Volume 11, Issue 2 (8-2024)
Journal of Spatial Analysis Environmental Hazards 2024, 11(2): 101-115 |
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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-en.html
URL: http://jsaeh.khu.ac.ir/article-1-3450-en.html
1- Graduate University of Advanced Technology
2- tarbiat modares university
3- Golestan Agricultural and Natural Resources Research and Education Center ,saeidshabani07@gmail.com
4- Golestan Agricultural and Natural Resources Research and Education Center
2- tarbiat modares university
3- Golestan Agricultural and Natural Resources Research and Education Center ,
4- Golestan Agricultural and Natural Resources Research and Education Center
Abstract: (2048 Views)
Forest fire in many ecosystems is a natural phenomenon, but also a serious and dangerous threat with environmental, ecological, and physical effects. Therefore, this research investigated the risk areas of fire in Zagros forests identification to evaluate the changes in the time series of deals with a potential fire hazard. To achieve this goal fuzzy layers of analysis network process and order weighted average method were used regularly. For this purpose, fire Zagros forests using satellite images Landsat and MODIS Lordegan city in the period between 2000, 2007, and 2014 and the factors affecting fire are examined. The high-risk areas based on classification utility area and the number of zones were identified as fire-prone areas. In the analytical network process procedure, the largest weighs were assigned to the distance from residential areas and roads, GVMI index, and maximum daily air temperature factors which were 0.209, 0.198, 0.09, and 0.0716, respectively. Time series analysis map showing the extent of critical areas from 2000 to 2014 decreased by investigating the factors affecting fire occurrence in critical areas, distance for roads and residential areas, slope, aspect, GVMI index, and NDVI and maximum temperatures have the greatest impact were on fire. The low-risk scenario and a small amount of compensation with the ROC higher than 0.7 as the best model was the estimated risk of forest fires. The preparation of a map of areas susceptible to fire, as well as analyzing and analyzing the time series of factors affecting the fire in different years, is an effective step in helping forest managers to plan and implement preventive operations in high-risk areas.
Article number: 6
Type of Study: Research |
Subject:
Special
Received: 2024/06/16 | Accepted: 2024/09/11 | Published: 2024/09/11
Received: 2024/06/16 | Accepted: 2024/09/11 | Published: 2024/09/11
References
1. 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
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
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.
8. 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.
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.
12. 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.
13. Malczewski, J. 2006b. GIS-based multicriteria decision analysis: a survey of the literature. International Journal of Geographical Information Science, 20 (7): 703–726.
14. 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.
15. 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.
16. 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.
17. 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.
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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