Investigating the Relationship between Vegetation Indices and the Occurrence of Wildfire in the Vegetation Areas of Iran

Document Type : Research Article

Authors

1 PhD Candidate in Climatology, Shahid Beheshti University, Tehran, Iran

2 Associate Professor in Climatology, Shahid Beheshti University, Tehran, Iran

3 Postdoctoral Research Associate in Climatology, Ferdowsi University of Mashhad, Mashhad, Iran

Abstract

The risk of wildfire in Iran has increasingly become a serious risk throughout the year and is no longer limited to a few months of the year. Accordingly, forest monitoring for identifying areas prone to fires is an effective step for the development of early warning systems. This research was conducted to see the relationship between the vegetation indices with the occurrence of fire in the vegetation areas of Iran. The MODIS sensor data of the Terra satellite, including the active fire product (MOD14A2) and vegetation indices NDVI and EVI (MOD13A3) were used from 2001 to 2020. The results showed that the maximum occurrence of wildfire in the vegetation areas of Iran occurs from the end of spring to the beginning of autumn. The increasing trend of wildfire occurrence in the warm period of the year is related to the increase in temperature, decrease in humidity, and early melting of snow in the spring season and the autumn season, simultaneously with the fall of vegetation and the late start of autumn precipitation. The maximum average height of wildfire occurrences in the Arsbaran vegetation area is 1791 meters in October and the maximum height of wildfire occurrences in the Irani-Turani area is 1565 meters in August. Spatial distribution of NDVI and EVI indices showed that the density of vegetation is effective in the intensity and spread of wildfire and provides the conditions for the spread of wildfire in such a way that barren areas with low vegetation cover the center, east, and southeast of Iran. Almost no wildfire occurred and the maximum number of active fires was observed in the north, west, and northwest of Iran.

Graphical Abstract

Investigating the Relationship between Vegetation Indices and the Occurrence of Wildfire in the Vegetation Areas of Iran

Keywords


امامی، حسن؛ شهریاری، حسن؛ 1398. کمّی سازی عوامل محیطی و انسانی در وقوع آتش‌­سوزی جنگل با روش‌­های RS و GIS؛ مناطق حفاظت شده ارسباران. فصلنامه علمی- پژوهشی اطلاعات جغرافیایی سپهر. 28 (112)، 53- 35. https://doi.org/10.22131/sepehr.2020.38606
جانباز قبادی، غلامرضا؛ 1398. بررسی مناطق خطر آتش­‌سوزی جنگل در استان گلستان، بر اساس شاخص خطر آتش­‌سوزی (FRSI) با بهره‌گیری از تکنیک (GIS). تحلیل فضایی مخاطرات محیطی. 6 (3)، 102-89.
خان‌محمدی، مرتضی؛ رحیمی، محمد؛ کرتولی نژاد، داود؛ 1395. تحلیل خطر آتش­‌سوزی جنگل‏­های هیرکانی شمال‌شرق ایران با استفاده از شاخص­‌های کچ-بایرام و مک-آرتور. تحقیقات حمایت و حفاظت جنگل‌ها و مراتع ایران. 14 (1)، 57-48. https://doi.org/10.22092/IJFRPR.2016.107641
رحیمی، داریوش؛ خادمی، سمانه؛ 1397. تحلیل الگوهای همدید خطر آتش‌سوزی در جنگل­‌های شمال ایران (استان گلستان). مخاطرات محیط طبیعی. 7 (17)، 36-19. https://doi.org/10.22111/JNEH.2017.3279
زرین، آذر؛ داداشی رودباری، عباسعلی؛ 1399. پیش­نگری چشم‌انداز بلندمدت دمای آینده ایران مبتنی بر برونداد پروژة مقایسة مدل­های جفت شدة فاز ششم (CMIP6). فیزیک زمین و فضا. 46 (3)، 602-583.
زرین، آذر؛ داداشی رودباری، عباسعلی؛ 1400 الف. پیش نگری همادی نمایه‌های خشکسالی در ایران مبتنی بر برونداد چند مدلی CMIP5. پژوهش­‌های تغییرات آب و هوایی. 2 (7)، 82-71.
زرین، آذر؛ داداشی رودباری، عباسعلی؛ 1400 ب. پیش­نگری دوره­های خشک و مرطوب متوالی در ایران مبتنی بر برونداد همادی مدل­های تصحیح شده اریبی CMIP6. فیزیک زمین و فضا. 47 (3)، 578-561.
شریف نژاد، طوبی؛ خاوریان نهزک، حسن؛ ورامش، سعید؛ 1398. قابلیت­های سنجش از دور در پایش آتش­سوزی­های عرصه­های منابع طبیعی، اولین کنفرانس بین المللی و چهارمین کنفرانس ملی صیانت از منابع طبیعی و محیط زیست. https://civilica.com/doc/961387
عالی محمودی سراب، سجاد؛ فقهی، جهانگیر؛ جباریان امیری، بهمن؛ 1391. پیش‌بینی وقوع آتش‌­سوزی در جنگل­ها و مراتع با استفاده از شبکه عصبی مصنوعی (مطالعه موردی: جنگل‌­های منطقه زاگرس، شهرستان ایذه). بوم شناسی کاربردی. 1 (2)، 75-86. http://ijae.iut.ac.ir/article-1-188-fa.html
عساکره، حسین؛ مسعودیان، سید ابوالفضل؛ ترکارانی، فاطمه؛ 1399. آشکارسازی روند بلندمدت بارش سالانۀ ایران­زمین در ارتباط با تغییر فراوانی فرین­های بارش روزانه. جغرافیا و مخاطرات محیطی. 9 (4)، 143-123.
فرج زاده اصل، منوچهر؛ قویدل رحیمی، یوسف؛ مکری، ساحل؛ 1394. تجزیه و تحلیل آتش‌­سوزی جنگل با منشأ آب و هوایی با داده­های ماهواره‌ای در منطقه البرز. تحلیل فضایی مخاطرات محیطی. 2 (3)،104-83.
محمدی، فریده؛ شعبانیان، نقی؛ پورهاشمی، مهدی؛ فاتحی، پرویز؛ 1389. تهیه نقشه خطر آتش­‌سوزی جنگل با استفاده از GIS و AHP در بخشی از جنگل­های پاوه. تحقیقات جنگل و صنوبر ایران. 18 (4)، 586-569.
 
Albar, I., Jaya, I.N.S., Saharjo, B.H., Kuncahyo, B. and Vadrevu, K.P., 2018. Spatio-temporal analysis of land and forest fires in Indonesia using MODIS active fire dataset. Land-atmospheric research applications in South and Southeast Asia, 105-127. https://doi.org/10.1007/978-3-319-67474-2_6
Barro, S.C. and Conard, S.G., 1991. Fire effects on California chaparral systems: an overview. Environment International, 17(2-3), 135-149. https://doi.org/10.1016/0160-4120(91)90096-9
Castro, F.X., Tudela, A. and Sebastià, M.T., 2003. Modeling moisture content in shrubs to predict fire risk in Catalonia (Spain). Agricultural and Forest Meteorology, 116(1-2), 49-59. https://doi.org/10.1016/S0168-1923(02)00248-4
Champin, L., Taïbi, A.N. and Ballouche, A., 2022. Spatial analysis of the occurrence and spread of wildfires in Southwest Madagascar. Fire, 5(4), 98. https://doi.org/10.3390/fire5040098.
Chuvieco, E., Aguado, I., Salas, J., García, M., Yebra, M. and Oliva, P., 2020. Satellite remote sensing contributions to wildland fire science and management. Current Forestry Reports, 6, 81-96. https://doi.org/10.1007/s40725-020-00116-5.
Collins, K.M., Price, O.F. and Penman, T.D., 2015. Spatial patterns of wildfire ignitions in south-eastern Australia. International Journal of Wildland Fire, 24(8), 1098-1108. https://doi.org/10.1071/wf15054.
Flannigan, M., Cantin, A.S., De Groot, W.J., Wotton, M., Newbery, A. and Gowman, L.M., 2013. Global wildland fire season severity in the 21st century. Forest Ecology and Management, 294, pp.54-61. https://doi.org/10.1016/j.foreco.2012.10.022
Fu, Y., Li, R., Wang, X., Bergeron, Y., Valeria, O., Chavardès, R.D., Wang, Y. and Hu, J., 2020. Fire detection and fire radiative power in forests and low-biomass lands in Northeast Asia: MODIS versus VIIRS Fire Products. Remote Sensing, 12(18), 2870. https://doi.org/10.3390/ rs12182870.
Giglio, L., Descloitres, J., Justice, C.O. and Kaufman, Y.J., 2003. An enhanced contextual fire detection algorithm for MODIS. Remote sensing of environment, 87(2-3),273-282. https://doi.org/10.1016/s0034-4257(03)00184-6.
Guan, X., Shen, H., Wang, Y., Chu, D., Li, X., Yue, L., Liu, X. and Zhang, L., 2021. Fusing MODIS and AVHRR products to generate a global 1-km continuous NDVI time series covering four decades. Earth System Science Data Discussions, 1-32. https://doi.org/10.5194/essd-2021-156.
Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X. and Ferreira, L.G., 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote sensing of environment, 83(1-2), 195-213. https://doi.org/10.1016/s0034-4257(02)00096-2.
Justice, C.O., Giglio, L., Roy, D., Boschetti, L., Csiszar, I., Davies, D., Korontzi, S., Schroeder, W., O’Neal, K. and Morisette, J., 2011. MODIS-derived global fire products. Land Remote Sensing and Global Environmental Change: NASA's Earth Observing System and the Science of ASTER and MODIS, 661-679. https://doi.org/10.1007/978-1-4419-6749-7_29.
Katagis, T. and Gitas, I.Z., 2022. Assessing the accuracy of MODIS MCD64A1 C6 and FireCCI51 burned area products in Mediterranean ecosystems. Remote Sensing, 14(3), 602. https://doi.org/10.3390/rs14030602.
 Kerr, J.T. and Ostrovsky, M., 2003. From space to species: ecological applications for remote sensing. Trends in ecology & evolution, 18(6), pp.299-305. https://doi.org/10.1016/s0169-5347(03)00071-5.
Kumari, B. and Pandey, A.C., 2020. MODIS based forest fire hotspot analysis and its relationship with climatic variables. Spatial Information Research, 28(1), 87-99. https://doi.org/10.1007/ s41324-019-00275-z
Li, Y., Gao, X., Li, Z., Jiang, J. and Li, P., 2022. Analysis of Atmospheric Factors affecting wildfires. Authorea Preprints. https://essopenarchive.org/doi/full/10.1002/essoar.10506515.1
Littell, J. S., Peterson, D. L., Riley, K. L., Liu, Y., & Luce, C. H., 2016. A review of the relationships between drought and forest fire in the United States. Global Change Biology, 22(7), 2353–2369. Portico.  https://doi.org/10.1111/gcb.13275
Lizundia-Loiola, J., Otón, G., Ramo, R. and Chuvieco, E., 2020. A spatio-temporal active-fire clustering approach for global burned area mapping at 250 m from MODIS data. Remote Sensing of Environment, 236, p.111493. https://doi.org/10.1016/j.rse.2019.111493.
Mangeon, S., Field, R., Fromm, M., McHugh, C. and Voulgarakis, A., 2016. Satellite versus ground-based estimates of burned area: A comparison between MODIS based burned area and fire agency reports over North America in 2007. The Anthropocene Review, 3(2),76-92. https://doi.org/10.1177/2053019615588790.
Martyn, I., Petrov, Y., Stepanov, S., Sidorenko, A. and Vagizov, M., 2020. Monitoring forest fires and their consequences using MODIS spectroradiometer data. In IOP conference series: earth and environmental science,507(1),012019. https://doi.org/10.1088/1755-1315/507/1/012019.
Oton, G., Ramo, R., Lizundia-Loiola, J. and Chuvieco, E., 2019. Global detection of long-term (1982–2017) burned area with AVHRR-LTDR data. Remote Sensing, 11(18), 2079. https://doi.org/10.3390/rs11182079.
Parajuli, A., Gautam, A.P., Sharma, S.P., Bhujel, K.B., Sharma, G., Thapa, P.B., Bist, B.S. and Poudel, S., 2020. Forest fire risk mapping using GIS and remote sensing in two major landscapes of Nepal. Geomatics, Natural Hazards and Risk, 11(1), pp.2569-2586.  https://doi.org/10.1080/19475705.2020.1853251.
Pradhan, B., Suliman, M.D.H.B. and Awang, M.A.B., 2007. Forest fire susceptibility and risk mapping using remote sensing and geographical information systems (GIS). Disaster Prevention and Management: An International Journal, 16(3), 344-352. https://doi.org/ 10.1108/09653560710758297.
Subedi, P.B., Ayer, K., Miya, M.S., Parajuli, B. and Sharma, B., 2022. Forest Fire Risk Zone Mapping of Aalital Rural Municipality, Dadeldhura District, Nepal. Journal of Multidisciplinary Applied Natural Science, 2(2),70-81. https://doi.org/10.47352/jmans.2774-3047.115.
Sullivan, A.L., 2009. Wildland surface fire spread modelling, 1990–2007. 3: Simulation and mathematical analogue models. International Journal of Wildland Fire, 18(4), 387-403.  https://doi.org/10.1071/wf06144.
Tanase, M.A., Aponte, C., Mermoz, S., Bouvet, A., Le Toan, T. and Heurich, M., 2018. Detection of windthrows and insect outbreaks by L-band SAR: A case study in the Bavarian Forest National Park. Remote Sensing of Environment, 209, 700-711. https://doi.org/10.1016/ j.rse.2018.03.009.
Thome, K.J., Czapla-Myers, J.S. and Biggar, S.F., 2003. Vicarious calibration of Aqua and Terra MODIS. In Earth Observing Systems VIII (Vol. 5151, 395-405). SPIE. https://doi.org/ 10.1117/ 12.506364.
Tomshin, O. and Solovyev, V., 2022. Spatio-temporal patterns of wildfires in Siberia during 2001–2020. Geocarto International, 37(25), 7339-7357. https://doi.org/10.1080/ 10106049. 2021.1973581
Tosic, I., Mladjan, D., Gavrilov, M.B., Zivanovic, S., Radakovic, M.G., Putnikovic, S., Petrovic, P., Mistridzelovic, I.K. and Markovic, S.B., 2019. Potential influence of meteorological variables on forest fire risk in Serbia during the period 2000-2017. Open Geosciences, 11(1), 414-425. https://doi.org/10.1515/geo-2019-0033.
Wang, J., Wang, G., Qi, J., Liu, Y., & Zhang, W., 2021). Research of Forest Fire Points Detection Method Based on MODIS Active Fire Product. In 2021 28th International Conference on Geoinformatics (pp. 1-5). IEEE. https://doi.org/10.1109/IEEECONF54055.2021.9687646
Wu, Z., Li, M., Wang, B., Quan, Y. and Liu, J., 2021. Using artificial intelligence to estimate the probability of forest fires in Heilongjiang, northeast China. Remote Sensing, 13(9), 1813. https://doi.org/10.3390/rs13091813.
Xiao, Y., Zhang, X. and Ji, P., 2015. Modeling forest fire occurrences using count-data mixed models in qiannan autonomous prefecture of Guizhou Province in China. PloS one, 10(3), e0120621. https://doi.org/10.1371/journal.pone.0120621.
Yankovich, K.S., Yankovich, E.P. and Baranovskiy, N.V., 2019. Classification of vegetation to estimate forest fire danger using landsat 8 images: Case study. Mathematical Problems in Engineering, 2019,1-14. https://doi.org/10.1155/2019/6296417.
Zeng, A., Yang, S., Zhu, H., Tigabu, M., Su, Z., Wang, G. and Guo, F., 2022. Spatiotemporal dynamics and climate influence of forest fires in Fujian Province, China. Forests, 13(3), p.423. https://doi.org/10.3390/f13030423.
 
CAPTCHA Image