مدل سری زمانی رخدادهای آتش سوزی در جنگل‌ها و مراتع استان کرمانشاه با استفاده از داده های سنجنده MODIS از سال 2002 تا 2018

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانش‌آموخته کارشناسی ارشد علوم و مهندسی جنگل، گروه منابع طبیعی، دانشگاه رازی، کرمانشاه، ایران

2 استادیار، گروه منابع طبیعی، دانشگاه رازی، کرمانشاه، ایران

3 استادیار، گروه منابع طبیعی، دانشگاه رازی، کرمانشاه

چکیده

بررسی و پایش تغییرات رژیم‌های آتش‌سوزی در جنگل‌ها و مراتع اهمیت ویژه‌ای در برنامه‌ریزی‌های کنونی و آینده مدیریت آتش‌سوزی در عرصه‌های طبیعی دارد. هدف از پژوهش پیش‌رو بررسی تغییرات زمانی رخدادهای آتش‌سوزی در گذشته و پیش‌بینی رخدادهای آتش‌سوزی در آینده با استفاده از مدل سری‌های زمانی بود. به این منظور داده‌های سری زمانی رخدادهای آتش‌سوزی 2002 تا 2018 که با استفاده از سنجنده مادیس ثبت شده بود، گردآوری شد. سپس این داده‌ها جهت پیروی از مدل سری زمانی و انتخاب بهترین مدل بررسی شدند. در اینجا با توجه به فصلی بودن رخدادهای آتش‌سوزی، از مدل SARIMA (Seasonal Autoregressive Integrated Moving Average) استفاده و پارامترهای مدل جهت پیش‌بینی تعداد رخدادها برآورد شد. نتایج نشان داد که آتش‌سوزی در جنگل‌ها و مراتع استان کرمانشاه طی این بازه زمانی روند افزایشی اندک اما معنی‌دار داشت. همچنین مشاهده شد که رخدادهای آتش‌سوزی پراکنش فصلی داشتند و بیشینه رخدادها به ترتیب در اواخر تابستان و اوایل پاییز ثبت شده است. با توجه به فصلی بودن رخدادهای آتش‌سوزی و معیارهای برازش مدل، پارامتر‌های مدل آریمای فصلی انتخاب شد و نتایج نشان داد که مدل اعتبار قابل قبولی برای پیش‌بینی آتش‌سوزی سال‌های آینده دارد. با توجه به پیش‌بینی انجام شده، رخدادهای آتش‌سوزی نسبت به سال‌های قبل با حدود اعتماد آماری 98درصد در مراتع و در جنگل‌ها افزایش خواهند داشت. نتایج بدست آمده از این پژوهش چگونگی روند کلی رخدادهای آتش‌سوزی، فصلی بودن و همچنین ماه‌های پر خطر از نظر رخدادهای آتش‌سوزی را نشان داد که می‌تواند به عنوان راهنمایی در مدیریت آتش‌سوزی جنگل‌ها و مراتع استفاده شود.

کلیدواژه‌ها


عنوان مقاله [English]

Time Series model of fires forests and rangelands of Kermanshah province using MODIS data from 2002 to 2018

نویسندگان [English]

  • Masoume Azizi 1
  • Mohammad Khosravi 2
  • M. Pourreza 3
1 Department of Natural Resources, Razi University, Kermanshah, Iran
2 Department of Natural Resources, Razi University, Kermanshah, Iran
3 Department of Natural Resources, Razi University, Kermanshah, Iran
چکیده [English]

Examining and monitoring the history of changes in fire regimes in forests and pastures has a key role in the current and future planning of fire management in natural areas. The purpose of this study was to monitor the temporal changes of fire events in the past and the possibility of predicting future fire events using the time series model in Kermashah Province. For this purpose, MODIS time series data of fire events were collected from 2002 to 2018. Then an appropriate time series model was used to predict fire events. The results showed that the incidence of fire in the forests and pastures of Kermashah during the study period had a slight but significant upward trend. It was also observed seasonality in fire events and the maximum incidents in the forests and rangelands of Kermanshah occurred in late summer and early autumn, respectively. Considering the seasonality of fire events and the model fit criteria, the seasonal ARIMA (Seasonal Autoregressive Integrated Moving Average) model was selected and the results showed that the model had an acceptable validity for furcating the future fire events. According to the forecast, the incidence of fires will relatively increase in both forests and rangelands compared to previous years with a relative statistical confidence of 98%. The results of this study, which showed the general trend, seasonality and the possibility of predicting fires, can be considered as a good solution in proper planning for fire management in forests and pastures.

کلیدواژه‌ها [English]

  • ARIMA
  • Auto correlation
  • Forest
  • Rangeland
  • Trend
-Aminghafari, M., 2016. Time series forecasting-from elementary to advance with applications in R. Amir Kabir University of Technology, 258p.
-Azizi, M., Khosravi, M. and Pourreza, M., 2020. Frequency of fire incidence in relation to Zagros forests and rangelands physiography (Kermanshah province) using MODIS Active Fire Data. Iranian Journal of Forest and Range Protection Research, 18: 42-55.
-Brockwell, P.J. and Davis, R.A., 1996. Introduction to time series and forecasting. Springer Verlag, New York, Inc., 449p.
-Costafreda-Aumedes, S., Comas, C. and Vega-Garcia, C., 2017. Human-caused fire occurrence modelling in perspective: a review. International Journal of Wildland Fire, 26: 983-998.
-Eskandari, S. and Chuvieco, E., 2015. Fire danger assessment in Iran based on geospatial information. International Journal of Applied Earth Observation and Geoinformation, 42: 57-64.
-Ferreira, L.N., Vega-Oliveros, D.A., Zhao, L., Cardoso, M.F. and Macau, E.E.N., 2020. Global fire season severity analysis and forecasting. arXiv, 1903, 06667v3:1-16.
-Garavand, S., Yaralli, N. and Sadeghi, H., 2013. Spatial pattern and mapping fire risk occurrence at natural lands of Lorestan province. Iranian Journal of Forest and Range Protection Research, 21: 231-242 (In Persian).
-Giglio, L., Boschetti, L., Roy, D.P., Humber, M.L. and Justice, C.O., 2018 The Collection 6 MODIS burned area mapping algorithm and product. Remote Sensing of Environment, 217: 72-85.
-Giglio, L., Schroeder, W. and Justice, C.O., 2018. MODIS collection 6 active fire product user's guide, revision B. Technical Report, University of Maryland, 64p.
-Huesca, M., Litago, J., Merino-de-Miguel, S., Cicuendez-López-Ocaña, V. and Palacios- rueta, A., 2014. Modeling and forecasting MODIS-based Fire Potential Index on a pixel basis using time series models. International Journal of Applied Earth Observation and Geoinformation, 26: 363.
-Hyndman, R., Athanasopoulos, G., Bergmeir, C., Caceres, G., Chhay, L., O'Hara-Wild, M., Petropoulos, F., Razbash, S., Wang E. and Yasmeen, F., 2021. Forecast: Forecasting functions for time series and linear models. R package version 8,14.
-Jaafari, A., Mafi Gholami, D. and Zenner, E., 2017. A Bayesian modeling of wildfire probability in the Zagros Mountains, Iran. Ecological Informatics, 39: 32-44 (In Persian).
-Jazirehi, M.H. and Ebrahimi Rastaghi, M., 2003. Silviculture in Zagros. University of Tehran, Tehran, 560p (In Persian).
-Jiménez-Ruano, A., Rodrigues, M. and de la Riva Fernández, J., 2020. Fire regime dynamics in mainland Spain. Part 2: a near -future prospective of fire activity. Science of the Total Environment, 705: 135842.
-Kouassi, J-L., Wandan, N. and Mbow, C., 2020. Predictive modeling of wildfire occurrence and damage in a Tropical Savanna ecosystem of west Africa. Fire, 3: 42.
-Krebs, P., Pezzatti, G.B., Mazzoleni, S., Talbot, L.M. and Conedera, M., 2010. Fire regime: history and definition of a key concept in disturbance ecology. Theory in Biosciences, 129: 53-69.
-Liu, T., Mickley, L.J., Marlier, M.E., DeFries, R.S., Khan, M.d.F., Latif, M.T. and Alexandra, K., 2020. Diagnosing spatial biases and uncertainties in global fire emissions inventories: Indonesia as regional case study. Remote Sensing of Environment, 237: 111557.
-Liu, Y., Stanturf, J. and Goodrick, S., 2010. Trends in global wildfire potential in a changing climate. Forest Ecology and Management, 259: 685-697.
-Miller, J.D., Safford, H.D., Crimmins, M. and Thode, A.E., 2009. Quantitative evidence for increasing forest fire severity in the Sierra Nevada and southern Cascade mountains, California and Nevada, USA. Ecosystems, 12: 16-32.
-Nemati Paykani, M. and Jalilian, N., 2012. Medicinal plants of Kermanshah province. Taxonomy and Biosystematics, 4: 69-78.
-Oliveira, S., Rocha, J. and Sá, A., 2021. Wildfire risk modeling. Current Opinion in Environmental Science and Health, 23: 100274.
-Pourreza, M., Hosseini, S.M., Safari Sinegani, A.A., Matinizadeh, M. and Dick, W.A., 2014. Soil microbial activity in response to fire severity in Zagros oak (Quercus brantii Lindl.) forests, Iran, after one year. Geoderma, 213: 95-102.
-Pourreza, M., Safari, H., Khodakarami, Y. and Mashayekhi, SH., 2009. Preliminary results of post-fire resprouting of manna oak (Quercus brantii Lindl.) in the Zagros forests, Kermanshah. Iranian Journal of Forest and Poplar Research, 17: 225-236.
-Renard, Q., Pe´lissier, R., Ramesh, B.R. and Kodandapani, N., 2012. Environmental susceptibility model for predicting forest fire occurrence in the Western Ghats of India. International Journal of Wildland Fire, 21: 368-379.
-Rodrigues, M., Miguel, J.S, Oliveira, S., Moreira, F. and Camia, A., 2013. An insight into spatial-temporal trends of fire ignitions and burned areas in the European Mediterranean countries. Journal of Earth Science and Engineering, 3: 497-505.
-Satendra and Kaushik, A.D., 2014. Forest fire diaster management. National Institute of Disaster Management, Ministry of Home Affairs, New Delhi, 302p.
-Viganó, H.H.d.G., Souza, C.C.de., Reis Neto, J.F., Cristaldo, M.F. and Jesus, L.de. 2018. Prediction and modeling of forest fires in the Pantanal. Marcia Ferreira Cristaldo, Leandro de Jesus, 33: 306-316.
-Wahyuningsih, S., Goejantoro, R., Siringoringo, M., Saputra, A.R. and Aminah, S., 2019. Application seasonal autoregressive integrated moving average to forecast the number of east Kalimantan hotspots. Journal of Physics: Conference Series, 1351, 012085.
-Wuertz, D., 2020. TimeSeries: Financial Time Series Objects (Rmetrics). R package version, 3062, 100.
-Ye, T., Wang, Y., Guo, Z.X. and Li, Y.J., 2017. Factor contribution to fire occurrence, size, and burn probability in a subtropical coniferous forest in east China. PLoS ONE, 12: e0172110.