نشریه محیط زیست طبیعی

مدل پیش‌بینی ارزیابی اثر گردشگری بر درصد پوشش تاجی گیاهی پارک ملی و پناهگاه حیات‌وحش قمیشلو

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشیار دانشکده محیط زیست کرج

2 دکترای مرتعداری، گروه احیای مناطق خشک و کوهستانی، دانشکده منابع‌طبیعی، دانشگاه تهران

چکیده

مدیریت اکوسیستم‌های طبیعی در مناطق تحت حفاظت گردشگری اثرات زیست محیطی بسیاری ایجاد می‌کند که یک موضوع چالش‌انگیز جهانی در زمینه حفاظت است. هدف از این پژوهش مدل‌سازی کاهش درصد پوشش تاجی گیاهی جهت ارزیابی اثر گردشگری با استفاده از شبکه عصبی مصنوعی و تعیین میزان اثرگذاری متغیرهای اکولوژیکی و شدت گردشگری بر آن است. پژوهش حاضر در پارک ملی و پناه‌گاه حیات وحش قمیشلو با مساحت 10 هکتار زون تفرج متمرکز و 100 هکتار زون تفرج گسترده انجام شده است. در این مطالعه از 100 قطعه نمونه آماربرداری و اندازه‌گیری متغیرهای اکولوژیکی و گردشگری و تغییرات درصد پوشش تاجی گیاهی در طی یک‌سال (بهار 1396 تا بهار 1397) استفاده شد. روش مدل‌سازی شبکه عصبی مصنوعی جهت پیش‌بینی کاهش درصد پوشش تاجی گیاهی با استفاده از 11 متغیر محیطی انجام شده است. با توجه به نتایج، مدل با ساختار 1-8-11 (11 متغیر ورودی، 8 نورون در لایه مخفی و یک متغیر خروجی) با توجه به بیشترین مقدار ضریب تبیین در سه دسته داده آموزش، اعتبارسنجی و آزمون معادل 95/0، 87/0 و 93/0، بهترین عملکرد بهینه‌سازی ساختار را نشان می‌دهد. بر این اساس طبقه شدت گردشگری، شیب زمین، شوری خاک، عمق خاک و درصد ماده آلی خاک با ضریب اثرگذاری 59/8، 02/2، 88/1، 81/1 و 65/1 به ترتیب بیشترین تأثیر را در میزان کاهش درصد پوشش تاجی گیاهی در منطقه از خود نشان می‌دهند. مدل ارائه شده در این پژوهش یک سیستم پشتیبان تصمیم‌گیری در ارزیابی اثرات گردشگری در مناطق تحت حفاظت شناخته شده و امکان پیش-بینی میزان کاهش درصد پوشش تاجی گیاهی در زون‌های تفرجی پارک‌های ملی را فراهم می‌کند.

کلیدواژه‌ها

عنوان مقاله [English]

The prediction model of tourism impact assessment in vegetation canopy cover of Qhamishloo National park and Wildlife Refuge

نویسندگان [English]

  • Ali Jahani 1
  • Maryam Saffariha 2

1 College of Environment

2 Ph.D in Rangeland Management, College of Natural Resources, University of Tehran, Tehran, Iran

چکیده [English]

The protected areas are managed by ecological targets including ecological and biological protection of nature and at the same time the goal of tourism use. The management of natural ecosystems in protected areas and where tourism creates many environmental impacts is a global challenging issue in the field of protection. The aim of this study is to model the vegetation canopy cover reduction in Qhamishloo national park and wildlife refuge in order to evaluate the impact of tourism using artificial neural network to determine the influence of the ecological variables and severity of tourism on protected areas. This study has been performed in Qhamishloo national park and wildlife refuge with an area of 10 hectares intensive tourism zone and 100 hectares extensive tourism zone. In this study to evaluate the impact of tourism on vegetation canopy cover reduction, 100 inventory sample plots were used to measure ecological and tourism variables and vegetation canopy cover density changes along one year (spring 2017-2018). Artificial neural network modeling method has been used to predict the reduction of vegetation canopy cover density using 11 environmental variables. According to the results, the model with the structure of 1-8-11 (11 input variables, 8 neurons in hidden layer and 1 output variable) with regard to the maximum coefficient of determination in the three categories of training, validation and test data set which equal 0.95, 0.87, and 0.93 respectively, declare the best function of structural optimization. On this basis, the intensity of tourism, land slope, soil salinity, soil depth and percentage of organic matter of soil with the coefficient of determination of 8.59, 2.02, 1.88, 1.81 and 1.65 respectively show the highest effect on the vegetation canopy cover reduction in the region. The proposed model in this study provides a decision support system in tourism impacts assessment in the protected areas and enables prediction of the vegetation canopy cover reduction in recreational zones of national parks.

کلیدواژه‌ها [English]

  • National park
  • canopy cover
  • tourism
  • artificial neural network
  • recreational zone
Aghajani, H., Marvi Mohadjer, M.R., Jahani, A., Asef, M.R., Shirvany, A., & Azaryan, M. 2014. Investigation of affective habitat factors affecting on abundance of wood macro fungi and sensitivity analysis using the artificial neural network (case study: Kheyrud forest, Noshahr). Iranian Journal of Forest and Poplar Research, 21(4), 617-628.)in Persian)
Bahraminejad, M., Rayegani, B., Jahani, A., & Nezami, B. 2018. Proposing an early-warning system for optimal management of protected areas (Case study: Darmiyan protected area, Eastern Iran), Journal for Nature Conservation, 46, 79-88.
Canteiro, M., Córdova-Tapiab, F., & Brazeiro, A. 2018. Tourism impact assessment: A tool to evaluate the environmental impacts of touristic activities in Natural Protected Areas. Tourism Management Perspectives, 28, 220–227.
Growcock, A., 2005. Impacts of camping and trampling on Australian alpine and subalpine vegetation and soils. PhD Thesis, School of Environmental and Applied Sciences, Griffith University, Gold Coast.
Jahani, A. 2016. Modeling of forest canopy density confusion in environmental assessment using artificial neural network. Journal of Forest and Poplar Research, 24(2), 310-322. )in Persian)
Jahani, A. 2017. Sycamore failure hazard risk modeling in urban green space. Journal of Spatial Analysis Environmental Hazards, 3(4), 35-48. )in Persian)
Jahani, A. 2019. 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.
Jahani, A., & Mohammadi Fazel, A. 2017. Aesthetic quality modeling of landscape in urban green space using artificial neural network, Journal of Natural Environment, 69(4), 951-963.
Jahani, A., Goshtasb, H., Saffariha, M. 2020. Tourism impact assessment modeling in vegetation density of protected areas using data mining techniques. Land Degradation & Development, in press: DOI:10.1002/ldr.3549
Jahani, A., Makhdoum, F.M., Feghhi, J., & Omid, M. 2016. Environmental decision support systems (EDSSs): the study of concepts, developments and challenges from past to present. Journal of Environmental researches, 7(13), 175-188. )in Persian)
Jahani, A., Makhdoum, M.F., Feghhi, J., & Etemad, V. 2011. Landscape quality appraisal from look outs for ecotourism land use (Case Study: Patom District of Kheyrud Forest). Journal of Environmental researches, 2(3), 13-20. )in Persian)
Kalantary, S., Jahani, A., Pourbabaki, R., & Beigzadeh, Z. 2019. Application of ANN modeling techniques in the prediction of the diameter of PCL/gelatin nanofibers in environmental and medical studies. RSC Advances, 9 (43), 24858-24874.
Kelly, C., Pickering, C.M., & Buckley, R.C., 2003. Impacts of tourism on threatened plants taxa and communities in Australia. Ecological Restoration and Management, 4, 37–44.
Khaleghpanah R., Jahani, A., Khorasani, N., & Goshtasb, H. 2019. Prediction model of citizens' satisfaction in urban parks using artificial neural network. Natural Environment Journal. 72(2), 239-250. )in Persian)
Marion, L., Leung, J.Y.F., Eagleston, H., & Burroughs, K. 2016. A Review and Synthesis of Recreation Ecology Research Findings on Visitor Impacts to Wilderness and Protected Natural Areas, J. Forestry, 114(3), 352–362.
Moghaddam, M. R. 1998. Rangeland and range management in Iran. 6th edition. University of Tehran press, Tehran.
Mosaffaei, Z., Jahani, A., Zare Chahouki, M.A., Goshtasb, H., Etemad, V., & Saffariha, M. 2020. Soil texture and plant degradation predictive model (STPDPM) in national parks using artificial neural network (ANN). Modeling Earth Systems and Environment 6:715–729.
Pongpattananurak, N. 2018. Impacts from tourism development and agriculture on forest degradation in Thap Lan National Park and adjacent areas. Agriculture and Natural Resources, 52, 290-297.
Rezazadeh, S., Jahani, A., Makhdoum, M., & Goshtasb Meigooni, H. 2017. Evaluation of the Strategic Factors of the Management of Protected Areas Using SWOT Analysis—Case Study: Bashgol Protected Area-Qazvin Province. Open Journal of Ecology, 7, 55-68.
Saffariha, M., Azarnivand, H., & Tavili, A. 2014. Effects of grazed exclosure on some of nutrient elements of aerial and underground organs of Artemisia sieberi, Stipa hohenacheriana and Salsola rigida. International Journal of Agronomy and Agricultural Research (IJAAR), 4(2): 62-70.
Shirani Sarmazeh, N., Jahani, A., Goshtasb, H., & Etemad, V. 2018a. Ecological Impacts Assessment of Recreation on Quality of Soil and Vegetation in Protected Areas (Case Study: Qhamishloo National park and Wildlife Refuge), Journal of Natural Environment, 70(4), 881-891. )in Persian)
Shirani Sarmazeh, N., Jahani, A., Goshtasb, H., & Etemad, V. 2018b. Environmental Impact Assessment of Ecotourism in Protected Areas, Journal of Environment and Development, 9(17), 25-36. )in Persian)
Silva, E.S., Hassani, H., Heravi, S., & Huang, X. 2019. Forecasting tourism demand with denoised neural networks. Annals of Tourism Research, 74: 134–154.
Whinam, J., Chilcott, & N.M., 2003. Impacts after four years of experimental trampling on alpine/sub-alpine environments in western Tasmania. Journal of Environmental Management, 67, 339–351.
Zhang, C., Fyall, A. & Zheng, Y. 2014. Heritage and Tourism Conflict within World Heritage Sites in China: A Longitudinal Study. Current Issues in Tourism, 18 (2), 110-136.
نشریه محیط زیست طبیعی
دوره 73، شماره 2
مرداد 1399
صفحه 257-270