environmentally responsible behavior in urban natural scenic spots. Journal of China Population, Resources and Environment, 27(5), 161-169. - Luo, W., Tang, P., Jiang, L., & Su, M. M. (2020). Influencing mechanism of tourist social responsibility awareness on environmentally responsible behavior. Journal of Cleaner Production, 271, 1-27. - Naim, U., & Sağlam, N. (2006). Orta öğretim öğrencileri için çevresel tutum ölçeği geliştirme ve geçerliliği. Hacettepe Üniversitesi Eğitim Fakültesi Dergisi, 30(30), 240-250. - Organisation for Economic Co-operation and Development (OECD) (2020). Chapter 3. Rethinking tourism success for sustainable growth. Retrieved from https://www.oecd-ilibrary. org/docserver/82b46508-en.pdf?expires =1622722023&id= id&accname= guest&checksum= 80ECA3E68F177FBD387886D6447AB443. - Oskamp, S., Harrington, M. J., Edwards, T. C., Sherwood, D. L., Okuda, S. M., & Swanson, D. C. (1991). Factors influencing household recycling behavior. Journal of Envirnment and Behavior, 23(4), 494-519. - Paco, A., & Lavrador, T. (2017). Environmental knowledge and attitudes and behaviours towards energy consumption. Journal of Environmental Management, 197, 384-392. - Rauwald, K. S., & Moore, C. F. (2002). Environmental Attitudes as predictors of policy support across three countries. Journal of Environment and Behavior, 34(6), 709-739. - Robbins, S. (1997). Essentials of organizational behavior. United Kingdom: Prentice Hall. - Roberts, D. W. (1996). Landscape vegetation modelling with vital attributes and fuzzy systems theory. Journal of Ecological Modelling, 90(2), 175-184. - Sarabia-Sanchez, F. J., & Rodriguez-Sanchez, C. (2016). The role of credibility and negative feelings in comparative perceptual bias related to environmental hazards. Journal of Environmental Psychology, 47, 1-13. - Shobeiri, S. M., Omidvar, B., & Prahallada, N. N. (2007). A comperative study of environmental awareness among secondary school students in Iran and India. Journal of Environmental Research, 1, 28-34. - Steg, L., & Vlek, C. (2009). Encouraging pro-environmental behaviour: An integrative review and research agenda. Journal of Environmental Psychology, 29(3), 309-317. - Stern, C. P. (2000). Toward a coherent theory of environmentally significant behavior. Journal of Social Issues, 56(3), 407-425. - Su, L., Huang, S. S., & Pearce, J. (2018). How does destination social responsibility contribute to environmentally responsible behaviour? A destination resident perspective. Journal of Business Research, 86, 179-189. - Su, L., & Swanson, S. R. (2017). The effect of destination social responsibility on tourist environmentally responsible behavior: Compared analysis of ﬁrst time and repeat tourists. Journal of Tourism Management, 60, 308-321. - United Nations Office for Disaster Risk Reduction (UNISDR) (2007). The international strategy for disaster reduction: “Terminology”. (n.p). - Varela-Candamio, L., Novo-Corti, I., & García-Álvarez, M. T. (2018). The importance of environmental education in the determinants of green behavior: A meta-analysis approach. Journal of Cleaner Production, 170, 1565-1578. - Vicente-Molina, M. A., Fernández-Sáinz, A., & Izagirre-Olaizola, J. (2013). Environmental knowledge and other variables affecting pro-environmental behaviour: comparison of university students from emerging and advanced countries. Journal of Cleaner Production, 61, 130-138. - Vicente-Molina, M. A., Fernández-Sainz, A., & Izagirre-Olaizola, J. (2018). Does gender make a difference in pro-environmental behavior? The case of the Basque Country University students. Journal of Cleaner Production, 176, 89-98. - Weber, E. U. (2006). Experience-Based and description-based perceptions of long-term risk: Why global warming does not scare us (yet). Journal of Climatic Change, 77(1-2), 103-120. - Wiek, A., Withycombe, L., & Redman, C. L. (2011). Key competencies in sustainability: A reference framework for academic program development. Journal of Sustainability Science, 6, 203-218. - Williams, K. J., & Cary, J. (2002). Landscape preferences, ecological quality and biodiversity protection. Journal of Environment and Behavior, 34(2), 257-274. - Xiao, C., Dunlap, R. E., & Hong, D. (2013). The nature and bases of environmental concern among Chinese citizens. Social Science Quarterly, 94(3), 672-690. - Zhang, H., Zhang, Y., Song, Z. & Lew, A. (2019). Assessment bias of environmental quality (AEQ), consideration of future consequences (CFC), and environmentally responsible behavior (ERB) in tourism. Journal of Sustainable Tourism, 27(5), 609-628. - Zhang, L., Fukuda, H., & Liu, Z. (2019). Public willingness to pay for sand and dust weather mitigation: A case study in Beijing, China. Journal of Cleaner production, 217, 639-645. Abstract The most important aspect of nature-oriented tourists’ behaviors is paying attention to environmental sustainability. Much environmental research in the field of tourism has focused on the roles of values, attitudes, beliefs, and norms in shaping environmental behavior. In this study, according to the theory adopted by the majority of psychologists concerning the effect of attitude on behavior, it was used as an intermediate variable for explaining the relationship between knowledge and tourists’ environmental behaviors in the tourist areas of Tehran. This research was applied in terms of purpose and a descriptive survey in terms of data collection method. To conduct it, a researcher-made questionnaire with 75 variables was prepared and adjusted. Its reliability was confirmed by a Cronbach's alpha of 0.939 and its validity was formally confirmed. The statistical population included the tourists referring to 5 mountain tourism areas of Tehran. To estimate the sample size of 302 people from an unknown community, Cochran's formula was used. The study area included 5 mountain tourism areas of Tehran. Structural equation modeling was used to analyze the data. The analytical results of the behavioral models of the nature-oriented tourists showed a positive and significant relationship between attitude and environmental behavior and also between attitude and environmental knowledge with the factor loads of 0.68 and 0.82 at the significant level of less than 0.05, respectively. Also, the tourists’ environmental behaviors were studied based on their educational levels. According to the results, the tourists’ environmental behaviors depended on their levels of education. Thus, it could be stated that the existence of a cognitive analysis system called “tourist environmental knowledge” in combination with an emotional system would lead to the formation of their environmental attitudes, which could act as a basis for improving and optimizing their behaviors in the tourism destinations. Keywords: Environmental Behavior, Place Attachment, Environmental Commitment, Environmental Attitude, Nature-Based Tourists Introduction Environmental education is as a key solution to the community in the world. It makes people sensitive to the events, as well as physical, biological, social, and political changes in the environment. According to previous studies, nature-oriented tourists in the tourism process cause damage to the environment, while the most important goal of tourism is development by preserving natural resources for the next generation. Therefore, the present study examined the tourists’ environmental behaviors and the relationship between them and the independent factors, such as environmental attitude, environmental knowledge, environmental commitment, gender, and education, in order to identify the causes of the tourists’ environmental behaviors in the tourism areas of Tehran, including Darakeh, Darabad, Bam-e Tehran, Ahar Fasham Village, and Vardij Village. Methodology The present study was applied in terms of purpose and descriptive-analytical in nature. Due to the lack of accurate statistics of the tourists and uncertainty of the statistical community, 300 selected tourists aged 10 to 71 years answered the research questionnaire randomly. After having a conversation with the municipalities and Cultural Heritage centers of the regions, as well as Tourism and Handicraft Organization of Tehran Province, no exact statistics on the number of tourists in each region per year were found. Still, it was mentioned that in terms of regional geographical location and tourism history, their highest and lowest numbers were in Darakeh and Vardij regions, respectively. 80, 70, 70, 45, and 35 questionnaires were distributed in Darakeh, Darabad, Bam-e Tehran, Ahar Fasham, and Vardij, respectively. The method of data collection was based on a combination of library and field methods. The data collection tool was a researcher-made questionnaire with a combination of general and special questions. The validity of the research instrument was confirmed by the experts and its reliability was corroborated by using Cronbach's alpha coefficient of 0.939. To analyze the collected data, one-sample t-test, ANOVA analysis of variance, paired t-test, and Tukey post-test were applied in SPSS software. AMOS software was employed for structural equation modeling. Discussion The analytical results obtained from the application of one-sample t-test with the significance level of less than 0.05 and the positive upper and lower edges of the confidence level showed that the tourists had an attachment to the studied tourist sites. The findings of the study of the tourists’ environmental behaviors with respectively unfavorable and somewhat unfavorable situations of 48 and 11% of the studied indicators indicated the ecotourists’ inappropriate behaviors in the environment. Using structural equation modeling, the data analysis revealed that the effects of environmental attitude, environmental commitment, and place attachment on the occurrence of the tourists' environmental behaviors were significant. The factor loads of 0.62, 0.64, and 0.42 with the significance levels of less than 0.05 and appropriateness of the model fit indices confirmed the results. Also, environmental knowledge with the factor load of 0.68 directly and indirectly affected environmental attitude and formation of environmentally friendly behavior with the regression weight of 0.82, respectively. The effect of gender on the occurrence of environmental behavior showed that the effect of this factor was not significant between the men and women. However, the results of the ANOVA test identified a positive and significant relationship between the tourists’ environmental behaviors and their levels of education. Conclusion According to the research results, both environmental attitude and knowledge had positive and significant effects on the tourists’ behaviors. The relationship between environmental attitude and behavior has been discussed in many studies. In fact, by examining most domestic and foreign studies, a positive and significant relationship between environmental attitude and environmental behavior could be observed, which had been rarely denied by other researches. Although many researches in Iran have measured the relationship between environmental knowledge and attitude and their effects on individual’s environmental behavior, no studies have been professionally done on these two important components of tourists in Tehran. Of course, changing attitude alone is not necessary and sufficient, but other variables that affect behavior change should be considered. Researchers have also found that environmental knowledge has a positive and significant effect on environmental attitude. As mentioned in the previous sections of this study, there was a direct relationship between environmental behavior and knowledge as it was observed in the behaviors of the buyers of green goods. However, by studying different researches, we also got this point about environmental knowledge that this variable alone could not lead to a positive intention and behavior in the environment. The fact that environmental knowledge does not lead to environmental behavior by itself may be related to many factors, which can include traditional environmental knowledge in Iranian ethnic groups, traditional environmental education in schools based on knowledge transfer to the students, and lack of seriousness in environmental education. Therefore, environmental education alone cannot change the students' environmental behaviors even in high schools. Environmental knowledge can only motivate environmentalists to change behavior. Of course, the public media have been raising public awareness about the environment for several years and it can be hoped that this small awareness will have indirect effects and cause public concern and attention, thus creating a starting point for environmental action. This study also found that attitude as a mediator could play a positive and significant role between environmental knowledge and environmental behavior. Weber says cognition alone has no incentive to change environmental behavior, but when it is inspired by the emotional system, protective measures are more likely to be taken. Considering previous research, it is possible to understand that environmental attitude plays a role in environmental emotion to some extent. Jane Goodall describes the knowledge-feeling-action sequence as follows: "Only if we understand, we can we care. We will only help if we care. Only if we help, we will be saved." The role of education in improving environmental performance shows that educated people have a high understanding of such issues and problems. Thus, the following suggestions are recommended to improve the tourists’ environmental behaviors: - Making more films, documentaries, serials, and programs about environmental issues - Providing environmental education with the aim of changing environmental attitude and behavior in kindergarten children - Making public programs for the general public with the aim of becoming more familiar with the environment and its role in human life References - Arbuthnott, K. D. (2009). Education for sustainable development beyond attitude change. International Journal of Sustainability in Higher Education, 10, 152-163. - Blok, V., Wesselink, R., Studynka, O., & Kemp, R. (2015). Encouraging sustainability in the workplace: A survey on the pro-environmental behaviour of university employees. Journal of Cleaner Production, 106, 55-67. - Blomquist, G. C., & Whitehead, J. C. (1998). Resource quality information and validity of willingness to pay in contingent valuation. Journal of Resource and Energy Economics, 20(2), 179-196. - Brosdahl, D. J., & Carpenter, J. M. (2010). Consumer knowledge of the environmental impacts of textile and apparel production, concern for the environment, and environmentally friendly consumption behavior. Journal of Textile and Apparel, Technology and Management, 6(4), 1-9. - Budak, D. B., Budak, F., Zaimoglu, Z., Kekec, S., & Sucu, M. Y. (2005). Behaviour and attitudes of students towards environmental issues at faculty of agriculture, Turkey. Journal of Applied Sciences, 5(7), 1224-1227. - Chang, L. C. (2010). The effects of moral emotions and justiﬁcations on visitors’ intention to pick ﬂowers in a forest recreation area in Taiwan. Journal of Sustainable Tourism, 18(1), 137-150. - Denys, P., & Holmes, M. (1998). Animal magnetism: At home with celebrities and their animal companions. Smithmark Publishers. - Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. New York: Harcourt Brace Jovanovich College Publishers. - Erten, S. (2003). Okul Öncesi Öğretmen Adaylarında Çevre Dostu Davranışların Araştırılması. Hacettepe Üniversitesi Eğitim Fakültesi Dergisi, 28(28), 91-100. - Fernandez-Manzanal, R., Serra, L. M., Morales, M. J., Carrasquer, J., Rodríguez-Barreiro, L. M., del Valle, J., & Murillo, M. B. (2015). Environmental behaviours in initial professional development and their relationship with university education. Journal of Cleaner Production, 108, 830-840. - Frick, J., Kaiser, F. G., & Wilson, M. (2004). Environmental knowledge and conservation behavior: Exploring prevalence and structure in a representative sample. Journal of Personality and Individual Differences, 37(8), 1597-1613. - Gao, J., Huang, Z., & Zhang, C. (2017). Tourists' perceptions of responsibility: An application of norm-activation theory. Journal of Sustainable Tourism, 25(2), 276-291. - Goldman, D., Yavetz, B., & Pe'er, S. (2006). Environmental literacy in teacher training in Israel: Environmental behavior of new students. Journal of Environmental Education, 38(1), 3-22. - Gossling, S. (2002). Global environmental consequences of tourism. Global Environmental Change, 12(4), 283-302. - Gössling, S., & Schumacher, K. P. (2011). Implementing carbon neutral destination policies: Issues from the Seychelles. Journal of Sustainable Tourism, 18(3), 377-391. - Hess-Quimbita, G., & Pavel, M. (1996). Assessing an environmental attitude development model: Factors influencing the environmental attitudes of college students. New York. - Hines, J. M., Hungerford, H. R., & Tomera, A. N. (2013). Analysis and Synthesis of research on responsible environmental behavior: A meta-analysis. Journal of Environmental Education, 18(2), 1-8. - Hopper, J. R. (1991). Recycling as altruistic behavior: Normative and behavioral strategies to expand participation in a community recycling program. Journal of Environment and Behavior, 23(2), 195-220. - Huang, X., & Ge, J. (2019). Electric vehicle development in Beijing: An analysis of consumer purchase intention. Journal of Cleaner Production, 216, 361-372. - Hungerford, H. R., & Volk, T. L. (1990). Changing learner behavior through environmental education. Journal of Environmental Education, 21(3), 257-270. - Ilgar, R. (2007). Çevre eğitiminde yaygın eğitimin rolü ve önemi. Ondokuz Mayıs Üniversitesi Eğitim Fakültesi Dergisi, 23, 40-48. - Juvan, E., & Dolnicar, S. (2017). Drivers of pro-environmental tourist behaviours are not universal. Journal of Cleaner Production, 166, 879-890. - Kafyri, A., Hovardas, T., & Poirazidis, K. (2012). determinants of visitor pro-environmental intentions on two small Greek Islands: Is Ecotourism possible at coastal protected areas?. Journal of Environmental Management, 50, 64-76. - Keniger, L. E., Gaston, K. J., Irvine, K. N., & Fuller, R. A. (2013). What are the benefits of interacting with nature?. International Journal of Environmental Research and Public Health, 10(3), 913-935. - Kiatkawsin, K., & Han, H. (2017). Young travelers' intention to behave pro-environmentally: Merging the value-belief-norm theory and the expectancy theory. Journal of Tourism Management, 59, 76-88. - Kollmuss, A., & Agyeman, J. (2002). Mind the gap: Why do people act environmentally and what are the barriers to pro-environmental behavior?. Journal of Environmental Education Research, 8(3), 239-260. - Laroche, M. (2001). Targeting consumers who are willing to pay more for environmentally friendly products. Journal of Consumer Marketing, 18, 203-520. - Laroche, M., Tomiuk, M. A., Bergeron, J., & Barbaro-Forleo, G. (2009). Cultural differences in environmental knowledge, attitudes, and behaviours of canadian consumers. Administrative Sciences / Revue Canadienne des Sciences de l Administration, 19(3), 267-282. - Latif, S. A., Bidin, Y. H., & Awang, Z. (2013). Towards the realization of green cities: The moderating role of the residents’ education level. Journal of Social and Behavioral Sciences, 85, 646-652. - Lee, T. H. (2011). How recreation involvement, place attachment and conservation commitment affect environmentally responsible behavior. Journal of Sustainable Tourism, 19(7), 895-915. - Lee, T. H., Jan, F. H., & Yang, C. C. (2013). Conceptualizing and measuring environmentally responsible behaviors from the perspective of community-based tourists. Journal of Tourism Management, 36, 454-468. - Levine, D. S., & Strube, M. J. (2012). Environmental attitudes, knowledge, intentions and behaviors among college students behaviors among college students. Journal of Social Psychology, 152(3), 308-326. - Li, D., Zhao, L., Ma, S., Shao, S., & Zhang, L. (2019). What inﬂuences an individual’s pro-environmental behavior? A literature review. Journal of Resources, Conservation & Recycling, 146, 28-34. - Lu, J. L., & Wang, C. Y. (2018). Investigating the impacts of air travellers’ environmental knowledge on attitudes toward carbon oﬀsetting and willingness to mitigate the environmental impacts of aviation. Transportation Research Part D: Transport and Environment, 59, 96-107. - Luo, W. B., Zhang, X. H., Zhong, C., Meng, B., & Timothy, D. J. (2017). Research on the influencing factors of tourists’ environmentally responsible behavior in urban natural scenic spots. Journal of China Population, Resources and Environment, 27(5), 161-169. - Luo, W., Tang, P., Jiang, L., & Su, M. M. (2020). Influencing mechanism of tourist social responsibility awareness on environmentally responsible behavior. Journal of Cleaner Production, 271, 1-27. - Naim, U., & Sağlam, N. (2006). Orta öğretim öğrencileri için çevresel tutum ölçeği geliştirme ve geçerliliği. Hacettepe Üniversitesi Eğitim Fakültesi Dergisi, 30(30), 240-250. - Organisation for Economic Co-operation and Development (OECD) (2020). Chapter 3. Rethinking tourism success for sustainable growth. Retrieved from https://www.oecd-ilibrary. org/docserver/82b46508-en.pdf?expires =1622722023&id= id&accname= guest&checksum= 80ECA3E68F177FBD387886D6447AB443. - Oskamp, S., Harrington, M. J., Edwards, T. C., Sherwood, D. L., Okuda, S. M., & Swanson, D. C. (1991). Factors influencing household recycling behavior. Journal of Envirnment and Behavior, 23(4), 494-519. - Paco, A., & Lavrador, T. (2017). Environmental knowledge and attitudes and behaviours towards energy consumption. Journal of Environmental Management, 197, 384-392. - Rauwald, K. S., & Moore, C. F. (2002). Environmental Attitudes as predictors of policy support across three countries. Journal of Environment and Behavior, 34(6), 709-739. - Robbins, S. (1997). Essentials of organizational behavior. United Kingdom: Prentice Hall. - Roberts, D. W. (1996). Landscape vegetation modelling with vital attributes and fuzzy systems theory. Journal of Ecological Modelling, 90(2), 175-184. - Sarabia-Sanchez, F. J., & Rodriguez-Sanchez, C. (2016). The role of credibility and negative feelings in comparative perceptual bias related to environmental hazards. Journal of Environmental Psychology, 47, 1-13. - Shobeiri, S. M., Omidvar, B., & Prahallada, N. N. (2007). A comperative study of environmental awareness among secondary school students in Iran and India. Journal of Environmental Research, 1, 28-34. - Steg, L., & Vlek, C. (2009). Encouraging pro-environmental behaviour: An integrative review and research agenda. Journal of Environmental Psychology, 29(3), 309-317. - Stern, C. P. (2000). Toward a coherent theory of environmentally significant behavior. Journal of Social Issues, 56(3), 407-425. - Su, L., Huang, S. S., & Pearce, J. (2018). How does destination social responsibility contribute to environmentally responsible behaviour? A destination resident perspective. Journal of Business Research, 86, 179-189. - Su, L., & Swanson, S. R. (2017). The effect of destination social responsibility on tourist environmentally responsible behavior: Compared analysis of ﬁrst time and repeat tourists. Journal of Tourism Management, 60, 308-321. - United Nations Office for Disaster Risk Reduction (UNISDR) (2007). The international strategy for disaster reduction: “Terminology”. (n.p). - Varela-Candamio, L., Novo-Corti, I., & García-Álvarez, M. T. (2018). The importance of environmental education in the determinants of green behavior: A meta-analysis approach. Journal of Cleaner Production, 170, 1565-1578. - Vicente-Molina, M. A., Fernández-Sáinz, A., & Izagirre-Olaizola, J. (2013). Environmental knowledge and other variables affecting pro-environmental behaviour: comparison of university students from emerging and advanced countries. Journal of Cleaner Production, 61, 130-138. - Vicente-Molina, M. A., Fernández-Sainz, A., & Izagirre-Olaizola, J. (2018). Does gender make a difference in pro-environmental behavior? The case of the Basque Country University students. Journal of Cleaner Production, 176, 89-98. - Weber, E. U. (2006). Experience-Based and description-based perceptions of long-term risk: Why global warming does not scare us (yet). Journal of Climatic Change, 77(1-2), 103-120. - Wiek, A., Withycombe, L., & Redman, C. L. (2011). Key competencies in sustainability: A reference framework for academic program development. Journal of Sustainability Science, 6, 203-218. - Williams, K. J., & Cary, J. (2002). Landscape preferences, ecological quality and biodiversity protection. Journal of Environment and Behavior, 34(2), 257-274. - Xiao, C., Dunlap, R. E., & Hong, D. (2013). The nature and bases of environmental concern among Chinese citizens. Social Science Quarterly, 94(3), 672-690. - Zhang, H., Zhang, Y., Song, Z. & Lew, A. (2019). Assessment bias of environmental quality (AEQ), consideration of future consequences (CFC), and environmentally responsible behavior (ERB) in tourism. Journal of Sustainable Tourism, 27(5), 609-628. - Zhang, L., Fukuda, H., & Liu, Z. (2019). Public willingness to pay for sand and dust weather mitigation: A case study in Beijing, China. Journal of Cleaner production, 217, 639-645.

https://gep.ui.ac.ir/article_26745_df8594892f570ead79459728687e7958.pdf

University of Isfahan Geography and Environmental Planning 2008-5362 34 3 2023 09 23 Identification and Structural Analysis of the Key Factors Affecting the Future Status of Sustainable Neighborhood Development (Case Study: Fahadan-e No Neighborhood in Yazd City) Identification and Structural Analysis of the Key Factors Affecting the Future Status of Sustainable Neighborhood Development (Case Study: Fahadan-e No Neighborhood in Yazd City) 27 50 27199 10.22108/gep.2023.133023.1503 FA Homeira Beiki Tafti Master Student of Regional Planning, School of Architecture and Environmental Design, Iran University of Science and Technology, Tehran, Iran Samaneh Jalili Sadrabad Assistant Professor of Regional & Urban Planning, School of Architecture and Environmental Design, Iran University of Science and Technology, Tehran, Iran. Journal Article 2022 03 13 Abstract The rapid growth of urbanization and physical growth of cities in most of the developing countries has led to the formations of cities that have faced a variety of problems, including neighborhood underdevelopment. The common approaches in planning them have not been able to develop a sustainable neighborhood. This issue has had a direct relationship with sustainable developments of the whole cities though attention has been already paid to neighborhood-based planning with the aim of integrating different groups involved or interested in planning in a collaborative manner with a decentralized and bottom-up perspective in some countries of the world. In Iran, despite the historical past information of cities and urbanization, as well as the great history of urban development and importance of urban neighborhoods, especially in historical cities, "neighborhood scale" has had no place in its official urban planning system. The purposes of this research were firstly evaluating neighborhood-based planning in sustainable urban development through explanation of planning components and indicators of the neighborhood development approach and secondly identifying the effective key strategies in the future status of sustainable neighborhood development. Keywords: Neighborhood Development, Sustainable Development, Structural Analysis, Foresight, MicMac Software Introduction As one of the old cities of Iran, Yazd City has witnessed rapid growth of urbanization since the 1940s, while this issue has been accompanied by a decline in the environmental qualities of its neighborhoods so that its old and historical neighborhoods have undergone functional and physical problems and inconsistency, all of which have threatened their residents. Due to the lack of a planning approach at the level of urban neighborhood, it is necessary to pay attention to the neighborhood scale in urban planning. Therefore, the purpose of this study was to identify the key factors affecting the future status of sustainable neighborhood development. Materials & Methods This research was based on an applied method and an analytical and exploratory approach in terms of nature. The research area was Fahadan-e No Neighborhood in Yazd City and the target community was the population living in this neighborhood. For collecting documentary and library information, qualitative data were used with an open questionnaire through interviews and document reviews, while the quantitative data were prepared numerically by weighting the Delphi questionnaires and examining the obtained indicators to find the key strategies for the future development of Fahadan-e No Neighborhood based on the principles of neighborhood-based development, structural analysis, and futuristic approach via MicMac software. Research findings In this study, a matrix with the dimension of 16×16 was set up. The number of replications was considered to be 3 times and the degree of saturation of the matrix was 93.75%, which indicated the great effects of the variables on each other and their dispersion. The distribution of the key variables affecting the future development status of Fahadan-e No Neighborhood in Yazd City based on the development principles of Mabna Neighborhood indicated the stability of the system. Also, 9 factors were selected as the key factors affecting the development of Fahadan-e No Neighborhood in Yazd City based on the principles of neighborhood development. Discussion of Results & Conclusion The research findings showed that the values and principles of the neighborhood development approach could provide the basis for planning for the future of dilapidated and historic neighborhoods, such as Fahadan-e No Neighborhood. The selected factors were found to affect the future status of neighborhood development, such as that of Fahadan-e No Neighborhood. Persian References: - Ahghar, M., & Malek Hosseini, A. (2018). An analysis of neighborhood-based planning indicators in sustainable urban development (Case study: Golpa neighborhood of Hamedan city). Journal of Environmental Studies, 12(47), 107-125. - Amin Naseri, A. (2006). Planning of places (basics, concepts, definitions and methods). Journal of Urbanization Essays, (19-20), 118-125. - Armanshahr Consulting Engineers (2017). Detailed plan of Yazd city. Yazd Municipality. - Ghasemi, V., Esmaeili, R., & Rabiei, K. (2006). Measurment and ranking of social capital in the townships of Isfahan province. Social Welfare Quarterly (Tehran, University of Welfare and Rehabilitation Sciences), 6(23), 220-229. - Habibi, L., & Jafari Mehrabadi, M. (2010). Foresight and the future of metropolises with an emphasis on Tehran metropolis. Quarterly Journal of Zagros Landscape Geographic, 2(6), 97-110. - Hajipour, Kh. (2006). Neighborhood planning is the basis of an efficient approach in creating sustainable urban management. Journal of Fine Arts (University of Tehran), 26, 37-46. - Houdsony, H. (2005). Development of sustainable places, theoretical concepts and historical origins. MA Thesis on Urban Planning, Tarbiat Modares University, Faculty of Arts. Internet base of the Future Studies Center. - Khakpour, B., Mafi, E., & Bavanpouri, A. (2009). The role of social capital in the sustainable development of neighborhoods (case study: Sajjadieh district, Mashhad). Journal of Geography and Regional Development, 8(12), 55-81. - Madanipour, A. (2004). Is it possible to build the management and development of cities on the basis of neighborhoods?. Conference on the Development of Neighborhoods, the Perspective of Sustainable Development of the City of Tehran, Tehran Municipality, 19, 2-18. - Moein, M. (1983). Persian dictionary. Fifth Edition. Tehran: Amirkabir Publication. - Mohammadi, A., & Rafiee, F. (2016). The role of neighborhood-based planning for neighborhood sustainable development (Case study: Hosseinieh neighborhood in Zanjan City). Geographical Urban Planning Research (Tehran University), 4(3), 347-367. - Mousavi, S. A. (2006). Local development planning with emphasis on social capital. MA Thesis in Urban Planning, Tarbiat Modares University, Faculty of Arts. - Naderi Buanlo, M. (2010). Feasibility of realizing neighborhood-centric approach in urban management (Case study: Mashhad city). BA Thesis on Urban and Regional Planning, University of Arts, Ministry of Science, Research and Technology. - Rafi Pourgavgani, S. (2009). Examining and explaining the factors affecting the planning of the base neighborhood (case study: Firouz Salar neighborhood of Gowgan city). MA Thesis in Urban Planning, Faculty of Architecture and Urban Planning, Tarbiat Modares University. - Sharifiani Sani, M. (2002). Social capital: Main concepts and conceptual framework. Social Welfare Quarterly (Tehran University of Welfare and Rehabilitation Sciences), 1(2), 5-18. - Zamani, B., & Shams, S. (2014). The necessity of the community-based approach in regeneration of urban historical fabrics. Journal of the Urban Development and Organization (Haft Shahr), 4(45-46), 72-93. English References: - Barton, H., Grant, M., & Guise, R. (2003). Shaping neighborhoods, London and New York, Spon press rapid urbanization: Unregulated assets and transitional neighborhoods. Habitat International, 34, 135-144. - Chermayeff, S., & Alexander, C. (1974). Community and privacy: Toward a new architecture of humanism. Translated by Manouchehr Mazini. Tehran: Tehran University Press. - Flanagan, W. C. (1993). Contemporary urban society. Cambridge: Cambridge University Press. - Irvin, J., & Martin B. R. (1984). Foresight in science. London: (n.p). - Kline, E. (1997). Sustainable community indicators. In: Roseland, M. (Ed). Eco-city Dimensions: Healthy. Communities, Healthy planet, New society Publishers. - Martin, B. R. (1995). Foresight in science and technology. Technology Analysis & Strategic Management, 2, 139-168. - Shea, C. P. (1994). Employment and sustainable development. Opportunities for Canada. Canada: International Institute for Sustainable Development Winnipeg. - Townsend, C. L. (2005). Building great neighborhood a citizens ’guide for neighborhood planning. Michigan: Michigan State University. - Zokin, S. (1997). The culture of cities. Blackwell. Abstract The rapid growth of urbanization and physical growth of cities in most of the developing countries has led to the formations of cities that have faced a variety of problems, including neighborhood underdevelopment. The common approaches in planning them have not been able to develop a sustainable neighborhood. This issue has had a direct relationship with sustainable developments of the whole cities though attention has been already paid to neighborhood-based planning with the aim of integrating different groups involved or interested in planning in a collaborative manner with a decentralized and bottom-up perspective in some countries of the world. In Iran, despite the historical past information of cities and urbanization, as well as the great history of urban development and importance of urban neighborhoods, especially in historical cities, "neighborhood scale" has had no place in its official urban planning system. The purposes of this research were firstly evaluating neighborhood-based planning in sustainable urban development through explanation of planning components and indicators of the neighborhood development approach and secondly identifying the effective key strategies in the future status of sustainable neighborhood development. Keywords: Neighborhood Development, Sustainable Development, Structural Analysis, Foresight, MicMac Software Introduction As one of the old cities of Iran, Yazd City has witnessed rapid growth of urbanization since the 1940s, while this issue has been accompanied by a decline in the environmental qualities of its neighborhoods so that its old and historical neighborhoods have undergone functional and physical problems and inconsistency, all of which have threatened their residents. Due to the lack of a planning approach at the level of urban neighborhood, it is necessary to pay attention to the neighborhood scale in urban planning. Therefore, the purpose of this study was to identify the key factors affecting the future status of sustainable neighborhood development. Materials & Methods This research was based on an applied method and an analytical and exploratory approach in terms of nature. The research area was Fahadan-e No Neighborhood in Yazd City and the target community was the population living in this neighborhood. For collecting documentary and library information, qualitative data were used with an open questionnaire through interviews and document reviews, while the quantitative data were prepared numerically by weighting the Delphi questionnaires and examining the obtained indicators to find the key strategies for the future development of Fahadan-e No Neighborhood based on the principles of neighborhood-based development, structural analysis, and futuristic approach via MicMac software. Research findings In this study, a matrix with the dimension of 16×16 was set up. The number of replications was considered to be 3 times and the degree of saturation of the matrix was 93.75%, which indicated the great effects of the variables on each other and their dispersion. The distribution of the key variables affecting the future development status of Fahadan-e No Neighborhood in Yazd City based on the development principles of Mabna Neighborhood indicated the stability of the system. Also, 9 factors were selected as the key factors affecting the development of Fahadan-e No Neighborhood in Yazd City based on the principles of neighborhood development. Discussion of Results & Conclusion The research findings showed that the values and principles of the neighborhood development approach could provide the basis for planning for the future of dilapidated and historic neighborhoods, such as Fahadan-e No Neighborhood. The selected factors were found to affect the future status of neighborhood development, such as that of Fahadan-e No Neighborhood. Persian References: - Ahghar, M., & Malek Hosseini, A. (2018). An analysis of neighborhood-based planning indicators in sustainable urban development (Case study: Golpa neighborhood of Hamedan city). Journal of Environmental Studies, 12(47), 107-125. - Amin Naseri, A. (2006). Planning of places (basics, concepts, definitions and methods). Journal of Urbanization Essays, (19-20), 118-125. - Armanshahr Consulting Engineers (2017). Detailed plan of Yazd city. Yazd Municipality. - Ghasemi, V., Esmaeili, R., & Rabiei, K. (2006). Measurment and ranking of social capital in the townships of Isfahan province. Social Welfare Quarterly (Tehran, University of Welfare and Rehabilitation Sciences), 6(23), 220-229. - Habibi, L., & Jafari Mehrabadi, M. (2010). Foresight and the future of metropolises with an emphasis on Tehran metropolis. Quarterly Journal of Zagros Landscape Geographic, 2(6), 97-110. - Hajipour, Kh. (2006). Neighborhood planning is the basis of an efficient approach in creating sustainable urban management. Journal of Fine Arts (University of Tehran), 26, 37-46. - Houdsony, H. (2005). Development of sustainable places, theoretical concepts and historical origins. MA Thesis on Urban Planning, Tarbiat Modares University, Faculty of Arts. Internet base of the Future Studies Center. - Khakpour, B., Mafi, E., & Bavanpouri, A. (2009). The role of social capital in the sustainable development of neighborhoods (case study: Sajjadieh district, Mashhad). Journal of Geography and Regional Development, 8(12), 55-81. - Madanipour, A. (2004). Is it possible to build the management and development of cities on the basis of neighborhoods?. Conference on the Development of Neighborhoods, the Perspective of Sustainable Development of the City of Tehran, Tehran Municipality, 19, 2-18. - Moein, M. (1983). Persian dictionary. Fifth Edition. Tehran: Amirkabir Publication. - Mohammadi, A., & Rafiee, F. (2016). The role of neighborhood-based planning for neighborhood sustainable development (Case study: Hosseinieh neighborhood in Zanjan City). Geographical Urban Planning Research (Tehran University), 4(3), 347-367. - Mousavi, S. A. (2006). Local development planning with emphasis on social capital. MA Thesis in Urban Planning, Tarbiat Modares University, Faculty of Arts. - Naderi Buanlo, M. (2010). Feasibility of realizing neighborhood-centric approach in urban management (Case study: Mashhad city). BA Thesis on Urban and Regional Planning, University of Arts, Ministry of Science, Research and Technology. - Rafi Pourgavgani, S. (2009). Examining and explaining the factors affecting the planning of the base neighborhood (case study: Firouz Salar neighborhood of Gowgan city). MA Thesis in Urban Planning, Faculty of Architecture and Urban Planning, Tarbiat Modares University. - Sharifiani Sani, M. (2002). Social capital: Main concepts and conceptual framework. Social Welfare Quarterly (Tehran University of Welfare and Rehabilitation Sciences), 1(2), 5-18. - Zamani, B., & Shams, S. (2014). The necessity of the community-based approach in regeneration of urban historical fabrics. Journal of the Urban Development and Organization (Haft Shahr), 4(45-46), 72-93. English References: - Barton, H., Grant, M., & Guise, R. (2003). Shaping neighborhoods, London and New York, Spon press rapid urbanization: Unregulated assets and transitional neighborhoods. Habitat International, 34, 135-144. - Chermayeff, S., & Alexander, C. (1974). Community and privacy: Toward a new architecture of humanism. Translated by Manouchehr Mazini. Tehran: Tehran University Press. - Flanagan, W. C. (1993). Contemporary urban society. Cambridge: Cambridge University Press. - Irvin, J., & Martin B. R. (1984). Foresight in science. London: (n.p). - Kline, E. (1997). Sustainable community indicators. In: Roseland, M. (Ed). Eco-city Dimensions: Healthy. Communities, Healthy planet, New society Publishers. - Martin, B. R. (1995). Foresight in science and technology. Technology Analysis & Strategic Management, 2, 139-168. - Shea, C. P. (1994). Employment and sustainable development. Opportunities for Canada. Canada: International Institute for Sustainable Development Winnipeg. - Townsend, C. L. (2005). Building great neighborhood a citizens ’guide for neighborhood planning. Michigan: Michigan State University. - Zokin, S. (1997). The culture of cities. Blackwell.

https://gep.ui.ac.ir/article_27199_a54b267fdbbcfb931b69ced9b2c927a0.pdf

University of Isfahan Geography and Environmental Planning 2008-5362 34 3 2023 09 23 A Survey and Analysis of Urban Governance Indicators in Tehran Metropolis A Survey and Analysis of Urban Governance Indicators in Tehran Metropolis 51 64 26769 10.22108/gep.2022.133499.1523 FA Saeid Khatam Postdoctoral Researcher in Political Geography, Tarbiat Modares University, Tehran, Iran Seyyed Abbas Ahmad Assistant Professor of Political Geography, University of Tehran, Iran Mahnaz Khatam Ph.D., Department of Management, Sirjan Branch, Islamic Azad University, Sirjan, Iran Journal Article 2022 05 01 Abstract: The city is considered a source of development, and the position of urban management plays a very important and decisive role in the process of urban development. One of the most successful models in the field of urban management is the model of urban governance, which is actually a management system in a participatory form, which consists of three institutions. Civil society, the private sector, and the government participate in all relevant decision-making. This research is descriptive in terms of its practical purpose and in terms of data collection. To collect data, according to experts, targeted sampling and snowball methods were used. Based on the findings, the accountability index has the highest impact on other indicators and the accountability index has the highest impact on other indicators. After accountability and responsibility and at a very close distance, the factors of effectiveness and efficiency are important for urban governance indicators. Based on the data of the D-R column, the rule of law index is one of the other antecedents in cause and effect relationships. On the other hand, other factors have an effective outcome, among which the specialization index is at the top. In this way, it can be concluded that accountability and responsibility indicators are closely related and are considered the most fundamental factor in the system of good governance indicators, and making constructive changes in it can lead to more serious and effective changes in improving urban governance. Keywords: Urban Governance, Urban Management, Fuzzy Dimtel, Tehran Metropolis Introduction: Sustainable development is a powerful tool for policymakers, city managers, and relevant executive organizations. It has great impacts on different areas, such as improving environmental, economic, social, and technological conditions of citizens’ lives. However, according to the results of some research and researchers’ observations, it can be acknowledged that sustainable urban development in a metropolis like Tehran still faces various challenges. One of the effective ways to overcome these challenges is paying attention to the concept of urban governance. This trend has led governments towards pursuing decentralization policy and transferring part of their powers and responsibilities to local institutions in order to gain their lost legitimacy. Nevertheless, many of them are still in unfavorable social, legal, economic, and political conditions as a result of poor urbanization. The optimal urban governance system is based on 7 principles: transparency, justice, accountability, efficiency, fairness, participation, and ownership. Correct urban management based on correct methods has been always a serious challenge for urban experts and discussed over time. Each city experiences special issues that are specific to that city and its surroundings. Therefore, creating a single pattern is not an acceptable version. In its social, cultural, economic, physical, and environmental dimensions and components, sustainable urban development faces many issues and challenges that require planning and training for increasing public awareness and active participation in solving its problems. Good urban governance is an important principle in urban management, which has unfortunately faced many obstacles in our country. Methodology: This study was an applied research in terms of purpose and descriptive in terms of data collection method, which was based on the causal method. The aim of this research was to identify the indicators of urban governance in the metropolis of Tehran by using fuzzy dimtel method and doing network analysis of cause-and-effect relationships between those indicators. The statistical population included academic experts and professors. Management and experimentation of Tehran metropolis in the field of urban governance were analyzed through non-probability and snowball sampling methods. To collect the research data, the views of a number of the experts were used. In the present study, the sample size was completed by using the purposive sampling method. To this end, a questionnaire was completed by 30 university academic members, managers, and experimenters in the field of urban governance in Tehran metropolis. Then, based on the identified factors, the experts were provided with a closed-ended questionnaire containing the mentioned factors and asked to give their opinions on the effects of those factors in each line, while taking into account the time horizon and expressing each column based on a range of 5 ineffective to very effective options. Discussion: This discussion is consistent with the research results of Mojtabazadeh Khaneghahi et al. (1400), Di Elvira et al. (2013), Hilayi (2015), Virtodos (2016), and Jerd and Sail (2018). Many experts in the metropolitan area believed that the emergence of challenges like political division meant the existence of multiple administrative and governmental domains, such as municipalities, governorates, settlements, etc., due to the expansion of urbanization process without any coordinating frameworks. It was necessary to adopt a unified view that was appropriate to the socio-political and cultural conditions of the day in the society so as to manage this challenge and meet public expectations and demands in this area. However, in the last decade of the 20th century and beginning of the 3rd millennium AD, the decline in the credibility and legitimacy of governments, as well as increasing complex global competitions and citizens’ demands, showed that the new paradigm of governance was incapable of dealing with the crises. This ship could be controlled and led the way home in the turbulent storms only in the light of the paradigm of good governance. The World Bank and the IMF see good governance in promoting transparency, accountability, efficiency, fairness, participation, and ownership. They focus more on transparency, while emphasizing that governments need to have reliable, comprehensive, timely, and understandable access and provide people with comparable information about activities of governments on a global scale. Elsewhere, they discuss the various aspects of good governance in the forms of reconsidering the rule of law, improving efficiency and accountability of the private sector, and combating corruption. They consider them a necessary condition for the country’s lasting prosperity. Conclusion: Based on the data in Column D in Table 6, it was found that the accountability index had the greatest impact on the other indicators of good urban governance. Numerous studies were found to have confirmed the indicators of good urban governance mentioned in this study. When executive plans are more carefully planned, they can create higher levels of the indicators of good urban governance to achieve a fundamental change and development. Similarly, according to the data in Column R in Table 6, it was found that the responsibility index had the highest impact on the other indicators of good urban governance. On the other hand, since the factor of accountability had the highest D+R value according to the results presented in Table 6, it was inferred that the indicators of accountability and responsibility were closely interrelated, making the most fundamental factor in the system of indicators. They were considered to provide good governance and make constructive changes in it, which could lead to more serious and effective changes in urban governance. Also, according to the data of Column D+R in Table 6, the factor of effectiveness and efficiency, which closely appeared after accountability, was an important factor among the indicators of urban governance. For justifying the importance and special place of effectiveness and efficiency in the network of the relationships between the factors affecting good governance, it is enough to say that some studies have introduced it as one of the 3 pillars of the indicators of good urban governance. Based on the data in Column D-R in Table 6, it turned out that the rule of law index was one of the other preconditions in the system of cause-and-effect relations, i.e., indicators of good urban governance in the metropolis of Tehran. In contrast, the other factors affected the outcome with ‘expertise’ at the top. As shown in the Dimtel diagram in Fig. 1, the indicator of ‘rule of law’ is specifically at the highest point among the antecedents. It has been identified as the most influential factor in the network of relations between the indicators of good urban governance in Tehran. Urban governance can lead to better qualities of urban places and more citizens' trust in urban management institutions. It can also align citizens with urban plans and initiatives and facilitate their participations. The degree of legitimacy of the participatory process plays an important role in the development of urban programs and city management. In Tehran metropolis, this case has not been seriously considered by the city managers and thus, it has not been legitimized. Also, in the ladder of participation, citizens usually appear in the position of partial participation, i.e., counseling and informing, and do not reach its higher levels. Cities can increase the quality of life for citizens, who are in a favorable environment in terms of quality of governance. Improving governance of cities will lead to citizens’ improved situations. References - Addink, H. (2019). Good governance: Concept and context. Oxford: Oxford University Press. - Benites, A. J., & Simoes, A. F. (2021). Assessing the urban sustainable development strategy: An application of a smart city services sustainability taxonomy. Journal of Ecological Indicators, 127, 107734. - Brenner, N. (2003). Metropolitan institutional reform and the rescaling of state space in contemporary Western Europe. Journal of European Urban and Regional Studies, 10(4), 297-324. - Broccardo, L., Culasso, F., & Mauro, S. G. (2019). Smart city governance: Exploring the institutional work of multiple actors towards collaboration. International Journal of Public Sector Management, 32(4), 367-387. - Carlton, B. (2014). A model for municipal institutional capacity analysis, FCM (Federation of Canadian Municipalities). Retrieved from http://www.cardinalgroup.ca/nua/aif/ aif02.Html. - Cento Bull, A., & Jones, B. (2006). Governance and social capital in urban regeneration: A comparison between Bristol and Naples. Journal of Urban Studies, 43(4), 767-786. - Chang, H. J. (2019). An analysis of Ha-Joon Chang’s kinking away the ladder: Development strategy in historical perspective. Macat Library. - De Guimarães, J. C. F., Severo, E. A., Júnior, L. A. F., Da Costa, W. P. L. B., & Salmoria, F. T. (2020). Governance and quality of life in smart cities: Towards sustainable development goals. Journal of Cleaner Production, 253, 1-33. - De Oliveira, J. A. P., Doll, C. N., Balaban, O., Jiang, P., Dreyfus, M., Suwa, A., ... & Dirgahayani, P. (2013). Green economy and governance in cities: Assessing good governance in key urban economic processes. Journal of Cleaner Production, 58, 138-152. - Ene, C., Gheorghiu, A., & Gheorghiu, A. (2011). A theoretical approach for dynamic modelling of ‎sustainable development. Arxiv, 20, 52-57. - Escribano, G., Paredes-Gazquez J., & San-Martin, E. (2020). The European union and the good governance of energy resources: Practicing what it preaches?. Journal of Energy Policy, 147, 111884. - Evans, B., Joas, M., Sundback, S., & Theobald, K. (2020). Governing sustainable cities. Routledge. - Gani, A., & Duncan, R. (2007). Measuring good governance using time series data: Fiji Islands. Journal of the Asia Pacific Economy, 12(3), 367-385. - Gjerde, M., & de Sylva, S. (2018). Governance and recovery: Comparing recent disaster recoveries in Sri Lanka and New Zealand. Procedia Engineering, 212, 527-534. - Healey, P. (2015). Planning theory: The good city and its governance. International Encyclopedia of the Social & Behavioral Sciences, 18, 202-207. - Joseph, J. (2013). Resilience as embedded neoliberalism: A governmentality approach. Resilience, 1(1), 38-52. - Kaufmann, D., Kraay, A., & Mastruzzil, M. (2017). The worldwide governance indicators project: Answering the critics. Washington: World Bank Publications. - Korosteleva, E. A., & Flockhart, T. (2020). Resilience in EU and international institutions: Redefining local ownership in a new global governance agenda. Journal of Contemporary Security Policy, 41(2), 153-175. - Krellenberg, K., Bergsträßer, H., Bykova, D., Kress, N., & Tyndall, K. (2019). Urban sustainability strategies guided by the SDGs- A tale of four cities. Sustainability, 11(4), 1116. - Lyall, C., & Tait, J. (2019). Beyond the limits to governance: New rules of engagement for the tentative governance of the life sciences. Journal of Research Policy, 48(5), 1128-1137. - Majuri, P., Kumpula, A., & Vourisalo, T. (2020). Geoenergy permit practices in Finnish municipalities – Challenges with good governance. Energy Strategy Reviews, 32, 100537. - Mallick, S. K. (2021). Prediction-Adaptation-Resilience (PAR) approach- A new pathway towards future resilience and sustainable development of urban landscape. Geography and Sustainability, 2(2), 127-133. - Markus, G. B., & Krings, A. (2020). Planning, participation, and power in a shrinking city: The Detroit works project. Journal of Urban Affairs, 42(8), 1141-1163. - Morel, M., Balm, S., Berden, M., & van Amstel, W. P. (2020). Governance models for sustainable urban construction logistics: Barriers for collaboration. Transportation Research Procedia, 46, 173-180. - Ni, P., & Kresl, P. K. (2018). The global urban competitiveness report. UK, Northampton: Edward Elgar. - Omri, A., & Mabrouk, N. (2020.( Good governance for sustainable development goals: Getting ahead of the pack or falling behind?. Environmental Impact Assessment Review, 83, 106388. - Patias, N., Rowe, F., Cavazzi, S., & Arribas-Bel, D. (2021). Sustainable urban development indicators in Great Britain from 2001 to 2016. Journal of Landscape and Urban Planning, 214, 104148. - Porter, J. (2018). Sustainability and good governance: Monitoring participation and process as well as outcomes. UTS Centre for Local Government. - Rao, S., & Perry, C. (2020). Convergent interviewing to build a theory in under-researched areas: Principles and an example investigation of Internet usage in inter-firm relationships. Qualitative Market Research: An International Journal, 6(4), 236-247. - Smith, B. C. (2007). Good governance and development. England: Macmillan International Higher Education. - Taşan-Kok, T., Atkinson, R., & Martins, M. L. R. (2020). Hybrid contractual landscapes of governance: Generation of fragmented regimes of public accountability through urban regeneration. Environment and Planning C: Politics and Space, 39(2), 371-392. - Tonne, C., Adair, L., Adlakha, D., Anguelovski, I., Belesova, K., Berger, M., ... & Adli, M. (2021). Defining pathways to healthy sustainable urban development. Environment International, 146, 106236. - Virtudes, A. (2016). ‘Good’governance principles in spatial planning at local scale. Procedia Engineering, 161, 1710-1714. - Wilson, A., Tewdwr-Jones, M., & Comber, R. (2019). Urban planning, public participation and digital technology: App development as a method of generating citizen involvement in local planning processes. Environment and Planning B: Urban Analytics and City Science, 46(2), 286–302. Yang, H. (2021). Holistic governance: An explanatory framework. Journal of Urban Governance in Transition, 57-95. Abstract: The city is considered a source of development, and the position of urban management plays a very important and decisive role in the process of urban development. One of the most successful models in the field of urban management is the model of urban governance, which is actually a management system in a participatory form, which consists of three institutions. Civil society, the private sector, and the government participate in all relevant decision-making. This research is descriptive in terms of its practical purpose and in terms of data collection. To collect data, according to experts, targeted sampling and snowball methods were used. Based on the findings, the accountability index has the highest impact on other indicators and the accountability index has the highest impact on other indicators. After accountability and responsibility and at a very close distance, the factors of effectiveness and efficiency are important for urban governance indicators. Based on the data of the D-R column, the rule of law index is one of the other antecedents in cause and effect relationships. On the other hand, other factors have an effective outcome, among which the specialization index is at the top. In this way, it can be concluded that accountability and responsibility indicators are closely related and are considered the most fundamental factor in the system of good governance indicators, and making constructive changes in it can lead to more serious and effective changes in improving urban governance. Keywords: Urban Governance, Urban Management, Fuzzy Dimtel, Tehran Metropolis Introduction: Sustainable development is a powerful tool for policymakers, city managers, and relevant executive organizations. It has great impacts on different areas, such as improving environmental, economic, social, and technological conditions of citizens’ lives. However, according to the results of some research and researchers’ observations, it can be acknowledged that sustainable urban development in a metropolis like Tehran still faces various challenges. One of the effective ways to overcome these challenges is paying attention to the concept of urban governance. This trend has led governments towards pursuing decentralization policy and transferring part of their powers and responsibilities to local institutions in order to gain their lost legitimacy. Nevertheless, many of them are still in unfavorable social, legal, economic, and political conditions as a result of poor urbanization. The optimal urban governance system is based on 7 principles: transparency, justice, accountability, efficiency, fairness, participation, and ownership. Correct urban management based on correct methods has been always a serious challenge for urban experts and discussed over time. Each city experiences special issues that are specific to that city and its surroundings. Therefore, creating a single pattern is not an acceptable version. In its social, cultural, economic, physical, and environmental dimensions and components, sustainable urban development faces many issues and challenges that require planning and training for increasing public awareness and active participation in solving its problems. Good urban governance is an important principle in urban management, which has unfortunately faced many obstacles in our country. Methodology: This study was an applied research in terms of purpose and descriptive in terms of data collection method, which was based on the causal method. The aim of this research was to identify the indicators of urban governance in the metropolis of Tehran by using fuzzy dimtel method and doing network analysis of cause-and-effect relationships between those indicators. The statistical population included academic experts and professors. Management and experimentation of Tehran metropolis in the field of urban governance were analyzed through non-probability and snowball sampling methods. To collect the research data, the views of a number of the experts were used. In the present study, the sample size was completed by using the purposive sampling method. To this end, a questionnaire was completed by 30 university academic members, managers, and experimenters in the field of urban governance in Tehran metropolis. Then, based on the identified factors, the experts were provided with a closed-ended questionnaire containing the mentioned factors and asked to give their opinions on the effects of those factors in each line, while taking into account the time horizon and expressing each column based on a range of 5 ineffective to very effective options. Discussion: This discussion is consistent with the research results of Mojtabazadeh Khaneghahi et al. (1400), Di Elvira et al. (2013), Hilayi (2015), Virtodos (2016), and Jerd and Sail (2018). Many experts in the metropolitan area believed that the emergence of challenges like political division meant the existence of multiple administrative and governmental domains, such as municipalities, governorates, settlements, etc., due to the expansion of urbanization process without any coordinating frameworks. It was necessary to adopt a unified view that was appropriate to the socio-political and cultural conditions of the day in the society so as to manage this challenge and meet public expectations and demands in this area. However, in the last decade of the 20th century and beginning of the 3rd millennium AD, the decline in the credibility and legitimacy of governments, as well as increasing complex global competitions and citizens’ demands, showed that the new paradigm of governance was incapable of dealing with the crises. This ship could be controlled and led the way home in the turbulent storms only in the light of the paradigm of good governance. The World Bank and the IMF see good governance in promoting transparency, accountability, efficiency, fairness, participation, and ownership. They focus more on transparency, while emphasizing that governments need to have reliable, comprehensive, timely, and understandable access and provide people with comparable information about activities of governments on a global scale. Elsewhere, they discuss the various aspects of good governance in the forms of reconsidering the rule of law, improving efficiency and accountability of the private sector, and combating corruption. They consider them a necessary condition for the country’s lasting prosperity. Conclusion: Based on the data in Column D in Table 6, it was found that the accountability index had the greatest impact on the other indicators of good urban governance. Numerous studies were found to have confirmed the indicators of good urban governance mentioned in this study. When executive plans are more carefully planned, they can create higher levels of the indicators of good urban governance to achieve a fundamental change and development. Similarly, according to the data in Column R in Table 6, it was found that the responsibility index had the highest impact on the other indicators of good urban governance. On the other hand, since the factor of accountability had the highest D+R value according to the results presented in Table 6, it was inferred that the indicators of accountability and responsibility were closely interrelated, making the most fundamental factor in the system of indicators. They were considered to provide good governance and make constructive changes in it, which could lead to more serious and effective changes in urban governance. Also, according to the data of Column D+R in Table 6, the factor of effectiveness and efficiency, which closely appeared after accountability, was an important factor among the indicators of urban governance. For justifying the importance and special place of effectiveness and efficiency in the network of the relationships between the factors affecting good governance, it is enough to say that some studies have introduced it as one of the 3 pillars of the indicators of good urban governance. Based on the data in Column D-R in Table 6, it turned out that the rule of law index was one of the other preconditions in the system of cause-and-effect relations, i.e., indicators of good urban governance in the metropolis of Tehran. In contrast, the other factors affected the outcome with ‘expertise’ at the top. As shown in the Dimtel diagram in Fig. 1, the indicator of ‘rule of law’ is specifically at the highest point among the antecedents. It has been identified as the most influential factor in the network of relations between the indicators of good urban governance in Tehran. Urban governance can lead to better qualities of urban places and more citizens' trust in urban management institutions. It can also align citizens with urban plans and initiatives and facilitate their participations. The degree of legitimacy of the participatory process plays an important role in the development of urban programs and city management. In Tehran metropolis, this case has not been seriously considered by the city managers and thus, it has not been legitimized. Also, in the ladder of participation, citizens usually appear in the position of partial participation, i.e., counseling and informing, and do not reach its higher levels. Cities can increase the quality of life for citizens, who are in a favorable environment in terms of quality of governance. Improving governance of cities will lead to citizens’ improved situations. References - Addink, H. (2019). Good governance: Concept and context. Oxford: Oxford University Press. - Benites, A. J., & Simoes, A. F. (2021). Assessing the urban sustainable development strategy: An application of a smart city services sustainability taxonomy. Journal of Ecological Indicators, 127, 107734. - Brenner, N. (2003). Metropolitan institutional reform and the rescaling of state space in contemporary Western Europe. Journal of European Urban and Regional Studies, 10(4), 297-324. - Broccardo, L., Culasso, F., & Mauro, S. G. (2019). Smart city governance: Exploring the institutional work of multiple actors towards collaboration. International Journal of Public Sector Management, 32(4), 367-387. - Carlton, B. (2014). A model for municipal institutional capacity analysis, FCM (Federation of Canadian Municipalities). Retrieved from http://www.cardinalgroup.ca/nua/aif/ aif02.Html. - Cento Bull, A., & Jones, B. (2006). Governance and social capital in urban regeneration: A comparison between Bristol and Naples. Journal of Urban Studies, 43(4), 767-786. - Chang, H. J. (2019). An analysis of Ha-Joon Chang’s kinking away the ladder: Development strategy in historical perspective. Macat Library. - De Guimarães, J. C. F., Severo, E. A., Júnior, L. A. F., Da Costa, W. P. L. B., & Salmoria, F. T. (2020). Governance and quality of life in smart cities: Towards sustainable development goals. Journal of Cleaner Production, 253, 1-33. - De Oliveira, J. A. P., Doll, C. N., Balaban, O., Jiang, P., Dreyfus, M., Suwa, A., ... & Dirgahayani, P. (2013). Green economy and governance in cities: Assessing good governance in key urban economic processes. Journal of Cleaner Production, 58, 138-152. - Ene, C., Gheorghiu, A., & Gheorghiu, A. (2011). A theoretical approach for dynamic modelling of ‎sustainable development. Arxiv, 20, 52-57. - Escribano, G., Paredes-Gazquez J., & San-Martin, E. (2020). The European union and the good governance of energy resources: Practicing what it preaches?. Journal of Energy Policy, 147, 111884. - Evans, B., Joas, M., Sundback, S., & Theobald, K. (2020). Governing sustainable cities. Routledge. - Gani, A., & Duncan, R. (2007). Measuring good governance using time series data: Fiji Islands. Journal of the Asia Pacific Economy, 12(3), 367-385. - Gjerde, M., & de Sylva, S. (2018). Governance and recovery: Comparing recent disaster recoveries in Sri Lanka and New Zealand. Procedia Engineering, 212, 527-534. - Healey, P. (2015). Planning theory: The good city and its governance. International Encyclopedia of the Social & Behavioral Sciences, 18, 202-207. - Joseph, J. (2013). Resilience as embedded neoliberalism: A governmentality approach. Resilience, 1(1), 38-52. - Kaufmann, D., Kraay, A., & Mastruzzil, M. (2017). The worldwide governance indicators project: Answering the critics. Washington: World Bank Publications. - Korosteleva, E. A., & Flockhart, T. (2020). Resilience in EU and international institutions: Redefining local ownership in a new global governance agenda. Journal of Contemporary Security Policy, 41(2), 153-175. - Krellenberg, K., Bergsträßer, H., Bykova, D., Kress, N., & Tyndall, K. (2019). Urban sustainability strategies guided by the SDGs- A tale of four cities. Sustainability, 11(4), 1116. - Lyall, C., & Tait, J. (2019). Beyond the limits to governance: New rules of engagement for the tentative governance of the life sciences. Journal of Research Policy, 48(5), 1128-1137. - Majuri, P., Kumpula, A., & Vourisalo, T. (2020). Geoenergy permit practices in Finnish municipalities – Challenges with good governance. Energy Strategy Reviews, 32, 100537. - Mallick, S. K. (2021). Prediction-Adaptation-Resilience (PAR) approach- A new pathway towards future resilience and sustainable development of urban landscape. Geography and Sustainability, 2(2), 127-133. - Markus, G. B., & Krings, A. (2020). Planning, participation, and power in a shrinking city: The Detroit works project. Journal of Urban Affairs, 42(8), 1141-1163. - Morel, M., Balm, S., Berden, M., & van Amstel, W. P. (2020). Governance models for sustainable urban construction logistics: Barriers for collaboration. Transportation Research Procedia, 46, 173-180. - Ni, P., & Kresl, P. K. (2018). The global urban competitiveness report. UK, Northampton: Edward Elgar. - Omri, A., & Mabrouk, N. (2020.( Good governance for sustainable development goals: Getting ahead of the pack or falling behind?. Environmental Impact Assessment Review, 83, 106388. - Patias, N., Rowe, F., Cavazzi, S., & Arribas-Bel, D. (2021). Sustainable urban development indicators in Great Britain from 2001 to 2016. Journal of Landscape and Urban Planning, 214, 104148. - Porter, J. (2018). Sustainability and good governance: Monitoring participation and process as well as outcomes. UTS Centre for Local Government. - Rao, S., & Perry, C. (2020). Convergent interviewing to build a theory in under-researched areas: Principles and an example investigation of Internet usage in inter-firm relationships. Qualitative Market Research: An International Journal, 6(4), 236-247. - Smith, B. C. (2007). Good governance and development. England: Macmillan International Higher Education. - Taşan-Kok, T., Atkinson, R., & Martins, M. L. R. (2020). Hybrid contractual landscapes of governance: Generation of fragmented regimes of public accountability through urban regeneration. Environment and Planning C: Politics and Space, 39(2), 371-392. - Tonne, C., Adair, L., Adlakha, D., Anguelovski, I., Belesova, K., Berger, M., ... & Adli, M. (2021). Defining pathways to healthy sustainable urban development. Environment International, 146, 106236. - Virtudes, A. (2016). ‘Good’governance principles in spatial planning at local scale. Procedia Engineering, 161, 1710-1714. - Wilson, A., Tewdwr-Jones, M., & Comber, R. (2019). Urban planning, public participation and digital technology: App development as a method of generating citizen involvement in local planning processes. Environment and Planning B: Urban Analytics and City Science, 46(2), 286–302. Yang, H. (2021). Holistic governance: An explanatory framework. Journal of Urban Governance in Transition, 57-95.

https://gep.ui.ac.ir/article_26769_62c1720088ebb22b7a4a089a21d0f49f.pdf

University of Isfahan Geography and Environmental Planning 2008-5362 34 3 2023 09 23 Explaining the Concept of Urban Green Network from the Perspective of Different Sciences by Developing a Theoretical Framework Explaining the Concept of Urban Green Network from the Perspective of Different Sciences by Developing a Theoretical Framework 65 94 27111 10.22108/gep.2022.133689.1526 FA Mohammad Motaghed Ph.D. Student of Urban Design, Faculty of Art and Architecture, Bu Ali Sina University, Hamadan, Iran. Hassan Sajadzadeh Associate Professor, Faculty of Arts and Architecture, Bu Ali Sina University, Hamadan, Iran. 0000-0002-3989-9389 Mohammad Saeed Izadi Assistant Professor, Faculty of Art and Architecture, Bu Ali Sina University, Hamadan, Iran. Journal Article 2022 05 17 Abstract Networking aims to define the principles and regulatory relationships between elements and optimize the performance of urban subsystems. Urban green networks support ecological functions and services that can help planning for the future of cities. The purpose of this research was to explain a conceptual model for the urban green network from the perspective of science according to the various perceptions of the concept of urban green network based on different scientific points of view by inferring and extracting the most important dimensions, approaches, and attitudes related to this field. By using the qualitative method and analyzing the written documents through the same lexical analysis, this research inferred and evaluated different points of view. The information was analyzed using the LCA analysis tool and SPAD software was used to analyze the relationships among the collected information by examining the effective environmental sciences. Using the VOSviewer software, the most important dimensions, approaches, and attitudes were determined. The findings of this research showed that the concept of the urban green network was directly related to the three environmental sciences of urban studies, urban geography, and ecological sciences. These sciences had a significant impact on the formation of a conceptual model to provide a clear picture of this concept in line with urban green infrastructure planning. Urban studies deal with concepts, such as protecting green spaces in cities, creating new spatial forms, etc., in urban geography, paying attention to the ecological principles in cities, meeting the ecological needs of nature and humans, etc., as the effective concepts. Ecological sciences are also mentioned as the most important research fields regarding the concept of urban green network to promote continuity in urban green spaces, create and design optimal corridors, etc. The results of this study showed that the concept of urban green network was a multi-research field. It appeared as a field supported by the urban planning method by means of decision-making tools, capable of modeling green infrastructure as an urban network formed from natural and semi-natural areas and was in line with the distribution of public services to increase the quality of life, as well as providing ecosystem services and sustainability in cities. Keywords: Urban Green Network, Green Infrastructure, Green Space, Vocabulary Analysis Introduction Currently, approximately 54% of the world's population live in urban areas and this number is projected to reach about 70% by 2030 due to population migration from rural to urban areas (Organization, 2019: 24). Urbanization generally causes many environmental problems that are mostly associated with the destruction or degradation of green spaces (WHO Regional Office for Europe, 2017: 112). Cities with higher population densities have less green space. For this reason, the relationship of city dwellers with their living environments requires their adaptation to semi-urban areas without borders, fences, hedges, and physical or administrative constraints. They leave nature to go to the city and use it as an opportunity and improvement (Narain, 2017: 147). Therefore, increasing population and the need to meet human needs can lead to the expansion of the use of natural resources and transformation of many ecosystems into urban areas (Ersoy et al., 2019: 310), which has increased the demand for land in many countries, especially in developing countries. With the increase in urban construction, the need for land and housing has led to land use change and non-land conversion. Urban areas have become a land for urban construction (Tannier, 2016: 84). Considering these issues, it can be said that urban green network has an important place for protection of biodiversity (Angold et al., 2006: 202; Karuppannan et al., 2014). Methodology To answer the questions, a collection of scientific articles from reputable scientific journals was analyzed by using lexical analysis tools. This approach made it possible to extract the main semantic dimensions in question in order to highlight the current meanings and practical contexts of the green network concept, especially from an urban planning perspective. To use this method, the data collection was performed in two different stages: 1) collecting highly cited scientific articles in the field of urban green infrastructure studies during the last 21 years and 2) selecting items that contained specific references to the concept of green network. The scientific articles were collected by searching in two scientific databases, Scopus and Google Scholar. They were selected and filtered by using the main keyword (urban green space) and collected according to the number of citations. Also, the collected information led to a conclusion for each of them by identifying the main keywords of each article and examining its key points. The data were analyzed using the analytical instrument of Life Cycle Assessment (LCA), which is an analytical tool that is able to identify the hidden meaning of a group of texts. This type of data analysis is basically based on "differences" (De Falco et al., 2021: 198). SPAD software was utilized to analyze the relationships between the collected information, such as factors and components, each of which showed an aspect of the type of communication contained in the information. In this way, the analysis allowed us to examine whether the role of urban green space towards specific issues, such as urban green network, in the scientific community had been evolving. Therefore, we could extract the main concepts that defined the stages of this evolution in the last 20 years. Discussion Extensive urbanization has made cities more complex than ever before. As a result, "new knowledge about cities" has emerged in the scientific debate and more advanced mathematical models, especially network models, have been developed to describe and optimize physical networks and human interactions. From this perspective, the elements of the natural environment in urban patterns can be also designed and managed as networks by integrating the findings of natural and social sciences into a multidisciplinary approach. Therefore, increasing attention to the different roles that green space can play for the sustainable development of urban areas has made the scientific community include the concept of green network in their vocabulary. However, by analyzing the results of the present study, which was done by using lexical analysis tools, it was clear that the concept of green network was still semantically ambiguous and unclear. The reason for this confusion was first of all due to the meanings of two terms in the concept: "green and network". In some areas of research, the term "green" refers to the definition of products, strategies, or processes that evoke the concept of sustainable development based on living conditions and the use of resources to meet human needs. From this perspective, the concept of green network can be applied not only to the system of urban natural areas, but also to other groups of interconnected elements, such as control systems of energy storage devices in electricity networks with a high share of renewable energy sources and consumer networks, which is even considered for environmental marketing strategies. For this reason, lexical analysis was performed taking into account the scientific articles published during the last 21 years in the important and prestigious journals of urban studies by using the main keyword of the concept of "green network". This indicated that the discussion about the function of green space as one of the main elements of urban green infrastructure was still very important and bold, which was characterized by the use of related research areas, such as environmental sciences, urban geography, and urban studies. Thus, the term "green" related to urban space described green spaces not only as public services, but also in a broader sense of ecosystem services, discussing new scientific evidence with regard to the relationship between ecological processes and their relative effects on the man-made environment. This issue had a special place due to the increasing importance of the consequences of climate change. Conclusion After reviewing the present study in relation to the various functions attributed to natural areas in urban contexts, a general definition of this concept could be considered: "A system consisting of similar parts that are connected to each other to allow movement or communication between them and also move along these sections." In fact, from the analysis of the results, it could be pointed out that the term "network" seemed to refer to a general set of natural areas in a city, which referred to the theory of network system that connected these elements, except for the ecological field, which used the same principles applied in larger ecosystems to describe the relationships between natural elements (green) on an urban scale and evaluate the ecological effectiveness of green infrastructure in a particular urban context. In short, it was obvious that the concept of green network still had many challenges as a defined topic in urban planning and system. Therefore, to avoid creating any ambiguity and provide a unique definition of the concept of green network from the perspective of urban planning, it could be stated as an urban planning action by decision support tools to create a green infrastructure as a the network consisted of natural and semi-natural areas whose connections and communications were modeled according to specific variables in order to distribute public services equally and increase the quality of life, as well as a wide range of ecosystem services. From this perspective, the concept of green network could create a multidisciplinary research field that combined natural and social sciences with computer science findings to provide tools for public administration in a way that the consequences of their selections could be understood in terms of access so as to evaluate public services and sustainability in cities. Consideration: This article was taken from the doctorate thesis of Mohammad Motaghed entitled "Evaluation of the role of urban green networks in improving life quality with an emphasis on the interlinking degree of natural and artificial elements (case study: Hamadan City)" under the guidance of Dr. Hassan Sajadzadeh and Dr. Mohammad Saeed Izadi at the School of Art and Architecture, Bu Ali Sina University, Hamadan, Iran. References - Ahern, J. (2007). Green infrastructure for cities: The spatial dimension. In V. Novotny and P. Brown (Eds.), Cities of the future: Towards integrated sustainable water and landscape management. IWA Publishing Center. Retrieved from: http://people.umass.edu/ jfa/pdf/Chapter17_ Ahern2%20copy. - Attwell, K., & Smith, D. T. (2017). Parenting as politics: Social identity theory and vaccine hesitant communities. International Journal of Health Governance, 22(3), 183-198. - Bai, Y., & Guo, R. (2021). The construction of green infrastructure network in the perspectives of ecosystem services and ecological sensitivity: The case of Harbin, China. Journal of Global Ecology and Conservation, 27, e01534. Retrieved from: https://doi.org/10.1016/j.gecco.2021.e01534. - Bastian, O., Haase, D., & Grunewald, K. (2012). Ecosystem properties, potentials, and services−The EPPS conceptual framework and an urban application example. Ecological Indicators, 21, 7-16. - Bennett, G. E., & Mulongoy, K. J. (2006). Review of experience with ecological networks, corridors, and buffer zones. Montreal: Secretariat of the Convention on Biological Diversity, Technical Series, 23, 100. - Brunetta, G., & Voghera, A. (2014). Resilience through Ecological Network. TeMA Journal of Land Use, Mobility, and Environment. - Chen, C., Bi, L., & Zhu, K. (2021). Study on spatial-temporal change of urban green space in Yangtze River economic belt and its driving mechanism. International Journal of Environmental Research and Public Health, 18(23), 12498. - De Falco, C., Punziano, G., & Trezza, B. (2021). A mixed content analysis design in the study of the Italian perception of COVID-19 on Twitter. Athens Journal of Social Sciences, 8(3), 191-210. - De Groot, R. S., Alkemade, R., Braat, L., Hein, L., & Willemen, L. (2010). Challenges in integrating the concept of ecosystem services and values in landscape planning, management, and decision-making. Journal of Ecological Complexity, 7(3), 260-272. - Ding, R., Ujang, N., Hamid, H. B., & Wu, J. (2015). Complex network theory applied to the growth of Kuala Lumpur’s public urban rail transit network. PloS One, 10(10), e0139961. doi: https://doi.org/10.1371/journal.pone.0139961 - Ersoy, E., Jorgensen, A., & Warren, P. H. (2019). Identifying multispecies connectivity corridors and the spatial pattern of the landscape. Journal of Urban Forestry & Urban Greening, 40, 308-322. - EU Commission (2017). Green Infrastructure. Retrieved from: http://ec.europa. eu/environment/ nature/ ecosystems/index_en.html. - Éva, S. (2011). Research of town structure and green space system of Sopron, opportunities of development with a historical approach. Ph.D. Thesis, University of West Hungary. - Fenu, G., & Pau, P. L. (2018). Connectivity analysis of ecological landscape networks by cut node ranking. Journal of Applied Network Science, 3(1), 22. - Fichera, A., Frasca, M., Palermo, V., & Volpe, R. (2016). Application of the complex network theory in urban environments: A case study in Catania. Energy Procedia, 101, 345-351. doi: https://doi.org/10.1016/j.egypro.2016.11.044 - Forman, R. T. T. (2016). Urban ecology principles: Are urban ecology and natural area ecology really different?. Journal of Landscape Ecology, 31(8), 1653-1662. - Frazier, A. E., & Bagchi-Sen, S. (2015). Developing open space networks in shrinking cities. Journal of Applied Geography, 59, 1-9. doi: http://doi.org/10.1016/j.apgeog.2015.02.010 - Gargiulo, C., & Tulisi, A. (2016). The building aspect ratio for an energy efficient green network design. In: G. Colombo, P. Lombardi, G. and Mondini (Eds.), E-agorà|e-aγορά for the Transition toward Resilient Communities, pp. 220-226, Torino: DIST-Politecnico di Torino, ISBN: 978-88-9052-964-1. - Gargiulo, C., & Tulisi, A. (2017). Climate change-oriented urban green network design: A decision support tool. In J. K. Gakis & P. Pardalos (Eds.), Network Design and Optimization for Smart Cities, Series on Computer and Operations Research, Vol. 8, 255-278. World Scientific. doi: https://doi.org/10.1142/9789813200012_0011 - Gargiulo Morelli, V., Weijnen, M., Van Bueren, E., Wenzel, I., De Reuver, M., & Salvati, L. (2013). Towards intelligently–sustainable cities? From intelligent and knowledge city programmes to the achievement of urban sustainability. TeMA Journal of Land Use, Mobility and Environment, 6(1), 73-86. doi: http://dx.doi.org/10.6092/1970- 9870/1496 - Girling, C. (2008). Mitigation, adaptation, uncertainty--univerCity: Building a new green neighborhood. Journal of Places, 20(2). - Grunwald, A. (2018). Diverging pathways to overcoming the environmental crisis: A critique of eco-modernism from a technology assessment perspective. Journal of Cleaner Production, 197, 1854-1862. - Hladnik, D., & Pirnat, J. (2011). Urban forestry—Linking naturalness and amenity: The case of Ljubljana, Slovenia. Journal of Urban Forestry & Urban Greening, 10(2), 105-112. Doi: 10.1016/j.ufug.2011.02.002 - Hofman, M. P., Hayward, M. W., Kelly, M. J., & Balkenhol, N. (2018). Enhancing conservation network design with graph-theory and a measure of protected area effectiveness: Refining wildlife corridors in Belize, Central America. Journal of Landscape and Urban Planning, 178, 51-59. - Hu, C. P., Hu, J. M., Deng, S. L., & Liu, Y. (2013). A co-word analysis of library and information science in China. Scientometrics, 97(2), 369-382. - Hunter, A. J., & Luck, G. W. (2015). Defining and measuring the social-ecological quality of urban green space: A semi-systematic review. Journal of Urban Ecosystems, 18, 1139-1163. - Inostroza, L. (2014). Open spaces and urban ecosystem services: Cooling effect towards urban planning in South American cities. TeMA-Journal of Land Use, Mobility, and Environment, 1. - IPBES (2019). Summary for policymakers of the global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. (n.p). - IPCC (2014). Climate Change: Synthesis Report. Contribution of Working Groups I, II, and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC, Geneva, Switzerland, pp. 151-159. Retrieved from: http://ar5syr.ipcc.ch/ipcc/ ipcc/resources /pdf/IPCC_ SynthesisReport. - Jim, C. Y. (2013). Sustainable urban greening strategies for compact cities in developing and developed economies. Journal of Urban Ecosystems, 16(4), 741-761. Doi: 10.1007/s11252-012-0268-x - Jim, C. Y., & Chen, S. S. (2003). Comprehensive green space planning based on landscape ecology principles in compact Nanjing city, China. Journal of Landscape and Urban Planning, 65(3), 95-116. Doi: 10.1016/S0169-2046(02)00244-X - Jim, C. Y., & Chen, W. Y. (2009). Ecosystem services and valuation of urban forests in China. Cities, 26(4), 187-194. - Jongman R., & Pungetti G. (2004). Ecological network and greenways. Cambridge, England: Cambridge University Press. - Kang, J., Zhang, X., Zhu, X., & Zhang, B. (2021). Ecological security pattern: A new idea for balancing regional development and ecological protection (A case study of the Jiaodong Peninsula, China). Journal of Global Ecology and Conservation, 26, e01472. https://doi.org/10.1016/j. gecco. 2021.e01472. - Kang, S., & Kim, J. O. (2015). Morphological analysis of green infrastructure in the Seoul metropolitan area, South Korea. Journal of Landscape and Ecological Engineering, 11(2), 259-268. Doi: 10.1007/s11355-014-0268-5 - Kong, F., Yin, H., Nakagoshi, N., & Zong, Y. (2010). Urban green space network development for biodiversity conservation: Identification based on graph theory and gravity modeling. Journal of Landscape and Urban Planning, 95(1-2), 16-27. - Kowalewska, A. (2011). Sustainable urban green network concept for the city of Gdynia, Poland. In Gdynia Sustainable Green Network, 47th ISOCARP Congress. - Li, F., Wang, R., Paulussen, J., & Liu, X. (2005). Comprehensive concept planning of urban greening based on ecological principles: A case study in Beijing, China. Journal of Landscape and Urban Planning, 72(4), 325-336. Doi:10.1016/j.landurbplan.2004.04.002 - Mahmoud, A. H. A., & El-Sayed, M. A. (2011). Development of sustainable urban green areas in Egyptian new cities: The case of El-Sadat City. Journal of Landscape and Urban Planning, 101(2), 157-170. Doi: 10.1061/(ASCE)UP.1943-5444.0000076 - Massa, P., & Campagna, M. (2014). Social media geographic information: Recent findings and opportunities for smart spatial planning. TeMA-Journal of Land Use, Mobility and Environment, 1. doi: http://dx.doi.org/10.6092/1970-9870/2500 - Matke, C., Bienstock, D., Muñoz, G., Yang, S., Kleinhans, D., & Sager, S. (2016). Robust optimization of power network operation: Storage devices and the role of forecast errors in renewable energies. In: Complex Networks & Their Applications V: Proceedings of the 5th International Workshop on Complex Networks and Their Applications, Springer International Publishing Center, pp. 809-820. Doi: 10.1007/978-3-319-50901-3_64 - Matthews, T., Lo, A. Y., & Byrne, J. A. (2015). Reconceptualizing green infrastructure for climate change adaptation: Barriers to adoption and drivers for uptake by spatial planners. Journal of Landscape and Urban Planning, 138, 155-163. Doi: 10.1016/j.landurbplan.2015.02.010 - Mell, I. C. (2010). Green infrastructure: Concepts, perceptions, and its use in spatial planning. PhD Thesis, Newcastle University. - Moseley, D., Marzano, M., Chetcuti, J., & Watts, K. (2013). Green networks for people: Application of a functional approach to support the planning and management of green space. Journal of Landscape and Urban Planning, 116, 1-12. Doi: http://doi.org/10.1016/j.landurbplan.2013.04.004. - Mougiakou, E., & Phoutis, Y. (2014). Urban green space network evaluation and planning: optimizing accessibility based on connectivity and raster GIS analysis. European Journal of Geography, 5(4), 19-46. - Nor, A. N. M., Corstanje, R., Harris, J. A., Grafius, D. R., & Siriwardena, G. M. (2017). Ecological connectivity networks in rapidly expanding cities. Heliyon, 3(6), 310-325. - Oh, K., Lee, D., & Park, C. (2011). Urban ecological network planning for sustainable landscape management. Journal of Urban Technology, 18(4), 39-59. Doi: 10.1080/10630732.2011.648433 - Ouin, A., Martin, M., & Burel, F. (2008). Agricultural landscape connectivity for the meadow brown butterfly (Maniola jurtina). Journal of Agriculture, Ecosystems, & Environment, 124(3-4), 193-199. Doi: 10.1016/j.agee.2007.09.010 - Pozoukidou, G. (2020). Designing a green infrastructure network for metropolitan areas: A spatial planning approach. Euro-Mediterranean Journal of Environmental Integration, 5(2), 40. Doi:https://doi.org/10.1007/s41207-020-00178-8 - Register, R. (2006). Ecocities: Rebuilding cities in balance with nature. Gabriola Island: New Society Publishers. - Roca, M. (2017). Green approaches in river engineering: Supporting implementation of Green Infrastructure. United Kingdom: HR Wallingford. - Rouse, D. C., & Bunster-Ossa, I. F. (2013). Green infrastructure: A landscape approach. (n.p). - Sadeghi Banis, M. (2015). Using landscape metrics in improving urban ecological network (case study: Tabriz City). Journal of Gardening News, 32, 53-62. - Salata, K., & Yiannakou, A. (2016). Green Infrastructure and climate change adaptation. TeMA-Journal of Land Use, Mobility and Environment, 9(1), 7-24. Doi: 10.6092/1970-9870/3723 - Sandström, U. G., Angelstam, P., & Mikusiński, G. (2006). Ecological diversity of birds in relation to the structure of urban green space. Landscape and Urban Planning, 77(1-2), 39-53. - Scottish Natural Heritage Information Note (SNH) (2012). Green networks in development planning. (n.p). - Senanayake, I. P., Welivitiya, W. D., & Nadeeka, P. M. (2013). Urban green spaces analysis for development planning in Colombo, Sri Lanka utilizing THEOS satellite imagery–a remote sensing and GIS approach. Journal of Urban Forestry and Urban Greening, 12(3), 307-314. - Şenik, B., & Uzun, O. (2021). A process approach to the open green space system planning. Journal of Landscape and Ecological Engineering, 18(2), 203-219. - Shafer, C. S., Lee, B. K., & Turner, S. H. (2000). A tale of three greenway trails: User perceptions related to quality of life. Journal of Landscape and Urban Planning, 49(3‐4), 163-178. - Sluis, T., & Jongman, R. (2019). Green infrastructure and network coherence. Routledge. - Tannier, C., Bourgeois, M., Houot, H., & Foltête, J. C. (2016). Impact of urban developments on the functional connectivity of forested habitats: A joint contribution of advanced urban models and landscape graphs. Journal of Land Use Policy, 52, 76-91. - Tian, Y., Jim, C. Y., Tao, Y., & Shi, T. (2011). Landscape ecological assessment of green space fragmentation in Hong Kong. Journal of Urban Forestry & Urban Greening, 10(2), 79-86. - Tulisi, A. (2017). Urban Green Network Design: Defining green network from an urban planning perspective. TeMA Journal of Land Use, Mobility and Environment, 10(2), 179-192. - Tzoulas, K., Korpela, K., Venn, S., Yli-Pelkonen, V., Kaźmierczak, A., Niemela, J., & James, P. (2007). Promoting ecosystem and human health in urban areas using green infrastructure: A literature review. Journal of Landscape and Urban Planning, 81(3), 167-178. - Van Eck, N. J., & Waltman, L. (2009). VOS viewer: A computer program for bibliometric mapping. Scientometrics, 84(2), 523-538. - Verdú Vázquez, A., Fernández Pablos, E., López Zaldívar, Ó., & Lozano Diez, R. (2020). Development of a methodology for the characterization of urban and peri-urban green spaces in a context of supra-municipal strategies. Journal of Land Use Policy, 69, 75-84. - Weller, B., & Ganzhorn, J. U. (2004). Carabid beetle community composition, body size, and fluctuating asymmetry along an urban-rural gradient. Journal of Basic and Applied Ecology, 5(2), 193-201. Doi: 10.1078/1439-1791-00220 - WHO Regional Office for Europe (2017). Briefing note on process and results of indicator mapping exercise and proposal for a joint monitoring framework. Copenhagen. - World Health Organization (2019). Global action plan on physical activity (2018-2030): More active people for a healthier world. World Health Organization. - Wu, X. Y., Liu, X. G., Wu, B., & Yuan, X. J. (2018). Regional green infrastructure space pattern identification based on urban air conditioning. Landsc. Archit., 29, 33-37. Doi: https:// doi.org/10.14085/j.fjyl.2018.01.0033.05 - WWF (2020). Living planet report 2020-bending the curve of biodiversity loss. Gland, Switzerland. - Yeang, K., & Threipland, E. (2021). At one with nature: Advances in Ecological architecture in the work of Ken Yeang. Routledge. - Zhang, B., Li, N., & Wang, S. (2015). Effect of urban green space changes on the role of rainwater runoff reduction in Beijing, China. Journal of Landscape and Urban Planning, 140, 8-16. Doi: 10.1016/j.landurbplan.2015.03.014 - Zhang, L., & Wang, H. (2006). Planning an ecological network of Xiamen Island (China) using landscape metrics and network analysis. Journal of Landscape and Urban Planning, 78(4), 449-456. - Zhang, Z., Meerow, S., Newell, J. P., & Lindquist, M. (2019). Enhancing landscape connectivity through multifunctional green infrastructure corridor modeling and design. Journal of Urban Forestry & Urban Greening, 38, 305-317. Abstract Networking aims to define the principles and regulatory relationships between elements and optimize the performance of urban subsystems. Urban green networks support ecological functions and services that can help planning for the future of cities. The purpose of this research was to explain a conceptual model for the urban green network from the perspective of science according to the various perceptions of the concept of urban green network based on different scientific points of view by inferring and extracting the most important dimensions, approaches, and attitudes related to this field. By using the qualitative method and analyzing the written documents through the same lexical analysis, this research inferred and evaluated different points of view. The information was analyzed using the LCA analysis tool and SPAD software was used to analyze the relationships among the collected information by examining the effective environmental sciences. Using the VOSviewer software, the most important dimensions, approaches, and attitudes were determined. The findings of this research showed that the concept of the urban green network was directly related to the three environmental sciences of urban studies, urban geography, and ecological sciences. These sciences had a significant impact on the formation of a conceptual model to provide a clear picture of this concept in line with urban green infrastructure planning. Urban studies deal with concepts, such as protecting green spaces in cities, creating new spatial forms, etc., in urban geography, paying attention to the ecological principles in cities, meeting the ecological needs of nature and humans, etc., as the effective concepts. Ecological sciences are also mentioned as the most important research fields regarding the concept of urban green network to promote continuity in urban green spaces, create and design optimal corridors, etc. The results of this study showed that the concept of urban green network was a multi-research field. It appeared as a field supported by the urban planning method by means of decision-making tools, capable of modeling green infrastructure as an urban network formed from natural and semi-natural areas and was in line with the distribution of public services to increase the quality of life, as well as providing ecosystem services and sustainability in cities. Keywords: Urban Green Network, Green Infrastructure, Green Space, Vocabulary Analysis Introduction Currently, approximately 54% of the world's population live in urban areas and this number is projected to reach about 70% by 2030 due to population migration from rural to urban areas (Organization, 2019: 24). Urbanization generally causes many environmental problems that are mostly associated with the destruction or degradation of green spaces (WHO Regional Office for Europe, 2017: 112). Cities with higher population densities have less green space. For this reason, the relationship of city dwellers with their living environments requires their adaptation to semi-urban areas without borders, fences, hedges, and physical or administrative constraints. They leave nature to go to the city and use it as an opportunity and improvement (Narain, 2017: 147). Therefore, increasing population and the need to meet human needs can lead to the expansion of the use of natural resources and transformation of many ecosystems into urban areas (Ersoy et al., 2019: 310), which has increased the demand for land in many countries, especially in developing countries. With the increase in urban construction, the need for land and housing has led to land use change and non-land conversion. Urban areas have become a land for urban construction (Tannier, 2016: 84). Considering these issues, it can be said that urban green network has an important place for protection of biodiversity (Angold et al., 2006: 202; Karuppannan et al., 2014). Methodology To answer the questions, a collection of scientific articles from reputable scientific journals was analyzed by using lexical analysis tools. This approach made it possible to extract the main semantic dimensions in question in order to highlight the current meanings and practical contexts of the green network concept, especially from an urban planning perspective. To use this method, the data collection was performed in two different stages: 1) collecting highly cited scientific articles in the field of urban green infrastructure studies during the last 21 years and 2) selecting items that contained specific references to the concept of green network. The scientific articles were collected by searching in two scientific databases, Scopus and Google Scholar. They were selected and filtered by using the main keyword (urban green space) and collected according to the number of citations. Also, the collected information led to a conclusion for each of them by identifying the main keywords of each article and examining its key points. The data were analyzed using the analytical instrument of Life Cycle Assessment (LCA), which is an analytical tool that is able to identify the hidden meaning of a group of texts. This type of data analysis is basically based on "differences" (De Falco et al., 2021: 198). SPAD software was utilized to analyze the relationships between the collected information, such as factors and components, each of which showed an aspect of the type of communication contained in the information. In this way, the analysis allowed us to examine whether the role of urban green space towards specific issues, such as urban green network, in the scientific community had been evolving. Therefore, we could extract the main concepts that defined the stages of this evolution in the last 20 years. Discussion Extensive urbanization has made cities more complex than ever before. As a result, "new knowledge about cities" has emerged in the scientific debate and more advanced mathematical models, especially network models, have been developed to describe and optimize physical networks and human interactions. From this perspective, the elements of the natural environment in urban patterns can be also designed and managed as networks by integrating the findings of natural and social sciences into a multidisciplinary approach. Therefore, increasing attention to the different roles that green space can play for the sustainable development of urban areas has made the scientific community include the concept of green network in their vocabulary. However, by analyzing the results of the present study, which was done by using lexical analysis tools, it was clear that the concept of green network was still semantically ambiguous and unclear. The reason for this confusion was first of all due to the meanings of two terms in the concept: "green and network". In some areas of research, the term "green" refers to the definition of products, strategies, or processes that evoke the concept of sustainable development based on living conditions and the use of resources to meet human needs. From this perspective, the concept of green network can be applied not only to the system of urban natural areas, but also to other groups of interconnected elements, such as control systems of energy storage devices in electricity networks with a high share of renewable energy sources and consumer networks, which is even considered for environmental marketing strategies. For this reason, lexical analysis was performed taking into account the scientific articles published during the last 21 years in the important and prestigious journals of urban studies by using the main keyword of the concept of "green network". This indicated that the discussion about the function of green space as one of the main elements of urban green infrastructure was still very important and bold, which was characterized by the use of related research areas, such as environmental sciences, urban geography, and urban studies. Thus, the term "green" related to urban space described green spaces not only as public services, but also in a broader sense of ecosystem services, discussing new scientific evidence with regard to the relationship between ecological processes and their relative effects on the man-made environment. This issue had a special place due to the increasing importance of the consequences of climate change. Conclusion After reviewing the present study in relation to the various functions attributed to natural areas in urban contexts, a general definition of this concept could be considered: "A system consisting of similar parts that are connected to each other to allow movement or communication between them and also move along these sections." In fact, from the analysis of the results, it could be pointed out that the term "network" seemed to refer to a general set of natural areas in a city, which referred to the theory of network system that connected these elements, except for the ecological field, which used the same principles applied in larger ecosystems to describe the relationships between natural elements (green) on an urban scale and evaluate the ecological effectiveness of green infrastructure in a particular urban context. In short, it was obvious that the concept of green network still had many challenges as a defined topic in urban planning and system. Therefore, to avoid creating any ambiguity and provide a unique definition of the concept of green network from the perspective of urban planning, it could be stated as an urban planning action by decision support tools to create a green infrastructure as a the network consisted of natural and semi-natural areas whose connections and communications were modeled according to specific variables in order to distribute public services equally and increase the quality of life, as well as a wide range of ecosystem services. From this perspective, the concept of green network could create a multidisciplinary research field that combined natural and social sciences with computer science findings to provide tools for public administration in a way that the consequences of their selections could be understood in terms of access so as to evaluate public services and sustainability in cities. Consideration: This article was taken from the doctorate thesis of Mohammad Motaghed entitled "Evaluation of the role of urban green networks in improving life quality with an emphasis on the interlinking degree of natural and artificial elements (case study: Hamadan City)" under the guidance of Dr. Hassan Sajadzadeh and Dr. Mohammad Saeed Izadi at the School of Art and Architecture, Bu Ali Sina University, Hamadan, Iran. References - Ahern, J. (2007). Green infrastructure for cities: The spatial dimension. In V. Novotny and P. Brown (Eds.), Cities of the future: Towards integrated sustainable water and landscape management. IWA Publishing Center. Retrieved from: http://people.umass.edu/ jfa/pdf/Chapter17_ Ahern2%20copy. - Attwell, K., & Smith, D. T. (2017). Parenting as politics: Social identity theory and vaccine hesitant communities. International Journal of Health Governance, 22(3), 183-198. - Bai, Y., & Guo, R. (2021). The construction of green infrastructure network in the perspectives of ecosystem services and ecological sensitivity: The case of Harbin, China. Journal of Global Ecology and Conservation, 27, e01534. Retrieved from: https://doi.org/10.1016/j.gecco.2021.e01534. - Bastian, O., Haase, D., & Grunewald, K. (2012). Ecosystem properties, potentials, and services−The EPPS conceptual framework and an urban application example. Ecological Indicators, 21, 7-16. - Bennett, G. E., & Mulongoy, K. J. (2006). Review of experience with ecological networks, corridors, and buffer zones. Montreal: Secretariat of the Convention on Biological Diversity, Technical Series, 23, 100. - Brunetta, G., & Voghera, A. (2014). Resilience through Ecological Network. TeMA Journal of Land Use, Mobility, and Environment. - Chen, C., Bi, L., & Zhu, K. (2021). Study on spatial-temporal change of urban green space in Yangtze River economic belt and its driving mechanism. International Journal of Environmental Research and Public Health, 18(23), 12498. - De Falco, C., Punziano, G., & Trezza, B. (2021). A mixed content analysis design in the study of the Italian perception of COVID-19 on Twitter. Athens Journal of Social Sciences, 8(3), 191-210. - De Groot, R. S., Alkemade, R., Braat, L., Hein, L., & Willemen, L. (2010). Challenges in integrating the concept of ecosystem services and values in landscape planning, management, and decision-making. Journal of Ecological Complexity, 7(3), 260-272. - Ding, R., Ujang, N., Hamid, H. B., & Wu, J. (2015). Complex network theory applied to the growth of Kuala Lumpur’s public urban rail transit network. PloS One, 10(10), e0139961. doi: https://doi.org/10.1371/journal.pone.0139961 - Ersoy, E., Jorgensen, A., & Warren, P. H. (2019). Identifying multispecies connectivity corridors and the spatial pattern of the landscape. Journal of Urban Forestry & Urban Greening, 40, 308-322. - EU Commission (2017). Green Infrastructure. Retrieved from: http://ec.europa. eu/environment/ nature/ ecosystems/index_en.html. - Éva, S. (2011). Research of town structure and green space system of Sopron, opportunities of development with a historical approach. Ph.D. Thesis, University of West Hungary. - Fenu, G., & Pau, P. L. (2018). Connectivity analysis of ecological landscape networks by cut node ranking. Journal of Applied Network Science, 3(1), 22. - Fichera, A., Frasca, M., Palermo, V., & Volpe, R. (2016). Application of the complex network theory in urban environments: A case study in Catania. Energy Procedia, 101, 345-351. doi: https://doi.org/10.1016/j.egypro.2016.11.044 - Forman, R. T. T. (2016). Urban ecology principles: Are urban ecology and natural area ecology really different?. Journal of Landscape Ecology, 31(8), 1653-1662. - Frazier, A. E., & Bagchi-Sen, S. (2015). Developing open space networks in shrinking cities. Journal of Applied Geography, 59, 1-9. doi: http://doi.org/10.1016/j.apgeog.2015.02.010 - Gargiulo, C., & Tulisi, A. (2016). The building aspect ratio for an energy efficient green network design. In: G. Colombo, P. Lombardi, G. and Mondini (Eds.), E-agorà|e-aγορά for the Transition toward Resilient Communities, pp. 220-226, Torino: DIST-Politecnico di Torino, ISBN: 978-88-9052-964-1. - Gargiulo, C., & Tulisi, A. (2017). Climate change-oriented urban green network design: A decision support tool. In J. K. Gakis & P. Pardalos (Eds.), Network Design and Optimization for Smart Cities, Series on Computer and Operations Research, Vol. 8, 255-278. World Scientific. doi: https://doi.org/10.1142/9789813200012_0011 - Gargiulo Morelli, V., Weijnen, M., Van Bueren, E., Wenzel, I., De Reuver, M., & Salvati, L. (2013). Towards intelligently–sustainable cities? From intelligent and knowledge city programmes to the achievement of urban sustainability. TeMA Journal of Land Use, Mobility and Environment, 6(1), 73-86. doi: http://dx.doi.org/10.6092/1970- 9870/1496 - Girling, C. (2008). Mitigation, adaptation, uncertainty--univerCity: Building a new green neighborhood. Journal of Places, 20(2). - Grunwald, A. (2018). Diverging pathways to overcoming the environmental crisis: A critique of eco-modernism from a technology assessment perspective. Journal of Cleaner Production, 197, 1854-1862. - Hladnik, D., & Pirnat, J. (2011). Urban forestry—Linking naturalness and amenity: The case of Ljubljana, Slovenia. Journal of Urban Forestry & Urban Greening, 10(2), 105-112. Doi: 10.1016/j.ufug.2011.02.002 - Hofman, M. P., Hayward, M. W., Kelly, M. J., & Balkenhol, N. (2018). Enhancing conservation network design with graph-theory and a measure of protected area effectiveness: Refining wildlife corridors in Belize, Central America. Journal of Landscape and Urban Planning, 178, 51-59. - Hu, C. P., Hu, J. M., Deng, S. L., & Liu, Y. (2013). A co-word analysis of library and information science in China. Scientometrics, 97(2), 369-382. - Hunter, A. J., & Luck, G. W. (2015). Defining and measuring the social-ecological quality of urban green space: A semi-systematic review. Journal of Urban Ecosystems, 18, 1139-1163. - Inostroza, L. (2014). Open spaces and urban ecosystem services: Cooling effect towards urban planning in South American cities. TeMA-Journal of Land Use, Mobility, and Environment, 1. - IPBES (2019). Summary for policymakers of the global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. (n.p). - IPCC (2014). Climate Change: Synthesis Report. Contribution of Working Groups I, II, and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC, Geneva, Switzerland, pp. 151-159. Retrieved from: http://ar5syr.ipcc.ch/ipcc/ ipcc/resources /pdf/IPCC_ SynthesisReport. - Jim, C. Y. (2013). Sustainable urban greening strategies for compact cities in developing and developed economies. Journal of Urban Ecosystems, 16(4), 741-761. Doi: 10.1007/s11252-012-0268-x - Jim, C. Y., & Chen, S. S. (2003). Comprehensive green space planning based on landscape ecology principles in compact Nanjing city, China. Journal of Landscape and Urban Planning, 65(3), 95-116. Doi: 10.1016/S0169-2046(02)00244-X - Jim, C. Y., & Chen, W. Y. (2009). Ecosystem services and valuation of urban forests in China. Cities, 26(4), 187-194. - Jongman R., & Pungetti G. (2004). Ecological network and greenways. Cambridge, England: Cambridge University Press. - Kang, J., Zhang, X., Zhu, X., & Zhang, B. (2021). Ecological security pattern: A new idea for balancing regional development and ecological protection (A case study of the Jiaodong Peninsula, China). Journal of Global Ecology and Conservation, 26, e01472. https://doi.org/10.1016/j. gecco. 2021.e01472. - Kang, S., & Kim, J. O. (2015). Morphological analysis of green infrastructure in the Seoul metropolitan area, South Korea. Journal of Landscape and Ecological Engineering, 11(2), 259-268. Doi: 10.1007/s11355-014-0268-5 - Kong, F., Yin, H., Nakagoshi, N., & Zong, Y. (2010). Urban green space network development for biodiversity conservation: Identification based on graph theory and gravity modeling. Journal of Landscape and Urban Planning, 95(1-2), 16-27. - Kowalewska, A. (2011). Sustainable urban green network concept for the city of Gdynia, Poland. In Gdynia Sustainable Green Network, 47th ISOCARP Congress. - Li, F., Wang, R., Paulussen, J., & Liu, X. (2005). Comprehensive concept planning of urban greening based on ecological principles: A case study in Beijing, China. Journal of Landscape and Urban Planning, 72(4), 325-336. Doi:10.1016/j.landurbplan.2004.04.002 - Mahmoud, A. H. A., & El-Sayed, M. A. (2011). Development of sustainable urban green areas in Egyptian new cities: The case of El-Sadat City. Journal of Landscape and Urban Planning, 101(2), 157-170. Doi: 10.1061/(ASCE)UP.1943-5444.0000076 - Massa, P., & Campagna, M. (2014). Social media geographic information: Recent findings and opportunities for smart spatial planning. TeMA-Journal of Land Use, Mobility and Environment, 1. doi: http://dx.doi.org/10.6092/1970-9870/2500 - Matke, C., Bienstock, D., Muñoz, G., Yang, S., Kleinhans, D., & Sager, S. (2016). Robust optimization of power network operation: Storage devices and the role of forecast errors in renewable energies. In: Complex Networks & Their Applications V: Proceedings of the 5th International Workshop on Complex Networks and Their Applications, Springer International Publishing Center, pp. 809-820. Doi: 10.1007/978-3-319-50901-3_64 - Matthews, T., Lo, A. Y., & Byrne, J. A. (2015). Reconceptualizing green infrastructure for climate change adaptation: Barriers to adoption and drivers for uptake by spatial planners. Journal of Landscape and Urban Planning, 138, 155-163. Doi: 10.1016/j.landurbplan.2015.02.010 - Mell, I. C. (2010). Green infrastructure: Concepts, perceptions, and its use in spatial planning. PhD Thesis, Newcastle University. - Moseley, D., Marzano, M., Chetcuti, J., & Watts, K. (2013). Green networks for people: Application of a functional approach to support the planning and management of green space. Journal of Landscape and Urban Planning, 116, 1-12. Doi: http://doi.org/10.1016/j.landurbplan.2013.04.004. - Mougiakou, E., & Phoutis, Y. (2014). Urban green space network evaluation and planning: optimizing accessibility based on connectivity and raster GIS analysis. European Journal of Geography, 5(4), 19-46. - Nor, A. N. M., Corstanje, R., Harris, J. A., Grafius, D. R., & Siriwardena, G. M. (2017). Ecological connectivity networks in rapidly expanding cities. Heliyon, 3(6), 310-325. - Oh, K., Lee, D., & Park, C. (2011). Urban ecological network planning for sustainable landscape management. Journal of Urban Technology, 18(4), 39-59. Doi: 10.1080/10630732.2011.648433 - Ouin, A., Martin, M., & Burel, F. (2008). Agricultural landscape connectivity for the meadow brown butterfly (Maniola jurtina). Journal of Agriculture, Ecosystems, & Environment, 124(3-4), 193-199. Doi: 10.1016/j.agee.2007.09.010 - Pozoukidou, G. (2020). Designing a green infrastructure network for metropolitan areas: A spatial planning approach. Euro-Mediterranean Journal of Environmental Integration, 5(2), 40. Doi:https://doi.org/10.1007/s41207-020-00178-8 - Register, R. (2006). Ecocities: Rebuilding cities in balance with nature. Gabriola Island: New Society Publishers. - Roca, M. (2017). Green approaches in river engineering: Supporting implementation of Green Infrastructure. United Kingdom: HR Wallingford. - Rouse, D. C., & Bunster-Ossa, I. F. (2013). Green infrastructure: A landscape approach. (n.p). - Sadeghi Banis, M. (2015). Using landscape metrics in improving urban ecological network (case study: Tabriz City). Journal of Gardening News, 32, 53-62. - Salata, K., & Yiannakou, A. (2016). Green Infrastructure and climate change adaptation. TeMA-Journal of Land Use, Mobility and Environment, 9(1), 7-24. Doi: 10.6092/1970-9870/3723 - Sandström, U. G., Angelstam, P., & Mikusiński, G. (2006). Ecological diversity of birds in relation to the structure of urban green space. Landscape and Urban Planning, 77(1-2), 39-53. - Scottish Natural Heritage Information Note (SNH) (2012). Green networks in development planning. (n.p). - Senanayake, I. P., Welivitiya, W. D., & Nadeeka, P. M. (2013). Urban green spaces analysis for development planning in Colombo, Sri Lanka utilizing THEOS satellite imagery–a remote sensing and GIS approach. Journal of Urban Forestry and Urban Greening, 12(3), 307-314. - Şenik, B., & Uzun, O. (2021). A process approach to the open green space system planning. Journal of Landscape and Ecological Engineering, 18(2), 203-219. - Shafer, C. S., Lee, B. K., & Turner, S. H. (2000). A tale of three greenway trails: User perceptions related to quality of life. Journal of Landscape and Urban Planning, 49(3‐4), 163-178. - Sluis, T., & Jongman, R. (2019). Green infrastructure and network coherence. Routledge. - Tannier, C., Bourgeois, M., Houot, H., & Foltête, J. C. (2016). Impact of urban developments on the functional connectivity of forested habitats: A joint contribution of advanced urban models and landscape graphs. Journal of Land Use Policy, 52, 76-91. - Tian, Y., Jim, C. Y., Tao, Y., & Shi, T. (2011). Landscape ecological assessment of green space fragmentation in Hong Kong. Journal of Urban Forestry & Urban Greening, 10(2), 79-86. - Tulisi, A. (2017). Urban Green Network Design: Defining green network from an urban planning perspective. TeMA Journal of Land Use, Mobility and Environment, 10(2), 179-192. - Tzoulas, K., Korpela, K., Venn, S., Yli-Pelkonen, V., Kaźmierczak, A., Niemela, J., & James, P. (2007). Promoting ecosystem and human health in urban areas using green infrastructure: A literature review. Journal of Landscape and Urban Planning, 81(3), 167-178. - Van Eck, N. J., & Waltman, L. (2009). VOS viewer: A computer program for bibliometric mapping. Scientometrics, 84(2), 523-538. - Verdú Vázquez, A., Fernández Pablos, E., López Zaldívar, Ó., & Lozano Diez, R. (2020). Development of a methodology for the characterization of urban and peri-urban green spaces in a context of supra-municipal strategies. Journal of Land Use Policy, 69, 75-84. - Weller, B., & Ganzhorn, J. U. (2004). Carabid beetle community composition, body size, and fluctuating asymmetry along an urban-rural gradient. Journal of Basic and Applied Ecology, 5(2), 193-201. Doi: 10.1078/1439-1791-00220 - WHO Regional Office for Europe (2017). Briefing note on process and results of indicator mapping exercise and proposal for a joint monitoring framework. Copenhagen. - World Health Organization (2019). Global action plan on physical activity (2018-2030): More active people for a healthier world. World Health Organization. - Wu, X. Y., Liu, X. G., Wu, B., & Yuan, X. J. (2018). Regional green infrastructure space pattern identification based on urban air conditioning. Landsc. Archit., 29, 33-37. Doi: https:// doi.org/10.14085/j.fjyl.2018.01.0033.05 - WWF (2020). Living planet report 2020-bending the curve of biodiversity loss. Gland, Switzerland. - Yeang, K., & Threipland, E. (2021). At one with nature: Advances in Ecological architecture in the work of Ken Yeang. Routledge. - Zhang, B., Li, N., & Wang, S. (2015). Effect of urban green space changes on the role of rainwater runoff reduction in Beijing, China. Journal of Landscape and Urban Planning, 140, 8-16. Doi: 10.1016/j.landurbplan.2015.03.014 - Zhang, L., & Wang, H. (2006). Planning an ecological network of Xiamen Island (China) using landscape metrics and network analysis. Journal of Landscape and Urban Planning, 78(4), 449-456. - Zhang, Z., Meerow, S., Newell, J. P., & Lindquist, M. (2019). Enhancing landscape connectivity through multifunctional green infrastructure corridor modeling and design. Journal of Urban Forestry & Urban Greening, 38, 305-317.

https://gep.ui.ac.ir/article_27111_875a0b35379cab6a26be52717612d306.pdf

University of Isfahan Geography and Environmental Planning 2008-5362 34 3 2023 09 23 Investigating the Effects of Land Use Changes on the Runoff of Qara Chai River Basin Using the SWAT Model Investigating the Effects of Land Use Changes on the Runoff of Qara Chai River Basin Using the SWAT Model 95 118 27198 10.22108/gep.2023.134432.1535 FA Sayyad Asghari Sersekanroud Professor, Department of Natural Geography, Faculty of Literature and Humanities, University of Mohaghegh Ardabili, Ardabil, Iran Alireza Saeedi Seta Master Student, Department of Natural Geography, Faculty of Literature and Humanities, University of Mohaghegh Ardabili, Ardabil, Iran Journal Article 2022 07 16 Abstract: The present study analyzed changes in land use and its effects on the runoff of Qara Chai Watershed in Hamadan Province using the SWAT model. To this aim, Landsat OLI-TM satellite images of the years of 2001-2020 were used. First, the relevant images were obtained and the necessary pre-processing steps, including atmospheric corrections, were applied by using the FLAASH method. To increase the classification accuracy, the multispectral images were combined with the panchromatic images and the spatial resolution was enhanced up to 15 m. Then, the classification process was done by using the object-oriented method and the nearest neighbor algorithm. The SWAT model was utilized for hydrological simulation of the basin and SUFI-2 algorithm was applied in SWAT-CUP software for conducting sensitivity analysis, calibration, and validation. Due to the sensitivity of the model to the initial loss parameter, it was recalibrated based on the initial loss values. The values of the coefficients for the calibration period were between 0.72 and 0.90 and the model validation results confirmed the calibration accuracy. Assessment of the efficiency of the model by using Nash Sutcliffe coefficients, p-factor, R, and r-factor indicated its high capability for simulating the related runoff. Keywords: Land Use, Runoff, SWAT model Introduction Land use and its fluctuations are among the factors that affect natural cycles in ecosystems. Land use change has several effects on the hydrology of watersheds, such as changing the characteristics of peak discharge, total volume of runoff water, water quality, and hydrological balance. Changing land use and soil cover has some effects on runoff, infiltration, retention, evaporation, transpiration, etc. The runoff caused by rainfall in watersheds causes erosion and loss of surface fertile soil and finally leads to sedimentation in canals, rivers, and reservoirs of dams in addition to human and financial losses triggered by floods. Nowadays, it is possible to study land use changes through satellite images and remote sensing science The geographic information system as a suitable tool for extracting and classifying information has greatly contributed to the studies related to the effects of land use changes. SWAT model is a hydrological simulator and a continuous spatio-temporal semi-distributed model with a physical base. It has been used to simulate hydrological processes in complex and vast watersheds with regard to soil changes, land use, and weather conditions during different periods for a long time. Methodology Qara Chai Basin with an area of about 11000 km2 (based on the political border) is the largest watershed in Hamadan Province. It has got the geographical coordinates of 13° 48° to 29° 49° east longitude and 12° 34° to 35° 44° north latitude. It is drained by Qara Chai River and its branches. This basin is the most flood-prone part of Hamadan Province in terms of flood situation. In the studied plan, the area of Qara Chai Catchment (11000 km2) was divided into 18 sub-basins. The SWAT model was used to obtain the data of the land use, soil, and slope maps and build the hydrological response units. Separate grids were introduced to the model. After introducing two maps of land use, as well as soil and slope classes, to the SWAT model, all the rasterized maps were combined and integrated to produce the hydrological reaction units of the basin. Due to the fact that the land use maps had changed in two time periods and thus had different characteristics, the hydrological response units were affected by the land use type in addition to the soil type and slope. Therefore, Therefore, the number of the hydrological reaction units changed After forming the basin and hydrological response units, the meteorological data required by the SWAT model were introduced to the model. Then, the output of this model was linked with SUFI-2 program in SWAT-CUP software and the important parameters of the SWAT model for the watershed were sensitized with the help of SUFI-2 algorithm for the statistical period of 2002-2021 by applying the method of one parameter at a time. The parameters, which were more sensitive, were identified and their optimal values were determined. In short, the model was recalibrated for the land uses of 2001 and 2020 during the statistical periods of 2001-2008 and of 2013-2020, respectively. It was validated for the statistical period of 2009-2012. Using the statistical criteria of Nash Sutcliffe coefficient, p-factor, and r-factor both for the calibration and validation periods, the statistical analysis was done and the statistical results were checked by using the SWAT-CUP program. Results and Discussion In the present research, land use changes and its effects on the runoff of Qara Chai Basin were analyzed by using the SWAT model. To simulate the runoff by using the mentioned model and SUFI-2 algorithm, the effective parameters on the runoff were identified from among the 29 parameters examined in the sensitivity analysis via the sensitivity analysis technique. 14 variables were found to affect the simulation. Finally, the runoff in the Qara Chai Catchment was identified. Due to the sensitivity of the model to the initial loss parameter, it was recalibrated based on the initial loss values. Then, using the SUFI-2 algorithm, the model was calibrated and validated for the period of 2001-2020. Examining the efficiency of the model by using Nash Sutcliffe coefficients, p-factor, R, and r-factor resulted in the values between 0.72 and 0.90, which indicated the high capability of the model for simulating the runoff. The validation results of the model also confirmed the correctness of the performed calibration. To measure the influences of land use change on the amount of runoff, two SWAT models were prepared by using two separate land use maps, including those of 2001 and 2020 for Qara Chai Watershed. The results of land use changes from 2001 to 2020 showed that the most changes in land use included those of the lands without vegetation cover with an area of 2550 km2 and mixed irrigated and rainfed agriculture lands with an area of 1298.5 km2. Changes in the lands without vegetation had been decreasing and those of the lands related to the mixture of irrigated and rainfed agriculture had been increasing. Also, the least changes were related to the pastures (16.6 km2) with poor canopy coverage. Conclusion The results related to land use changes from 2003 to 2021 showed that the most changes in land use included those related to the lands without vegetation cover and mixed irrigated and rainfed agriculture lands with the areas of 2550 and 1298.5 km2, respectively. The changes were decreasing in the lands without vegetation and increasing in the lands related to the mixture of irrigated and rainfed agriculture. Also, the least changes were related to the pastures (16.6 km2) with poor canopy cover. Due to the sensitivity of the model to the initial loss parameter, it was recalibrated based on the initial loss values. The validation results of the model showed good validity of calibration. Also, the results of this research revealed that the use of remote sensing techniques and combined images for extracting land use data provided a significant help to accurate studies of the effects of land use changes on runoff and their simulation by the SWAT model. References - Ali, M., Khan, S. J., Aslam, I., & Khan, Z. (2011). Simulation of the impacts of land-use change on surface runoff of Lai Nullah Basin in Islamabad, Pakistan. Landscape and Urban Planning, 102(4), 271-279. - Chen, Y., Xu, Y., & Yin, Y. (2009). Impacts of land use change scenarios on storm-runoff generation in Xitiaoxi basin, China. Quaternary International, 208(1-2), 121-128. - De Andrade Farias, C. W. L., Montenegro, S. M. G. L., de Assunção Montenegro, A. A., de Sousa Lima, J. R., Srinivasan, R., & Jones, C. A. (2020). Modeling runoff response to land-use changes using the SWAT model in the Mundaú watershed, Brazil. Journal of Environmental Analysis and Progress, 5(2), 194-206. - Ewen, J., & Parkin, G. (1996). Validation of catchment models for predicting land-use and climate change impacts. Method. Journal of Hydrology, 175(1-4), 583-594. - Fohrer, N., Steiner, N., & Moller, D. (2002). Multidisciplinary Trade-off Function for Land Use Option in Low Mountain Ranges Area: A Modelling Approach. Third International Conference on Water Resources and Environment Research. Dresden University of Technology, 387-391. - George, C., & James, E. J. (2013). Simulation of streamflow using soil and water assessment tool (SWAT) in Meenachil river basin of Kerala, India. Scholars Journal of Engineering and Technology, 1(2), 68-77. - Howarth, P. J., & Wickware, G. M. (1981). Procedures for change detection using Landsat digital data. International Journal of Remote Sensing, 2(3), 277-291. - Koneti, S., Sunkara, S. L., & Roy, P. S. (2018). Hydrological modeling with respect to impact of land-use and land-cover change on the runoff dynamics in Godavari River Basin using the HEC-HMS model. ISPRS International Journal of Geo-Information, 7(6), 206. - Li, K. Y., Coe, M. T., Ramankutty, N., & De Jong, R. (2007). Modeling the hydrological impact of land-use change in West Africa. Journal of Hydrology, 337(3-4), 258-268. - Li, Z., Liu, W. Z., Zhang, X. C., & Zheng, F. L. (2009). Impacts of land use change and climate variability on hydrology in an agricultural catchment on the Loess Plateau of China. Journal of Hydrology, 377(1-2), 35-42. - Melesse, A. M., & Shih, S. F. (2002). Spatially distributed storm runoff depth estimation using Landsat images and GIS. Computers and Electronics in Agriculture, 37(1-3), 173-183. - Neitsch, S. L., Arnold, J. G., Kiniry, J. R., & Williams, J. R. (2011). Soil and water assessment tool theoretical documentation version 2009. Texas Water Resources Institute. - Taia, S., Erraioui, L., Mbrenga, N. C., Chao, J., El Mansouri, B., Haida, S., & Taj-Eddine, K. (2021). Assessment of soil erosion using two spatial approaches: RUSLE and SWAT Model. In E3S Web of Conferences (Vol. 234, p. 00082). EDP Sciences. - Vaze, J., Post, D. A., Chiew, F. H. S., Perraud, J. M., Viney, N. R., & Teng, J. (2010). Climate non-stationarity validity of calibrated rainfall–runoff models for use in climate change studies. Journal of Hydrology, 394(3-4), 447-457. Abstract: The present study analyzed changes in land use and its effects on the runoff of Qara Chai Watershed in Hamadan Province using the SWAT model. To this aim, Landsat OLI-TM satellite images of the years of 2001-2020 were used. First, the relevant images were obtained and the necessary pre-processing steps, including atmospheric corrections, were applied by using the FLAASH method. To increase the classification accuracy, the multispectral images were combined with the panchromatic images and the spatial resolution was enhanced up to 15 m. Then, the classification process was done by using the object-oriented method and the nearest neighbor algorithm. The SWAT model was utilized for hydrological simulation of the basin and SUFI-2 algorithm was applied in SWAT-CUP software for conducting sensitivity analysis, calibration, and validation. Due to the sensitivity of the model to the initial loss parameter, it was recalibrated based on the initial loss values. The values of the coefficients for the calibration period were between 0.72 and 0.90 and the model validation results confirmed the calibration accuracy. Assessment of the efficiency of the model by using Nash Sutcliffe coefficients, p-factor, R, and r-factor indicated its high capability for simulating the related runoff. Keywords: Land Use, Runoff, SWAT model Introduction Land use and its fluctuations are among the factors that affect natural cycles in ecosystems. Land use change has several effects on the hydrology of watersheds, such as changing the characteristics of peak discharge, total volume of runoff water, water quality, and hydrological balance. Changing land use and soil cover has some effects on runoff, infiltration, retention, evaporation, transpiration, etc. The runoff caused by rainfall in watersheds causes erosion and loss of surface fertile soil and finally leads to sedimentation in canals, rivers, and reservoirs of dams in addition to human and financial losses triggered by floods. Nowadays, it is possible to study land use changes through satellite images and remote sensing science The geographic information system as a suitable tool for extracting and classifying information has greatly contributed to the studies related to the effects of land use changes. SWAT model is a hydrological simulator and a continuous spatio-temporal semi-distributed model with a physical base. It has been used to simulate hydrological processes in complex and vast watersheds with regard to soil changes, land use, and weather conditions during different periods for a long time. Methodology Qara Chai Basin with an area of about 11000 km2 (based on the political border) is the largest watershed in Hamadan Province. It has got the geographical coordinates of 13° 48° to 29° 49° east longitude and 12° 34° to 35° 44° north latitude. It is drained by Qara Chai River and its branches. This basin is the most flood-prone part of Hamadan Province in terms of flood situation. In the studied plan, the area of Qara Chai Catchment (11000 km2) was divided into 18 sub-basins. The SWAT model was used to obtain the data of the land use, soil, and slope maps and build the hydrological response units. Separate grids were introduced to the model. After introducing two maps of land use, as well as soil and slope classes, to the SWAT model, all the rasterized maps were combined and integrated to produce the hydrological reaction units of the basin. Due to the fact that the land use maps had changed in two time periods and thus had different characteristics, the hydrological response units were affected by the land use type in addition to the soil type and slope. Therefore, Therefore, the number of the hydrological reaction units changed After forming the basin and hydrological response units, the meteorological data required by the SWAT model were introduced to the model. Then, the output of this model was linked with SUFI-2 program in SWAT-CUP software and the important parameters of the SWAT model for the watershed were sensitized with the help of SUFI-2 algorithm for the statistical period of 2002-2021 by applying the method of one parameter at a time. The parameters, which were more sensitive, were identified and their optimal values were determined. In short, the model was recalibrated for the land uses of 2001 and 2020 during the statistical periods of 2001-2008 and of 2013-2020, respectively. It was validated for the statistical period of 2009-2012. Using the statistical criteria of Nash Sutcliffe coefficient, p-factor, and r-factor both for the calibration and validation periods, the statistical analysis was done and the statistical results were checked by using the SWAT-CUP program. Results and Discussion In the present research, land use changes and its effects on the runoff of Qara Chai Basin were analyzed by using the SWAT model. To simulate the runoff by using the mentioned model and SUFI-2 algorithm, the effective parameters on the runoff were identified from among the 29 parameters examined in the sensitivity analysis via the sensitivity analysis technique. 14 variables were found to affect the simulation. Finally, the runoff in the Qara Chai Catchment was identified. Due to the sensitivity of the model to the initial loss parameter, it was recalibrated based on the initial loss values. Then, using the SUFI-2 algorithm, the model was calibrated and validated for the period of 2001-2020. Examining the efficiency of the model by using Nash Sutcliffe coefficients, p-factor, R, and r-factor resulted in the values between 0.72 and 0.90, which indicated the high capability of the model for simulating the runoff. The validation results of the model also confirmed the correctness of the performed calibration. To measure the influences of land use change on the amount of runoff, two SWAT models were prepared by using two separate land use maps, including those of 2001 and 2020 for Qara Chai Watershed. The results of land use changes from 2001 to 2020 showed that the most changes in land use included those of the lands without vegetation cover with an area of 2550 km2 and mixed irrigated and rainfed agriculture lands with an area of 1298.5 km2. Changes in the lands without vegetation had been decreasing and those of the lands related to the mixture of irrigated and rainfed agriculture had been increasing. Also, the least changes were related to the pastures (16.6 km2) with poor canopy coverage. Conclusion The results related to land use changes from 2003 to 2021 showed that the most changes in land use included those related to the lands without vegetation cover and mixed irrigated and rainfed agriculture lands with the areas of 2550 and 1298.5 km2, respectively. The changes were decreasing in the lands without vegetation and increasing in the lands related to the mixture of irrigated and rainfed agriculture. Also, the least changes were related to the pastures (16.6 km2) with poor canopy cover. Due to the sensitivity of the model to the initial loss parameter, it was recalibrated based on the initial loss values. The validation results of the model showed good validity of calibration. Also, the results of this research revealed that the use of remote sensing techniques and combined images for extracting land use data provided a significant help to accurate studies of the effects of land use changes on runoff and their simulation by the SWAT model. References - Ali, M., Khan, S. J., Aslam, I., & Khan, Z. (2011). Simulation of the impacts of land-use change on surface runoff of Lai Nullah Basin in Islamabad, Pakistan. Landscape and Urban Planning, 102(4), 271-279. - Chen, Y., Xu, Y., & Yin, Y. (2009). Impacts of land use change scenarios on storm-runoff generation in Xitiaoxi basin, China. Quaternary International, 208(1-2), 121-128. - De Andrade Farias, C. W. L., Montenegro, S. M. G. L., de Assunção Montenegro, A. A., de Sousa Lima, J. R., Srinivasan, R., & Jones, C. A. (2020). Modeling runoff response to land-use changes using the SWAT model in the Mundaú watershed, Brazil. Journal of Environmental Analysis and Progress, 5(2), 194-206. - Ewen, J., & Parkin, G. (1996). Validation of catchment models for predicting land-use and climate change impacts. Method. Journal of Hydrology, 175(1-4), 583-594. - Fohrer, N., Steiner, N., & Moller, D. (2002). Multidisciplinary Trade-off Function for Land Use Option in Low Mountain Ranges Area: A Modelling Approach. Third International Conference on Water Resources and Environment Research. Dresden University of Technology, 387-391. - George, C., & James, E. J. (2013). Simulation of streamflow using soil and water assessment tool (SWAT) in Meenachil river basin of Kerala, India. Scholars Journal of Engineering and Technology, 1(2), 68-77. - Howarth, P. J., & Wickware, G. M. (1981). Procedures for change detection using Landsat digital data. International Journal of Remote Sensing, 2(3), 277-291. - Koneti, S., Sunkara, S. L., & Roy, P. S. (2018). Hydrological modeling with respect to impact of land-use and land-cover change on the runoff dynamics in Godavari River Basin using the HEC-HMS model. ISPRS International Journal of Geo-Information, 7(6), 206. - Li, K. Y., Coe, M. T., Ramankutty, N., & De Jong, R. (2007). Modeling the hydrological impact of land-use change in West Africa. Journal of Hydrology, 337(3-4), 258-268. - Li, Z., Liu, W. Z., Zhang, X. C., & Zheng, F. L. (2009). Impacts of land use change and climate variability on hydrology in an agricultural catchment on the Loess Plateau of China. Journal of Hydrology, 377(1-2), 35-42. - Melesse, A. M., & Shih, S. F. (2002). Spatially distributed storm runoff depth estimation using Landsat images and GIS. Computers and Electronics in Agriculture, 37(1-3), 173-183. - Neitsch, S. L., Arnold, J. G., Kiniry, J. R., & Williams, J. R. (2011). Soil and water assessment tool theoretical documentation version 2009. Texas Water Resources Institute. - Taia, S., Erraioui, L., Mbrenga, N. C., Chao, J., El Mansouri, B., Haida, S., & Taj-Eddine, K. (2021). Assessment of soil erosion using two spatial approaches: RUSLE and SWAT Model. In E3S Web of Conferences (Vol. 234, p. 00082). EDP Sciences. - Vaze, J., Post, D. A., Chiew, F. H. S., Perraud, J. M., Viney, N. R., & Teng, J. (2010). Climate non-stationarity validity of calibrated rainfall–runoff models for use in climate change studies. Journal of Hydrology, 394(3-4), 447-457.

https://gep.ui.ac.ir/article_27198_f30bc8af16a60f40e60a155eb3c29534.pdf

University of Isfahan Geography and Environmental Planning 2008-5362 34 3 2023 09 23 Feasibility of Identifying and Studying the Damage of Inner-City Streets Using Drone and Satellite Images (Case Study: A Part of Yazd City) Feasibility of Identifying and Studying the Damage of Inner-City Streets Using Drone and Satellite Images (Case Study: A Part of Yazd City) 119 142 27197 10.22108/gep.2023.135448.1551 FA Hamid Reza Ghafarian Malamiri M.Sc. student of Remote Sensing, Yazd University, Yazd, Iran Negar Naghipur M.Sc. student of Remote Sensing, Yazd University, Yazd, Iran Mohammad Mansourmoghaddam Ph.D. student of Remote Sensing, Shahid Beheshti University, Tehran, Iran Ahmad Mazidi Associate professor, Department of Geography, Yazd University, Yazd, Iran Journal Article 2022 10 22 Abstract Sustainability in the field of pavements is one of the subsets of sustainability topics in sustainable development. In the upcoming research, various supervised and object-oriented methods and fusions of satellite and drone images by using the Gram-Schmidt algorithm were used to investigate asphalt damage, including asphalt cracking and wear, in order to provide the best method for investigation. The results showed that the supervised methods of support vector machine with a kappa coefficient of 87% and overall accuracy of 90% provided the best and shortest distance method with the kappa coefficient and overall accuracy of 57% and 67%, respectively, while showing the lowest accuracy in the classification of supervised methods. Also, among the object-oriented methods, the support vector machine algorithm with a kappa coefficient of 86% and an overall accuracy of 91% had a more accurate output compared to the other studied algorithms. The lowest accuracy was related to the nearest neighbor algorithm with a kappa coefficient of 78% and an overall accuracy of 80%. In the UAV fusion output with Sentinel-2, the classification was done by using the most optimal algorithm and the support vector machine in the object-oriented method. The results showed an increase in classification accuracy up to the kappa coefficient of 91% and overall accuracy of 93%. Furthermore, the thresholding method with a Kappa coefficient of 90% showed the best result for detecting asphalt wear. Keywords: Asphalt, Remote Sensing, Drone, Classification, Road Introduction Sustainability in an environment highly depends on the sustainability of the components of that environment. Sustainability in the field of pavements is also one of the subsets of sustainability topics in sustainable development. Ways of communication are among the most important structures in a city; thus, monitoring and maintenance of urban and intercity communication networks are always among the most used cases of urban management. Materials & Methods In the upcoming research, various supervised classification methods, such as K-Nearest Neighbors, Artificial Neural Network, Support Vector Machine, Maximum Likelihood, and Minimum Distance, and object-oriented classification methods, such as Random Forest, Decision Tree, Naïve Bayes, and fusion of sentinel-2 images and drone images, by using the Gram-Schmidt algorithm were used to investigate asphalt damage, including asphalt cracking and wear, in order to provide the best method for investigation. Also, to study the lifespan of asphalt, first, the spectral profile of different points of asphalt was drawn. Then, it was compared with the existing spectral libraries and classified via classification based on the threshold limit. Research findings The obtained results indicated that in the supervised method, the algorithm of support vector machine achieved the highest accuracy with an overall accuracy of 90% and a kappa coefficient of 87%. Similarly, in the section of object-oriented algorithms, the method of support vector machine achieved the best accuracy with an overall accuracy of 91% and a kappa coefficient of 86%. The higher accuracy of the algorithm of support vector machine was probably due to the preparation of a more optimal decision boundary compared to other algorithms. Also, the minimum distance method obtained the lowest accuracy among all the classification algorithms. The inaccuracy of the shortest distance algorithm in road classification was proven in the research conducted by Li et al. (2020). The results also showed that the most optimal algorithm investigated in the current research, i.e., the support vector machine, in the fused image of the UAV with satellite images, increased the overall accuracy and Kappa coefficient up to 93% and 91%, respectively. This increase indicated the effective role of fusion in increasing classification accuracy, which was probably the result of merging the higher radiometric power of the Sentinel-2 image with an image that had a high spatial resolution of the UAV. Moreover, the results of this research revealed the higher accuracy of the threshold method with an overall accuracy of 80% and a Kappa coefficient of 90% compared to the object-oriented classification of the support vector machine for distinguishing worn asphalt from less worn asphalt. Discussion of Results & Conclusion The results demonstrated that the supervised methods of support vector machine with a kappa coefficient of 87% and overall accuracy of 90% provided the best and shortest distance method with the kappa coefficient and overall accuracy of 57% and 67%, respectively, thus showing the lowest accuracy in the classification of supervised methods. Also, among the object-oriented methods, the algorithm of support vector machine with a kappa coefficient of 86% and overall accuracy of 91% had a more accurate output compared to the other studied algorithms, while the lowest accuracy was related to the nearest neighbor algorithm with a kappa coefficient of 78% and an overall accuracy of 80%. In the UAV/Sentinel-2 data fusion, the classification was done by the most optimized algorithm, which was the support vector machine in the object-oriented method and the results showed an increase in classification accuracy with the kappa coefficient of 91% and overall accuracy of 93%. Mansourmoghaddam et al. (2022) also proved an increase in classification accuracy after fusing the Landsat-8 image with images of higher spatial resolution. Also, the thresholding method with a Kappa coefficient of 90% showed the best result for detecting asphalt wear. The efficiency of using the the threshold method to separate asphalt into two defined classes was proven in the research conducted in this field by Kim et al. (2012) and Amelian et al. (2014) as well. The results of this research are suitable for municipal and road construction organizations to monitor the asphalt conditions of urban roads and increase road safety, as well as sustainability of cities and citizens' well-being, while spending less money, time, and manpower. References - Aiazzi, B., Alparone, L., Baronti, S., & Selva, M. (2007). MS þ Pan image fusion by an enhanced Gram–Schmidt spectral sharpening. 26th Earsel Symp New Dev. Challenges Remote Sensing. - Akbari Mutlaq, A., & Mirzaei, I. (2017). Sustainable development in pavement. (n.p) (in Persian). - Aksoy, A., Şamlioglu, K., Tayfur, S., & Özen, H. (2005). Effects of various additives on the moisture damage sensitivity of asphalt mixtures. Journal of Construction and Building Materials, 19(1), 11-18. - Alavi Panah, S. K. (2003). Application of remote sensing in earth sciences (soil sciences). Tehran: Tehran University Press (in Persian). - Alimjan, G., Sun, T., Liang, Y., Jumahun, H., & Guan, Y. (2018). A new technique for remote sensing image classification based on combinatorial algorithm of SVM and KNN. International Journal of Pattern Recognition and Artificial Intelligence, 32(07), 1859012. - Alizade Rabiei, H. (1993). Remote sensing (principles and applications). Organization for studying and compiling humanities books of universities (Department). Geography, 9, 289-292 (in Persian). - Atkinson, P. M., & Tatnall, A. R. (1997). Introduction neural networks in remote sensing. International Journal of Remote Sensing, 18(4), 699-709. - Bekkari, A., Idbraim, S., Elhassouny, A., Mammass, D., El Yassa, M., & Ducrot, D. (2012). Spectral and spatial classification of high resolution urban satellites images using haralick features and SVM with SAM and EMD distance metrics. International Journal of Computer Applications, 46(11), 28-37. - Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24-31. - Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 2-16. - Blaschke, T., Hay, G. J., Kelly, M., Lang, S., Hofmann, P., Addink, E., . . . Van Coillie, F. (2014). Geographic object-based image analysis–towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 180-191. - Breiman, L. (1996). Bagging predictors. Journal of Machine Learning, 24(2), 123-140. - Cox, G., Curphey, D., Fronek, D., & Wilson, J. (1986). Remote video sensing of highway pavements at road speeds: Using the Motorola 68020 microprocessor. Computer‐Aided Civil and Infrastructure Engineering, 1(1), 1-13. - Cubero-Fernandez, A., Rodriguez-Lozano, F., Villatoro, R., Olivares, J., & Palomares, J. M. (2017). Efficient pavement crack detection and classification. EURASIP Journal on Image and Video Processing, 2017(1), 1-11. - Gonzalez-Audicana, M., Garcia, R., & Herrero-Isern, J. (2003). Fusion of multispectral and panchromatic images using new methods based on wavelet transforms-Evaluation of crop classification accuracy. Geoinfor. Eur. Wide Integr, 265-272. - Guo, H. (2010). Understanding global natural disastersand the role of earth observation. International Journal of Digital Earth, 3(3), 221-230. - Hartgen, D. T., Fields, M. G., & Feigenbaum, B. (2014). 21st annual report on the performance of state highway systems(1984-2012). (n.p). - Hoang, N. D., & Nguyen, Q. L. (2019). A novel method for asphalt pavement crack classification based on image processing and machine learning. Journal of Engineering with Computers, 35(2), 487-498. - Huang, C., Davis, L., & Townshend, J. (2002). An assessment of support vector machines for land cover classification. International Journal of Remote Sensing, 23(4), 725-749. - Jog, S., & Dixit, M. (2016). Supervised classification of satellite images. 2016 Conference on Advances in SignalProcessing (CASP). - Kim, H., Sim, S. H., & Cho, S. (2015). Unmanned aerial vehicle (UAV)-powered concrete crack detection based on digital image processing. In International Conference on Advances in Experimental Structural Engineering. - Kim, H., Soleymani, H., Han, S. H., & Nam, H. (2006). Evaluation of asphalt pavement crack sealing performance using image processing technique. Proceedings of the International Symposium on Automation and Robotics in Construction (ISARC), Tokyo, Japan. - Kim, Y. R., Pinto, I., & Park, S. W. (2012). Experimental evaluation of anti-stripping additives in bituminousmixtures through multiple scale laboratory test results. Journal of Construction and Building Materials, 29, 386-393. - Kingston, G. B., Lambert, M. F., & Maier, H. R. (2005). Bayesian training of artificial neural networks used forwater resources modeling. Journal of Water Resources Research, 41(12). - Koch, C., Georgieva, K., Kasireddy, V., Akinci, B., & Fieguth, P. (2015). A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure. Journal of Advanced Engineering Informatics, 29(2), 196-210. - Li, X., Liu, L., & Huang, L. (2020). Comparison of several remote sensing image classification methods basedon envi. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences,42, 605-611. - Li, Z., Cheng, C., Kwan, M. P., Tong, X., & Tian, S. (2019). Identifying asphalt pavement distress using UAV LiDAR point cloud data and random forest classification. ISPRS International Journal of GeoInformation, 8(1), 39. - Mahvash Mohammadi, N., & Hazarkhani, A. (2019). Comparison of classification methods of support vector machine and maximum likelihood for the separation of change units of flat dome area. Geology of Iran, 53(14) (in Persian). - Mansourmoghaddam, M., Ghafarian Malamiri, H. R., Arabi Aliabad, F., Fallah Tafti, M., Haghani, M., & Shojaei,S. (2022). The Separation of the Unpaved Roads and Prioritization of Paving These Roads UsingUAV Images. Journal of Air, Soil and Water Research, 15, 11786221221086285. - Mansourmoghaddam, M., Rousta, I., Ghaffarian, H., & Mokhtari, M. H. (2022). Evaluating the capability ofspatial and spectral fusion in land-cover mapping enhancement. Journal of Earth Observation and Geomatics Engineering, 6(1). - Mansourmoghaddam, M., Rousta, I., Zamani, M. S., Mokhtari, M. H., Karimi Firozjaei, M., & Alavipanah, S. K. (2022a). Investigating and modeling the effect of The composition and arrangement of the landscapesof Yazd city on the land surface temperature using machine learning and landsat-8 and sentinel-2data. Iranian Journal of Remote Sensing & GIS, (in press). https://doi.org/10.52547/gisj.2022.223918.1060. - Mansourmoghaddam, M., Rousta, I., Zamani, M. S., Mokhtari, M. H., Karimi Firozjaei, M., & Alavipanah, S. K. (2022b). Studying and predicting the changes in ground surface temperature in Yazd city: investigating the effect of proximity and land cover changes. Remote Sensing and Geographical Information System in Natural Resources, 12(4), 1-27 (in Persian). - Maroofi, S., Pak Shir, A. H., & Khorasgani, A. (2015). Investigation of warm asphalt and hot asphalt and its impact on sustainable development (review article). Isfahan International Conference on Sustainable Development and Urban Development (in Persian). - Mettas, C., Themistocleous, K., Neocleous, K., Christofe, A., Pilakoutas, K., & Hadjimitsis, D. (2015). Monitoringasphalt pavement damages using remote sensing techniques. Third international conference on remotesensing and geoinformation of the environment (RSCy2015). - Mokhtari, Mohammad. H., & Najafi, A. (2014). Comparison of classification methods of support vector machine and artificial neural network in extracting land uses from Landsat satellite images. TM Soil and Water Sciences, 19(72), 35-45 (in Persian). - Pal, M., & Mather, P. M. (2001). Decision tree based classification of remotely sensed data. 22nd Asian conference on remote Sensing. - Pan, Y., Zhang, X., Cervone, G., & Yang, L. (2018). Detection of asphalt pavement potholes and cracks basedon the unmanned aerial vehicle multispectral imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(10), 3701-3712. - Richards, J. A., & Richards, J. (1999). Remote sensing digital image analysis (Vol. 3). Springer. - Safavian, S. R., & Landgrebe, D. (1991). A survey of decision tree classifier methodology. IEEE Transactions Onsystems, Man and Cybernetics, 21(3), 660-674. - Sankarasrinivasan, S., Balasubramanian, E., Karthik, K., Chandrasekar, U., & Gupta, R. (2015). Health monitoringof civil structures with integrated UAV and image processing system. Procedia Computer Science, 54, 508-515. - Some, L. (2016). Automatic image-based road crack detection methods KTH. (n.p). - Statistical Center of Iran. (2019). Statistical Yearbook of Yazd Province. National Portal of Statistics (in Persian). - Taghipour, A. A., Rasouli, H., & Family, A. (2017). Investigating the power of accurate drone images in evaluating the asphalt wear phenomenon (case example: Alwar Safli village, near Tabriz city). Journal of Transportation Infrastructure Engineering, 4(15), 99-115 (in Persian). - Vapnik, V. (1999). The nature of statistical learning theory. Springer Science & Business Media. - Wettschereck, D., Aha, D .W., & Mohri, T. (1997). A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms. Artificial Intelligence Review, 11(1), 273-314. - Zhang, C. (2008). An UAV-based photogrammetric mapping system for road condition assessment. Int. Arch.Photogramm. Remote Sens. Spatial Inf. Sci, 37, 627-632. - Zhang, S., Bogus, S. M., Lippitt, C. D., Neville, P. R., Zhang, G., Chen, C., & Valentin, V. (2015). Extracting pavement surface distress conditions based on high spatial resolution multispectral digital aerialphotography. Photogrammetric Engineering & Remote Sensing, 81(9), 709-720. - Zhao, L., Shi, Y., Liu, B., Hovis, C., Duan, Y., & Shi, Z. (2019). Finer classification of crops by fusing UAV images and Sentinel-2A data. Journal of Remote Sensing, 11(24), 3012. Abstract Sustainability in the field of pavements is one of the subsets of sustainability topics in sustainable development. In the upcoming research, various supervised and object-oriented methods and fusions of satellite and drone images by using the Gram-Schmidt algorithm were used to investigate asphalt damage, including asphalt cracking and wear, in order to provide the best method for investigation. The results showed that the supervised methods of support vector machine with a kappa coefficient of 87% and overall accuracy of 90% provided the best and shortest distance method with the kappa coefficient and overall accuracy of 57% and 67%, respectively, while showing the lowest accuracy in the classification of supervised methods. Also, among the object-oriented methods, the support vector machine algorithm with a kappa coefficient of 86% and an overall accuracy of 91% had a more accurate output compared to the other studied algorithms. The lowest accuracy was related to the nearest neighbor algorithm with a kappa coefficient of 78% and an overall accuracy of 80%. In the UAV fusion output with Sentinel-2, the classification was done by using the most optimal algorithm and the support vector machine in the object-oriented method. The results showed an increase in classification accuracy up to the kappa coefficient of 91% and overall accuracy of 93%. Furthermore, the thresholding method with a Kappa coefficient of 90% showed the best result for detecting asphalt wear. Keywords: Asphalt, Remote Sensing, Drone, Classification, Road Introduction Sustainability in an environment highly depends on the sustainability of the components of that environment. Sustainability in the field of pavements is also one of the subsets of sustainability topics in sustainable development. Ways of communication are among the most important structures in a city; thus, monitoring and maintenance of urban and intercity communication networks are always among the most used cases of urban management. Materials & Methods In the upcoming research, various supervised classification methods, such as K-Nearest Neighbors, Artificial Neural Network, Support Vector Machine, Maximum Likelihood, and Minimum Distance, and object-oriented classification methods, such as Random Forest, Decision Tree, Naïve Bayes, and fusion of sentinel-2 images and drone images, by using the Gram-Schmidt algorithm were used to investigate asphalt damage, including asphalt cracking and wear, in order to provide the best method for investigation. Also, to study the lifespan of asphalt, first, the spectral profile of different points of asphalt was drawn. Then, it was compared with the existing spectral libraries and classified via classification based on the threshold limit. Research findings The obtained results indicated that in the supervised method, the algorithm of support vector machine achieved the highest accuracy with an overall accuracy of 90% and a kappa coefficient of 87%. Similarly, in the section of object-oriented algorithms, the method of support vector machine achieved the best accuracy with an overall accuracy of 91% and a kappa coefficient of 86%. The higher accuracy of the algorithm of support vector machine was probably due to the preparation of a more optimal decision boundary compared to other algorithms. Also, the minimum distance method obtained the lowest accuracy among all the classification algorithms. The inaccuracy of the shortest distance algorithm in road classification was proven in the research conducted by Li et al. (2020). The results also showed that the most optimal algorithm investigated in the current research, i.e., the support vector machine, in the fused image of the UAV with satellite images, increased the overall accuracy and Kappa coefficient up to 93% and 91%, respectively. This increase indicated the effective role of fusion in increasing classification accuracy, which was probably the result of merging the higher radiometric power of the Sentinel-2 image with an image that had a high spatial resolution of the UAV. Moreover, the results of this research revealed the higher accuracy of the threshold method with an overall accuracy of 80% and a Kappa coefficient of 90% compared to the object-oriented classification of the support vector machine for distinguishing worn asphalt from less worn asphalt. Discussion of Results & Conclusion The results demonstrated that the supervised methods of support vector machine with a kappa coefficient of 87% and overall accuracy of 90% provided the best and shortest distance method with the kappa coefficient and overall accuracy of 57% and 67%, respectively, thus showing the lowest accuracy in the classification of supervised methods. Also, among the object-oriented methods, the algorithm of support vector machine with a kappa coefficient of 86% and overall accuracy of 91% had a more accurate output compared to the other studied algorithms, while the lowest accuracy was related to the nearest neighbor algorithm with a kappa coefficient of 78% and an overall accuracy of 80%. In the UAV/Sentinel-2 data fusion, the classification was done by the most optimized algorithm, which was the support vector machine in the object-oriented method and the results showed an increase in classification accuracy with the kappa coefficient of 91% and overall accuracy of 93%. Mansourmoghaddam et al. (2022) also proved an increase in classification accuracy after fusing the Landsat-8 image with images of higher spatial resolution. Also, the thresholding method with a Kappa coefficient of 90% showed the best result for detecting asphalt wear. The efficiency of using the the threshold method to separate asphalt into two defined classes was proven in the research conducted in this field by Kim et al. (2012) and Amelian et al. (2014) as well. The results of this research are suitable for municipal and road construction organizations to monitor the asphalt conditions of urban roads and increase road safety, as well as sustainability of cities and citizens' well-being, while spending less money, time, and manpower. References - Aiazzi, B., Alparone, L., Baronti, S., & Selva, M. (2007). MS þ Pan image fusion by an enhanced Gram–Schmidt spectral sharpening. 26th Earsel Symp New Dev. Challenges Remote Sensing. - Akbari Mutlaq, A., & Mirzaei, I. (2017). Sustainable development in pavement. (n.p) (in Persian). - Aksoy, A., Şamlioglu, K., Tayfur, S., & Özen, H. (2005). Effects of various additives on the moisture damage sensitivity of asphalt mixtures. Journal of Construction and Building Materials, 19(1), 11-18. - Alavi Panah, S. K. (2003). Application of remote sensing in earth sciences (soil sciences). Tehran: Tehran University Press (in Persian). - Alimjan, G., Sun, T., Liang, Y., Jumahun, H., & Guan, Y. (2018). A new technique for remote sensing image classification based on combinatorial algorithm of SVM and KNN. International Journal of Pattern Recognition and Artificial Intelligence, 32(07), 1859012. - Alizade Rabiei, H. (1993). Remote sensing (principles and applications). Organization for studying and compiling humanities books of universities (Department). Geography, 9, 289-292 (in Persian). - Atkinson, P. M., & Tatnall, A. R. (1997). Introduction neural networks in remote sensing. International Journal of Remote Sensing, 18(4), 699-709. - Bekkari, A., Idbraim, S., Elhassouny, A., Mammass, D., El Yassa, M., & Ducrot, D. (2012). Spectral and spatial classification of high resolution urban satellites images using haralick features and SVM with SAM and EMD distance metrics. International Journal of Computer Applications, 46(11), 28-37. - Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24-31. - Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 2-16. - Blaschke, T., Hay, G. J., Kelly, M., Lang, S., Hofmann, P., Addink, E., . . . Van Coillie, F. (2014). Geographic object-based image analysis–towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 180-191. - Breiman, L. (1996). Bagging predictors. Journal of Machine Learning, 24(2), 123-140. - Cox, G., Curphey, D., Fronek, D., & Wilson, J. (1986). Remote video sensing of highway pavements at road speeds: Using the Motorola 68020 microprocessor. Computer‐Aided Civil and Infrastructure Engineering, 1(1), 1-13. - Cubero-Fernandez, A., Rodriguez-Lozano, F., Villatoro, R., Olivares, J., & Palomares, J. M. (2017). Efficient pavement crack detection and classification. EURASIP Journal on Image and Video Processing, 2017(1), 1-11. - Gonzalez-Audicana, M., Garcia, R., & Herrero-Isern, J. (2003). Fusion of multispectral and panchromatic images using new methods based on wavelet transforms-Evaluation of crop classification accuracy. Geoinfor. Eur. Wide Integr, 265-272. - Guo, H. (2010). Understanding global natural disastersand the role of earth observation. International Journal of Digital Earth, 3(3), 221-230. - Hartgen, D. T., Fields, M. G., & Feigenbaum, B. (2014). 21st annual report on the performance of state highway systems(1984-2012). (n.p). - Hoang, N. D., & Nguyen, Q. L. (2019). A novel method for asphalt pavement crack classification based on image processing and machine learning. Journal of Engineering with Computers, 35(2), 487-498. - Huang, C., Davis, L., & Townshend, J. (2002). An assessment of support vector machines for land cover classification. International Journal of Remote Sensing, 23(4), 725-749. - Jog, S., & Dixit, M. (2016). Supervised classification of satellite images. 2016 Conference on Advances in SignalProcessing (CASP). - Kim, H., Sim, S. H., & Cho, S. (2015). Unmanned aerial vehicle (UAV)-powered concrete crack detection based on digital image processing. In International Conference on Advances in Experimental Structural Engineering. - Kim, H., Soleymani, H., Han, S. H., & Nam, H. (2006). Evaluation of asphalt pavement crack sealing performance using image processing technique. Proceedings of the International Symposium on Automation and Robotics in Construction (ISARC), Tokyo, Japan. - Kim, Y. R., Pinto, I., & Park, S. W. (2012). Experimental evaluation of anti-stripping additives in bituminousmixtures through multiple scale laboratory test results. Journal of Construction and Building Materials, 29, 386-393. - Kingston, G. B., Lambert, M. F., & Maier, H. R. (2005). Bayesian training of artificial neural networks used forwater resources modeling. Journal of Water Resources Research, 41(12). - Koch, C., Georgieva, K., Kasireddy, V., Akinci, B., & Fieguth, P. (2015). A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure. Journal of Advanced Engineering Informatics, 29(2), 196-210. - Li, X., Liu, L., & Huang, L. (2020). Comparison of several remote sensing image classification methods basedon envi. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences,42, 605-611. - Li, Z., Cheng, C., Kwan, M. P., Tong, X., & Tian, S. (2019). Identifying asphalt pavement distress using UAV LiDAR point cloud data and random forest classification. ISPRS International Journal of GeoInformation, 8(1), 39. - Mahvash Mohammadi, N., & Hazarkhani, A. (2019). Comparison of classification methods of support vector machine and maximum likelihood for the separation of change units of flat dome area. Geology of Iran, 53(14) (in Persian). - Mansourmoghaddam, M., Ghafarian Malamiri, H. R., Arabi Aliabad, F., Fallah Tafti, M., Haghani, M., & Shojaei,S. (2022). The Separation of the Unpaved Roads and Prioritization of Paving These Roads UsingUAV Images. Journal of Air, Soil and Water Research, 15, 11786221221086285. - Mansourmoghaddam, M., Rousta, I., Ghaffarian, H., & Mokhtari, M. H. (2022). Evaluating the capability ofspatial and spectral fusion in land-cover mapping enhancement. Journal of Earth Observation and Geomatics Engineering, 6(1). - Mansourmoghaddam, M., Rousta, I., Zamani, M. S., Mokhtari, M. H., Karimi Firozjaei, M., & Alavipanah, S. K. (2022a). Investigating and modeling the effect of The composition and arrangement of the landscapesof Yazd city on the land surface temperature using machine learning and landsat-8 and sentinel-2data. Iranian Journal of Remote Sensing & GIS, (in press). https://doi.org/10.52547/gisj.2022.223918.1060. - Mansourmoghaddam, M., Rousta, I., Zamani, M. S., Mokhtari, M. H., Karimi Firozjaei, M., & Alavipanah, S. K. (2022b). Studying and predicting the changes in ground surface temperature in Yazd city: investigating the effect of proximity and land cover changes. Remote Sensing and Geographical Information System in Natural Resources, 12(4), 1-27 (in Persian). - Maroofi, S., Pak Shir, A. H., & Khorasgani, A. (2015). Investigation of warm asphalt and hot asphalt and its impact on sustainable development (review article). Isfahan International Conference on Sustainable Development and Urban Development (in Persian). - Mettas, C., Themistocleous, K., Neocleous, K., Christofe, A., Pilakoutas, K., & Hadjimitsis, D. (2015). Monitoringasphalt pavement damages using remote sensing techniques. Third international conference on remotesensing and geoinformation of the environment (RSCy2015). - Mokhtari, Mohammad. H., & Najafi, A. (2014). Comparison of classification methods of support vector machine and artificial neural network in extracting land uses from Landsat satellite images. TM Soil and Water Sciences, 19(72), 35-45 (in Persian). - Pal, M., & Mather, P. M. (2001). Decision tree based classification of remotely sensed data. 22nd Asian conference on remote Sensing. - Pan, Y., Zhang, X., Cervone, G., & Yang, L. (2018). Detection of asphalt pavement potholes and cracks basedon the unmanned aerial vehicle multispectral imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(10), 3701-3712. - Richards, J. A., & Richards, J. (1999). Remote sensing digital image analysis (Vol. 3). Springer. - Safavian, S. R., & Landgrebe, D. (1991). A survey of decision tree classifier methodology. IEEE Transactions Onsystems, Man and Cybernetics, 21(3), 660-674. - Sankarasrinivasan, S., Balasubramanian, E., Karthik, K., Chandrasekar, U., & Gupta, R. (2015). Health monitoringof civil structures with integrated UAV and image processing system. Procedia Computer Science, 54, 508-515. - Some, L. (2016). Automatic image-based road crack detection methods KTH. (n.p). - Statistical Center of Iran. (2019). Statistical Yearbook of Yazd Province. National Portal of Statistics (in Persian). - Taghipour, A. A., Rasouli, H., & Family, A. (2017). Investigating the power of accurate drone images in evaluating the asphalt wear phenomenon (case example: Alwar Safli village, near Tabriz city). Journal of Transportation Infrastructure Engineering, 4(15), 99-115 (in Persian). - Vapnik, V. (1999). The nature of statistical learning theory. Springer Science & Business Media. - Wettschereck, D., Aha, D .W., & Mohri, T. (1997). A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms. Artificial Intelligence Review, 11(1), 273-314. - Zhang, C. (2008). An UAV-based photogrammetric mapping system for road condition assessment. Int. Arch.Photogramm. Remote Sens. Spatial Inf. Sci, 37, 627-632. - Zhang, S., Bogus, S. M., Lippitt, C. D., Neville, P. R., Zhang, G., Chen, C., & Valentin, V. (2015). Extracting pavement surface distress conditions based on high spatial resolution multispectral digital aerialphotography. Photogrammetric Engineering & Remote Sensing, 81(9), 709-720. - Zhao, L., Shi, Y., Liu, B., Hovis, C., Duan, Y., & Shi, Z. (2019). Finer classification of crops by fusing UAV images and Sentinel-2A data. Journal of Remote Sensing, 11(24), 3012.

https://gep.ui.ac.ir/article_27197_2798fca9f218f6acf5bad43586f06883.pdf

University of Isfahan Geography and Environmental Planning 2008-5362 34 3 2023 09 23 Spatial Autocorrelation of Land Surface Temperature and its Relationship With Land Use in Ahvaz City Spatial Autocorrelation of Land Surface Temperature and its Relationship With Land Use in Ahvaz City 141 166 27325 10.22108/gep.2023.136187.1564 FA Mohammad Reza Ansari Assistant Professor, Department of Soil Science, Faculty of Agriculture, Agricultural Sciences and Natural Resources University of Khuzestan, Khuzestan, ,Mollasani ,Iran Azin Norouzi MA, Department of Soil Science, Faculty of Agriculture, Agricultural Sciences and Natural Resources University of Khuzestan, Khuzestan, Mollasani ,Iran 0000-0002-7576-684X Journal Article 2022 12 23 Abstract Urban heat islands could have destructive effects on the environment and increase energy consumption. The present study aimed to investigate the spatial autocorrelation of land surface temperature and its relationship with land use in Ahvaz city. Daily and Night Land surface temperature maps were extracted from the split window method for the Landsat 8 satellite image and MOD11A1 product, respectively. Land use maps of the study were extracted into five classes: Built-up area, vegetation, bare land, water, and Karun River. Based on the results, Global Moran's index on day and night was equal to 0.94 and 0.9, respectively, according to which Land Surface temperature is distributed in a cluster form. Results of Getis-ord GI statistics showed that the largest area of daily and nightly hot spots in the east of Ahvaz was obtained with 5050.53 and 8055.69 ha, respectively. The results of the Kruskal Wallis test showed that there was a statistically significant difference between the average temperature of land uses (p-value<0.05), so industrial areas and bare land had the highest temperature and the water bodies and vegetation had the lowest temperature. The results also showed that LST has a negative relationship with NDVI (r = -0.47); however, the average NDVI index in the study area was equal to 0.17 which indicated the poor quality of vegetation in Ahvaz. Therefore, it is necessary to take measures to improve the quality of vegetation and expand the urban green space and preserve water resources to adjust the land surface temperature in Ahvaz. Keywords: Landsat 8, Modis, Split Window, Global Moran, Getis-Ord GI. Introduction Land surface temperature (LST) is one of the essential components of climate in any region. It controls physical, chemical, and environmental processes on the earth, which are influenced by factors such as surface reflection and roughness of different types of land use/land cover, topography, geographical location, biophysical characteristics, and climate parameter changes over time. Population growth and urbanization, industrial development, and human activities such as unprincipled changes in land use/land cover have created urban heat islands, which have devastating effects on the environment and the health of the community, as well as energy consumption, especially in cities with hot and dry climates. Using traditional methods to estimate land surface temperature is impossible and time-consuming for all ground conditions. On the other hand, meteorological stations only measure temperature information for specific points. In contrast, due to land cover and other temperature conditions, temperatures in different regions are different from the recorded temperatures for a particular point, and this data cannot be generalized to the whole region. Currently, remote sensing technology with continuity and data collection in broad ranges can be very efficient in estimating the land surface temperature. This study aimed to investigate the spatial autocorrelation of surface temperature and to identify the thermal islands of Ahvaz using remote sensing and GIS. Materials and Methods Daily land surface temperature maps were extracted from the Landsat 8 satellite image in June 2020 using a split window method. The method of comparison with the temperature of the synoptic station was used to evaluate the accuracy of the LST map. To investigate the changes in land surface temperature at night, the land surface temperature product of the MODIS sensor (MOD11A1) was used. In the next step, the spatial autocorrelation of Land surface temperature was investigated using the global Moran index, and hot and cold spots were identified by Getis-Ord GI statistics. In the next step, land use maps of the study were extracted into five classes (Built-up area, vegetation, bare land, water, and Karun River) using the support vector machine classification method. To evaluate the efficiency of the classification method, the overall accuracy and kappa coefficient were used. The relationship between land surface temperature (LST) and land use type, and normalized vegetation difference index (NDVI) was investigated using the Kruskal-Wallis test and regression analysis, respectively. Research Findings The results showed that the difference between the air temperature recorded by Ahwaz Synoptic Station and the air temperature obtained from Landsat images is equal to 0.5 ° C, which indicates the high accuracy of the LST map. According to the results, Region 8 had the highest, and Region 6 had the lowest average surface temperature, equal to 53.72 and 51.53 ° C, respectively. The average LST was 51.96 ° C. The results of calculating the global Moran index showed that the LST in Ahvaz has a cluster pattern. The global Moran index for day and night was equal to 0.94 and 0.9, respectively, which was significant at the level of one percent probability. The results of calculating the Getis-Ord GI statistic for identifying the areas with high and low clusters showed that daily cold spots in the west and east of Ahvaz were equal to 4541.22 and 2644.74 ha, respectively. The largest area of daily and night cold spots related to region 3 is equal to 1404.27 and 344.68 ha, respectively, and the lowest area of daily cold spots related to region 7 is equal to 728.83 ha. In general, parks, areas around the Karun River, cultivated lands, tree and forest cover, and water areas formed the cold spots of Ahvaz city. The area of daily hot spots in the east and west of Ahvaz were obtained at 5050.53 and 2022.48 ha, respectively, and the area of night hot spots in the east and west of Ahvaz was obtained at 8055.69 and 4509.66 ha, respectively. The highest area of hot spots day and night is related to region eight equal to 2734.92 and 3160.65 ha, respectively. The hot spots mainly covered industrial areas and surrounding urban areas, hospitals, passenger terminals, gas stations, locations near busy roads, and bare land. Based on the result, the kappa coefficient and overall accuracy of classification were 95.1% and 91%, respectively. According to the results of the Kruskal-Wallis test, there was a statistically significant difference between the mean temperature of land (p-value <0.05), so industrial areas and bare land had the highest average mean, i.e., the highest temperature and the water bodies and vegetation had the lowest mean rank, i.e., the lowest temperature. The result also showed that LST has a negative relationship with NDVI (r = -0.47), and with increasing the positive values of the NDVI index, the average surface temperature decreased. Discussion of Results and Conclusion The results showed that the type of land use affects the land surface temperature. The high concentration of industrial centers, large areas of barren lands, and lack of vegetation are the main reasons for the formation of thermal islands in Ahvaz city. Based on the results, the presence of vegetation and water zone modulates the land surface temperature. The average NDVI index was obtained at 0.17, which indicated the thin vegetation of Ahvaz city. Measures such as using bright colors on the facades of buildings, roads, streets, and parking, expanding vegetation and green space, creating green roofs, changing the use of bare land, paying more attention to the agricultural sector, and preventing construction on agricultural lands, planning, and optimal traffic management in busy places, streets, and squares with high traffic and preservation of water resources in the region and creating waterfalls in high-temperature areas are necessary and recommended adjusting the land surface temperature in Ahvaz. In this research, the study year was selected as near to normal conditions and summer season. It is suggested that further studies should be done in wet and drought conditions and other seasons and compared the results with the results of this research. References - Abdul Athick, A.S., Mohammed, K., Shankar, D., & Raja Naqvi, R. (2019). Data on time series analysis of land surface temperature variation in response to vegetation indices in twelve wereda of Ethiopia using mono window, split window algorithm and spectral radiance model. Data in Brief, 27, 1-12. - Bhargava, A., Lakmini, S., & Bhargava, S. (2017). Urban heat island effect: It’s relevance in urban planning. Journal of Biodiversity and Endangered Species, 5(1), 1-4. - Ca, V.T., Asaede, T., & Abu. E. (1998). Reductions in air conditioning energy caused by a nearby park. Journal of Energy and Building, 29, 83-92. - Choudhury, D., Kalikinkar, D., & Arijit, D. (2019). Assessment of land use land cover changes and its impact on variations of land surface temperature in Asansol-Durgapur Development Region. The Egyptian Journal of Remote Sensing and Space Sciences, 12(22), 203–218. - Das, N., Mondal, P., Sutradhar, S., & Ghosh, R. (2021). Assessment of variation of land use/land cover and its impact on land surface temperature of Asansol subdivision. The Egyptian Journal of Remote Sensing and Space Sciences, 24(1), 131-149. - Das, S., & Angadi, D. P. (2020). Land use-land cover (LULC) transformation and its relation with land surface temperature changes: A case study of Barrackpore Subdivision, West Bengal, India. Remote Sensing Applications: Society and Environment, 19, 1-28. - Feng, Y., Gao, C., Tong, X., Chen, S., Lei, Z., & Wang, J. (2019). Spatial Patterns of Land Surface Temperature and Their Influencing Factors: A Case Study in Suzhou, China. Remote Sensing, 11(82), 2-20. - Gaffin, S. R., Rosenzweig, C., Eichenbaum-Pikser, J., Khanbilvardi, R., & Susca, T. (2010). A Temperature and Seasonal Energy Analysis of Green, White, and Black Roofs, Columbia University. Center for Climate Systems Research, New York, 1-19. - Govil, H., Guha, S., Diwan, P., Gill, N., & Dey, A. (2020). Analyzing linear relationships of lst with ndvi and mndisi using various resolution levels of landsat 8 oli and tirs data. Journal of Data Management, Analytics and Innovation, 1, 171-184. - Guha, S., & Govil, H. (2020). An assessment on the relationship between land surface temperature and normalized difference vegetation index, Environment. Journal of Development and Sustainability, 23, 1944-1963. - Guo, G., Wu, Z., Xiao, R., Chen, Y., Liu, X., & Zhang, X. (2015). Impacts of urban biophysical composition on land surface temperature in urban heat island clusters. Journal of Landscape and Urban Planning, 135, 1-10. - He, J., Zhao, W., Li, A., Wen, F., & Yu, D. (2019). The impact of the terrain effect on land surface temperature variation based on Landsat-8 observations in mountainous areas. International Journal of Remote Sensing, 40(5), 1808-1827. - Kabano, P., Lindley, S., & Harris, A. (2021). Evidence of urban heat island impacts on the vegetation growing season length in a tropical city. Journal of Landscape and Urban Planning, 206, 1-9. - Kaufmann, R., Zhou, L., Myneni, R., Tucker, C., Slayback, D., Shabanov, N., & Pinzon, J. (2003). The effect of vegetation on surface temperature: A statistical analysis of NDVI and climate data. Journal of Geophysical Research Letters, 30(22), 1-4. - Kumari, M., Sarma, K., & Sharma, R. (2019). Using Moran's I and GIS to study the spatial pattern of land surface temperature in relation to land use/cover around a thermal power plant in Singrauli district, Madhya Pradesh, India. Remote Sensing Applications: Society and Environment, 15, 1-6. - Liu, L., & Zhang, Y. (2011). Urban Heat Island Analysis Using the Landsat TM Data and ASTER Data: A Case Study in Hong Kong. Remote Sensing, 3, 1535-1552. - McKee, T. B., Doesken, N. J., & Kleist, J. (1993). The relationship of drought frequency and duration to time scales. Eighth Conference on Applied Climatology. Anaheim, California, 17-22 January, 1-6. - Mujabar, S., & Rao, V. (2018). Estimation and analysis of land surface temperature of Jubail Industrial City, Saudi Arabia, by using remote sensing and GIS technologies. Arabian Journal of Geosciences, 11, 1-13. - QIN, Z., & Karnieli, A. (2001). A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region. International Journal of Remote Sensing, 22(18), 3719–3746. - Rongali, G., Ashok K. K., Gosain, A. K., & Khosa, R. (2018). Split-Window algorithm for retrieval of land surface temperature using Landsat 8 thermal infrared data. Journal of Geo Visualization and Spatial Analysis, 2(14), 1-19. - Sekertekin, A., & Zadbagher, E. (2021). Simulation of future land surface temperature distribution and evaluating surface urban heat island based on impervious surface area. Journal of Ecological Indicators, 122, 1-11. - Shahfahad., Kumari, B., Tayyab, M., Ahmed, I. A., Imam Baig, M. R., Firoz Khan, M., & Rahman, A.(2020) Longitudinal study of land surface temperature (LST) using mono- and split-window algorithms and its relationship with NDVI and NDBI over selected metro cities of India. Arabian Journal of Geosciences, 13(104), 1-19. - Sharma, R., Pradhan, L., Kumari, M., & Bhattacharya, P. (2021). Assessing urban heat islands and thermal comfort in Noida City using geospatial technology. Journal of Urban Climate, 35, 1-15. - Sun, C., Wu, Z., Lv, Z., & Wei, J. (2010). Spatial-temporal analysis of land surface temperature and its interplay with land use change. Information Engineering and Computer Science (ICIECS), 25, 1-4. - Tafesse, B., & Suryabhagavan, K. V. (2018). Systematic modeling of impacts of land-use and land-cover changes on land surface temperature in Adama Zuria District, Ethiopia. Journal of Modeling Earth Systems and Environment, 9, 1-13. - Wang, R., & Murayama, Y. (2020). Geo-simulation of land use/cover scenarios and impacts on land surface temperature in Sapporo, Japan. Journal of Sustainable Cities and Society, 36, 1-11. - Weng, Q., Karimi firozjaei, M., Kiavarz, M., Alavipanah S. K., & Hamzeh, S. (2019). Normalizing land surface temperature for environmental parameters in mountainous and urban areas of a cold semi-arid climate. Journal of Science of the Total Environment, 650, 515-529. - Xu, H., Yunhao, C., Shangming, D., & Wenxia, Q. (2011). Dynamical monitoring and evaluation methods to urban heat island effects based on RS & GIS. Procedia Environmental Sciences, 10, 1228-1237. - Zhang, C., Luo, L., Xub, W., & Ledwitha, V. (2018). Use of local Moran's I and GIS to identify pollution hotspots of Pb in urban soils of Galway, Ireland. Journal of Science of the Environment, 398, 212-221. Abstract Urban heat islands could have destructive effects on the environment and increase energy consumption. The present study aimed to investigate the spatial autocorrelation of land surface temperature and its relationship with land use in Ahvaz city. Daily and Night Land surface temperature maps were extracted from the split window method for the Landsat 8 satellite image and MOD11A1 product, respectively. Land use maps of the study were extracted into five classes: Built-up area, vegetation, bare land, water, and Karun River. Based on the results, Global Moran's index on day and night was equal to 0.94 and 0.9, respectively, according to which Land Surface temperature is distributed in a cluster form. Results of Getis-ord GI statistics showed that the largest area of daily and nightly hot spots in the east of Ahvaz was obtained with 5050.53 and 8055.69 ha, respectively. The results of the Kruskal Wallis test showed that there was a statistically significant difference between the average temperature of land uses (p-value<0.05), so industrial areas and bare land had the highest temperature and the water bodies and vegetation had the lowest temperature. The results also showed that LST has a negative relationship with NDVI (r = -0.47); however, the average NDVI index in the study area was equal to 0.17 which indicated the poor quality of vegetation in Ahvaz. Therefore, it is necessary to take measures to improve the quality of vegetation and expand the urban green space and preserve water resources to adjust the land surface temperature in Ahvaz. Keywords: Landsat 8, Modis, Split Window, Global Moran, Getis-Ord GI. Introduction Land surface temperature (LST) is one of the essential components of climate in any region. It controls physical, chemical, and environmental processes on the earth, which are influenced by factors such as surface reflection and roughness of different types of land use/land cover, topography, geographical location, biophysical characteristics, and climate parameter changes over time. Population growth and urbanization, industrial development, and human activities such as unprincipled changes in land use/land cover have created urban heat islands, which have devastating effects on the environment and the health of the community, as well as energy consumption, especially in cities with hot and dry climates. Using traditional methods to estimate land surface temperature is impossible and time-consuming for all ground conditions. On the other hand, meteorological stations only measure temperature information for specific points. In contrast, due to land cover and other temperature conditions, temperatures in different regions are different from the recorded temperatures for a particular point, and this data cannot be generalized to the whole region. Currently, remote sensing technology with continuity and data collection in broad ranges can be very efficient in estimating the land surface temperature. This study aimed to investigate the spatial autocorrelation of surface temperature and to identify the thermal islands of Ahvaz using remote sensing and GIS. Materials and Methods Daily land surface temperature maps were extracted from the Landsat 8 satellite image in June 2020 using a split window method. The method of comparison with the temperature of the synoptic station was used to evaluate the accuracy of the LST map. To investigate the changes in land surface temperature at night, the land surface temperature product of the MODIS sensor (MOD11A1) was used. In the next step, the spatial autocorrelation of Land surface temperature was investigated using the global Moran index, and hot and cold spots were identified by Getis-Ord GI statistics. In the next step, land use maps of the study were extracted into five classes (Built-up area, vegetation, bare land, water, and Karun River) using the support vector machine classification method. To evaluate the efficiency of the classification method, the overall accuracy and kappa coefficient were used. The relationship between land surface temperature (LST) and land use type, and normalized vegetation difference index (NDVI) was investigated using the Kruskal-Wallis test and regression analysis, respectively. Research Findings The results showed that the difference between the air temperature recorded by Ahwaz Synoptic Station and the air temperature obtained from Landsat images is equal to 0.5 ° C, which indicates the high accuracy of the LST map. According to the results, Region 8 had the highest, and Region 6 had the lowest average surface temperature, equal to 53.72 and 51.53 ° C, respectively. The average LST was 51.96 ° C. The results of calculating the global Moran index showed that the LST in Ahvaz has a cluster pattern. The global Moran index for day and night was equal to 0.94 and 0.9, respectively, which was significant at the level of one percent probability. The results of calculating the Getis-Ord GI statistic for identifying the areas with high and low clusters showed that daily cold spots in the west and east of Ahvaz were equal to 4541.22 and 2644.74 ha, respectively. The largest area of daily and night cold spots related to region 3 is equal to 1404.27 and 344.68 ha, respectively, and the lowest area of daily cold spots related to region 7 is equal to 728.83 ha. In general, parks, areas around the Karun River, cultivated lands, tree and forest cover, and water areas formed the cold spots of Ahvaz city. The area of daily hot spots in the east and west of Ahvaz were obtained at 5050.53 and 2022.48 ha, respectively, and the area of night hot spots in the east and west of Ahvaz was obtained at 8055.69 and 4509.66 ha, respectively. The highest area of hot spots day and night is related to region eight equal to 2734.92 and 3160.65 ha, respectively. The hot spots mainly covered industrial areas and surrounding urban areas, hospitals, passenger terminals, gas stations, locations near busy roads, and bare land. Based on the result, the kappa coefficient and overall accuracy of classification were 95.1% and 91%, respectively. According to the results of the Kruskal-Wallis test, there was a statistically significant difference between the mean temperature of land (p-value <0.05), so industrial areas and bare land had the highest average mean, i.e., the highest temperature and the water bodies and vegetation had the lowest mean rank, i.e., the lowest temperature. The result also showed that LST has a negative relationship with NDVI (r = -0.47), and with increasing the positive values of the NDVI index, the average surface temperature decreased. Discussion of Results and Conclusion The results showed that the type of land use affects the land surface temperature. The high concentration of industrial centers, large areas of barren lands, and lack of vegetation are the main reasons for the formation of thermal islands in Ahvaz city. Based on the results, the presence of vegetation and water zone modulates the land surface temperature. The average NDVI index was obtained at 0.17, which indicated the thin vegetation of Ahvaz city. Measures such as using bright colors on the facades of buildings, roads, streets, and parking, expanding vegetation and green space, creating green roofs, changing the use of bare land, paying more attention to the agricultural sector, and preventing construction on agricultural lands, planning, and optimal traffic management in busy places, streets, and squares with high traffic and preservation of water resources in the region and creating waterfalls in high-temperature areas are necessary and recommended adjusting the land surface temperature in Ahvaz. In this research, the study year was selected as near to normal conditions and summer season. It is suggested that further studies should be done in wet and drought conditions and other seasons and compared the results with the results of this research. References - Abdul Athick, A.S., Mohammed, K., Shankar, D., & Raja Naqvi, R. (2019). Data on time series analysis of land surface temperature variation in response to vegetation indices in twelve wereda of Ethiopia using mono window, split window algorithm and spectral radiance model. Data in Brief, 27, 1-12. - Bhargava, A., Lakmini, S., & Bhargava, S. (2017). Urban heat island effect: It’s relevance in urban planning. Journal of Biodiversity and Endangered Species, 5(1), 1-4. - Ca, V.T., Asaede, T., & Abu. E. (1998). Reductions in air conditioning energy caused by a nearby park. Journal of Energy and Building, 29, 83-92. - Choudhury, D., Kalikinkar, D., & Arijit, D. (2019). Assessment of land use land cover changes and its impact on variations of land surface temperature in Asansol-Durgapur Development Region. The Egyptian Journal of Remote Sensing and Space Sciences, 12(22), 203–218. - Das, N., Mondal, P., Sutradhar, S., & Ghosh, R. (2021). Assessment of variation of land use/land cover and its impact on land surface temperature of Asansol subdivision. The Egyptian Journal of Remote Sensing and Space Sciences, 24(1), 131-149. - Das, S., & Angadi, D. P. (2020). Land use-land cover (LULC) transformation and its relation with land surface temperature changes: A case study of Barrackpore Subdivision, West Bengal, India. Remote Sensing Applications: Society and Environment, 19, 1-28. - Feng, Y., Gao, C., Tong, X., Chen, S., Lei, Z., & Wang, J. (2019). Spatial Patterns of Land Surface Temperature and Their Influencing Factors: A Case Study in Suzhou, China. Remote Sensing, 11(82), 2-20. - Gaffin, S. R., Rosenzweig, C., Eichenbaum-Pikser, J., Khanbilvardi, R., & Susca, T. (2010). A Temperature and Seasonal Energy Analysis of Green, White, and Black Roofs, Columbia University. Center for Climate Systems Research, New York, 1-19. - Govil, H., Guha, S., Diwan, P., Gill, N., & Dey, A. (2020). Analyzing linear relationships of lst with ndvi and mndisi using various resolution levels of landsat 8 oli and tirs data. Journal of Data Management, Analytics and Innovation, 1, 171-184. - Guha, S., & Govil, H. (2020). An assessment on the relationship between land surface temperature and normalized difference vegetation index, Environment. Journal of Development and Sustainability, 23, 1944-1963. - Guo, G., Wu, Z., Xiao, R., Chen, Y., Liu, X., & Zhang, X. (2015). Impacts of urban biophysical composition on land surface temperature in urban heat island clusters. Journal of Landscape and Urban Planning, 135, 1-10. - He, J., Zhao, W., Li, A., Wen, F., & Yu, D. (2019). The impact of the terrain effect on land surface temperature variation based on Landsat-8 observations in mountainous areas. International Journal of Remote Sensing, 40(5), 1808-1827. - Kabano, P., Lindley, S., & Harris, A. (2021). Evidence of urban heat island impacts on the vegetation growing season length in a tropical city. Journal of Landscape and Urban Planning, 206, 1-9. - Kaufmann, R., Zhou, L., Myneni, R., Tucker, C., Slayback, D., Shabanov, N., & Pinzon, J. (2003). The effect of vegetation on surface temperature: A statistical analysis of NDVI and climate data. Journal of Geophysical Research Letters, 30(22), 1-4. - Kumari, M., Sarma, K., & Sharma, R. (2019). Using Moran's I and GIS to study the spatial pattern of land surface temperature in relation to land use/cover around a thermal power plant in Singrauli district, Madhya Pradesh, India. Remote Sensing Applications: Society and Environment, 15, 1-6. - Liu, L., & Zhang, Y. (2011). Urban Heat Island Analysis Using the Landsat TM Data and ASTER Data: A Case Study in Hong Kong. Remote Sensing, 3, 1535-1552. - McKee, T. B., Doesken, N. J., & Kleist, J. (1993). The relationship of drought frequency and duration to time scales. Eighth Conference on Applied Climatology. Anaheim, California, 17-22 January, 1-6. - Mujabar, S., & Rao, V. (2018). Estimation and analysis of land surface temperature of Jubail Industrial City, Saudi Arabia, by using remote sensing and GIS technologies. Arabian Journal of Geosciences, 11, 1-13. - QIN, Z., & Karnieli, A. (2001). A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region. International Journal of Remote Sensing, 22(18), 3719–3746. - Rongali, G., Ashok K. K., Gosain, A. K., & Khosa, R. (2018). Split-Window algorithm for retrieval of land surface temperature using Landsat 8 thermal infrared data. Journal of Geo Visualization and Spatial Analysis, 2(14), 1-19. - Sekertekin, A., & Zadbagher, E. (2021). Simulation of future land surface temperature distribution and evaluating surface urban heat island based on impervious surface area. Journal of Ecological Indicators, 122, 1-11. - Shahfahad., Kumari, B., Tayyab, M., Ahmed, I. A., Imam Baig, M. R., Firoz Khan, M., & Rahman, A.(2020) Longitudinal study of land surface temperature (LST) using mono- and split-window algorithms and its relationship with NDVI and NDBI over selected metro cities of India. Arabian Journal of Geosciences, 13(104), 1-19. - Sharma, R., Pradhan, L., Kumari, M., & Bhattacharya, P. (2021). Assessing urban heat islands and thermal comfort in Noida City using geospatial technology. Journal of Urban Climate, 35, 1-15. - Sun, C., Wu, Z., Lv, Z., & Wei, J. (2010). Spatial-temporal analysis of land surface temperature and its interplay with land use change. Information Engineering and Computer Science (ICIECS), 25, 1-4. - Tafesse, B., & Suryabhagavan, K. V. (2018). Systematic modeling of impacts of land-use and land-cover changes on land surface temperature in Adama Zuria District, Ethiopia. Journal of Modeling Earth Systems and Environment, 9, 1-13. - Wang, R., & Murayama, Y. (2020). Geo-simulation of land use/cover scenarios and impacts on land surface temperature in Sapporo, Japan. Journal of Sustainable Cities and Society, 36, 1-11. - Weng, Q., Karimi firozjaei, M., Kiavarz, M., Alavipanah S. K., & Hamzeh, S. (2019). Normalizing land surface temperature for environmental parameters in mountainous and urban areas of a cold semi-arid climate. Journal of Science of the Total Environment, 650, 515-529. - Xu, H., Yunhao, C., Shangming, D., & Wenxia, Q. (2011). Dynamical monitoring and evaluation methods to urban heat island effects based on RS & GIS. Procedia Environmental Sciences, 10, 1228-1237. - Zhang, C., Luo, L., Xub, W., & Ledwitha, V. (2018). Use of local Moran's I and GIS to identify pollution hotspots of Pb in urban soils of Galway, Ireland. Journal of Science of the Environment, 398, 212-221.

https://gep.ui.ac.ir/article_27325_0383450c394980db406eb6c4605c8963.pdf