دوره 12، شماره 1 و 45 - ( 5-1404 )
جلد 12 شماره 1 و 45 صفحات 62-49 |
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shabani S, mohseni B, kornejady A, ahmadi A, faramarzi H, silakhori E. Spatial Prediction of Deforestation in Iran’s Hyrcanian Forests: Integrating Climatic, Topographic, and Anthropogenic Factors. Journal of Spatial Analysis Environmental Hazards 2025; 12 (1 and 45) : 4
URL: http://jsaeh.khu.ac.ir/article-1-3492-fa.html
URL: http://jsaeh.khu.ac.ir/article-1-3492-fa.html
شعبانی سعید، محسنی بهروز، کرنژادی آیدینگ، احمدی اکرم، فرامرزی حسن، سیلاخوری اسماعیل. پیش‌بینی مکانی جنگل‌زدایی در جنگل‌های هیرکانی ایران: تلفیق عوامل اقلیمی، توپوگرافی و انسانی. تحلیل فضایی مخاطرات محیطی. 1404; 12 (1 و 45) :49-62
سعید شعبانی1 
، بهروز محسنی2 ، آیدینگ کرنژادی2 ، اکرم احمدی2 ، حسن فرامرزی3 ، اسماعیل سیلاخوری4
، بهروز محسنی2 ، آیدینگ کرنژادی2 ، اکرم احمدی2 ، حسن فرامرزی3 ، اسماعیل سیلاخوری4
1- سازمان تحقیقات آموزش و ترویج کشاورزی ، s.shabani@areeo.ac.ir
2- سازمان تحقیقات آموزش و ترویج کشاورزی
3- دانشکدە منابع طبیعی و علوم دریایی نور، مازندران
4- دانشگاه علوم کشاورزی گرگان
2- سازمان تحقیقات آموزش و ترویج کشاورزی
3- دانشکدە منابع طبیعی و علوم دریایی نور، مازندران
4- دانشگاه علوم کشاورزی گرگان
چکیده: (1405 مشاهده)
پدیده جنگل‌زدایی یکی از چالش‌ها و مخاطرات اصلی در اکوسیستم‌های جنگلی از جمله جنگل‌های هیرکانی است که تحت تأثیر عوامل متنوع طبیعی و انسانی رخ می‌دهد. این مطالعه با هدف مدلسازی احتمال وقوع جنگل‌زدایی در حوزه جنگلداری لوه واقع در شمال ایران انجام شد. داده‌های این پژوهش شامل ۱۰۴ نقطه جنگل‌زدایی ثبت‌شده و ۱۴ متغیر تبیینی بود که از طریق تحلیل مکانی در محیط GIS و داده‌های اقلیمی، توپوگرافی و انسانی استخراج گردید. برای تحلیل رابطه بین متغیرها و پیش‌بینی احتمال جنگل‌زدایی، از دو مدل آماری شامل رگرسیون لجستیک و مدل جمعی تعمیم‌یافته استفاده شد. نتایج نشان داد که مدل جمعی تعمیم‌یافته با ضریب کاپای 84/0 و سطح زیر منحنی عملکرد برابر 956/0 عملکرد بهتری نسبت به مدل لجستیک داشته و توزیع واقع‌گرایانه‌تری از سطوح خطر ارائه داده است. متغیرهای فاصله از جاده، شیب، اثر باد و ارتفاع از سطح دریا بیشترین تأثیر را بر احتمال جنگل‌زدایی داشتند. بر اساس خروجی مدل GAM، حدود ۲۰ درصد منطقه در طبقه خطر بالا و بسیار بالا قرار گرفت. یافته‌ها حاکی از نقش تعیین‌کننده زیرساخت‌های دسترسی، فشار انسانی و عوامل اقلیمی در تسریع روند جنگل‌زدایی است. نتایج این پژوهش می‌تواند در اولویت‌بندی مداخلات حفاظتی، بازنگری در توسعه جاده‌ها و برنامه‌ریزی فضایی مؤثر برای مدیریت پایدار جنگل‌های شمال کشور مورد استفاده قرار گیرد.
شماره‌ی مقاله: 4
فهرست منابع
1. میرآخورلو، خ.، اخوان، ر.، 1396. ارزیابی تغییرات سطح جنگل‌های هیرکانی 1395 تا1383. طبیعت ایران، 2 (3)، 40–45.
2. یخکشی، ع.، آفتاب طلب، ن.، 1387. منابع تجدیدپذیر و توسعه پایدار، انتشارات سازمان حفاظت محیط زیست، 176 ص.
3. Abdullah, S. A.; and Nakagoshi, N. (2007). Forest fragmentation and its correlation to human land use change in the state of Selangor, peninsular Malaysia. Forest Ecology and Management, 241 (1-3), 39-48.
4. Abman, R.; and Carney, C. (2020). Land rights, agricultural productivity, and deforestation. Food Policy, 94, 101841.
5. Ahmadi, V. (2018). Using GIS and Artificial Neural Network for Deforestation Prediction. Remote Sensing, 2018030048, pp. 15. https://doi: 10.20944/preprints201803.0048.v1
6. Bera, B.; Saha, S.; and Bhattacharjee, S. (2020). Forest cover dynamics (1998 to 2019) and prediction of deforestation probability using binary logistic regression (BLR) model of Silabati watershed, India. Trees, Forests and People, 100034.
7. 034
8. Chen, S.; Woodcock, C.; Dong, L.; Tarrio, K.; Mohammadi, D.; and Olofsson, P. (2024). Review of drivers of forest degradation and deforestation in Southeast Asia. Remote Sensing Applications: Society and Environment, 33, 101129, 11 pp.
9. Davison, C. W.; Rahbek, C.; and Morueta-Holme, N. (2021). Land-use change and biodiversity: Challenges for assembling evidence on the greatest threat to nature. Global Change Biology, 27, 5414-5429. https://doi:10.1111/gcb.15846
10. Dutt, S.; Batar, A. K.; Sulik, S.; and Kunz, M. (2024). Forest ecosystem on the edge: Mapping forest fragmentation susceptibility in Tuchola Forest, Poland. Ecological Indicators, 161: 111980.
11. FAO (Food and Agriculture Organization). (2020). Global Forest Resources Assessment 2020, (Iran Report). Rome, 54 pp. https://openknowledge.fao.org/server/api/core/bitstreams/70aae432-4a3e-4d96-9083-f0800bd959af/content
12. Feng, Y.; Yang, Q.; Tong, X.; and Chen, L. (2018). Evaluating land ecological security and examining its relationships with driving factors using GIS and generalized additive model. Science of the Total Environment, 633, 1469-1479. https://doi: 10.1016/j.scitotenv.2018.03.272
13. Foley, J. A.; DeFries, R.; Asner, G. P.; Barford, C.C.; Bonan, G.; Carpenter, S. R.; Chapin, F. S.; Coe, M. T.; Daily, G. C.; Gibbs, H.; Helkowski, J. H.; Holloway, T.; Howard, E.; Kucharik, C. J.; Patz, J.; Prentice, I. C.; Ramankutty, N.; and Snyder, P. K. (2005). Global consequences of land use. Science. Science, 309 (5734), 570-574. https://doi: 10.1126/science.11117
14. Hosonuma, N.; Herold, M.; Sy, V. D.; Fries, R. S. D.; Brockhaus, M.; Verchot, L.; Angelsen, A.; and Romijn, E. (2012). An assessment of deforestation and forest degradation drivers in developing countries. Environmental Research Letters, 7 (4), 044009. https://doi:10.1088/1748-9326/7/4/044009
15. Jellouli, O.; and Bernoussi, A.S. (2022). The impact of dynamic wind flow behavior on forest fire spread using cellular automata: Application to the watershed BOUKHALEF (Morocco). Ecological Modelling, 468, 109938.
16. Kayet, N.; Pathak, K.; Kumar, S.; Singh, C.P.; Chowdary, V.M.; Chakrabarty, A.; Sinha, N.; Shaik, I.; Ghosh A. (2021). Deforestation susceptibility assessment and prediction in hilltop mining-affected forest region. Journal of Environmental Management, 112504.
17. Knapp, M.; Strobl, M.; Venturo, A.; Seidl, M.; Jakubíkova, L.; Tajovský, K.; Kadlec, T.; and Gonzalez, E. (2022). Importance of grassy and forest non-crop habitat islands for overwintering of ground-dwelling arthropods in agricultural landscapes: A multi-taxa approach. Biological Conservation, 275, 109757.
18. Laurance, W.F.; Goosem, M.; and Laurance, S.G.W. (2009). Impacts of roads and linear clearings on tropical forests. Trends in Ecology and Evolution, 24 (12), 659-669. https://doi:10.1016/j.tree.2009.06.009
19. Looze, B.E. (2009). Forest fragmentation patterns in Maine watersheds and prediction of visible crown diameter in recent undisturbed forest, MSc thesis, University of Wisconsin-Superior, 130 pp. https://digitalcommons.library.umaine.edu/cgi/viewcontent.cgi?article=2772&context=etd
20. López, S. 2022. Deforestation, forest degradation, and land use dynamics in the Northeastern Ecuadorian Amazon. Applied Geography, 145, 102749.
21. López-Bedoya, P. A.; Bohada-Murillo, M.; Ángel-Vallejo, M. C.; Audino, L. D.; Davis, A. L. V.; Gurr, G.; and Noriega, J. A. (2022). Primary forest loss and degradation reduces biodiversity and ecosystem functioning: A global meta-analysis using dung beetles as an indicator taxon. Journal of Applied Ecology, 59, 1572-1585.
22. Mirakhorlou, Kh.; and Akhavan, R. (2017). Area changes of Hyrcanian Forests during 2004 to 2016. Nature Iran, 2 (3), 40-45. https://doi:10.22092/irn.2017.112967 (in Persian)
23. Netzel, P.; Tyminska, L.; Feleha, D. D.; Socha, J. (2024). New approach to assess forest fragmentation based on multiscale similarity index. Ecological Indicators, 158, 111530. https://doi: 10.1016/j.ecolind.2023.111530
24. Ojoatre, S.; Zhang, C.; Yesuf, G.; and Rufino, M.C. (2023). Mapping deforestation and recovery of tropical montane forests of East Africa. Frontiers in Environmental Science, 11, 1084764. 17 pp. https://www.10.3389/fenvs.2023.1084764
25. Sahana, M.; Hong, H.; Sajjad, H.; Liu, J.; and Zhu, A.X. (2018). Assessing deforestation susceptibility to forest ecosystem in Rudraprayag district, India using fragmentation approach and frequency ratio model. Science of the Total Environment, 627, 1264-1275.
26. Sahana, M.; Hong, H.; Sajjad, H.; Liu, J.; and Zhu, A.X. (2018). Assessing deforestation susceptibility to forest ecosystem in Rudraprayag district, India using fragmentation approach and frequency ratio model. Science of the Total Environment, 627, 1264-1275.
27. Silva, A.C.O.; Fonseca, L.M.G.; Körting, T.S.; and Escada, M.I.S. (2020). A spatio-temporal Bayesian Network approach for deforestation prediction in an Amazon rainforest expansion frontier. Spatial Statistics, 35, 100393.
28. Silvério, D. V.; Brando, P. M.; Bustamante, M. M. C.; Putz, F. E.; Marra, D. M.; Levick, S. R.; and Trumbore, S. E. (2019). Fire, fragmentation, and windstorms: A recipe for tropical forest degradation. Journal of Ecology, 107 (2), 656-667.
29. Tavares das Neves, P. B.; Blanco, C. J. C.; Duarte, A. A. A. M.; das Neves, F. B. S.; das Neves, L. B. S.; de Paula dos Santos, M. H. (2021). Amazon rainforest deforestation influenced by clandestine and regular roadway network. Land Use Policy, 108, 105510.
30. Worku, A. (2023). Review on drivers of deforestation and associated socio-economic and ecological impacts. Advances in Agriculture. Food Science and Forestry, 11 (1), 1-12. https://creativecommons.org/licenses/by-nc-nd/4.0/
31. Yakhkeshi, A.; and Aftabtalab, N. (2008). Renewable resources and sustainable development. Department of Environment press, 176 pp. (in Persian)
32. Yamamoto, Y.; Shigetomi, Y.; Ishimura, Y. and Hattori, M. (2019). Forest change and agricultural productivity: Evidence from Indonesia. World Development, 114, 196-207. https://ideas.repec.org/a/eee/wdevel/v114y2019icp196-207.html
33. Abdullah, S. A.; and Nakagoshi, N. (2007). Forest fragmentation and its correlation to human land use change in the state of Selangor, peninsular Malaysia. Forest Ecology and Management, 241 (1-3), 39-48.
34. Abman, R.; and Carney, C. (2020). Land rights, agricultural productivity, and deforestation. Food Policy, 94, 101841.
35. Ahmadi, V. (2018). Using GIS and Artificial Neural Network for Deforestation Prediction. Remote Sensing, 2018030048, pp. 15. https://doi: 10.20944/preprints201803.0048.v1
36. Bera, B.; Saha, S.; and Bhattacharjee, S. (2020). Forest cover dynamics (1998 to 2019) and prediction of deforestation probability using binary logistic regression (BLR) model of Silabati watershed, India. Trees, Forests and People, 100034.
37. 034
38. Chen, S.; Woodcock, C.; Dong, L.; Tarrio, K.; Mohammadi, D.; and Olofsson, P. (2024). Review of drivers of forest degradation and deforestation in Southeast Asia. Remote Sensing Applications: Society and Environment, 33, 101129, 11 pp.
39. Davison, C. W.; Rahbek, C.; and Morueta-Holme, N. (2021). Land-use change and biodiversity: Challenges for assembling evidence on the greatest threat to nature. Global Change Biology, 27, 5414-5429. https://doi:10.1111/gcb.15846
40. Dutt, S.; Batar, A. K.; Sulik, S.; and Kunz, M. (2024). Forest ecosystem on the edge: Mapping forest fragmentation susceptibility in Tuchola Forest, Poland. Ecological Indicators, 161: 111980.
41. FAO (Food and Agriculture Organization). (2020). Global Forest Resources Assessment 2020, (Iran Report). Rome, 54 pp. https://openknowledge.fao.org/server/api/core/bitstreams/70aae432-4a3e-4d96-9083-f0800bd959af/content
42. Feng, Y.; Yang, Q.; Tong, X.; and Chen, L. (2018). Evaluating land ecological security and examining its relationships with driving factors using GIS and generalized additive model. Science of the Total Environment, 633, 1469-1479. https://doi: 10.1016/j.scitotenv.2018.03.272
43. Foley, J. A.; DeFries, R.; Asner, G. P.; Barford, C.C.; Bonan, G.; Carpenter, S. R.; Chapin, F. S.; Coe, M. T.; Daily, G. C.; Gibbs, H.; Helkowski, J. H.; Holloway, T.; Howard, E.; Kucharik, C. J.; Patz, J.; Prentice, I. C.; Ramankutty, N.; and Snyder, P. K. (2005). Global consequences of land use. Science. Science, 309 (5734), 570-574. https://doi: 10.1126/science.11117
44. Hosonuma, N.; Herold, M.; Sy, V. D.; Fries, R. S. D.; Brockhaus, M.; Verchot, L.; Angelsen, A.; and Romijn, E. (2012). An assessment of deforestation and forest degradation drivers in developing countries. Environmental Research Letters, 7 (4), 044009. https://doi:10.1088/1748-9326/7/4/044009
45. Jellouli, O.; and Bernoussi, A.S. (2022). The impact of dynamic wind flow behavior on forest fire spread using cellular automata: Application to the watershed BOUKHALEF (Morocco). Ecological Modelling, 468, 109938.
46. Kayet, N.; Pathak, K.; Kumar, S.; Singh, C.P.; Chowdary, V.M.; Chakrabarty, A.; Sinha, N.; Shaik, I.; Ghosh A. (2021). Deforestation susceptibility assessment and prediction in hilltop mining-affected forest region. Journal of Environmental Management, 112504.
47. Knapp, M.; Strobl, M.; Venturo, A.; Seidl, M.; Jakubíkova, L.; Tajovský, K.; Kadlec, T.; and Gonzalez, E. (2022). Importance of grassy and forest non-crop habitat islands for overwintering of ground-dwelling arthropods in agricultural landscapes: A multi-taxa approach. Biological Conservation, 275, 109757.
48. Laurance, W.F.; Goosem, M.; and Laurance, S.G.W. (2009). Impacts of roads and linear clearings on tropical forests. Trends in Ecology and Evolution, 24 (12), 659-669. https://doi:10.1016/j.tree.2009.06.009
49. Looze, B.E. (2009). Forest fragmentation patterns in Maine watersheds and prediction of visible crown diameter in recent undisturbed forest, MSc thesis, University of Wisconsin-Superior, 130 pp. https://digitalcommons.library.umaine.edu/cgi/viewcontent.cgi?article=2772&context=etd
50. López, S. 2022. Deforestation, forest degradation, and land use dynamics in the Northeastern Ecuadorian Amazon. Applied Geography, 145, 102749.
51. López-Bedoya, P. A.; Bohada-Murillo, M.; Ángel-Vallejo, M. C.; Audino, L. D.; Davis, A. L. V.; Gurr, G.; and Noriega, J. A. (2022). Primary forest loss and degradation reduces biodiversity and ecosystem functioning: A global meta-analysis using dung beetles as an indicator taxon. Journal of Applied Ecology, 59, 1572-1585.
52. Mirakhorlou, Kh.; and Akhavan, R. (2017). Area changes of Hyrcanian Forests during 2004 to 2016. Nature Iran, 2 (3), 40-45. https://doi:10.22092/irn.2017.112967 (in Persian)
53. Netzel, P.; Tyminska, L.; Feleha, D. D.; Socha, J. (2024). New approach to assess forest fragmentation based on multiscale similarity index. Ecological Indicators, 158, 111530. https://doi: 10.1016/j.ecolind.2023.111530
54. Ojoatre, S.; Zhang, C.; Yesuf, G.; and Rufino, M.C. (2023). Mapping deforestation and recovery of tropical montane forests of East Africa. Frontiers in Environmental Science, 11, 1084764. 17 pp. https://www.10.3389/fenvs.2023.1084764
55. Sahana, M.; Hong, H.; Sajjad, H.; Liu, J.; and Zhu, A.X. (2018). Assessing deforestation susceptibility to forest ecosystem in Rudraprayag district, India using fragmentation approach and frequency ratio model. Science of the Total Environment, 627, 1264-1275.
56. Sahana, M.; Hong, H.; Sajjad, H.; Liu, J.; and Zhu, A.X. (2018). Assessing deforestation susceptibility to forest ecosystem in Rudraprayag district, India using fragmentation approach and frequency ratio model. Science of the Total Environment, 627, 1264-1275.
57. Silva, A.C.O.; Fonseca, L.M.G.; Körting, T.S.; and Escada, M.I.S. (2020). A spatio-temporal Bayesian Network approach for deforestation prediction in an Amazon rainforest expansion frontier. Spatial Statistics, 35, 100393.
58. Silvério, D. V.; Brando, P. M.; Bustamante, M. M. C.; Putz, F. E.; Marra, D. M.; Levick, S. R.; and Trumbore, S. E. (2019). Fire, fragmentation, and windstorms: A recipe for tropical forest degradation. Journal of Ecology, 107 (2), 656-667.
59. Tavares das Neves, P. B.; Blanco, C. J. C.; Duarte, A. A. A. M.; das Neves, F. B. S.; das Neves, L. B. S.; de Paula dos Santos, M. H. (2021). Amazon rainforest deforestation influenced by clandestine and regular roadway network. Land Use Policy, 108, 105510.
60. Worku, A. (2023). Review on drivers of deforestation and associated socio-economic and ecological impacts. Advances in Agriculture. Food Science and Forestry, 11 (1), 1-12. https://creativecommons.org/licenses/by-nc-nd/4.0/
61. Yakhkeshi, A.; and Aftabtalab, N. (2008). Renewable resources and sustainable development. Department of Environment press, 176 pp. (in Persian)
62. Yamamoto, Y.; Shigetomi, Y.; Ishimura, Y. and Hattori, M. (2019). Forest change and agricultural productivity: Evidence from Indonesia. World Development, 114, 196-207. https://ideas.repec.org/a/eee/wdevel/v114y2019icp196-207.html
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