Combining AHP with GIS for mapping the vulnerability to forest fire risk

• Autorzy: Loumi Kahina , Redjem Ali

Identyfikatory

DOI

10.15576/GLL/2024.2.07

• Języki publikacji: EN

Abstrakty

EN

This article deals with the problem of forest fires in the province of Tizi Ouzou this tragic phenomenon which has always struck the region and which often causes degas at the same time human, social, economic, ecological and sanitary. The methodology applied to the Tizi Ouzou region aims to study the vulnerability of its territory to forest fires. The AHP-GIS integration greatly facilitates this work because in this study several qualitative and quantitative criteria come into play. The construction of this structure was based on the construction of a grid of criteria applied to the entire area of The study, using geomantic operations as integrating and generating tools. To choose the most vulnerable area, a geographic information system (GIS) was combined with an analytical hierarchy process (AHP) in order to analyze several criteria, such as land use, climatological condition and Topography. The AHP was applied to determine the importance weights of each criterion. to assess the vulnerability of areas, a simple additive weighting method was used. Each criterion was evaluated with the aid of AHP and mapped by GIS. The main advantage of such an approach is to facilitate the analysis of complex data in the form of graphical representations. In particular, this is an essential decision-making tool for elected representatives of local authorities. These results can be used effectively to plan fire control measures in advance and the methodology suggested in this study can be adopted in other areas too for delineating potential fire risk zones.

Słowa kluczowe

EN

forest fires; AHP-GIS integration; Tizi Ouzou region; risk; decision-making

PL

pożar lasu; region Tizi Ouzou; podejmowanie decyzji; ryzyko

• Wydawca: Wydawnictwo Uniwersytetu Rolniczego w Krakowie
• Czasopismo: Geomatics, Landmanagement and Landscape
• Rocznik: 2024
• Tom: no. 2
• Strony: 99--110
• Opis fizyczny: Bibliogr. 37 poz., rys., tab.

Twórcy

autor

Loumi Kahina

• Urban Techniques Management Institute, City, Environment, Hydraulic, and Sustainable Development Laboratory, M’sila, University of M’sila, Algeria

• https://orcid.org/0000-0002-3840-8306

autor

Redjem Ali

• Urban Techniques Management Institute, City, Environment, Hydraulic, and Sustainable Development Laboratory, M’sila, University of M’sila, Algeria

• https://orcid.org/0000-0001-8527-397X

Bibliografia

• Ajin R.S., Ciobotaru A.M., Vinod P.G., Jacob M.K. 2015. Forest and wildland fire risk assessment using geospatial techniques: a case study of Nemmara forest division, Kerala, India. J. Wetlands Biodivers., 5, 29–37.
• Alexandrian D. 1990. Analyse des données contenues dans le fichier Prométhée Région Provence Alpes Côte d’Azur. Ministère de l’Agriculture, Entente.
• Algeria: Forest Wildfires – Operation Update Report. DREF Operation no. MDRDZ008, Operation update no. 01. Situation Report, Source IFRC, originally published 28.03.2023.
• Barmpoutis P., Papaioannou P., Dimitropoulos K., Grammalidis N. 2020. A Review on Early Forest Fire Detection Systems Using Optical Remote Sensing. Sensors, 20, 6442. https://doi.org/10.3390/s20226442
• Bonan G.B. 2008. Forests and climate change: forcings, feedback, and the climate benefits of forests. Science, 320(5882), 1444–1449.
• Burgan R.E. 1984. Behave: fire behaviour prediction and fuel modelling system, fuel subsystem. https://doi.org/10.2737/INT-GTR-167
• Canelles Q., Aquilué N., James P.M.A., Lawler J., Brotons L. 2021. Global review on interactions between insect pests and other forest disturbances. Landscape Ecology, 7, 1–28. https://doi.org/10.1007/s1098 0- 021-01209-7
• Coomes D.A., Dalponte M., Jucker T., Asner G.P., Banin L.F., Burslem D.F., Lewis S.L., Nilus R., Phillips O.L., Phua M.H., Qie L. 2017. Areabased vs. tree-centric approaches to mapping forest carbon in Southeast Asian forests from airborne laser scanning data. Remote Sens. Environ., 194, 77–88.
• Dampage U., Bandaranayake L., Wanasinghe R., Kottahachchi K., Jayasanka B. 2022. Forest fire detection system using wireless sensor networks and machine learning. Scientific Reports,12, 46. https://doi.org/10.1038/s41598-021-03882-9
• Dupuy J.L. 1995. Slope and fuel load effects on fire behaviour: laboratory experiments in pine needles fuel beds. Int. J. Wildland Fire, 5(3), 153–164.
• Dupuy J.L. 1997. Mieux comprendre et prédire la propagation des feux de forêts: expérimentation, test et proposition de modèles. Thèse INRA/Univ. Claude Bernard, Lyon I, 272 p.
• El Amraoui S., Rouchdi M., Bouziani M., El Idrissi A. 2017. Intégration du SIG et de l’analyse hiérarchique multicritère pour l’aide dans la planification urbaine: étude de cas de la province de Khemisset, Maroc. Papeles de Geografía 63. http://dx.doi.org/10.6018/geografia/2017/280211
• Fatih S., Ömer K. 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
• Gupta Anil K., Nair Sreeja S. 2012. Ecosystem Approach to Disaster Risk Reduction. National Institute of Disaster Management, New Delhi, p. 202.
• Hossein M., Mohammad M., Biswajeet P., Loke Kok F. 2020. Fuzzy-metaheuristic ensembles for spatial assessment of forest fire susceptibility. Journal of Environmental Management, 260, 109867. https://doi.org/10.1016/j.jenvman.2019.109867
• Joerin F. 1995. Méthode multicritère d’aide à la décision et SIG pour la recherche d’un site. Revue Internationale de Géomatique, 5, 37–51.
• Kim T., Hwang S., Choi J. 2021. Characteristics of Spatiotemporal Changes in the Occurrence of Forest Fires. Remote Sensing, 13, 4940. https://doi.org/10.3390/rs13234940
• Lambert J.L. 1977. Forests fires in Morocco in relation to weather and fuel conditions. Annales de la recherche forestière au Maroc, 17. http://archives.cnd.hcp.ma/uploads/news/015618.pdf
• Loumi K. 2021. Croissance urbaine et risque d’inondation cas de la ville de M’sila. Ph.D. thesis, University of M’sila. Algeria. http://dspace.univ msila.dz:8080//xmlui/handle/123456789/29134
• Loumi K., Redjem A. 2021. Integration of GIS and Hierarchical Multi-Criteria Analysis for Mapping Flood Vulnerability. The Case Study of M’sila, Algeria. https://doi.org/10.48084/ etasr.4266
• Lukić T., Bjelajac D., Fitzsimmons K.E., Marković S.B., Basarin B., Mlađan D., Micić T., Schaetzl J.R., Gavrilov M.B., Milanović M., Sipos G., Mezősi G., Knežević Lukić N.,Milinčić M., Létal A., Samardžić I. 2018. Factors triggering landslide occurrence on the Zemun loess plateau, Belgrade area, Serbia. Environmental Earth Sciences, 77. https://doi.org/10.1007/s12665-018-7712-z
• Lukić T., Marić P., Hrnjak I., Gavrilov M.B., Mladjan D., Zorn M., Komac B., Milošević Z., Marković S.B., Sakulski D., Jordaan A., Đorđević J., Pavić D., Stojsavljević R. 2017. Forest fire analysis and classification based on Serbian case study. Acta Geographica, Slovenica 57. https://doi.org/10.3986/AGS.918
• Lukić T., Micić Ponjiger T., Basarin B., Sakulski D., Gavrilov M., Marković S., Zorn M., Komac B., Milanović M., Pavić D., Mesaroš M., Marković N., Durlević U., Morar C., Petrović A. 2021. Application of Angot precipitation index in the assessment of rainfall erosivity: Vojvodina Region case study (North Serbia). Acta Geographica Slovenica, 61–2. https://doi.org/10.3986/AGS.8754
• Luković J., Blagojević D., Kilibarda M., Bajat B. 2015. Spatial pattern of North Atlantic Oscillation impact on rainfall in Serbia. Spatial Statistics 14-A. https://doi.org/10.1016/j.spasta.2015.04.007
• Martins A., Novais A., Santos J.L., Canadas M.J. 2022. Promoting Landscape-Level Forest Management in Fire-Prone Areas: Delegate Management to a Multi-Owner Collaborative, Rent the Land, or Just Sell It? Forests, 13, 22. https://doi.org/10.3390/f13010022
• Parajuli A., Gautam P.A., Sharma P.S., Bhujel B.K., Sharma G., Thapa B.P., Bist S.B., Poudel S. 2020. Forest fire risk mapping using GIS and remote sensing in two major landscapes of Nepal. Geomantic, Natura Hazards and Risk, 11-1.
• Saaty T.L. 1990. How to make a decision. The Analytical Hierarchy Process. European Journal of Operational Research, 48, 9–26.
• San-Miguel-Ayanz J., Durrant T., Boca R., Liberta G., Branco A., De Rigo D., Ferrari D., Maianti P., Artes Vivancos T., Costa H. et al. 2017. Forest Fires in Europe, Middle East and North Africa 2017. Publications Office of the European Union: Luxembourg.
• Seidl R., Schelhaas M.-J., Lexer M.J. 2011. Unraveling the drivers of intensifying forest disturbance regimes in Europe. Global Change Biol., 17, 2842–2852.
• Seidl R., Thom D., Kautz M. et al. 2017. Forest disturbances under climate change. Nature Clim. Change, 7, 395–402. https://doi.org/10.1038/nclimate3303
• Sirin A., Medvedeva M. 2022. Remote Sensing Mapping of Peat-Fire-Burnt Areas: Identification among Other Wildfires. Remote Sensing, 14.
• Sol B. 1991. Comparaison de diverses méthodes d’estimation du danger météorologique d’incendie sur le Sud-Est de la France: feux d’été de la zone côtière et feux d’hiver des Alpes de Haute-Provence. Météo France, Direction interrégionale Sud-Est, Note DIR/SE no.13.
• Thom D., Seidl R. 2016. Natural disturbance impacts on ecosystem services and biodiversity in temperate and boreal forests. Biol. Rev., 91, 760–781.
• United Nations Environmental Programme (UNEP). 2002. Global Environment Outlook, 3, 92- 807-2087-2, 1–424.
• Uusivuori J., Lehto E., Palo M. 2002. Population, income and ecological conditions as determinants of forest area variation in the tropics. Global Environ. Chang., 12(4), 313–323.
• Van Wagner C.E. 1987. Elaboration et structure de la méthode canadienne de l’Indice Forêt Météo. Service Canadien des forêts, Institut forestier national de Petawawa, 34.
• Vladimir Ć., Uroš D., Nemanja R., Ivan N., Nina Č. 2022. GIS application in analysis of threat of forest fires and landslides in the svrljiški timok basin (Serbia), 102-1. https://doi.org/10.2298/GSGD2201107C