Modeling Wildfire Risk Using GIS-Integrated Analytical Hierarchy Process Method: A Case Study of Zouagha Forest (Northeastern Algeria)

Year 2025, Volume: 11 Issue: 2, 129 - 143

Intissar Meghzili , Ahmed Laala , Hichem Rais Hocine Mennour , Zineb Bouamrane

https://doi.org/10.33904/ejfe.1604500

Abstract

The growing demand for land increases the risk of forest fire, threatening ecosystems and human health. This study integrates Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA) to assess natural phenomena through fire susceptibility mapping in the Zouagha Forest, northeastern Algeria. This forest area, vital both environmentally and economically, frequently faced fires. In this study, factors that affect fire risk and spread included slope, aspect, Topographic Wetness Index (TWI), altitude, distance from roads, urban areas, and water resources, Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and flammability of species. The analytical Hierarchy Process (AHP) was used to determine the significance of these various factors, and it was found that anthropogenic factors (proximity to roads and urban areas) were the most important. Fire map results indicated that 61.71% of the forest area was at high and very high risk, with 9.18% specifically at very high risk of fire. The accuracy of the map was validated using the Receiver Operating Characteristic (ROC) curve method, achieving an 81% accuracy rate. Historical wildfire ignition points confirmed the model’s reliability, with over 83% located in high- or very high-risk areas. This model will undoubtedly assist local decision-makers and firefighters in implementing preventive measures and taking necessary precautions to reduce the damage caused by fires in this region.

Keywords

Wildfire , AHP , GIS , ROC , Fire Susceptibility , Zouagha Forest

References

• Akay, A.E., Şahi̇N, H. 2019. Forest Fire Risk Mapping by using GIS Techniques and AHP Method: A Case Study in Bodrum (Turkey). European Journal of Forest Engineering, 5(1): 25‑35. https://doi.org/ 10.33904/ejfe.579075
• Al-Qassab, O.A.I. 2021. Integration of Geographic Information Systems and Remote Sensing in Cartographic Modeling of Land Use: A Case Study of Erbil Plain District, PhD Dissertation, Faculty of Education, University of Mosul. Iraq.
• Anteur, D., Benaradj, A., Fekir, Y., Baghdadi, D. 2021. Zakour Forest fire risk map assessment in the commune of Mamounia (Mascara, Algeria). Folia Forestalia Polonica, 63(1): 21 35. https://doi.org/ 10.2478/ffp-2021-0003.
• Antoniya, A., Gerdzheva, A. 2014. A comparative analysis of different wildfire risk assessment models (a case study for Smolyan districts Bilgaria). Eur. J. Geogr, 5(3): 22-36.
• Baskent, E.Z., Keles, S. 2009. Developing alternative forest management planning strategies incorporating timber, water and carbon values: an examination of their interactions. Environ. Model. Assess. 14, 467-480. https://doi.org/10.1007/s10666-008-9148-4.
• Belkaid, H. 2016. Analyse spatiale et environnementale du risque d’incendie de forêt en Algérie: Cas de la Kabylie maritime. https://tel.archives-ouvertes.fr/tel-01355757.
• Bentekhici, N., Bellal, S., Zegrar, A. 2020. Contribution of remote sensing and GIS to mapping the fire risk of Mediterranean forest case of the forest massif of Tlemcen (North-West Algeria). Natural Hazards, 104(1): 811-831. https://doi.org/10.1007/s11069-020-04191-6.
• Bonora, L., Conese, C., Marchi, E. 2013. Wildfire occurrence: integrated model for risk analysis and operative suppression aspects management. Am. J. Plant Sci. 4: 705-710.
• Boudy, P. 1955. Économie forestière nord-africaine, Tome IV, Description Forestière de l’Algérie et de la Tunisie. Edition Larose Paris. 483 p.
• Busico, G., Giuditta, E., Kazakis, N., Colombani, N. 2019. A Hybrid GIS and AHP Approach for Modelling Actual and Future Forest Fire Risk Under Climate Change Accounting Water Resources Attenuation Role. Sustainability, 11(24): 7166. https://doi.org/10.3390/su11247166
• Butler, B.W., Anderson, W.R., Catchpole, E.A. 2007. Influence of slope on fire spread rate. USDA Forest Service Proceedings RMRS-P-46 CD. In: Butler, B.W., Cook, W. (Eds.): The Fire Environment-Innovations, Management, and Policy; Conference Proceedings, Fort Collins, CO, 26–30 March.
• Çolak, E., Sunar, F. 2020. Evaluation of forest fire risk in the Mediterranean Turkish forests: A case study of Menderes region, Izmir. International Journal of Disaster Risk Reduction, 45: 101479. https://doi.org/10.1016/j.ijdrr.2020.101479
• Demeke, D., Afework, B. 2014. Habitat association and distribution of rodents and insectivores in Chebera Churchura National Park, Ethiopia. Trop. Ecol. 55: 221–229.
• Dimitrakopulos, A.M., Bemmerzouk, A.M., Mitsopoulos, I.D. 2011. Evaluation of the Canadian fire weather index system in eastern Mediterranean environment. Meteorol. Appl. 18: 83-93.
• Demir, A., Akay, A. E. 2024. Forest Fire Risk Mapping Using GIS Based Analytical Hierarchy Process Approach. European Journal of Forest Engineering, 10(1): 15-28. https://doi.org/10.33904/ejfe.1400233
• Dieu, T. B., Le, K. T., Van, N., Le, H., Inge, R. 2016. Tropical Forest Fire Susceptibility Mapping at the Cat Ba National Park Area, Hai Phong City, Vietnam, Using GIS-Based Kernel Logistic Regression. Remote Sensing, 8: 347. https://doi.org/10.3390/rs8040347
• Fekir, Y., Hamadouche, M.A., Anteur, D. 2022. Integrated approach for the assessment of forest fire risk and burn severity mapping using GIS, AHP method, and Google Earth Engine in Western Algeria. Euro-Mediterranean Journal for Environmental Integration, 7(4): 531 544. https://doi. org/10.1007/s41207-02200338-y.
• Freden, S.C., Mercanti, E.P., Becker, M.A. 1974. Third Earth Resources Technology Satellite-1 Symposium: The Proceedings of a Symposium Held by Goddard Space Flight Center at Washington, D.C. on December 10-14, 1973: Prepared at Goddard Space Flight Center. Scientific and Technical Information Office, National Aeronautics and Space Administration.
• GDF, 2024. General Directorate of Forestry, Annual Activity Report, Mila 2024. 22p.
• George, S.L., Kantamneni, k., Allat, R.V., Prasad, K.A., Shekhar, S., Panneer, S., Rice, L., Balasubramani, K. 2022. A multi data geospatial approach for understanding flood risk in the coastal plains of Tamil Nadu, India. Earth, 3(1): 383-400. https://doi.org/10.3390/earth3010023
• Gheshlaghi, H.A., Feizizadeh, B., Blaschke, T. 2019. GIS-based forest fire risk mapping using the analytical network process and fuzzy logic. Journal of Environmental Planning and Management, 63(3): 481‑499. https://doi.org/10.1080/09640568.2019.1594726
• Gouveia, C.M., Bastos, A., Trigo, R.M., DaCamara, C.C. 2012. Drought impacts on vegetation in the pre- and post-fire events over Iberian Peninsula. Natural Hazards and Earth System Sciences, 12(10): 3123 3137. https://doi.org/10.5194/nhess-12-3123-2012
• Gülci, N. 2014. Researches on precision forestry in forest planning. Ph.D. thesis, Kahramanmaras Sutcu Imam University, Kahramanmaraş. 264 p.
• Jaiswal, R.K., Mukherjee, S., Raju, D.K., Saxena, R., 2002. Forest fire risk zone mapping from satellite imagery and GIS. Intr. Journal of Applied Earth Observation and Geoinformation, 4(2002):1-10
• Güngoroglu, 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 Inter. Journal, 23(2): 388–406. https://doi.org/10.1080/10807039. 2016.1255136.
• Jiang, Z., Huete, A.R., Chen, J., Chen, Y., Li, J., Yan, G., Zhang, X. 2006. Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction. Remote Sensing of Environment, 101, 366 378. https://doi.org/10.1016/j.rse.2006.01.003.
• Ju, W., Xing, Z., Wu, J., Kang, Q. 2023. Evaluation of forest fire risk based on multicriteria decision analysis techniques for Changzhou, China. International Journal of Disaster Risk Reduction, 98: 104082. https://doi.org/10.1016/j.ijdrr.2023.104082.
• Kloster, S., Lasslop, G. 2017. Historical and future fire occurrence (1850 to 2100) simulated in CMIP5 Earth System Models. Global and Planetary Change, 150: 58-69. https://doi.org/10.1016/j.gloplacha.2016.12. 017
• Kouachi, M. E., Khairoun, A., Moghli, A., Rahmani, S., Mouillot, F., Baeza, M.J., Moutahir, H. 2024. Forty-Year Fire History Reconstruction from Landsat Data in Mediterranean Ecosystems of Algeria following International Standards. Remote Sensing, 16(13): 2500. https://doi.org/10.3390/rs16132500
• Krawchuk, M.A., Moritz, M.A., Parisien, M., Van Dorn, J., Hayhoe, K. 2009. Global Pyro geography: The Current and Future Distribution of Wildfire. PLoS ONE, 4(4): e5102. https://doi.org/10.1371/journal.pone.0005102
• Laala, A., Beldjazia, A., Alatou, D. 2020. Mapping the Wildland-Urban Interfaces for Forest Fire Prevention in the Province of Mila (Algeria). Environmental Research Engineering and Management, 76(2): 76 90. https://doi.org/10.5755/j01.erem.76.2.24782
• Madoui, A. 2002. Les incendies de forêt en Algérie. Historique, bilan et analyse. Forêt Méditerranéenne, 23, 23–30. https://hal.science/hal-03558711/
• Maktite, A., Faleh, A. 2017. Cartographie des zones à risque d’incendies de forêts à l’aide du SIG et la télédétection dans L’arrière-Pays du port Tanger Med. European Scientific Journal ESJ, 13(32): 205. https://doi.org/10.19044/esj.2017.v13n32p205
• Matin, M., Chitale, V., Murthy, S.R., Uddin, M., Bajracharya, K., Pradhan, S.B. 2017.Understanding Forest fire patterns and risk in Nepal using remote sensing, geographic information system and historical fire data. International Journal of Wildland Fire, 26(4):276. http://dx.doi.org/10.1071/WF16056.
• McFeeters, S.K. 1996. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7): 1425-1432. https://doi.org/10.1080/01431169608948714
• Meddour-Sahar, O. 2015. Wildfires in Algeria: problems and challenges. iForest - Biogeosciences and Forestry, 8(6): 818-826. https://doi.org/10.3832/ifor1279-007
• Mitchell, J.W., 2013. Power line failures and catastrophic wildfires under extreme weather conditions. Engineering Failure Analysis, 35: 726–735.National Forest Park, Nanjing. Remote Sens. 2021, 13: 3704. https://doi.org/10.1016/j.engfailanal. 2013.07.006
• Mohajane, M., Costache, R., Karimi, F., Pham, Q.B., Essahlaoui, A., Nguyen, H., Laneve, G., Oudija, F. 2021. Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area. Ecological Indicators, 129: 107869. https://doi.org/10.1016/j.ecolind.2021. 107869.
• Ndalila, M.N., Williamson, G.J., Bowman, D.M.J.S. 2018. Geographic Patterns of Fire Severity Following an Extreme Eucalyptus Forest Fire in Southern Australia: 2013 Forcett-Dunalley Fire. Fire, 1(3): 40. https://doi.org/10.3390/fire1030040
• Nikhil, S., Danumah, J.H., Saha, S., Prasad, M.K., Rajaneesh, A., Mammen, P.C., Ajin, R.S., Kuriakose, S.L. 2021. Application of GIS and AHP Method in Forest Fire Risk Zone Mapping: a Study of the Parambikulam Tiger Reserve, Kerala, India. Journal of Geovisualization and Spatial Analysis, 5(1). https://doi.org/10.1007/s41651-021-00082-x
• Özcan, Z., Caglayan, İ., Kabak, Ö., Gül, F.K. 2024. Integrated risk mapping for forest fire management using the analytical hierarchy process and ordered weighted average: a case study in southern Turkey. Natural Hazards. 121(1): 959-1001. https://doi.org/10.1007/s11069-024-06810-y.
• Pacheco, A.d.P., da Silva Junior, J.A., Ruiz-Armenteros, A.M., Henriques, R.F.F., de Oliveira Santos, I. 2023. Analysis of spectral separability for detecting burned areas using Landsat-8 OLI/TIRS images under different biomes in Brazil and Portugal. Forests, 14(4): 663. https://doi.org/10.3390/f14040663.
• Pallikarakis, A., Konstantopoulou, F. 2024. Multi-Criteria Wildfire Risk Hazard Assessment in GIS Environment: Projection for the Future and Impact on RES Projects Installation Planning. Journal of Geoscience and Environment Protection, 12(05): 242 265. https://doi.org/10.4236/gep.2024.125014
• Parajuli, A., Gautam, A.P., Sharma, S.P., Bhujel, K. B., Sharma, G., Thapa, P.B., Bist, B.S., 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): 2569 2586. https://doi.org/10.1080/19475705.2020.1853251
• Parajuli, A., Manzoor, S.A., Lukac, M. 2023. Areas of the Terai Arc landscape in Nepal at risk of forest fire identified by fuzzy analytic hierarchy process. Environmental Development, 45, 100810. https://doi.org/10.1016/j.envdev.2023.100810.
• Pham, Q.B., Mohammadpour, R., Linh, N.T.T., Mohajane, M., Pourjasem, A., Sammen, S. S., Anh, D.T. 2021. Application of soft computing to predict water quality in wetland. Environmental Science and Pollution Research, 28 (1): 185–200. https://doi.org/ 10.1007/s11356-020-10344-8.
• Pourtaghi, Z.S., Pourghasemi, H.R., Rossi, M. 2015. Forest fire susceptibility mapping in the Minudasht forests, Golestan Province, Iran. Environmental Earth Sciences, 73(4): 1515–1533. https://doi.org/ 10.1007/s12665-014-3502-4.
• Rasooli, S.B., 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(5): 1447–1458. https://doi. org/10.1007/s11676-018-0765-8.
• Ribeiro, L., Koproski, L.D.P., Stolle, L., Lingnau, C., Soares, R.V., Batista, A.C. 2008. Zoneamento de riscos de incêndios florestais para a Fazenda Experimental do Canguiri, Pinhais (PR). Floresta, 38(3): 561-572.
• Rivière, M., Lenglet, J., Noirault, A., Pimont, F., Dupuy, J. 2023. Mapping territorial vulnerability to wildfires: A participative multi-criteria analysis. Forest Ecology and Management, 539: 121014. https://doi.org/10.1016/j.foreco.2023.121014
• Saaty, T.L. 1980. Analytic Hierarchy Process, McGraw-Hill.
• Saaty, T.L. 1990. How to make a decision: The analytic hierarchy process? European Journal of Operational Research, 48(1): 9–26. https://doi.org/10.1016/0377-2217(90)90057-I.
• Saaty, T.L. 2008. Decision making with the Analytic Hierarchy Process, International Journal of Services, 1(1): 85 p.
• Saaty, T., Vargas, L. 2012. Models, methods, concepts & applications of the analytic hierarchy process. In Driven demand and operations management models. https://doi.org/10.1007/978-1-4614-3597-6.
• Sahar, O., Leone, V., Limani, H., Rabia, N., Meddour, R. 2018. Wildfire risk and its perception in Kabylia (Algeria). iForest - Biogeosciences and Forestry, 11(3): 367 373. https://doi.org/10.3832/ifor2546-011
• Şakar, D. 2010. Determining the optimum route providing the fastest transportation to the fire areas by using GIS based decision support system (Master’s Thesis). KSU, Faculty of Forestry, Kahramanmaraş. Turkiye. 81 p.
• Sakellariou, S., Cabral, P., Caetano, M., Pla, F., Painho, M., Christopoulou, O., Sfougaris, A., Dalezios, N., Vasilakos, C. 2020. Remotely sensed data fusion for spatiotemporal geostatistical analysis of forest fire hazard. Sensors, 20: 5014. https://doi.org/10.3390/s20175014.
• Sari, F. 2021. Forest fire susceptibility mapping via multi-criteria decision analysis techniques for Mugla, Turkey: A comparative analysis of VIKOR and TOPSIS. Forest Ecology and Management, 480: 118644. https://doi.org/10.1016/j.foreco.2020.118644.
• Satir, O., Berberoğlu, S., Dönmez, 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. https://doi.org/10.1080/19475705.2015.1084541
• Seidl, R., Schelhaas, M., Rammer, W., Verkerk, P.J. 2014. Increasing forest disturbances in Europe and their impact on carbon storage. Nature Climate Change, 4(9): 806-810. https://doi.org/10.1038/nclimate2318.
• Silva, I., Valle, M., Barros, L., Meyer, J. 2020. A wildfire warning system applied to the state of Acre in the Brazilian Amazon. Applied Soft Computing, 89, 106075. https://doi.org/10.1016/j.asoc.2020.106075
• Sivrikaya, F., Küçük, Ö. 2022. Modeling forest fire risk based on GIS-based analytical hierarchy process and statistical analysis in Mediterranean region. Ecological Informatics, 68: 101537. https://doi.org/10.1016/j.ecoinf.2021.101537
• Talbi, O. 2019. Contribution à la mise en place d’un Système d’Information Géographique pour la prévention des feux de forêts dans la région de Saïda. Thèse de Doctorat Ès Sciences, Option: Foresterie. Département des ressources forestières. Faculté des Sciences de la Nature et de la Vie et des Sciences de la Terre et de l’Univers. Université Abou Bekr Belkaid de Tlemcen.
• Teng, J., Xia, S., Liu, Y., Yu, X., Duan, H., Xiao, H., Zhao, C. 2021. Assessing habitat suitability for wintering geese by using Normalized Difference Water Index (NDWI) in a large floodplain wetland, China. Ecological Indicators, 122: 107260. https://doi.org/10.1016/j.ecolind.2020.107260.
• Tiwari, A., Shoab, M., Dixit, A. 2021. GIS-based forest fire susceptibility modeling in Pauri Garhwal, India: a comparative assessment of frequency ratio, analytic hierarchy process and fuzzy modeling techniques. Natural Hazards, 105(2): 1189–1230. https://doi.org/ 10.1007/s11069-020-04351-8.
• Truong, T.X., Nhu, V., Phuong, D.T.N., Nghi, L.T., Hung, N.N., Hoa, P.V., Bui, D.T. 2023. A New Approach Based on TensorFlow Deep Neural Networks with ADAM Optimizer and GIS for Spatial Prediction of Forest Fire Danger in Tropical Areas. Remote Sensing, 15(14): 3458. https://doi.org/ 10.3390/rs15143458.
• Van Hoang, T., Chou, T.Y., Fang, Y.M., Nguyen, N.T., Nguyen, Q.H., Canh, P.X., Toan, D.N.B., Nguyen, X.L., Meadows, M.E. 2020. Mapping Forest Fire Risk and Development of Early Warning System for NW Vietnam Using AHP and MCA/GIS Methods. Applied Sciences, 10(12): 4348. https://doi.org/10.3390/app10124348.
• Verbesselt, J., Jonsson, P., Lhermitte, S., van Aardt, J., Coppin, P. 2006. Evaluating satellite and climate data-derived indices as fire risk indicators in savanna ecosystems. IEEE Transactions on Geoscience and Remote Sensing, 44(6): 1622–1632. https://doi.org/10.1109/TGRS.2005.862262.
• Vilar, L., Woolford, D.G., Martell, D.L., Martín, M.P. 2010. A model for predicting human-caused wildfire occurrence in the region of Madrid, Spain. International Journal of Wildland Fire, 19(3): 325. https://doi.org/10.1071/wf09030
• World Bank. World Bank Note Sur Les Forêts Algériennes : Gestion Durable Des Forêts Pour Lutter Contre Les Feux de Forêts; World Bank: Washington, DC, USA, 2023.
• Wu, C., Venevsky, S., Sitch, S., Mercado, L.M., Huntingford, C., Staver, A.C. 2021. Historical and future global burned area with changing climate and human demography. One Earth, 4(4): 517 530. https://doi.org/10.1016/j.oneear.2021.03.002
• Xofis, P., Tsiourlis, G., Konstantinidis, P. 2020. A Fire Danger Index for the early detection of areas vulnerable to wildfires in the Eastern Mediterranean region. Euro-Mediterranean Journal for Environmental Integration, 5(2). https://doi.org/10.1007/s41207-020-00173-z
• Yang, C.C., Prasher, S.O., Enright, P., Madramootoo, C., Burgess, M., Goel, P.K., Callum, I. 2003. Application of decision tree technology for image classification using remote sensing data. Agricultural Systems, 76: 1101-1117. https://doi.org/10.1016/S0308521X(02)00051-3.
• Yasin, H.E., Kornel, C. 2024. Evaluating Satellite Image Classification: Exploring Methods and Techniques. IntechOpen eBooks. https://doi.org/10.5772/intechopen.1003196.
• Yavuz, M., Sağlam, B., Küçük, Ö., Tüfekçioğlu, A. 2018. Assessing forest fire behavior simulation using FlamMap software and remote sensing techniques in Western Black Sea Region, Turkey. Kastamonu Univ. J. For. Faculty, 18(2): 171–188.
• Yeşilnacar, E.K. 2005. The Application of Computational Intelligence to Landslide Susceptibility Mapping in Turkey. PhD. Dissertation, Department of Geomatics, The University of Melbourne. 709 p.
• Zhao, P., Zhang, F., Lin, H., Xu, S. 2021. GIS-Based Forest Fire Risk Model: A Case Study in Laoshan National Forest Park, Nanjing. Remote Sensing, 13(18): 3704. https://doi.org/10.3390/rs13183704