Original Article
Wildfire Susceptibility Mapping using NBR Index and Frequency Ratio Model
Authors
Abstract
Quantifying fire hazards in natural areas and their spatial patterns are essential for developing appropriate fire management strategies, especially in countries with limited historical data on past fires. In this study, a fire hazard map for the Andika region of Iran was constructed by examining the correlation of past fires with the criteria of topography, meteorology, land cover, and human factors. The locations of eight-year fire points from 2013 to 2020 of Nova satellite sensor VIIRS were received and the fire map of each was constructed using the NBR (Normalized Burn Ratio). The wildfire events distribution maps were randomly divided into 70 and 30 percent ratios for training (modeling) and testing (validation) data, respectively. Using the frequency ratio, a fire hazard map of the region was created. Four fire hazard areas ranging from very high to low were identified. The results of past fires and the frequency ratio model showed that in the study area, land cover (2.982), elevation (2.778), and annual precipitation (2.419) have the greatest prediction rate and influence on fire occurrence. The results also showed that a large proportion of past fires (71.37%) were located in high and very high-risk areas. The evaluation results of the area under the curve method showed an accuracy of 71.1% using evaluation data and 74.4% using training data, which can be considered desirable. The small differences between the validation results using test and training data indicate an unbiased fire hazard map.
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References
Abatzoglou JT & Williams AP (2016). Impact of anthropogenic climate change on wildfire across western US forests. Proceedings of the National Academy of Sciences of the United States of America. 113(42):11770–11775. doi.org/10.1073/pnas.1607171113.
Adab H, Devi Kanniah K, Solaimani K, Kanniah KD & Solaimani K(n.d.). GIS-based probability assessment of fire risk in grassland and forested landscapes of Golestan Province, Iran. researchgate.net. https://www.researchgate.net/profile/Hamed-Adab-2/publication/268268777_GIS-based_Probability_Assessment_of_Fire_Risk_in_Grassland_and_Forested_Landscapes_of_Golestan_Province_Iran/links/0046352ade9ee26f2a000000/GIS-based-Probability-Assessment-of-Fire-Ri [Retrieved 11 December 2021].
Allen CD, Macalady AK, Chenchouni H, Bachelet D, McDowell N, Vennetier M, Kitzberger T, Rigling A, et al. (2010). A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Forest Ecology and Management. 259(4):660–684. doi.org/10.1016/j.foreco.2009.09.001.
Arca D, Hacısalihoğlu M & Kutoğlu H (2020). Producing forest fire susceptibility map via multi-criteria decision analysis and frequency ratio methods. Natural Hazards. 104(1):73–89. doi.org/10.1007/s11069-020-04158-7.
Argañaraz JP, Gavier Pizarro G, Zak M, Landi MA& Bellis LM (2015). Human and biophysical drivers of fires in Semiarid Chaco mountains of Central Argentina. Science of the Total Environment. 520:1–12. doi.org/10.1016/j.scitotenv.2015.02.081.
Bar Massada A, Syphard AD, Hawbaker TJ, Stewart SI & Radeloff VC (2011). Effects of ignition location models on the burn patterns of simulated wildfires. Environmental Modelling and Software. 26(5): 583–592. doi.org/10.1016/j.envsoft.2010.11.016.
Choubin B, Soleimani F, Pirnia A, Sajedi-Hosseini F, Alilou H, Rahmati O, Melesse AM, Singh VP, et al. (2019) Effects of drought on vegetative cover changes: Investigating spatiotemporal patterns. In Extreme Hydrology and Climate Variability (eds. Melesse AM, Abtew W, Senay G). Elsevier, Amsterdam..
Chuvieco E & Congalton RG (1989). Application of remote sensing and geographic information systems to forest fire hazard mapping. Remote Sensing of Environment. 29(2): 147–159. doi.org/10.1016/0034-4257(89)90023-0.
Clark JS (1990). Fire and climate change during the last 750 yr in northwestern Minnesota. Ecological Monographs. 60(2):135–159. doi.org/10.2307/1943042.
Continuation of the fire in the heights of Mount Della Andika (2020, August 23). Khabaronline. https://www.khabaronline.ir/xg7md. Retrieved April, 2022
Eastaugh CS & Hasenauer H (2014). Deriving forest fire ignition risk with biogeochemical process modelling. Environmental Modelling and Software. 55:132–142. doi.org/10.1016/j.envsoft.2014.01.018.
Eskandari S & Chuvieco E (2015). Fire danger assessment in Iran based on geospatial information. International Journal of Applied Earth Observation and Geoinformation. 42: 57–64. doi.org/10.1016/j.jag.2015.05.006.
Eskandari S & Miesel JR (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. doi.org/10.1080/19475705.2017.1289249.
Fischer AP, Spies TA, Steelman TA, Moseley C, Johnson BR, Bailey JD, Ager AA, Bourgeron P, et al. (2016). Wildfire risk as a socioecological pathology. Frontiers in Ecology and the Environment. 14(5):276–284. doi.org/10.1002/fee.1283.
Gigović L, Jakovljević G, Sekulović D & Regodić M (2018). GIS multi-criteria analysis for identifying and mapping forest fire hazard: Nevesinje, Bosnia and Herzegovina. Tehnicki Vjesnik. 25(3):891–897. doi.org/10.17559/TV-20151230211722.
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. 23(2): 388–406. doi.org/10.1080/10807039.2016.1255136.
Ivanilova TN (1985). Set probability identification in forest fire simulation. Annual Review in Automatic Programming. 12(2):185–188. doi.org/10.1016/0066-4138(85)90357-X.
Jaafari A, Zenner EK, Panahi M & 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. 266–267:198–207. doi.org/10.1016/j.agrformet.2018.12.015.
Jiang B (2011). GIS-based Multi-criteria Analysis Used in Forest Fire Estimation : A Case Study of Northernmost Gä vleborg County in Sweden. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-9626 [Accessed 11 December 2021].
Kayet N, Chakrabarty A, Pathak K, Sahoo S, Dutta T & Hatai BK (2020). Comparative analysis of multi-criteria probabilistic FR and AHP models for forest fire risk (FFR) mapping in Melghat Tiger Reserve (MTR) forest. Journal of Forestry Research. 31(2):565–579. doi.org/10.1007/s11676-018-0826-z.
Key CH & Benson NC (2005). Landscape assessment: remote sensing of severity, the normalized burn ratio and ground measure of severity, the composite burn index. In FIREMON: Fire effects monitoring and inventory system Ogden, Utah: USDA Forest Service, Rocky Mountain Res. Station.
Lee S & Pradhan B (2007). Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides. 4(1):33–41. doi.org/10.1007/s10346-006-0047-y.
Nami MH, Jaafari A, Fallah M & 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. doi.org/10.1007/s13762-017-1371-6.
Oh HJ, Kim YS, Choi JK, Park E & Lee S (2011). GIS mapping of regional probabilistic groundwater potential in the area of Pohang City, Korea. Journal of Hydrology. 399(3–4):158–172. doi.org/10.1016/j.jhydrol.2010.12.027.
Pimont F, Parsons R, Rigolot E, de Coligny F, Dupuy JL, Dreyfus P & Linn RR (2016). Modeling fuels and fire effects in 3D: Model description and applications. Environmental Modelling and Software. 80:225–244. doi.org/10.1016/j.envsoft.2016.03.003.
Pourtaghi ZS, Pourghasemi HR & Rossi M (2015). Forest fire susceptibility mapping in the Minudasht forests, Golestan province, Iran. Environmental Earth Sciences. 73(4): 1515–1533. doi.org/10.1007/s12665-014-3502-4.
Pradhan B, Suliman MDH Bin & Awang MA Bin (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. doi.org/10.1108/09653560710758297.
Renard Q, Pélissier R, Ramesh BR, Kodandapani N, Renard Q, Pélissier R, Ramesh BR & Kodandapani N (2012). Environmental susceptibility model for predicting forest fire occurrence in the Western Ghats of India. International Journal of Wildland Fire. 21(4):368–379. doi.org/10.1071/WF10109.
Rother MT & Veblen TT (2016). Limited conifer regeneration following wildfires in dry ponderosa pine forests of the Colorado Front Range. Ecosphere. 7(12):e01594. doi.org/10.1002/ecs2.1594.
Satir O, Berberoglu S & 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. doi.org/10.1080/19475705.2015.1084541.
Şen Z & Habib Z (2000). Spatial Precipitation Assessment with Elevation by Using Point Cumulative Semivariogram Technique. Water Resources Management 2000 14:4. 14(4): 311–325. doi.org/10.1023/A:1008191012044.
Shahabi H, Hashim M & Ahmad B Bin (2015). Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio, logistic regression, and fuzzy logic methods at the central Zab basin, Iran. Environmental Earth Sciences. 73(12):8647–8668. doi.org/10.1007/s12665-015-4028-0.
Stevens-Rumann CS, Kemp KB, Higuera PE, Harvey BJ, Rother MT, Donato DC, Morgan P& Veblen TT (2018). Evidence for declining forest resilience to wildfires under climate change. Ecology Letters. 21(2):243–252. doi.org/10.1111/ele.12889.
Takayama N (2018). Monitoring for Circumstance of Wildfire Occurrence in Kalimantan by Using Satellite Remote Sensing Data Based on GIS. Jurnal Terapan Manajemen Dan Bisnis. 4(1): 79. doi.org/10.26737/jtmb.v4i1.600.
Teodoro A, Duarte L, Sillero N, Gonçalves JA, Fonte J, Gonçalves-Seco L, Pinheiro da Luz LM & dos Santos Beja NMR (2015). An integrated and open source GIS environmental management system for a protected area in the south of Portugal. Earth Resources and Environmental Remote Sensing/GIS Applications VI. 9644:96440U. doi.org/10.1117/12.2193578.
Tien Bui D, Bui QT, Nguyen QP, Pradhan B, Nampak H & Trinh PT (2017). A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area. Agricultural and Forest Meteorology. 233:32–44. doi.org/10.1016/j.agrformet.2016.11.002.
Tshering K, Thinley P, Shafapour Tehrany M, Thinley U & 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. doi.org/10.3390/forecast2020003.
Vadrevu KP, Eaturu A & Badarinath KVS (2009). Fire risk evaluation using multicriteria analysis—a case study. Environmental Monitoring and Assessment 2009 166:1. 166(1):223–239. doi.org/10.1007/S10661-009-0997-3.
Westerling ALR (2016). Increasing western US forest wildfire activity: Sensitivity to changes in the timing of spring. Philosophical Transactions of the Royal Society B: Biological Sciences. 371(1696) doi.org/10.1098/rstb.2015.0178.
Yilmaz I (2009). Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: A case study from Kat landslides (Tokat-Turkey). Computers and Geosciences. 35(6):1125–1138. doi.org/10.1016/j.cageo.2008.08.007.
