Modeling and Zoning of Fire Prone Areas in Zagros Forests Using Geographic Information System Based on Logistic Regression

Document Type : Research Article

Authors

• Mehdi Heydari ¹
• Sina Attar Roshan ²
• Elham Jaferyan ³
• Mohammad Abiyat ⁴

¹ Associate Professor, Head of Forest Sciences Department, Faculty of Agriculture, Ilam University, Ilam Province, Iran

² Assistant Professor, Department of Environment, Persian Gulf Research Center, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran.

³ Agricultural Jihad Organization of Lorestan Province.

⁴ MSc in Natural Resources Engineering–Environment, Islamic Azad University, Science and Research Branch of Tehran (Khuzestan), Ahvaz, Iran

10.22067/geoeh.2021.68903.1019

Abstract

Forests play a vital role in the sustainability of ecosystems as one of the most important natural renewable resources. One of the most important disturbances affecting Zagros forest ecosystems is forest fires. Therefore, identifying critical fire areas to reduce potential damage is necessary. The purpose of this study is to investigate the effect of effective variables in causing fire and to prepare a fire risk map. For this purpose, the variables affecting the fire including altitude, slope, direction, Distance from residential areas, distance from waterways and distance from the road were determined to determine the impact of each on the fire. Elevation map of sea level, slope and geographical direction was prepared with the help of digital elevation model. Distance maps of residential areas and distance from the road were prepared from digital maps of 1.25000. Also, the areas where fires occurred during the years 90-94 were harvested by GPS. In this study, logistic regression method was used to investigate the effect of various factors on fire. The results showed that altitude, distance from waterway and slope percentage were the most important factors influencing forest fires in the region. Modeling was performed based on three variables that had a significant relationship with forest fires in the region and the coefficients obtained from the logistic regression method. The validation results of the model with a Nagelkerke's R² coefficient of about 0.500 and a rock curve coefficient of 0.701 showed the accuracy, fit and validity of the obtained model. The results also showed that 81% of the area is located in critical and dangerous areas.

Keywords

• Fire
• Rock
• Logistic Regression
• Modeling

20.1001.1.23221682.1400.10.2.3.0

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References

ابراهیمی، حمید؛ رسولی، علی‌اکبر؛ مختاری، داوود؛ 1397. بررسی تغییرات خطر آتش­سوزی و عوامل مؤثر بر آن با استفاده از مدل حداکثر بی­نظمی، مطالعه موردی: جنگل­ها و مراتع استان آذربایجان شرقی. جغرافیا و مخاطرات محیطی. 7(25): 57-73.

اسکندری، سعیده؛ 1392. ارائه الگوی پتانسیل آتش­سوزی جنگل و گسترش آن با استفاده از RS و GIS، مطالعه موردی: جنگل­های بخش سه نکا- ظالمرود. پایان­نامه دکتری، دانشگاه علوم کشاورزی و منابع طبیعی ساری، دانشکده منابع طبیعی، گروه جنگل­داری. 121 صفحه.

اسکندری، سعیده؛ 1396. روش­های مدل­سازی و ارزیابی خطر وقوع آتش­سوزی در جنگل­های جهان و ایران. انسان و محیط­زیست. 15(42): 81-110.

آمار آتش­سوزی 4 سال. 1394. سازمان جنگل­ها و مراتع کل کشور. 36 صفحه.

زرع کار، آزاده؛ کاظمی زمانی، بهاره؛ قربانی، ساره؛ عاشق معلا، مریم؛ جعفری، حمیدرضا؛ 1392. تهیه نقشه پراکندگی فضایی خطر آتش­سوزی جنگل با استفاده از روش تصمیم­گیری چندمعیاره و سامانه اطلاعات جغرافیایی (مطالعه موردی: سه حوزه جنگلی در استان گیلان). تحقیقات جنگل و صنوبر ایران. 21(2): 218-230.

فرامرزی، حسن؛ حسینی، سید محسن؛ غلامعلی فرد، مهدی؛ 1393. پهنه­بندی مخاطره آتش­سوزی پارک ملی گلستان با استفاده از رگرسیون لجستیک. جغرافیا و مخاطرات محیطی. 3(2): 73-90.

محمدی، فریده؛ 1388. تهیه نقشه خطر آتش­سوزی جنگل با استفاده از تصاویر ماهواره­ای و GIS در بخشی از جنگل­های پاوه. پایان­نامه کارشناسی ارشد، گروه جنگل­داری. دانشکده منابع طبیعی، دانشگاه کردستان. 64 صفحه.

میردیلمی، طیبه؛ شتایی جویباری، شعبان؛ کاووسی، محمدرضا؛ 1394. پهنه­بندی خطر آتش­سوزی در پارک ملی گلستان با استفاده از روش رگرسیون لجستیک. پژوهش­های علوم و فناوری چوب و جنگل. 22(1): 1-16.

نجفی، احمد؛ ایران‌نژاد، محمدحسین؛ ستوده، احد؛ مختاری، محمدحسین؛ کیانی، بهمن؛ 1394. مدل‌سازی و تهیه نقشه خطر وقوع آتش‌سوزی جنگل با استفاده از سنجش‌ازدور و سامانه اطلاعات جغرافیایی (مطالعه موردی: منطقه حفاظت ‌شده باغ شادی یزد). بوم‌شناسی کاربردی. ۴ (۱۴): ۱۳-۲۶.

 

Chang, Y., Zhu, Z., Bu, R., Chen, H., Feng, Y., Li, Y., Hu, Y., & Wang, Z., 2013. Predicting fire occurrence patterns with logistic regression in Heilongjiang Province, China. Landscape Ecology, 28(10): 1989-2004. https://doi.org/10.1007/s10980-013-9935-4

Chuvieco, E., Salas, J., 1994. Mapping the spatial distribution of forest fire danger using GIS. International Journal of Geographical Information Systems, 10(3): 333-345.

Deng, O., Su, G., Huang, Q., Li, Y., 2013. Forest Fire Risk Mapping Based on Spatial Logistic Model of Northeastern China Forest Zone. In: Bian F., Xie Y., Cui X., Zeng Y. (eds) Geo-Informatics in Resource Management and Sustainable Ecosystem. GRMSE 2013. Communications in Computer and Information Science, vol 399. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41908-9_18

Dong, X., Li-min, D., Guo-fan, Sh., Lei, T., Hui, W., 2005. Forest fire risk zone mapping from satellite images and GIS for Baihe forestry Bureau, Jilin, China. Journal of Forestry Research, 3(16): 169- 174.

Eastman, J. R., 2002. Idrisi for windows user’s guide ver. 32, Clark labs for cartography technology and geographic analysis, (Clark University).

Heydari, M., Salehi, A., Mahdavi, A. Adibnejad, M., 2012. Effects of different fire severity levels on soil chemical and physical properties in Zagros forests of western Iran. Folia Forestalia Polonica, 54, 241–250.

Lemke, D., Hulme, P. E., Brown, J. A., Tadesse, W., 2011. Distribution modelling of Japanese honeysuckle (Lonicera japonica) invasion in the Cumberland Plateau and Mountain Region, USA. Forest Ecology and Management, 9:1-11.

Mahiny, A. S., Turner, B., 2001. Modeling past vegetation change through remote sensing and GIS: a comparison of neural networks and logistic regression methods. School of resources, environment and society, the Australian national University, Canberra, 0200, Australia. 315 p.

Sakr, G. E., Elhajj, I. H., Mitri, G., 2011. Efficient forest fire occurrence prediction for developing countries using two weather parameters. Engineering applications of artificial intelligence, 24: 888-894.

Setiawan, I., Mahmud, A. R., Mansor, S., Mohamed Shariff, A. R. Nuruddin, A. A., 2004. GIS‐grid‐based and multi‐criteria analysis for identifying and mapping peat swamp forest fire hazard in Pahang, Malaysia. Disaster Prevention and Management, 13(5): 379-386.

Somashekar, R., Ravikumar, P., Mohankumar, C., Prakash, K., Nagaraja, B., 2009. Burnt area mapping of Bandipur National Park, India using IRS1C/1D LISS III data. Journal of the Indian Society of Remote Sensing, 37: 37-50.

Zhang, Q. F., Chen, W. J., 2007. Fire cycle of the Canada’s boreal region and its potential response to global change. Journal of forestry research, 18(1): 55-61.