ارزیابی اثرات اکوتوریسم بر منطقه رودبار قصران و لواسانات با استفاده از چارچوب DPSIR

نوع مقاله : علمی - پژوهشی

نویسندگان

1 هیات علمی

2 علوم زمین

چکیده

اکوتوریسم یا طبیعت گردی گرایشی از صنعت گردشگری است که طی سالهای  اخیر توجه بسیاری از مسولین و مردم را به خود جلب کرده و یکی از اهرمهای توسعه اقتصادی و اجتماعی بسیاری از کشورهای توسعه یافته و در حال توسعه بوده است. از آنجا که فعالیت  غیر نظام مند اکوتوریسم می تواند اثرات منفی بر محیط زیست داشته باشد، ارزیابی  فعالیتهای اکوتوریسمی با استفاده از چارچوب ها و روش های معتبر علمی من جمله  DPSIRمی تواند در برنامه ریزی های مدیران این صنعت، موثر و مفید واقع گردد. هدف اصلی این پژوهش ارزیابی وضعیت اکوتوریسم در منطقه رودبار قصران و لواسانات  با استفاده از چارچوب DPSIR می باشد. هر کدام از پنج بخش این مدل ارزیابی مورد بررسی و تحلیل قرار گرفت و یافته ها در قالب جداولی ارائه گردیدند. طبق نتایج به دست آمده از طبقه بندی تصاویر سال های 2004 و 2016، فضاهای ساخته شده از 3625 متر مربع به 8744 متر مربع افزایش یافته است. از جمله دلایل این امر را افزایش جمعیت، نزدیکی به پایتخت و سهولت در رفت و آمد در نتیجه گسترش خانه های دوم و افزایش ساخت و ساز اماکن مرتبط با خدمات گردشگران دانست. ارزیابی های صورت گرفته و نتایج بدست آمده از این تحقیق می‌تواند به عنوان یک ساختار پشتیبان تصمیم برای مدیران و برنامه ریزان در این حوزه و اتخاذ استراتژی های مناسب جهت پیاده سازی اکوتوریسم پایدار، مورد استفاده قرار گیرند.

کلیدواژه‌ها


عنوان مقاله [English]

Evaluation of Ecotourism Effects on Rudbar-e Qasran and Lavasanat Zone Using the DPSIR Framework

نویسندگان [English]

  • Jamileh Tavakkoli-nia 1
  • Aliakbar Matkan 1
  • Mozaffar Sarrafi, 1
  • faezeh borbori 2
1 Associate Professor, Faculty of Earth Sciences, Shahid Beheshti University
2 earth
چکیده [English]

Ecotourism is a part of the tourism industry that has attracted the attention of many officials and people in recent years and it is one of the levers of economic and social development of many developed and developing countries. Since the non-systematic activity of the ecotourism can negatively affect the environment, evaluating the ecotourism activities using valid scientific frameworks and methods, such as DPSIR, can be effective and useful in the managers’ planning of this industry. The main purpose of this research was to investigate the ecotourism status in Rudbar-e Qasran and Lavasanat Zone using the DPSIR framework. Each of the five sections of this evaluation model was analyzed and the findings were presented in the form of a table. According to the results from the classification of images in 2004 and 2016, the constructed spaces have increased from 3625 square meters to 8744 square meters. One of the reasons for this can be the increase in the population, proximity to the capital, the ease of commuting, the expansion of second homes, and increasing the construction of tourist-related service sites.
The conducted evaluations and the obtained results of this research can be used as a decision support structure for managers and planners in this area to adopt appropriate strategies for implementing sustainable ecotourism.

کلیدواژه‌ها [English]

  • DPSIR
  • Sustainable Ecotourism
  • GIS
  • remote sensing
  1. منابع:
  2. محلاتی، صلاح الدین.(1380) درآمدی بر جهانگردی، چـاپ اول، انتشارات شهید بهشتی
  3. رنجبریان، بهرام.؛ زاهدی، محمد. (1384)، «شناخت گردشگری»، اصفهان، انتشارات چهار باغ
  4. Agyemang, I., McDonald, A., & Carver, S. (2007). Application of the DPSIR framework to environmental degradation assessment in northern Ghana. Paper presented at the Natural Resources Forum.
  5. Anderies, J., Janssen, M., & Ostrom, E. (2004). A framework to analyze the robustness of social-ecological systems from an institutional perspective. Ecology and society, 9(1).
  6. Bartelmus, P., Pinter, L., & Hardi, P. (2005). Sustainable development indicators, proposals for a way forward. IISD/United Nations Division for Sustainable Development, NewYork.
  7. Bayot, K., Paran, J., Santiago, M., & Villanueva, C. (2012). DPSIR Assessment of Donsol Fisheries and Eco-tourism.
  8. Begley, S. (1996). Beware of the humans (eco-tourism is hurting ecosystems) newsweek.
  9. Bell, S. (2012). DPSIR= A problem structuring method? An exploration from the “Imagine” approach. European Journal of Operational Research, 222(2), 350-360.
  10. Bidone, E., & Lacerda, L. (2004). The use of DPSIR framework to evaluate sustainability in coastal areas. Case study: Guanabara Bay basin, Rio de Janeiro, Brazil. Regional Environmental Change, 4(1), 5-16.
  11. Board, M. E. A. (2005). Ecosystems and Human well-being, Biodiversity Synthesis, A Report of the Millennium Ecosystem Assessment. World Resources Institute, Washington, DC.
  12. Caeiro, S., Mourão, I., Costa, M., Painho, M., Ramos, T., & Sousa, S. (2004). Application of the DPSIR model to the Sado Estuary in a GIS context–Social and Economical Pressures. Paper presented at the Proceedings of 7th Conference on Geographic Information Science. Crete University Press. AGILE, Crete.
  13. Cobbinah, P. B. (2015). Contextualising the meaning of ecotourism. Tourism Management Perspectives, 16, 179-189.
  14. de Jonge, V. N., Pinto, R., & Turner, R. K. (2012). Integrating ecological, economic and social aspects to generate useful management information under the EU Directives'‘ecosystem approach’. Ocean & Coastal Management, 68, 169-188.
  15. Dixon, B., & Candade, N. (2008). Multispectral landuse classification using neural networks and support vector machines: one or the other, or both? International Journal of Remote Sensing, 29(4), 1185-1206.
  16. Gigović, L., Pamučar, D., Lukić, D., & Marković, S. (2016). GIS-Fuzzy DEMATEL MCDA model for the evaluation of the sites for ecotourism development: A case study of “Dunavski ključ” region, Serbia. Land Use Policy, 58, 348-365.
  17. Goodwin, H. (1996). In pursuit of ecotourism. Biodiversity & Conservation, 5(3), 277-29
  18. Guo, Y., De Jong, K., Liu, F., Wang, X., & Li, C. (2012). A comparison of artificial neural networks and support vector machines on land cover classification Computational Intelligence and Intelligent Systems (pp. 531-539): Springer
  19. Jiang, X., Lin, M., & Zhao, J. (2011). Woodland cover change assessment using decision trees, support vector machines and artificial neural networks classification algorithms. Paper presented at the Intelligent Computation Technology and Automation (ICICTA), 2011 International Conference on.
  20. Kagalou, I., Leonardos, I., Anastasiadou, C., & Neofytou, C. (2012). The DPSIR approach for an integrated river management framework. A preliminary application on a Mediterranean site (Kalamas River-NW Greece). Water resources management, 26(6), 1677-1692.
  21. Karageorgis, A. P., Kapsimalis, V., Kontogianni, A., Skourtos, M., Turner, K. R., & Salomons, W. (2006). Impact of 100-year human interventions on the deltaic coastal zone of the inner Thermaikos Gulf (Greece): A DPSIR framework analysis. Environmental Management, 38(2), 304-315.
  22. Kristensen, P. (2004). The DPSIR framework. National Environmental Research Institute, Denmark, 10.
  23. Mangi, S. C., Roberts, C. M., & Rodwell, L. D. (2007). Reef fisheries management in Kenya: Preliminary approach using the driver–pressure–state–impacts–response (DPSIR) scheme of indicators. Ocean & Coastal Management, 50(5), 463-480.
  24. Maxim, L., & Spangenberg, J. H. (2009). Driving forces of chemical risks for the European biodiversity. Ecological Economics, 69(1), 43-54.
  25. Mokhtari, M., & Najafi, A. (2015). Comparison of Support Vector Machine and Neural Network Classification Methods in Land Use Information Extraction through Landsat TM Data. Journal of Water and Soil Science, 19(72), 35-45. doi: 10.18869/acadpub.jstnar.19.72.4
  26. Moradi Estalkhzir, G. (2015). Economic Effects of Rural Tourism based on (DPSIR) by Using Fuzzy-TOPSIS Method (Case Study: city of Rezvanshahr). European Online Journal of Natural and Social Sciences: Proceedings, 4(3 (s)), pp. 789-800.
  27. OECD. (1993). OECD Core Set of Indicators for Environmental Performance Reviews. Paris: Organisation for Economic Co 鄄 operation and Development.
  28. Ostrom, E. (2009). A general framework for analyzing sustainability of social-ecological systems. Science, 325(5939), 419-422.
  29. Pacheco, A., Carrasco, A., Vila-Concejo, A., Ferreira, Ó., & Dias, J. (2007). A coastal management program for channels located in backbarrier systems. Ocean & Coastal Management, 50(1), 119-143.
  30. Pirrone, N., Trombino, G., Cinnirella, S., Algieri, A., Bendoricchio, G., & Palmeri, L. (2005). The Driver-Pressure-State-Impact-Response (DPSIR) approach for integrated catchment-coastal zone management: preliminary application to the Po catchment-Adriatic Sea coastal zone system. Regional Environmental Change, 5(2-3), 111-137.
  31. Reby, D., Lek, S., Dimopoulos, I., Joachim, J., Lauga, J., & Aulagnier, S. (1997). Artificial neural networks as a classification method in the behavioural sciences. Behavioural processes, 40(1), 35-43.
  32. Richards, J. A., & Richards, J. (1999). Remote sensing digital image analysis (Vol. 3): Springer.
  33. Roura-Pascual, N., Richardson, D. M., Krug, R. M., Brown, A., Chapman, R. A., Forsyth, G. G., . . . Van Wilgen, B. W. (2009). Ecology and management of alien plant invasions in South African fynbos: accommodating key complexities in objective decision making. Biological Conservation, 142(8), 1595-1604.
  34. Stanners, D., & Bourdeau, P. (1995). Europe's environment: the Dobris assessment Europe's environment: the Dobris assessment: Office for Official Publication of the European Communities
  35. Svarstad, H., Petersen, L. K., Rothman, D., Siepel, H., & Wätzold, F. (2008). Discursive biases of the environmental research framework DPSIR. Land Use Policy, 25(1), 116-125.
  36. Svozil, D., Kvasnicka, V., & Pospichal, J. (1997). Introduction to multi-layer feed-forward neural networks. Chemometrics and intelligent laboratory systems, 39(1), 43-62.
  37. Turner, R. K., Subak, S., & Adger, W. N. (1996). Pressures, trends, and impacts in coastal zones: interactions between socioeconomic and natural systems. Environmental management, 20(2), 159-173.