دوره 11، شماره 2 - ( 6-1403 )
جلد 11 شماره 2 صفحات 115-101 |
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hamedi N, esmaeily A, faramarzi H, shabani S, mohseni B. Analysis of Wildfire Hazard Potential in Zagros Forests: Investigating Spatial and Temporal Changes and Influential Factors. Journal of Spatial Analysis Environmental Hazards 2024; 11 (2) : 6
URL: http://jsaeh.khu.ac.ir/article-1-3450-fa.html
URL: http://jsaeh.khu.ac.ir/article-1-3450-fa.html
حامدی نگار، اسماعیلی علی، فرامرزی حسن، شعبانی سعید، محسنی بهروز. تحلیل پتانسیل خطر آتش‌سوزی در جنگل‌های زاگرس: بررسی تغییرات مکانی و زمانی و عوامل مؤثر. تحلیل فضایی مخاطرات محیطی. 1403; 11 (2) :101-115
نگار حامدی1 
، علی اسماعیلی1 ، حسن فرامرزی2 ، سعید شعبانی 3، بهروز محسنی4
، علی اسماعیلی1 ، حسن فرامرزی2 ، سعید شعبانی 3، بهروز محسنی4
1- دانشگاه تحصیلات تکمیلی صنعتی و فناوری پیشرفته
2- دانشگاه تربت مدرس
3- مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان گلستان ،saeidshabani07@gmail.com
4- مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان گلستان
2- دانشگاه تربت مدرس
3- مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان گلستان ،
4- مرکز تحقیقات و آموزش کشاورزی و منابع طبیعی استان گلستان
چکیده: (2018 مشاهده)
آتش‌سوزی جنگل پدیده‌ای طبیعی و در عین حال یک تهدید بالقوه با اثرات محیطی، اکولوژیکی و فیزیکی می‌باشد. با توجه به اهمیت پیش‌بینی خطر آتش‌سوزی جنگل، این تحقیق مناطق خطر وقوع حریق در جنگل‌های زاگرس را شناسایی و به بررسی تغییرات پتانسیل خطر آتش‌سوزی در سری‌های زمانی مختلف پرداخته است. جهت رسیدن به این هدف، با فازی‌سازی لایه‌ها از روش تحلیل شبکه‌ای و روش میانگین وزنی مرتب استفاده گردید. آتش‌سوزی جنگل‌های زاگرس شهرستان لردگان با استفاده از تصاویر ماهواره لندست و مادیس در بازه زمانی سال‌های 2000، 2007 و 2014 مشخص و عوامل مؤثر برآتش‌سوزی بررسی شد. سپس مناطق پرخطر طبقه‌بندی و مساحت مناطق مستعد آتش‌سوزی و تعداد زون‌ها مشخص گردید. در روش تحلیل شبکه‌ای، بیشترین وزن‌ها را عوامل فاصله از مناطق مسکونی و جاده، شاخص GVMIو حداکثر دمای روزانه هوا (به ترتیب با وزن‌های 209/، 198/0، 09/0 و 0716/0) بدست آوردند. تحلیل سری زمانی نقشه‌ها نشان داد از عوامل مؤثر بر وقوع حریق در مناطق بحرانی، عامل فاصله از جاده و مناطق مسکونی، شیب، جهت، شاخص GVMI، NDVI و حداکثر دما بیشترین تأثیر را در ایجاد حریق داشتند. سناریوی سطح ریسک پایین و مقدار اندک جبران نیز با میزان ROC بالاتر از 7/0 به‌عنوان بهترین مدل ریسک آتش‌سوزی جنگل برآورد گردید. بر اساس یافته‌ها، تهیه نقشه مناطق مستعد آتش‌سوزی و همچنین بررسی و تحلیل سری زمانی عوامل مؤثر بر آتش‌سوزی در سال‌های مختلف، به عنوان گامی موثر در کمک به حافظان جنگل و مسئولین و جهت برنامه‌ریزی و اجرای عملیات پیشگیرانه در مناطق پرخطر مفید است.
شماره‌ی مقاله: 6
فهرست منابع
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7. Dang, A.T.N; L, Kumar. M, Reid, and O, Mutanga. 2021. Fire danger assessment using geospatial modelling in Mekong delta, Vietnam: effects on wetland resources. Remote Sensing Applications: Society and Environment, 21: 100456.
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9. Denham, M.M; S, Waidelich, and K, Laneri. 2022. Visualization and modeling of forest fire propagation in Patagonia. Environmental Modelling & Softwar, 158: 105526.
10. Hansen, W.D; M.A, Krawchuk. A.T, Trugman, and A.P, Williams. 2022. The Dynamic Temperate and Boreal Fire and Forest-Ecosystem Simulator (DYNAFFOREST): Development and evaluation. Environmental Modelling & Softwar, 156: 105473.
11. Kumar, G; A, Kumar. P, Saikia. P.S, Roy, and M.L, Khan. 2022. Ecological impacts of forest fire on composition and structure of tropical deciduous forests of central India, Physics and Chemistry of the Earth, Parts A/B/, 128: 103240.
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18. Rihan, M; M.A, Bindajam. S, Talukdar. M.W, Shahfahad Naikoo. J, Mallick, and A, Rahman. 2023. Forest fire susceptibility mapping with sensitivity and uncertainty analysis using machine learning and deep learning algorithms. Advances in Space Research, 72 (2): 426–443.
19. Rinner, C, and J, Malczewski. 2000. Web-enabled spatial decision analysis using Ordered Weighted Averaging (OWA). Journal of Geographical System, 385-403.
20. Saha, S; B, Bera. P.K, Shit. S, Bhattacharjee, and N, Sengupta. 2023. Prediction of forest fire susceptibility applying machine and deep learning algorithms for conservation priorities of forest resources. Remote Sensing Applications: Society and Environment, 29: 100917.
21. Saleh, A; M.A, Zulkifley. H.H, Harun. F, Gaudreault. I, Davison, and M, Spraggon. 2024. Forest fire surveillance systems: A review of deep learning methods. Heliyon, 10 (1): e23127.
22. Si, L; L, Shu. M, Wang. F, Zhao. F, Chen. W, Li, and W, .Li. 2022. Study on forest fire danger prediction in plateau mountainous forest area. Natural Hazards Researc, 2 (1): 25-32.
23. Singh, S.S, C, Jeganathan. 2024. Using ensemble machine learning algorithm to predict forest fire occurrence probability in Madhya Pradesh and Chhattisgarh, India. Advances in Space Research, 73 (6): 2969-2987.
24. Talukdar, N.R; F, Ahmad. L, Goparaju. P, Choudhury. A, Qayum, and J, Rizvi. 2024. Forest fire in Thailand: Spatio-temporal distribution and future risk assessment. Natural Hazards Research, 4 (1): 87-96.
25. Tuyen, T.T; A, Jaafari. H.P.H, Yen. T, Nguyen-Thoi. T.V, Phong. H.D, Nguyen. H.V, Le. T.T.M, Phuong. S.H, Nguyen. I. Prakash, and B.T, Pham. 2021. Mapping forest fire susceptibility using spatially explicit ensemble models based on the locally weighted learning algorithm. Ecological Informatics, 63 (3).
26. Veraverbeke, S; S, Hook, and G, Hulley. 2012. An alternative spectral index for rapid fire severity assessments. Remote Sensing of Environmen, 123: 72–80.
27. Wang, S.D; L.L, Miao, and G.X, Peng. 2012. An Improved Algorithm for Forest Fire Detection Using HJ Data. Procedia Environmental Science, 13: 140-150.
28. William, A.H; B, Orthen, and K.V, Paula. 2011. Comparative fire ecology of tropical savanna and forest trees. Functional Ecology, 17 (6): 44- 47.
29. Wood, D.A. 2021. Prediction and data mining of burned areas of forest fires: Optimized data matching and mining algorithm provides valuable insight. Artificial Intelligence in Agricultur, 5: 24-42.
30. Xu, Q; W, Li. J, Liu, and X, Wang. 2023. A geographical similarity-based sampling method of non-fire point data for spatial prediction of forest fires. Forest Ecosystems, 10: 100104.
31. Yager, RR. 1996. Quantifier guided aggregation using OWA operators. International Journal of Intelligent Systems, 11 (1): 49–73. https://doi.org/10.1002/(SICI)1098-111X(199601)11:1<49::AID-INT3>3.0.CO;2-Z [DOI:10.1002/(SICI)1098-111X(199601)11:13.0.CO;2-Z]
32. Zhao, L; Y, Ge. S, Guo. H, Li. X, Li. L, Sun, and J. Chen. 2024. Forest fire susceptibility mapping based on precipitation-constrained cumulative dryness status information in Southeast China: A novel machine learning modeling approach. Forest Ecology and Management, 558: 121771.
33. فرامرزی، حسن؛ سیدمحسن حسینی، حمیدرضا پورقاسمی، مهدی فرنقی. 1397. ارزیابی نقش شاه‌راه آسیایی بر روی آتش‌سوزی‌های پارک ملی گلستان در محیط GIS. پژوهش‌های علوم و فن‌آوری چوب و جنگل، 25 (3): 33–48. https://doi,org/10.22069/JWFST.2018.14655.1729
34. Babu, K.N; R. Gour. K, Ayushi. N, Ayyappan, and N, Parthasarathy. 2023. Environmental drivers and spatial prediction of forest fires in the Western Ghats biodiversity hotspot, India: An ensemble machine learning approach. Forest Ecology and Management, 540: 121057.
35. Bargali, H; A, Pandey. D, Bhatt. R.C, Sundriyal, and V.P, Uniyal. 2024. Forest fire management, funding dynamics, and research in the burning frontier: A comprehensive review. Trees. Forests and People, 16: 100526.
36. Barros-Rosa, L; P.H.Z, de Arruda. N.G, Machado. J.C, Pires-Oliveira, and P.V, Eisenlohr. 2022. Fire probability mapping and prediction from environmental data: What a comprehensive savanna-forest transition can tell us. Forest Ecology and Managemen, 520: 120354.
37. Bhadoria, R.S; M.K, Pandey, and P, Kundu. 2021. RVFR: Random vector forest regression model for integrated & enhanced approach in forest fires predictions. Ecological Informatic, 66: 101471.
38. Chicas, S.D, and J.Q, Nielsen. 2022. Who are the actors and what are the factors that are used in models to map forest fire susceptibility? A systematic review. Natural Hazards, 114: 2417–2434.
39. Dang, A.T.N; L, Kumar. M, Reid, and O, Mutanga. 2021. Fire danger assessment using geospatial modelling in Mekong delta, Vietnam: effects on wetland resources. Remote Sensing Applications: Society and Environment, 21: 100456.
40. de Dios, V.R; J, Hedo. A.C, Camprubí. P, Thapa. E.M, del Castillo. J.M, de Aragón. J.A, Bonet. R, Balaguer-Romano. R, Díaz-Sierra. M, Yebra, and M.M, Boer. 2021. Climate change induced declines in fuel moisture may turn currently fire-free Pyrenean Mountain forests into fire-prone ecosystems. Science of The Total Environmen, 797: 149104.
41. Denham, M.M; S, Waidelich, and K, Laneri. 2022. Visualization and modeling of forest fire propagation in Patagonia. Environmental Modelling & Softwar, 158: 105526.
42. Hansen, W.D; M.A, Krawchuk. A.T, Trugman, and A.P, Williams. 2022. The Dynamic Temperate and Boreal Fire and Forest-Ecosystem Simulator (DYNAFFOREST): Development and evaluation. Environmental Modelling & Softwar, 156: 105473.
43. Kumar, G; A, Kumar. P, Saikia. P.S, Roy, and M.L, Khan. 2022. Ecological impacts of forest fire on composition and structure of tropical deciduous forests of central India, Physics and Chemistry of the Earth, Parts A/B/, 128: 103240.
44. Malczewski, J. 2006a. Integrating multicriteria analysis and geographic information systems: the ordered weighted averaging (OWA) approach. International Journal Environmental Technology and Management, 6 (1/2): 7–19.
45. Malczewski, J. 2006b. GIS-based multicriteria decision analysis: a survey of the literature. International Journal of Geographical Information Science, 20 (7): 703–726.
46. Martins, F; J, Santos. L, Galvão Magalhães, and H, Xau. 2016. Sensitivity of ALOS/PALSAR imagery to forest degradation by fire in northern Amazon. International Journal of Applied Earth Observation and Geoinformatio, 163-174.
47. Mishra, M; R, Guria. B, Baraj. A.P, Nanda. C.A.G, Celso. A.G, Santos. R.M, da Silva, and F.A.T, Laksono. 2024. Spatial analysis and machine learning prediction of forest fire susceptibility: a comprehensive approach for effective management and mitigation. Science of the Total Environment, 926: 171713.
48. Pham, V.T; T.A.T, Do. H.D, Tran, and A.N.T, Do. 2024. Classifying forest cover and mapping forest fire susceptibility in Dak Nong province, Vietnam utilizing remote sensing and machine learning. Ecological Informatics, 79: 102392.
49. Pradhan, B; B, Arshad, and M, Binawing. 2005. Application of remote sensing and GIS for forest fire susceptibility mapping using likelihood ratio model. Forest Managemen, 1-5.
50. Rihan, M; M.A, Bindajam. S, Talukdar. M.W, Shahfahad Naikoo. J, Mallick, and A, Rahman. 2023. Forest fire susceptibility mapping with sensitivity and uncertainty analysis using machine learning and deep learning algorithms. Advances in Space Research, 72 (2): 426–443.
51. Rinner, C, and J, Malczewski. 2000. Web-enabled spatial decision analysis using Ordered Weighted Averaging (OWA). Journal of Geographical System, 385-403.
52. Saha, S; B, Bera. P.K, Shit. S, Bhattacharjee, and N, Sengupta. 2023. Prediction of forest fire susceptibility applying machine and deep learning algorithms for conservation priorities of forest resources. Remote Sensing Applications: Society and Environment, 29: 100917.
53. Saleh, A; M.A, Zulkifley. H.H, Harun. F, Gaudreault. I, Davison, and M, Spraggon. 2024. Forest fire surveillance systems: A review of deep learning methods. Heliyon, 10 (1): e23127.
54. Si, L; L, Shu. M, Wang. F, Zhao. F, Chen. W, Li, and W, .Li. 2022. Study on forest fire danger prediction in plateau mountainous forest area. Natural Hazards Researc, 2 (1): 25-32.
55. Singh, S.S, C, Jeganathan. 2024. Using ensemble machine learning algorithm to predict forest fire occurrence probability in Madhya Pradesh and Chhattisgarh, India. Advances in Space Research, 73 (6): 2969-2987.
56. Talukdar, N.R; F, Ahmad. L, Goparaju. P, Choudhury. A, Qayum, and J, Rizvi. 2024. Forest fire in Thailand: Spatio-temporal distribution and future risk assessment. Natural Hazards Research, 4 (1): 87-96.
57. Tuyen, T.T; A, Jaafari. H.P.H, Yen. T, Nguyen-Thoi. T.V, Phong. H.D, Nguyen. H.V, Le. T.T.M, Phuong. S.H, Nguyen. I. Prakash, and B.T, Pham. 2021. Mapping forest fire susceptibility using spatially explicit ensemble models based on the locally weighted learning algorithm. Ecological Informatics, 63 (3).
58. Veraverbeke, S; S, Hook, and G, Hulley. 2012. An alternative spectral index for rapid fire severity assessments. Remote Sensing of Environmen, 123: 72–80.
59. Wang, S.D; L.L, Miao, and G.X, Peng. 2012. An Improved Algorithm for Forest Fire Detection Using HJ Data. Procedia Environmental Science, 13: 140-150.
60. William, A.H; B, Orthen, and K.V, Paula. 2011. Comparative fire ecology of tropical savanna and forest trees. Functional Ecology, 17 (6): 44- 47.
61. Wood, D.A. 2021. Prediction and data mining of burned areas of forest fires: Optimized data matching and mining algorithm provides valuable insight. Artificial Intelligence in Agricultur, 5: 24-42.
62. Xu, Q; W, Li. J, Liu, and X, Wang. 2023. A geographical similarity-based sampling method of non-fire point data for spatial prediction of forest fires. Forest Ecosystems, 10: 100104.
63. Yager, RR. 1996. Quantifier guided aggregation using OWA operators. International Journal of Intelligent Systems, 11 (1): 49–73. https://doi.org/10.1002/(SICI)1098-111X(199601)11:1<49::AID-INT3>3.0.CO;2-Z [DOI:10.1002/(SICI)1098-111X(199601)11:13.0.CO;2-Z]
64. Zhao, L; Y, Ge. S, Guo. H, Li. X, Li. L, Sun, and J. Chen. 2024. Forest fire susceptibility mapping based on precipitation-constrained cumulative dryness status information in Southeast China: A novel machine learning modeling approach. Forest Ecology and Management, 558: 121771.
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