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تهیه نقشه حساسیت به آتشسوزی در شهرستان آستارا با بهرهگیری از روش تحلیل سلسله مراتبی (AHP) و مدل فراوانی نسبی (FR) | ||
| تحقیقات حمایت و حفاظت جنگلها و مراتع ایران | ||
| مقاله 2، دوره 22، شماره 2 - شماره پیاپی 44، اسفند 1403، صفحه 167-183 اصل مقاله (749.44 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijfrpr.2024.364802.1613 | ||
| نویسندگان | ||
| اباذر اسمعلی عوری* 1؛ محمد گلشن2؛ رقیه جهدی3 | ||
| 1استاد، گروه آبخیزداری، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، esmaliouri@uma.ac.ir | ||
| 2دانشآموخته دکتری آبخیزداری، گروه آبخیزداری، دانشکده منابع طبیعی، دانشگاه علوم کشاورزی و منابع طبیعی ساری، ایران. | ||
| 3دانشیار، گروه جنگل، دانشکده کشاورزی و منابع طبیعی، دانشگاه محقق اردبیلی، ایران | ||
| چکیده | ||
| سابقه و هدف: یکی از خطراتی که در دهههای اخیر منابع طبیعی را بهطور جدی تهدید میکند، وقوع آتشسوزیهای عمدی و غیرعمدی در مناطق جنگلی است. این آتشسوزیها علاوهبر خسارتهای مستقیم جانی و مالی، با کاهش تراکم پوشش گیاهی بهصورت غیرمستقیم منجر به تخریب منابع طبیعی و پیامدهای منفی در سطح حوزههای آبخیز میشود. مواد و روشها: تهیه نقشه حساسیت به آتشسوزی میتواند در تعیین نقاط بحرانی و مقابله با این خطر طبیعی بسیار کارآمد باشد، برای این منظور در این پژوهش از کارایی سیستم اطلاعات جغرافیایی (GIS) و روشهای تحلیل سلسلهمراتبی (AHP) و فراوانی نسبی (FR) در جنگلهای هیرکانی شهرستان آستارا با مساحت 42 هزار هکتار در غرب استان گیلان استفاده شد. این شهرستان با میانگین بلندمدت 650 میلیمتر بارندگی و 1/13 درجه سانتیگراد حرارت، اقلیمی مرطوب دارد. در روش AHP و FR با اعمال وزنهای بهدستآمده بهترتیب با استفاده از پرسشنامه و 70 درصد نقاط آتشسوزی مشاهداتی روی معیارهای مطالعاتی، نقشه وزنی محاسبه شد. با ترکیب این نقشهها با یکدیگر، نقشه حساسیت به آتشسوزی در هر دو روش تهیه و در پنج کلاس با حساسیت بسیار کم تا بسیار بالا طبقهبندی گردید. بهمنظور تعیین صحت نتایج از نقشه 30 درصد نقاط آتشسوزی و منحنی ROC استفاده شد. نتایج و یافتهها: در روش AHP ارزش ضریب ناسازگاری (CI) کمتر از 1/0 بهدست آمد که نشاندهنده متناسب بودن پارامترهای انتخابی است. نتایج اعتبارسنجی مدلها نشان داد، روش AHP دارای سطح زیر منحنی برابر با 826/0 و روش FR دارای سطح زیر منحنی برابر با 894/0 است. براساس مقادیر AUC، مدل FR نسبت به مدل AHP در تهیه نقشه مناطق مستعد آتشسوزی دقت بیشتری دارد، بنابراین، استفاده از مدل FR برای این شهرستان توصیه میشود. با توجه به نتایج، از میان معیارهای مطالعاتی، معیار فاصله از روستا و جاده ارتباطی بهترتیب با امتیاز 436/0 و 314/0 مهمترین معیارهای مؤثر در وقوع آتشسوزی منطقه هستند. معیار جمعیت با امتیاز 062/0 کمترین تأثیر را بر وقوع آتشسوزی در منطقه مطالعاتی دارد. نتیجهگیری: در این شهرستان تمرکز آتشسوزیها بیشتر در نواحی شمالی و جنوبشرقی شامل حوزههای آبخیز شماره 1، 4 و 7 است، نقشه پهنهبندی خطر آتشسوزی تهیهشده نشان میدهد، 82/1 درصد از مساحت منطقه موردمطالعه دارای حساسیت بسیار بالا و 9/22 درصد دارای حساسیت بالا به آتشسوزی است که با مدیریت صحیح میتوان بهسرعت این آتشسوزیها را خاموش کرد؛ در حوزههای حساس به آتشسوزی، تمرکز روستاها بالا و فاصله بین روستاها از جادههای ارتباطاتی کمتر است، بنابراین، در مناطق بحرانی راهکارهای جلب مشارکت مردمی، همیاران طبیعت و تجهیز و آموزش روستاییان برای اطفای حریق میتواند در کاهش وقوع حریق در شهرستان آستارا مؤثر باشد. | ||
| کلیدواژهها | ||
| تخریب؛ حریق؛ جنگل هیرکانی؛ استان گیلان | ||
| عنوان مقاله [English] | ||
| Determining the forest fire susceptibility map in the Astara city using the Analytical Hierarchy Process (AHP) and Frequency Ratio Model (FR) | ||
| نویسندگان [English] | ||
| Abazar Esmali Ouri1؛ Mohammad Golshan2؛ Roghayeh Jahdi3 | ||
| 1Professor, Department of watershed management, Faculty of Agriculture and Natural Resource, University of Mohaghegh Ardabili, Iran. esmaliouri@uma.ac.ir | ||
| 2PhD. Graduated, Department of Watershed Management, Faculty of Natural Resources, Sari Agricultural sciences and Natural Resources University, Iran. | ||
| 3Associate Professor, Faculty of Faculty of Agriculture and Natural Resource, University of Mohaghegh Ardabili, Iran. | ||
| چکیده [English] | ||
| Abstract Background and objectives: In recent decades, intentional and unintentional forest fires have posed a serious threat to natural resources. These fires not only lead to direct human and financial losses but also contribute to the destruction of ecosystems, soil degradation, and loss of biodiversity. Additionally, they disrupt the balance of watersheds by reducing vegetation cover, increasing the risk of soil erosion, altering hydrological cycles, and ultimately affecting water quality and availability. Understanding fire-prone areas and their contributing factors is essential for effective fire management, risk reduction, and the development of preventive strategies. Fire risk mapping serves as a valuable tool for identifying critical areas and implementing targeted mitigation measures. Methodology: Fire risk mapping is an effective tool for identifying critical areas and mitigating fire hazards. This study evaluates the efficiency of the Geographic Information System (GIS), Analytical Hierarchy Process (AHP), and Frequency Ratio (FR) methods in mapping fire-prone areas in Astara, a city in western Gilan province covering 42,000 hectares. Astara is part of the Hyrcanian forests and has a humid climate with an average annual rainfall of 650 mm and a mean temperature of 13.1°C. In this study, the AHP method assigned weights to fire risk criteria based on expert surveys, while the FR method used the distribution of 70% of observed fire incidents. The obtained weights were applied to the study criteria to generate weighted maps. These maps were then integrated to produce a fire susceptibility map, categorizing fire risk into five classes, from very low to very high. To validate the results, 30% of recorded fire incidents and the ROC curve were used. The validation data were not included in the modeling phase. Results: In the AHP method, the consistency index (CI) was below 0.1, indicating reliable parameter selection. Model validation showed that the AHP method had an area under the curve (AUC) of 0.826, while the FR method achieved an AUC of 0.894. These results suggest that the FR model is more accurate for mapping fire-prone areas in Astara. The lower accuracy of the AHP model is likely due to potential errors in the pairwise comparison of criteria. Among the evaluated factors, proximity to villages (weight: 0.436) and roads (weight: 0.314) were the most influential in fire occurrence. In contrast, population density had the least impact (weight: 0.062). Conclusion: In Astara, fires are concentrated in the northern and southeastern regions, particularly in watersheds No. 1, 4, and 7. The fire susceptibility map indicates that 1.82% of the study area is highly fire-prone, while 22.9% has moderate sensitivity. With proper management, fires in these areas can be controlled effectively. High-risk basins have a dense village distribution with short distances to roads, making them more susceptible to fires. Therefore, promoting public participation and equipping villagers with fire extinguishers can help prevent rapid fire spread. | ||
| کلیدواژهها [English] | ||
| Destruction, Fire, Hyrcanian forest, Gilan Province | ||
| مراجع | ||
|
-Abdollahi, M., Hosseini, S.A. and Najafi, A., 2022. Predicting the decline of forest road pavement during the logging ban period in the north mountain forests of Iran. Natural Environmental, 75(3): 502-514 (In Persian).
-Adab, H., Kanniah, D. and Solaimani, K., 2011. GIS based Probability Assessment of fire risk in grassland and forested landscapes of Golestan province, Iran. International Conference on Environmental and Computer Science, IPCBEE vol.19: 170-175, IACSIT Press, Singapore.
-Almedia, R., 1994. Forest fire risk areas and definition of the prevention priority planning actions using GIS. Proceedings of the Fifth European Conference and Exhibition on Geographic Information Systems, EGIS 94. Utrecht: EGIS Foundation, 2: 1700-1706.
-Amin Amlashi, M., Jafari, M., Ghodskhah, M., Salehi, M. and Alidoost, M., 2016. A preliminary study of fire-resistant trees in the forests of Guilan province (Case study: Noqlebar Forest, Watershed number 18 Reshterood). Forest and Poplar Research, 24(3): 463-473. (In Persian).
-Amiri, T., Banj Shafiei, A., Erfanian, M., Hosseinzadeh, O. and Beygiheidarlou, H., 2018. Locating suitable areas for forest fire fighting stations in Sardasht, NW Iran. Iranian Journal of Forest, 10(3): 319-335.
-Andison, D.W., 2024. A new Meso-Scale coarse-filter indicator for the Canadian boreal forest. Forest Ecology and Management, 553: 121624.
-Bagherabadi, R., Shikhkanloo Milan, F. and Zarei Mohammadabad, M., 2022. Evaluation of fire risk in the Zagros forests (Case study: Dalahu County). Ecosystem Management, 1(3): 60-72.
-Calabrese, A., Costa, R., Levialdi, N. and Menichini, T., 2019. Integrating sustainability into strategic decision-making: A fuzzy AHP method for the selection of relevant sustainability issues. Technological Forecasting and Social Change, 139: 155-168.
-Chandra, S., 2005. Application of remote sensing and GIS technology in forest fire risk modeling and Management of forest fires: A case study in the Garhwal Himalayan Region: 1239-1254. In: van Oosterom, P., Zlatanova, S. and Fendel, E.M., (Eds.). Geo-information for Disaster Management. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-27468-5_86
-Eskandari, S., 2015. Evaluation of forest fire risk potential using Dong model, case study: District three of Neka-Zalemroud forests. Geographical Planning of Space quarterly Journal, 15(5): 189-203.
-Feizizadeh, B., Omarzadeh, D., Mohammadnejad, V., Khallaghi, H., Sharifi, A. and Karkarg, B.G., 2023. An integrated approach of artificial intelligence and geoinformation techniques applied to forest fire risk modeling in Gachsaran, Iran. Journal of Environmental Planning and Management, 66(6): 1369-1391.
-Golshan, M., Esmali Ouri, A. and Khosravi, K., 2018. Flood susceptibility assessments using frequency ratio model in Talar watershed. Journal of Natural Environmental Hazards, 7(15): 1-16 (In Persian).
-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. Ecological Modeling, 468: 109938.
-Jianning, R., Erin, J.H., John T.A., Crystal A.K., Christina, L.T., Maureen C.K., Mingliang L. and Jennifer C.A., 2021. Projecting future fire regimes in a semiarid watershed of the inland northwestern United States, vegetation productivity and fuel dynamics. Earths Future: 1-23.
-Kermani Rajabi, A. and Mahdavi, A., 2024. Prioritizing the influencing factors of customer satisfaction in electronic service systems using fuzzy Delphi hierarchical analysis criteria. Accounting and Management Perspective, 7(89): 118-129.
-Kim, S.J., Lim, C.H., Kim, G.S., Lee, J., Geiger, T., Rahmati, O. and Lee, W.K., 2019. Multi-temporal analysis of forest fire probability using socio-economic and environmental variables. Remote Sensing, 11(1): 86.
-Li, J., Zhu, C. and Li, Q., 2022. Application of AHP in WUI fire risk assessment. ITM Web of Conferences, 47(03039): 1-6.
-Moayedi, H. and Khasmakhi, M.A.S.A., 2023. Wildfire susceptibility mapping using two empowered machine learning algorithms. Stochastic Environmental Research and Risk Assessment, 37(1): 49-72.
-Mohammadi, F., Shabanian, N., Pourhashemi, H. and Fatehi, P., 2011. Risk zone mapping of forest fire using GIS and AHP in a part of Paveh forests. Forest and Poplar Research, 4(18): 569-586.
-Mosavi, A., Golshan, M., Janizadeh, S., Choubin, B., Melesse, A.M. and Dineva, A.A., 2022. Ensemble models of GLM, FDA, MARS, and RF for flood and erosion susceptibility mapping: a priority assessment of sub-basins. Geocarto International, 37(9): 2541-2560.
-Najafi, A., Irannezhad, M., Sotodeh, A., Mokhtari, M.H. and Keiani, B., 2015. Modeling and preparation of forest fire risk map using measurement from Duro geographic information system. Applied Ecology, 4(14): 13-25 (In Persian).
-Nobre, C.A., Sampaio, G., Borma, L.S., Castilla-Rubio, J.C., Silva, J.S. and Cardoso, M., 2016. Land-use and climate change risks in the Amazon and the need of a novel sustainable development paradigm. Proceedings of the National Academy of Sciences, 113(39): 10759-10768.
-Omidi, M., Mafi Gholami, D., Mahmoodi, B. and Jafari, A., 2020. Spatial modeling the probability of wildfire occurrence using frequency ratio and weight-of-evidence models. Iranian Journal of Forest and Range Protection Research, 17(2): 125-144 (In Persian).
-Ren, J., Hanan, E., Abatzoglou, J. and Kolden, C., 2022. Projecting future fire regimes in a semiarid watershed of the Inland Northwestern United States: Interactions among Climate Change, Vegetation Productivity, and Fuel Dynamics. Earth Future, 1-23. https://doi.org/10.1029/2021EF002518
-Sari, F., 2024. Assessment of the effects of different variable weights on wildfire susceptibility. European Journal of Forest Research, 1-20.
Savari, M., Limoei, M. and Eskandari, Z., 2023. Solutions to boost production in rural areas of Khuzestan province: application of hierarchical analysis. Geography and human relations, 6(3): 165-177.
-Shafiei, A., Beygi Heidarlu, H. and Erfanian, M., 2015. Evaluating the fuzzy weighted linear combination method in forest fire risk mapping (Case study: Sardasht Forests, West Azerbaijan Province, Iran). Journal of Wood and Forest Science and Technology, 22(3): 29-52 (In Persian).
-Sivrikaya, F., Günlü, A., Küçük, Ö. and Ürker, O., 2024. Forest fire risk mapping with Landsat 8 OLI images: Evaluation of the potential use of vegetation indices. Ecological Informatics, 102461.
-Tavakoli, R., 2011. Determining the most important factors affecting the spread of fire and the zoning of fire risk in the forests and pastures of Chaharmahal and Bakhtiari province. Master's thesis, Range, Shahrekord University, 115p (In Persian).
-Tshering, K., Thinley, P., Shafapour Tehrany, M., Thinley, U. and Shabani, F., 2020. A comparison of the qualitative analytic hierarchy process and the quantitative frequency ratio techniques in predicting forest fire-prone areas in Bhutan using GIS. Forecasting, 2(2): 36-58.
-Vadrevu, K.P., Eaturu, A. and Badarinath, A.V.S., 2009. Fire risk evaluation using multi-criteria Analysis-a case study. Journal of Environmental Monitoring Assessment, 166: 223-239.
-Yin, H.W., Kong, F.H. and Li, X.Z., 2004. RS and GIS-based forest fire risk zone mapping in da hinggan mountains. Chinese Geographical Science, 14 (3): 251-257. https://doi.org/10.1007/s11769-003-0055-y
-Zarekar, A., Kazemi Zamani, B., Ghorbani, S., Ashegh Moalla, M. and Jafari, H.R., 2013. Mapping spatial distribution of forest fire using MCDM and GIS (case study: three forest zones in Guilan province. Forest and Poplar Research, 21(2): 218-230 (In Persian).
-Zolghadry, Sh., GhodsKhah Daryaei, M., Nasirahmadi, K. and Ghajar, E., 2021. Comparison of the performance of Fuzzy C-Means and K-Medoids in modeling forest fire occurrence (Case study: Saravan forests, Gilan). Ecology of Iranian Forest, 9(17): 163-174 (In Persian). | ||
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آمار تعداد مشاهده مقاله: 603 تعداد دریافت فایل اصل مقاله: 434 |
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