| تعداد نشریات | 284 |
| تعداد نشریات سازمان | 82 |
| تعداد شماره‌ها | 3,036 |
| تعداد مقالات | 33,970 |
| تعداد مشاهده مقاله | 45,519,438 |
| تعداد دریافت فایل اصل مقاله | 72,753,617 |
مدلسازی مکانی احتمال وقوع آتشسوزی در جنگلها و مراتع با استفاده از مدل‌های نسبت فراوانی و وزن شاهد | ||
| تحقیقات حمایت و حفاظت جنگلها و مراتع ایران | ||
| مقاله 2، دوره 17، شماره 2 - شماره پیاپی 34، اسفند 1398، صفحه 125-144 اصل مقاله (1.09 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijfrpr.2020.121301 | ||
| نویسندگان | ||
| مرجان امیدی1؛ داود مافی غلامی* 2؛ بیت‌الله محمودی3؛ ابوالفضل جعفری4 | ||
| 1دانشجوی کارشناسی ارشد، گروه علوم جنگل، دانشکده منابع طبیعی و علوم زمین، دانشگاه شهرکرد، شهرکرد، ایران | ||
| 2گروه علوم جنگل، دانشکده منابع طبیعی و علوم زمین، دانشگاه شهرکرد | ||
| 3استادیار، گروه علوم جنگل، دانشکده منابع طبیعی و علوم زمین، دانشگاه شهرکرد، شهرکرد، ایران | ||
| 4استادیار پژوهش، مؤسسه تحقیقات جنگلها و مراتع کشور، سازمان تحقیقات، آموزش و ترویج کشاورزی، تهران، ایران | ||
| چکیده | ||
| طی سالهای اخیر، تلاشهای زیادی برای مدیریت و مهار آتشسوزی در جنگلها و مراتع شده است. از مهمترین این اقدامات، مدلسازی احتمال وقوع آتشسوزی و تهیه نقشههای پهنهبندی در مناطق حساس به آتشسوزی است. در این تحقیق، قابلیت مدل‌های نسبت فراوانی و وزن شاهد در پیشبینی احتمال وقوع آتشسوزی در جنگلها و مراتعاستان کهگیلویه و بویراحمد بررسی شده است. فرایند مدلسازی و پیشبینی وقوع آتشسوزیهای آینده بر مبنای بررسی ارتباط بین 271 مورد آتشسوزی از دوره 1395-1381 و 10 عامل درجه شیب، جهت، ارتفاع، درجه حرارت، سرعت باد، کاربری اراضی، شاخص تفاضلی نرمال شده پوشش گیاهی (NDVI) و فاصله تا رودخانه، جاده و مناطق مسکونی انجام شد. طی فرایند مدلسازی، میزان تأثیر هر طبقه از عوامل بر وقوع آتشسوزی محاسبه شد. نتایج مدلها مبنای ساخت نقشههای حساسیت به آتشسوزی در سطح استان قرار گرفت. نتایج ارزیابی و مقایسه مدلها که با استفاده از روش منحنی مشخصه نسبی، میزان موفقیت، نرخ پیشبینی و آزمون مقایسه جفتی ویلکاکسون انجام شد، اختلاف معنیداری را در عملکرد دو مدل نشان داد. به‌طوری‌که مدل وزن شاهد با نرخ موفقیت و پیشبینی 862/0 و 821/0 عملکرد بهتری نسبت به مدل نسبت فراوانی در تحلیل دادههای آموزشی و پیشبینی آتشسوزیهای آینده داشت. بر‌اساس نتایج به‌دست آمده حدود 30 درصد از وسعت جنگلها و مراتع استان کهگیلویه و بویراحمد در طبقات حساسیت زیاد تا بسیار زیاد به آتشسوزی قرار میگیرد که نیازمند اقدامات پیشگیرانه و مدیریت صحیح برای کاهش مخاطرات ناشی از آتش است. | ||
| کلیدواژه‌ها | ||
| مخاطرات طبیعی؛ مدلسازی؛ آتش سوزی؛ پهنه بندی حساسیت | ||
| عنوان مقاله [English] | ||
| Spatial modeling the probability of wildfire occurrence using frequency ratio and weight- of-evidence models | ||
| نویسندگان [English] | ||
| M. Omidi1؛ Davood Mafi Gholami2؛ B. Mahmoodi3؛ A. Jafari4 | ||
| 2Department of forest science, faculty of natural resources and earth sciences, Sahrekord university, Sahrekord, Iran | ||
| چکیده [English] | ||
| In recent years, many attempts have been made to manage and control wildfires. Modeling and mapping wildfire probability across fire-prone landscapes is one of the most important measures. In the present study, the capability of frequency ratio and weight-of-evidence models for predicting the probability of wildfires occurrence in the Kohgiluyeh and Boyer-Ahmad province were investigated. The modeling process and prediction of future fires were based on an analysis of the relationship between 271 historical fires occurred during the 2002-2014 period and 10 predictor variables including slope degree, aspect, altitude, temperature, wind speed, land use, NDVI, and proximity to rivers, roads, and human settlement. During the modeling process, the significance of each variable class on wildfire occurrence was quantified. The model results were used to produce distribution maps of wildfire probability. The results of the evaluation and comparison of the models, which were carried out using the receiver operating characteristic method, success rate, prediction rate, and Wilcoxon test showed that the weight-of-evidence model with success and prediction rates of 0.886 and 0.821 performed better than the frequency ratio model in both training and validation datasets. Overall, the results revealed that approximately 30% of the forests and rangelands of the province fall within the high and very high probability to wildfire occurrence, which requires prudent management measures to mitigate the risk of fire. | ||
| کلیدواژه‌ها [English] | ||
| Natural hazards, Modeling, Wildfire, susceptibility mapping | ||
| مراجع | ||
|
Abdullahi, S. and Pradhan, B. 2018. Land use change modeling and the effect of compact city paradigms: integration of GIS-based cellular automata and weights-of-evidence techniques. Environmental Earth Sciences, 77(6): 251. -Adab, H., Kanniah, K.D., Solaimani, K. and Sallehuddin, R. 2015. Modelling static fire hazard in a semi-arid region using frequency analysis. International Journal of Wildland Fire, 24(6): 763-777. -Adab, H., Kanniah, K.D. and Solaimani, K. 2013. Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques. Natural hazards, 65(3): 1723-1743. -Anonymous, 2014. Administrative office of natural resources of the Kohgiluyeh and Boyer-Ahmad Province, 284p. -Arpaci, A., Malowerschnig, B., Sass, O. and Vacik, H. 2014. Using multi variate data mining techniques for estimating fire susceptibility of Tyrolean forests. Applied Geography, 53: 258-270. -Bedia, J., Herrera, S., Gutiérrez, J.M., Benali, A., Brands, S., Mota, B. and Moreno, J.M. 2015. Global patterns in the sensitivity of burned area to fire-weather: Implications for climate change. Agricultural and Forest Meteorology, 214: 369-379. -Beygi Heidarlou, H., Shafiei, A.B. and Erfanian, M. 2014. Forest fire risk mapping using analytical hierarchy process technique and frequency ratio method (case study: Sardasht Forests, NW Iran. Iranian Journal of Forest and Poplar Research, 22(4): 559–573 (In Persian). -Bonham-Carter, G.F., Agterberg, F.P. and Wrigh, D.F. 1989. Weights of evidence modelling: a new approach to mapping mineral potential. Statistical applications in the earth science, geological survey of Canada, 171–183. -Catry, F.X., Rego, F.C., Bação, F.L. and Moreira, F. 2010. Modeling and mapping wildfire ignition risk in Portugal. International Journal of Wildland Fire, 18(8): 921-931. -Chen, F., Du, Y., Niu, S. and Zhao, J. 2015. Modeling forest lightning fire occurrence in the Daxinganling Mountains of Northeastern China with MAXENT. Forests, 6(5): 1422-1438. -Chuvieco, E. and Congalton, R.G. 1989. Application of remote sensing and geographic information systems to forest fire hazard mapping. Remote sensing of Environment, 29(2): 147-159. -Chuvieco, E. and Salas, J. 1996. Mapping the spatial distribution of forest fire danger using GIS. International Journal of Geographical Information Science, 10(3): 333-345. -Conedera, M., Cesti, G., Pezzatti, G.B., Zumbrunnen, T. and Spinedi, F. 2006. Lightning-induced fires in the Alpine region: An increasing problem. Forest Ecology and Management, 234(1): S68. -Dormann, C.F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G. and Münkemüller, T. 2013. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography, 36(1): 27-46. -Eskandari, S. and Miesel, J.R. 2017. Comparison of the fuzzy AHP method, the spatial correlation method, and the Dong model to predict the fire high-risk areas in Hyrcanian forests of Iran. Geomatics, Natural Hazards and Risk, 8(2): 933-949. -FAO, Food and Agriculture Organization of the United Nations, 2007. Fire Management-Global Assessment 2006. A Thematic Study Prepared in the Framework of the Global Forest Resources Assessment 2005. FAO, Rome, Italy. -Fischer, A.P., Spies, T.A., Steelman, T.A., Moseley, C., Johnson, B.R., Bailey, J.D. and Kline, J.D. 2016. Wildfire risk as a socioecological pathology. Frontiers in Ecology and the Environment, 14(5): 276-284. -Güngöroğlu, C. 2017. Determination of forest fire risk with fuzzy analytic hierarchy process and its mapping with the application of GIS: The case of Turkey/Çakırlar. Human and Ecological Risk Assessment: An International Journal, 23(2): 388-406. -Goleiji, E., Hosseini, S.M., Khorasani, N. and Monavari, S.M. 2017. Forest fire risk assessment an integrated approach based on multicriteria evaluation. Environmental Monitoring and Assessment, 189(12): 612. -Guo, F., Zhang, L., Jin, S., Tigabu, M., Su, Z. and Wang, W. 2016. Modeling anthropogenic fire occurrence in the boreal forest of China using logistic regression and random forests. Forests, 7(11): 250. -Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E. and Tatham, R.L. 2006. Multivariate data analysis Vol. 6, Pearson Prentice Hall, Upper Saddle River, 734p. -Hong, H., Jaafari, A. and Zenner, E.K. 2019. Predicting spatial patterns of wildfire susceptibility in the Huichang County, China: An integrated model to analysis of landscape indicators. Ecological Indicators, 101: 878-891. -Hong, H., Naghibi, S.A., Dashtpagerdi, M.M., Pourghasemi, H.R. and Chen, W. 2017. A comparative assessment between linear and quadratic discriminant analyses (LDA-QDA) with frequency ratio and weights-of-evidence models for forest fire susceptibility mapping in China. Arabian Journal of Geosciences, 10(7): 167. -Hong, H., Tsangaratos, P., Ilia, I., Liu, J., Zhu, A.X. and Xu, C. 2018. Applying genetic algorithms to set the optimal combination of forest fire related variables and model forest fire susceptibility based on data mining models. The case of Dayu County, China. Science of the Total Environment, 630: 1044-1056. -Ivanilova, T.N. 1985. Set probability identification in forest fire simulation. Annual Review in Automatic Programming, 12: 185-188. -Jaafari, A. 2018. LiDAR-supported prediction of slope failures using an integrated ensemble weights-of-evidence and analytical hierarchy process. Environmental Earth Sciences, 77(2): 42. -Jaafari, A. and Mafi Gholami, D. 2017. Wildfire hazard mapping using an ensemble method of frequency ratio with Shannon's entropy. Iranian Journal of Forest and Poplar Research, 25(2): 232-243 (In Persian). -Jaafari, A., Gholami, D.M. and Zenner, E.K. 2017. A Bayesian modeling of wildfire probability in the Zagros Mountains, Iran. Ecological informatics, 39: 32-44. -Jaafari, A., Najafi, A., Pourghasemi, H.R., Rezaeian, J. and Sattarian, A. 2014. GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. International Journal of Environmental Science and Technology, 11(4): 909-926. -Jaafari, A., Najafi, A., Rezaeian, J., Sattarian, A. and Ghajar, I. 2015. Planning road networks in landslide-prone areas: a case study from the northern forests of Iran. Land Use Policy, 47: 198-208. -Jaafari, A., Zenner, E.K. and Pham, B.T. 2018. Wildfire spatial pattern analysis in the Zagros Mountains, Iran: A comparative study of decision tree based classifiers. Ecological Informatics, 43: 200-211. -Jaafari, A., Zenner, E.K., Panahi, M. and Shahabi, H. 2019. Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability. Agricultural and Forest Meteorology, 266: 198-207. -Jahdi, R., Salis, M., Darvishsefat, A.A., Alcasena, F., Mostafavi, M.A., Etemad, V., Lozano, O.M. and Spano, D. 2015. Evaluating fire modelling systems in recent wildfires of the Golestan National Park, Iran. Forestry, 89(2): 136-149. -Jenks, G.F. and Caspall, F.C. 1971. Error on choroplethic maps: definition, measurement, reduction. Annals of the Association of American Geographers, 61(2): 217-244. -Kayastha, P., Dhital, M.R. and De Smedt, F. 2012. Landslide susceptibility mapping using the weight of evidence method in the Tinau watershed, Nepal. Natural hazards, 63(2): 479-498. -Lee, S. and Pradhan, B. 2007. Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides, 4(1): 33-41. -Meng, Y., Deng, Y. and Shi, P. 2015. Mapping forest wildfire risk of the world. In World Atlas of Natural Disaster Risk. Springer, Berlin, Heidelberg, 261-275. -Nami, M.H., Jaafari, A., Fallah, M. and Nabiuni, S. 2018. Spatial prediction of wildfire probability in the Hyrcanian ecoregion using evidential belief function model and GIS. International Journal of Environmental Science and Technology, 15(2): 373-384. -Oliveira, S., Oehler, F., San-Miguel-Ayanz, J., Camia, A. and Pereira, J.M. 2012. Modeling spatial patterns of fire occurrence in Mediterranean Europe using Multiple Regression and Random Forest. Forest Ecology and Management, 275: 117-129. -Pourtaghi, Z.S., Pourghasemi, H. R., Aretano, R. and Semeraro, T. 2016. Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques. Ecological indicators, 64: 72-84. -Pourtaghi, Z.S., Pourghasemi, H.R. and Rossi, M. 2015. Forest fire susceptibility mapping in the Minudasht forests, Golestan province, Iran. Environmental Earth Sciences, 73(4): 1515-1533. -Rahmati, O., Pourghasemi, H.R. and Zeinivand, H. 2016. Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran. Geocarto International, 31(1): 42-70. -Rasooli, S.B. and Bonyad, A.E. 2019. Evaluating the efficiency of the Dong model in determining fire vulnerability in Iran’s Zagros forests. Journal of Forestry Research, 30(4): 1447-1458. -Ricotta, C., Bajocco, S., Guglietta, D. and Conedera, M. 2018. Assessing the Influence of Roads on Fire Ignition: Does Land Cover Matter?. Fire, 1(2): 24. -Sarvaleh, F.M., Bavaghar, M.P. and Shabanian, N. 2014. Application of artificial neural network for forest fire risk mapping based on physiographic, human and climate factors in Sarvabad, Kurdistan province. Iranian Journal of Forest and Range Protection Research, 11(2): 97-107 (In Persian). -Satir, O., Berberoglu, S. and Donmez, C. 2016. Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem. Geomatics, Natural Hazards and Risk, 7(5): 1645-1658. -Semeraro, T., Mastroleo, G., Aretano, R., Facchinetti, G., Zurlini, G. and Petrosillo, I. 2016. GIS Fuzzy Expert System for the assessment of ecosystems vulnerability to fire in managing Mediterranean natural protected areas. Journal of environmental management, 168: 94-103. -Silva, G.L., Soares, P., Marques, S., Dias, M.I., Oliveira, M.M. and Borges, J.G. 2015. A Bayesian Modelling of Wildfires in Portugal. In Dynamics, Games and Science, Springer International Publishing, 723-733. -Swets, J.A. 1988. Measuring the accuracy of diagnostic systems. Science, 240(4857): 1285-1293. -Syphard, A.D., Radeloff, V.C., Keuler, N.S., Taylor, R.S., Hawbaker, T.J., Stewart, S.I. and Clayton, M.K. 2008. Predicting spatial patterns of fire on a southern California landscape. International Journal of Wildland Fire, 17(5): 602-613. -Wilcoxon, F. 1945. Individual comparisons by ranking methods. Biometrics bulletin, 1(6): 80-83. | ||
|
آمار تعداد مشاهده مقاله: 837 تعداد دریافت فایل اصل مقاله: 693 |
||