Modeling landscape visual aesthetic quality assessment towards tourism development in protected areas

Document Type : Original Article

Authors

1 Department of Environmental Science, Faculty of Natural Resources, University of Tehran, Karaj, Iran

2 Department of Environmental Sciences, Faculty of Natural Resources, University of Tehran, Karaj, Iran

3 Department of Environment, Research Center of Environment and Sustainable Development, Tehran, Iran

Abstract

Introduction: Landscape in protected areas is an advantage of tourism in nature, and as a result, aesthetic assessment and landscape quality assessment are necessary in these areas. Although this issue is important in other outdoor recreation sites, but in protected areas where the form of tourism is eco-tourism and based on visits, it is more important. Therefore, this study was done with the aim of modeling landscape visual aesthetic quality assessment with the purpose of tourism in protected areas using artificial neural network to predict the aesthetic value of the landscape and prioritize the influential variables of the model.
Material and methods: The current research was carried out in the Central Alborz protected area under the management of Alborz province. In this study, in order to assess the landscape visual aesthetic quality of protected areas with the aim of tourism, a combination of comprehensive assessment perspective and the artificial neural network modeling method was used; a comprehensive assessment perspective includes the user perspective by completing the questionnaire and using 19objective variables including 15landscape objective criteria (diversity of natural and human covers,vegetation form diversity,trees composition form,water body form,color diversity,water landscape,rock landscape,roads and trails landscape,tree and shrub vegetation landscape,grassy and bushy vegetation landscape,bare and uncovered surfaces landscape,buildings and structures landscape,sky landscape,hard surface ratio,soft surface ratio) and 4criteria related to viewpoints characteristics (altitude,slope,vegetation type,vegetation coverage). For this purpose, first, homogeneous landscape assessment units were characterized using six spatial indicators (elevation,slope(%),vegetation landscape,village visibility, permanent and seasonal river visibilities). Then 100photos of the scenery of the area were taken and modeling was done using the multilayer perceptron network method. In the next step, the sensitivity analysis of the model for assessing the aesthetic quality of the landscape was done in order to prioritize and determine the most effective visual aesthetic criteria in the assessment of the visual aesthetic quality of the landscapes in the region. Finally, the decision support system for assessing the landscape visual aesthetic quality in protected areas for the purpose of tourism was designed.
Results and discussion: The model with the structure of 19-6-1 (19input variables, 6neurons in the hidden layer, and one output variable) with Log-Sigmoid transfer functions in the hidden layer and linear in the output layer and Levenberg–Marquardt optimization algorithm, with explanatory coefficients in the three data sets, namely training, validation and test equal to 0.7, 0.75 and 0.70 were introduced as the optimal structure of the model for assessing the landscapes visual aesthetic quality of protected areas with the purpose of tourism.
According to the sensitivity analysis results, the parameters of the water landscape, the composition of trees, and the vegetation coverage with the sensitivity coefficients of 0.223, 0.147, and 0.104, respectively, showed the most significant impact on the landscapes visual aesthetic quality in protected areas. The trend of the changes in the landscape aesthetic quality according to the changes in the water landscape and the trees’ composition form was that, with the increase of the mentioned variables in the landscapes of the region, the visual aesthetic quality increases in a non-linear way; thus, with 8.23% increase in the water landscape and in changing the trees’ composition form from single to group, an increase of 0.46 and 0.48 units, respectively, was observed in the landscape visual aesthetic quality. Also, the process of changing the variable of vegetation coverage showed a decrease in the landscape visual aesthetic quality of the region with the increase of the said variable.
Conclusion: The sensitivity analysis and identification of the most significant variables and criteria on the visual aesthetic quality of the landscapes of protected areas with the purpose of tourism showed that in order to achieve a high landscape visual aesthetic value and determine the intensive and extensive recreational zones in the protected areas, the water landscape should take the first priority of planning. The model presented in this research is a decision support system for assessing the landscapes visual aesthetic quality in protected areas for tourism and provides the possibility of predicting the visual aesthetic quality of landscapes in protected areas with similar ecological conditions and ecosystems. Also, the model presented in this study can be used in preparing the management plan and zoning of protected areas, especially in recreational zones.

Keywords

References

Aboufazeli, S., Jahani, A. and Farahpour, M., 2021. A method for aesthetic quality modelling of the form of plants and water in the urban parks landscapes: an artificial neural network approach. Methods X. 8, 101489. https://doi.org/10.1016/j.mex.2021.101489.
ADE: Alborz Department of Environment, 2020a. Introduction of Central Alborz Protected Area. Available online at: https://alborz.doe.ir/. (In Persian).
ADE: Alborz Department of Environment, 2020b. The physiognomy of the natural environment of the central (southern) Alborz protected area. Available online at: https://alborz.doe.ir/.
Aghajani, H., Marvie Mohadjer, M.R., Jahani, A., Asef, M.R., Shirvany, A. and Azarian, M., 2014. Investigation of affective habitat factors affecting on abundance of wood macrofungi and sensitivity analysis using the artificial neural network (case study: Kheyrud forest, Noshahr). Iranian Journal of Forest and Poplar Research. 21(4), 617-628. https://doi.org/10.22092/ijfpr.2014.5135. (In Persian with English abstract).
Ahmadi Mirghaed, F., Mohammadzadeh, M., Salman Mahini, A.R. and Mirkarimi, S.H., 2016. Integrating visual and environmental elements using fuzzy and multi criteria evaluation methods for aesthetic quality assessment of Gharahsoo watershed, Golestan province. Journal of RS and GIS for Natural Resources. 7(3), 46-60. (In Persian with English abstract).
Ahmadi Mirghaed, F., Mohammadzadeh, M., Salmanmahiny, A. and Mirkarimi, S.H., 2020. Assessing the interactions between landscape aesthetic quality and spatial indices in Gharasoo watershed, North of Iran. International Journal of Environmental Science and Technology. 17, 231-242. https://doi.org/10.1007/s13762-019-02342-2.
Akhoondi, L., Arjmandi, R., Danehkar, A. and Shaban Ali Fami, H., 2015. Investigation the tourist's perception about improvement of tourism benefits in Karaj- Chalous road's resorts. Journal of Sustainability, Development and Environment. 2(1), 57-70. (In Persian with English abstract).
Attar Sahragard, F., 2023. Modeling landscape visual aesthetic quality assessment with the aim of tourism in protected areas using artificial neural network (case study: central Alborz protected area under the management of Alborz province). MS.c. Thesis. University of Tehran, Karaj, Iran.
Attar Sahragard, F., Danehkar, A. and Jahani, A., 2023. Ranking homogeneous natural landscape units by shannon entropy -vikor method (case study: central Alborz protected area under the management of Alborz province). Journal of Environmental Studies. 48(4), 555-575. https://doi.org/10.22059/jes.2023.348672.1008359. (In Persian with English abstract).
Bairam Komaki, C., Asadikia, R. and Niknahad Gharmakhar, H., 2019. Estimation of vegetation cover percentage and biomass using remote sensing indices (Case study: protected areas of Southern Alborz, Karaj). Journal of RS and GIS for Natural Resources, 10(1), 1-16. http://dorl.net/dor/20.1001.1.26767082.1398.10.1.1.8. (In Persian with English abstract).
Daniel, T.C., 2001. Whither scenic beauty? Visual landscape quality assessment in the 21st century. Landscape and Urban Planning, 54(1-4), 267-281. https://doi.org/10.1016/S0169-2046(01)00141-4.
De Val, G.D.L.F., Atauri, J.A. and de Lucio, J.V., 2006. Relationship between landscape visual attributes and spatial pattern indices: A test study in Mediterranean-climate landscapes. Landscape and Urban Planning, 77(4), 393-407. https://doi.org/10.1016/j.landurbplan.2005.05.003.
Dupont, L., Ooms, K., Antrop, M. and Van Eetvelde, V., 2016. Comparing saliency maps and eye-tracking focus maps: The potential use in visual impact assessment based on landscape photographs. Landscape and urban planning, 148, 17-26. https://doi.org/10.1016/j.landurbplan.2015.12.007.
Franco, D., Franco, D., Mannino, I. and Zanetto, G., 2003. The impact of agroforestry networks on scenic beauty estimation: The role of a landscape ecological network on a socio-cultural process. Landscape and Urban Planning, 62(3), 119-138. https://doi.org/10.1016/S0169-2046(02)00127-5.
Gobster, P.H., Ribe, R.G. and Palmer, J.F., 2019. Themes and trends in visual assessment research: Introduction to the Landscape and Urban Planning special collection on the visual assessment of landscapes. Landscape and Urban Planning, 191, 103635. https://doi.org/10.1016/j.landurbplan.2019.103635.
Gundersen, V.S. and Frivold, L.H., 2008. Public preferences for forest structures: A review of quantitative surveys from Finland, Norway and Sweden. Urban Forestry & Urban Greening, 7(4), 241-258. https://doi.org/10.1016/j.ufug.2008.05.001.
Harmon, D., 2004, June. Intangible values of protected areas: what are they? Why do they matter?. The George Wright Forum, 21(2), 9-22.
Hoseini Bay, M.S., Jahani, A. and Mohamadzade, M., 2015. Landscape aesthetic quality evaluation: approaches and criteria. In Proceedings 12th National Conference on Environmental Impact Assessment of Iran, 25th-26th February, Tehran, Iran. (In Persian with English abstract).
Howley, P., 2011. Landscape aesthetics: Assessing the general publics' preferences towards rural landscapes. Ecological Economics, 72, 161-169. https://doi.org/10.1016/j.ecolecon.2011.09.026.
Itten, J., 2020. The Art of Color (A. Sharveh).  Yassavoli Press, 192. (In Persian).
Jafari, Z., Mikaeali-Tabrizy, A.R., Mohammadzadeh, M. and Abdi, O., 2012. Evaluation of ecotourism competence in Golestan national park through weighted linear combination method. Journal of Renewable Natural Resources Research, 2(4), 25-37. (In Persian with English abstract).
Jahani, A., 2016. Modeling of forest canopy density confusion in environmental assessment using artificial neural network. Iranian Journal of Forest and Poplar Research, 24(2), 310-322. https://doi.org/10.22092/ijfpr.2016.106998. (In Persian with English abstract).
Jahani, A., 2017. Aesthetic quality evaluation modeling of forest landscape using artificial neural network. Journal of Wood and Forest Science and Technology, 24(3), 17-34. https://doi.org/10.22069/jwfst.2017.11235.1590. (In Persian with English abstract).
Jahani, A., 2019a. Forest landscape aesthetic quality model (FLAQM): A comparative study on landscape modelling using regression analysis and artificial neural networks. Journal of Forest Science, 65(2), 61-69. https://doi.org/10.17221/86/2018-JFS.
Jahani, A., 2019b. Sycamore failure hazard classification model (SFHCM): an environmental decision support system (EDSS) in urban green spaces. International Journal of Environmental Science and Technology. 16(2), 955-964. https://doi.org/10.1007/s13762-018-1665-3.
Jahani, A., Allahverdi, S., Saffariha, M., Alitavoli, A. and Ghiyasi, S., 2022. Environmental modeling of landscape aesthetic value in natural urban parks using artificial neural network technique. Modeling Earth Systems and Environment, 8(1), 163-172. https://doi.org/10.1007/s40808-020-01068-2.
Jahani, A., Hatef Rabiee, Z. and Saffariha, M., 2021. Modeling and prediction of the aesthetics of urban parks based on landscape complexity criterion. Journal of Natural Environment, 74(1), 27-40. https://doi.org/10.22059/jne.2021.305142.2020. (In Persian with English abstract).
Jahani, A. and Mohammadi Fazel, A., 2016. Aesthetic quality modeling of landscape in urban green space using artificial neural network. Journal of Natural Environment, 69(4), 951-963. https://doi.org/10.22059/jne.2017.127667.949. (In Persian with English abstract).
Jahani, A. and Rayegani, B., 2020. Forest landscape visual quality evaluation using artificial intelligence techniques as a decision support system. Stochastic Environmental Research and Risk Assessment, 34(10), 1473-1486. https://doi.org/10.1007/s00477-020-01832-x.
Jahani, A. and Saffariha, M., 2020. Aesthetic preference and mental restoration prediction in urban parks: An application of environmental modeling approach. Urban Forestry & Urban Greening, 54, 126775. https://doi.org/10.1016/j.ufug.2020.126775.
Jahani, A., Saffariha, M. and Ghiyasi, S., 2019. Evaluating the aesthetic quality of the landscape in the environment: A review of the concepts and scientific developments in the world. International Journal Environmental Science and Bioengineering, 8(1), 35-44. https://doi.org/10.22034/uoe.2019.103618.
Kazemeini, F., Rezaei, R. and Arabi, S.A., 2013. Investigating the values ​​of protected areas on the development of tourism and ecotourism. The Second National Conference on Tourism and Nature Tourism in Iran, 22nd June Hamedan, Iran. (In Persian with English abstract).
Khaleghpanah, R., Jahani, A., Khorasani, N. and Goshtasb, H., 2019. Prediction model of citizens' satisfaction in urban parks using artificial neural network. Journal of Natural Environment. 72(2), 239-250. https://doi.org/10.22059/jne.2019.267929.1572. (In Persian with English abstract).
Lothian, A., 1999. Landscape and the philosophy of aesthetics: Is landscape quality inherent in the landscape or in the eye of the beholder?. Landscape and Urban Planning, 44(4), 177-198. https://doi.org/10.1016/S0169-2046(99)00019-5.
Moshiri, S., 2002. A review on chaos and its applications in economics. Iranian Journal of Economic Research, 4(12), 29-68. (In Persian with English abstract).
Nordh, H. and Østby, K., 2013. Pocket parks for people–A study of park design and use. Urban Forestry & Urban Greening, 12(1), 12-17. https://doi.org/10.1016/j.ufug.2012.11.003.
Palmer, J.F., 2004. Using spatial metrics to predict scenic perception in a changing landscape: Dennis, Massachusetts. Landscape and Urban Planning. 69(2-3), 201-218. https://doi.org/10.1016/j.landurbplan.2003.08.010.
Pflüger, Y., Rackham, A. and Larned, S., 2010. The aesthetic value of river flows: An assessment of flow preferences for large and small rivers. Landscape and Urban Planning, 95(1-2), 68-78. https://doi.org/10.1016/j.landurbplan.2009.12.004.
Real, E., Arce, C. and Sabucedo, J.M., 2000. Classification of landscapes using quantitative and categorical data, and prediction of their scenic beauty in north-western Spain. Journal of Environmental Psychology. 20(4), 355-373. https://doi.org/10.1006/jevp.2000.0184.
Saeidi, S. and Salmanmahiny, A., 2015. Modeling landscape aesthetic values using artificial neural network method (case study: Ziarat watershed basin, Gorgan, Golestan). Environmental Researches. 5(10), 3-10. (In Persian with English abstract).
Saeidi, S., Mohammadzadeh, M., Salmanmahiny, A. and Mirkarimi, S.H., 2014. Survey of different methods of evaluating landscape aesthetic quality. Environment and Development Journal. 4(8), 59-70. (In Persian with English abstract).
Saeidi, S., Mohammadzadeh, M., Salmanmahiny, A. and Mirkarimi, S.H., 2017. Performance evaluation of multiple methods for landscape aesthetic suitability mapping: a comparative study between multi-criteria evaluation, logistic regression and multi-layer perceptron neural network. Land Use Policy. 67, 1-12. https://doi.org/10.1016/j.landusepol.2017.05.014.
Shams, S.R., Jahani, A., Moeinaddini, M. and Khorasani, N., 2020. Air carbon monoxide forecasting using an artificial neural network in comparison with multiple regression. Modeling Earth Systems and Environment. 6, 1467-1475. https://doi.org/10.1007/s40808-020-00762-5.
Sheikh Goodarzi, M., Jabbarian Amiri, B. and Jafari, S., 2017. Investigating the performance of artificial neural network-based model in simulating the urban growth using relative perating characteristics and landscape ecological metrics (study area: Hashtpar coastal city). Environmental Researches, 7(14), 181-190. https://dorl.net/dor/20.1001.1.20089597.1395.7.14.23.0. (In Persian with English abstract).
Shirani, H., 2017. Artificial Neural Networks with an Application in Agricultural and Natural Resource Sciences. Vali-E-Asr University, 295p. (In Persian)
Soleimanpourmoghadam, N., Agah, A. and Joulidehsar, F., 2013. Artificial neural network and its usage in the environment. In Proceedings 1st National Conference on Urban Services and Environment, 9th -10th October, Mashhad, Iran.
Tveit, M.S., 2009. Indicators of visual scale as predictors of landscape preference; a comparison between groups. Journal of Environmental Management. 90(9), 2882-2888. https://doi.org/10.1016/j.jenvman.2007.12.021.
Wang, Z., Li, M., Zhang, X. and Song, L., 2020. Modeling the scenic beauty of autumnal tree color at the landscape scale: A case study of Purple mountain, Nanjing, China. Urban Forestry & Urban Greening, 47, 126526. https://doi.org/10.1016/j.ufug.2019.126526.
Wang, R., Zhao, J. and Meitner, M.J., 2017. Urban woodland understory characteristics in relation to aesthetic and recreational preference. Urban Forestry and Urban Greening. 24, 55-61. https://doi.org/10.1016/j.ufug.2017.03.019.
Wang, R., Zhao, J., Meitner, M.J., Hu, Y. and Xu, X., 2019. Characteristics of urban green spaces in relation to aesthetic preference and stress recovery. Urban Forestry and Urban Greening. 41, 6-13. https://doi.org/10.1016/j.ufug.2019.03.005.
Weiss, G., 1992. Chaos hits wall street-the theory that is!. Business week, 2, 138-140.
Wherrett, J.R., 2000. Creating landscape preference models using internet survey techniques. Landscape Research. 25(1), 79-96. https://doi.org/10.1080/014263900113181.
Worboys, G.L., Lockwood, M., Kothari, A., Feary, S. and Pulsford, I., 2015. Protected Area Governance and Management. (Eds.), Anu Press. http://doi.org/10.22459/PAGM.04.2015.
Ye, M. and Hill, M.C., 2017. Global sensitivity analysis for uncertain parameters, models, and scenarios. In: Petropoulos, G.P. and Srivastava, P.K., Sensitivity Analysis in Earth Observation Modelling. Elsevier, pp. 177-210. https://doi.org/10.1016/B978-0-12-803011-0.00010-0.
Environmental Sciences
Volume 22, Issue 1
2024
Pages 123-144

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