تحلیل فضایی مخاطرات محیطی

تحلیل فضایی مخاطرات محیطی

مدل‌سازی ریسک کاهش تنوع و انقراض گونه‌های گیاهی در پارک ملی سرخه‌حصار

نویسندگان
زهرا مصفایی 1
علی جهانی 1
محمدعلی زارع چاهوکی 2
حمید گشتاسب میگونی 1
وحید اعتماد 2
1 دانشکده محیط زیست
2 دانشگاه تهران
10.52547/jsaeh.8.3.157
چکیده
شناسایی کامل مخاطرات و اولویت‌بندی آن‌ها در جهت عدم آسیب به طبیعت از اولین گام‌های مدیریت منابع طبیعی می‌باشد. لذا معرفی یک سیستم جامع قابل ارزیابی، درک و ارزشیابی، درجهت کنترل مخاطرات ضروری می‌باشد این پژوهش با هدف مدلسازی و پیش‌بینی میزان مخاطرات محیطی به دنبال افزایش تخریب در محیط‌های طبیعی به کمک شبکه عصبی مصنوعی (ANN) انجام گرفت. به این ترتیب تعداد 600 نمونه خاک و پوشش گیاهی در واحدهای همگن اکولوژیک برداشت شد. نمونه‌های خاک با روش ترانسکت نواری به توجه به عمق خاک و در چهار پروفیل (cm5،10،15،20) تهیه شد. نمونه های گیاهی نیز با روش سطح حداقل و با استفاده از پلات‌های مربع 2 2 با توجه به نوع، تراکم و پراکنش پوشش گیاهی برداشت شد. نمونه‌برداری در دو زون امن و سایر استفاده‌ها مدل‌سازی با کمک ANN در محیط متلب انجام شد. مدل بهینه پرسپترون چندلایه با دو لایه پنهان، تابع تانژانت سیگموئید و 19 نورون در هر لایه و ضریب تبیین 90/0 انتخاب شد. نتایج آنالیز حساسیت نشان داد، رطوبت وزنی خاک در شدت کاهش تنوع زیستی و ریسک سیل و همچنین افزایش ریسک انقراض گونه‌های اندمیک منطقه اثرگذار خواهد بود، و پس از آن وزن مخصوص ظاهری و حقیقی و تخلخل خاک و فاصله از جاده نقش کلیدی در تخریب پوشش گیاهی، افزایش سیل و افزایش ریسک انقراض پوشش گیاهی را دارند. لذا پیشنهاد می‌شود اقدامات مرتبط با احیای خاک و پوشش گیاهی در این پارک به منظور کاهش تخریب‌های آتی هرچه سریعتر انجام شود.
کلیدواژه‌ها
مدل‌سازی
شبکه عصبی مصنوعی
مخاطرات محیطی
پارک ملی
پوشش گیاهی

عنوان مقاله English

Risk modeling of plant species diversity and extinction in Sorkheh_hesar National Park

نویسندگان English

Zahra Mosaffaei 1
Ali Jahani 1
Mohammad ALi Zare Chahouki 2
Hamid Goshtasb Meygoni 1
Vahid Etemad 2
1 Collage of Environmen
2 University of Tehran
چکیده English

Risk modeling of plant species diversity and extinction in Sorkheh_hesar National Park



Zahra Mosaffaei1, Ali Jahani2*, 3MohammadAli ZareChahouki, 4Hamid GoshtasbMeygoni, 5Vahid Etemad



1 Masters of Natural Resources Engineering, Environmental Sciences, College of Environment, Karaj

*2Associate Professor, Department of Natural Environment and Biodiversity, College of Environment, Karaj.

3 Professor, Department of Restoration of arid and mountainous regions, University of Tehran, Karaj

4 Associate Professor, Department of Natural Environment and Biodiversity, College of Environment, Karaj

5 Associate Professor, Department of Forestry and Forest Economics, University of Tehran, Karaj





Abstract

Full identification of hazards and prioritizing them for non-harm to nature is one of the first steps in natural resource management. Therefore, introducing a comprehensive system of evaluation, understanding, and evaluation is essential for controlling hazards. This study aimed to model and predict environmental hazards following increased degradation in natural environments by ANN. Thus, 600 soil and vegetation samples were collected from inhomogeneous ecological units. Soil samples were prepared by strip transect method according to soil depth in four profiles (5, 10, 15, 20 cm). Vegetation samples were also collected using a minimum level method using 2 2 square plots according to the type, density, and distribution of vegetation. Sampling was done in two safe zones and other uses were modeled using ANN in MATLAB environment. The optimal model of multilayer perceptron with two hidden layers, sigmoid tangent function and 19 neurons per layer and coefficient of determination of 0.90. The results of sensitivity analysis showed that soil moisture content would be effective in decreasing biodiversity and flood risk as well as increasing the risk of extinction of endemic species in the region, and then the apparent and true gravity and soil porosity and distance from the road play a key role in the degradation of cover. Vegetation has increased flooding and extinction risk. Therefore, it is recommended that measures related to soil and vegetation restoration in this park be taken to reduce future damages as soon as possible.



Keywords: Modeling, Artificial Neural Network, Environmental Hazards, National Park, Vegetation

کلیدواژه‌ها English

modeling
Artificial neural network
Environmental hazards
National Park
Vegetation
14. Abella, S.R., Covington, W.W. 2006. Vegetation environment relationships and ecological speciesgroups of an arizona Pinus ponderosa landscape. Plant Ecology, 185(2): 225-268.
15. Adriaenssens, V., De Baets, B. Goethals, P.L. and De Pauw, N. 2004. Fuzzy rule-based models for decision support in ecosystem management. Science of the Total Environment, 319(1-3): pp.1-12.
16. Ashcroft, M.B. 2006. A method for improving landscape scale temperature predictions and the implications for vegetation modeling. Ecological Modelling, 197(3-4): 394-404.
17. Baral, H., Keenan, R.J. Sharma, S.K. Stork, N.E. and Kasel, S. 2014. Spatial assessment and mapping of biodiversity and conservation priorities in a heavily modified and fragmented production landscape in north-central Victoria, Australia. Ecological Indicators, 36: 552-562.
18. Biglouei, M., Akbarzadeh, H. A. and Yousefi, K. 2008. Effect of composted wood barks (CWBs) on some soil physical and hydraulic properties. International Journal of Applied Agricultural Research, 4(1): 1-14.
19. Connelly, J.W., Knick, S.T. Schroeder, M.A. and Stiver, S.J. 2004. Conservation assessment of greater sage-grouse and sagebrush habitats. All US Government Documents (Utah Regional Depository), 73.
20. Dewan, A. M., and Yamaguchi, Y. 2009. Land use and land cover change in Greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization. Applied geography, 29(3): 390-401.
21. Enea, M. and Salemi, G. 2001. Fuzzy approach to the environmental impact evaluation. Ecological Modelling, 136(2-3): pp.131-147.
22. Ervin, J. 2003. Rapid assessment of protected area management effectiveness in four countries. BioScience, 53(9): 833-841.
23. Goda, T., and Matsuoka, Y. 1986. Synthesis and analysis of a comprehensive lake model—with the evaluation of diversity of ecosystems. Ecological Modelling, 31(1-4):11-32.
24. Ibisch, P.L., Nowicki, C. Müller, R. and Araujo, N. 2002. Methods for the assessment of habitat and species conservation status in data-poor countries–case study of the Pleurothallidinae (Orchidaceae) of the Andean rain forests of Bolivia. Conservation of biodiversity in the Andes and the Amazon, 225-246.
25. 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): 61-9.
26. Jahani A. 2019. 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): 955-64.
27. Jahani, A., Feghhei, J. Makhdoum, M.F. and Omid, M. 2016. Optimized forest degradation model (OFDM): an environmental decision support system for environmental impact assessment using an artificial neural network. Journal of Environmental Planning and Management, 59(2): 222-244.
28. Kazmierczak, A. and Handley, J. 2011. The vulnerability concept: use within GRaBS. School of Environment and Development, University of Manchester, Manchester, UK.
29. Nikpour, N., Negaresh, H. Fotoohi, S. Hosseini, S.Z. Bahrami, S. 2019. Monitoring the trend of vegetation changes one of the most important indicators of land degradation (in Ilam province). Journal of Spatial Analysis Environmental Hazards, 5(4-3): 21-48.
30. Omann, I., Jäger, J. Grünberger, S. and Wesely, J. 2010. Report on the development of the conceptual framework for the vulnerability assessment. CCIA The CLIMSAVE Project, Methodology for Cross-Sectoral, Adaptation and Vulnerability in Europe.
31. Quan, R.C., Wen, X. and Yang, X. 2002. Effects of human activities on migratory waterbirds at Lashihai Lake, China. Biological Conservation, 108(3): 273-279.
32. Rouget, M., Richardson, D.M. Cowling, R.M. Lloyd, J.W. and Lombard, A.T. 2003. Current patterns of habitat transformation and future threats to biodiversity in terrestrial ecosystems of the Cape Floristic Region, South Africa. Biological conservation, 112(1-2): 63-85.
33. Tayebi, M.H., Tangestani, M.H. Roosta, H. 2010. Environmental impact assessment using neural network model: A case study of the jahani, Konarsiah and Kohe Gach salt pluges: SE Shiraz, Iran. Abstract of the 7th ISPRS TC VII Symposium. Austria, 557-562.
34. Thoisy, B., Richard-Hansen C. Goguillon B. Joubert P. Obstancias J. Winterton P. and Brosse, S. 2010. Rapid evaluation of threats to biodiversity: human footprint score and large vertebrate species responses in French Guiana. . Biodiversity Conservation. 19: 1567-1589.
35. Xiaofeng, L., Yi, Q. Diqiang, L. Shirong, L. Xiulei, W. Bo, W. and Chunquan, Z. 2011. Habitat evaluation of wild Amur tiger (Panthera tigris altaica) and conservation priority setting in north-eastern China. Journal of environmental management, 92(1): 31-42.