Wildfire hazard mapping in the eastern Mediterranean landscape
Andrea Trucchia
A * , Giorgio Meschi A , Paolo Fiorucci A , Antonello Provenzale B , Marj Tonini C and Umberto Pernice A D
+ Author Affiliations
- Author Affiliations
A CIMA Research Foundation, I-17100 Savona, Italy.
B Istituto di Geoscienze e Georisorse del CNR, Via Moruzzi 1, 56124 Pisa, Italy.
C Institute of Earth Surface Dynamics, Faculty of Geosciences and Environment, University of Lausanne, CH-1015 Lausanne, Switzerland.
D University of Rome ‘La Sapienza’, Scuola di Ingegneria Aerospaziale, Via Salaria 851, 00138, Rome, Italy.
* Correspondence to: andrea.trucchia@cimafoundation.org
International Journal of Wildland Fire 32(3) 417-434 https://doi.org/10.1071/WF22138
Submitted: 7 July 2022 Accepted: 10 February 2023 Published: 16 March 2023
© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)
Abstract
Background: Wildfires are a growing threat to many ecosystems, bringing devastation to human safety and health, infrastructure, the environment and wildlife.
Aims: A thorough understanding of the characteristics determining the susceptibility of an area to wildfires is crucial to prevention and management activities. The work focused on a case study of 13 countries in the eastern Mediterranean and southern Black Sea basins.
Methods: A data-driven approach was implemented where a decade of past wildfires was linked to geoclimatic and anthropic descriptors via a machine learning classification technique (Random Forest). Empirical classification of fuel allowed linking of fire intensity and hazard to environmental drivers.
Key results: Wildfire susceptibility, intensity and hazard were obtained for the study area. For the first time, the methodology is applied at a supranational scale characterised by a diverse climate and vegetation landscape, relying on open data.
Conclusions: This approach successfully allowed identification of the main wildfire drivers and led to identification of areas that are more susceptible to impactful wildfire events.
Implications: This work demonstrated the feasibility of the proposed framework and settled the basis for its scalability at a supranational level.
Keywords: crossboundary wildfire management, eastern Mediterranean, hazard mapping, machine learning, Random Forest, risk management, susceptibility mapping, wildfire drivers.
References
Ascoli D, Moris J, Marchetti M, Sallustio L (2021) Land use change towards forests and wooded land correlates with large and frequent wildfires in Italy. Annals of Silvicultural Research 46, 177–188.| Land use change towards forests and wooded land correlates with large and frequent wildfires in Italy.Crossref | GoogleScholarGoogle Scholar |
Bachmann A, Allgower B (2000) The need for a consistent wildfire risk terminology. In ‘Joint Fire Science Conference and Workshop Proceedings: Crossing the Millennium: Integrating Spatial Technologies and Ecological Principles for a New Age in Fire Management. Vol. I’, Boise, Idaho. (Eds LF Neuenschwander, KC Ryan, GE Gollberg) pp. 67–77. (University of Idaho and the International Association of Wildland Fire: Moscow, ID and Fairfield, WA)
Barredo Arrieta A, Díaz-Rodríguez N, Del Ser J, Bennetot A, Tabik S, Barbado A, Garcia S, Gil-Lopez S, Molina D, Benjamins R, Chatila R, Herrera F (2020) Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion 58, 82–115.
| Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI.Crossref | GoogleScholarGoogle Scholar | ISSN 1566-2535.
Beck HE, Zimmermann NE, McVicar TR, Vergopolan N, Berg A, Wood EF (2018) Present and future Köppen-Geiger climate classification maps at 1-km resolution. Scientific Data 5, 180214
| Present and future Köppen-Geiger climate classification maps at 1-km resolution.Crossref | GoogleScholarGoogle Scholar |
Breiman L (2001) Random Forests. Machine Learning 45, 5–32.
| Random Forests.Crossref | GoogleScholarGoogle Scholar |
ERCC - Emergency Response Coordination Centre (2021) ECHO Daily Map of 02 August 2021 - Turkey - Forest Fire - August 2021. Available at https://erccportal.jrc.ec.europa.eu/ECHO-Products/Maps#/maps/3782 [last accessed 30 June 2022]
European Environment Agency (2002) Europe’s biodiversity – biogeographical regions and seas. Biogeographical regions in Europe. Introduction. EEA Report No 1/2002. Available at https://www.eea.europa.eu/publications/report_2002_0524_154909 [last accessed 5 December 2022]
Gholamnia K, Gudiyangada Nachappa T, Ghorbanzadeh O, Blaschke T (2020) Comparisons of Diverse Machine Learning Approaches for Wildfire Susceptibility Mapping. Symmetry 12, 604
| Comparisons of Diverse Machine Learning Approaches for Wildfire Susceptibility Mapping.Crossref | GoogleScholarGoogle Scholar |
Ghorbanzadeh O, Blaschke T, Gholamnia K, Aryal J (2019a) Forest Fire Susceptibility and Risk Mapping Using Social/Infrastructural Vulnerability and Environmental Variables. Fire 2, 50
| Forest Fire Susceptibility and Risk Mapping Using Social/Infrastructural Vulnerability and Environmental Variables.Crossref | GoogleScholarGoogle Scholar |
Ghorbanzadeh O, Blaschke T, Gholamnia K, Meena SR, Tiede D, Aryal J (2019b) Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection. Remote Sensing 11, 196
| Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection.Crossref | GoogleScholarGoogle Scholar |
Hardy CC (2005) Wildland fire hazard and risk: Problems, definitions, and context. Forest Ecology and Management 211, 73–82.
| Wildland fire hazard and risk: Problems, definitions, and context.Crossref | GoogleScholarGoogle Scholar |
Hersbach H, Bell B, Berrisford P, Biavati G, Horányi A, Muñoz Sabater J, Nicolas J, Peubey C, Radu R, Rozum I, Schepers D, Simmons A, Soci C, Dee D, Thépaut J-N (2018) ‘ERA5 hourly data on single levels from 1959 to present.’ (Copernicus Climate Change Service (C3S) Climate Data Store (CDS))
| Crossref | [accessed 14 April 2021]
Langanke T, Büttner G, Dufourmont H, Iasillo D, Probeck M, Rosengren M, Sousa A, Strobl P, Weichselbaum J (2013) GIO land (GMES/Copernicus initial operations land) High Resolution Layers (HRLs) – Summary of product specifications. In ‘European Environment Agency Copernicus Report’. (European Environmental Agency: Copenhagen, Denmark) Available at https://land.copernicus.eu/user-corner/technical-library/gio-land-high-resolution-layers-hrls-2013-summary-of-product-specifications [accessed 18 January 2021]
Ministry of Forests (MOF) (1997) ‘Glossary of Forest Terms.’ (Ministry of Forests: Province of British Columbia, Canada)
Ministry of Forests and Range (MOF) (2008) Glossary of Forestry Terms in British Columbia. Available at https://www.for.gov.bc.ca/hfd/library/documents/glossary/glossary.pdf [accessed 5 December 2022]
Nachappa TG, Tavakkoli Piralilou S, Gholamnia K, Ghorbanzadeh O, Rahmati O, Blaschke T (2020a) Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory. Journal of Hydrology 590, 125275
| Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory.Crossref | GoogleScholarGoogle Scholar | ISSN 0022-1694
Nachappa TG, Ghorbanzadeh O, Gholamnia K, Blaschke T (2020b) Multi-Hazard Exposure Mapping Using Machine Learning for the State of Salzburg, Austria. Remote Sensing 12, 2757
| Multi-Hazard Exposure Mapping Using Machine Learning for the State of Salzburg, Austria.Crossref | GoogleScholarGoogle Scholar |
National Wildfire Coordinating Group (NWCG) 2003 Glossary of Wildland Fire Terminology. https://www.nwcg.gov/publications/pms205
Novara A, Gristina L, Sala G, Galati A, Crescimanno M, Cerdà A, Badalamenti E, La Mantia T (2017) Agricultural land abandonment in Mediterranean environment provides ecosystem services via soil carbon sequestration. Science of the Total Environment 576, 420–429.
| Agricultural land abandonment in Mediterranean environment provides ecosystem services via soil carbon sequestration.Crossref | GoogleScholarGoogle Scholar | ISSN 0048-9697
Olaya V (2009) Basic Land-Surface Parameters. In ‘Geomorphometry. Concepts, Software, Applications. Developments in Soil Science. Vol. 33’. (Eds Hengl T, Reuter H) pp. 141–169. (Elsevier: Amsterdam, The Netherlands)
Parente J, Pereira MG (2016) Structural fire risk: the case of Portugal. Science of the Total Environment 573, 883–893.
| Structural fire risk: the case of Portugal.Crossref | GoogleScholarGoogle Scholar |
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12, 2825–2830.
Plieninger T, Hui C, Gaertner M, Huntsinger L (2014) The Impact of Land Abandonment on Species Richness and Abundance in the Mediterranean Basin: A Meta-Analysis. PLoS One 9, e98355
| The Impact of Land Abandonment on Species Richness and Abundance in the Mediterranean Basin: A Meta-Analysis.Crossref | GoogleScholarGoogle Scholar |
Potdar K, Pardawala TS, Pai CD (2017) A Comparative Study of Categorical Variable Encoding Techniques for Neural Network Classifiers. International Journal of Computer Applications 175, 7–9.
| A Comparative Study of Categorical Variable Encoding Techniques for Neural Network Classifiers.Crossref | GoogleScholarGoogle Scholar |
Pradhan B, Dini Hairi Bin Suliman M, Arshad Bin Awang M (2007) Forest fire susceptibility and risk mapping using remote sensing and geographical information systems (GIS). Disaster Prevention and Management 16, 344–352.
| Forest fire susceptibility and risk mapping using remote sensing and geographical information systems (GIS).Crossref | GoogleScholarGoogle Scholar |
ReliefWeb (2021) Algeria: Forest Wildfires - Emergency Appeal №: MDRDZ007, Posted: 2 November 2021, Originally published 30 October 2021. Available at https://reliefweb.int/report/algeria/algeria-forest-wildfires-emergency-appeal-mdrdz007
San-Miguel-Ayanz J, Durrant T, Boca R, Maianti P, Libertá G, Artés-Vivancos T, Oom D, Branco A, de Rigo D, Ferrari D, Pfeiffer H, Grecchi R, Nuijten D (2022) ‘Advance Report on Forest Fires in Europe, Middle East and North Africa 2021’. JRC128678. EUR 31028 EN. (Publications Office of the European Union: Luxembourg)
| Crossref | ISBN 978-92-76-49633-5.
Scott JH, Thompson MP, Calkin DE (2013) A wildfire risk assessment framework for land and resource management. Gen. Tech. Rep. RMRS-GTR-315. 83 p. (USDA Forest Service, Rocky Mountain Research Station)
| Crossref |
Tavakkoli Piralilou S, Einali G, Ghorbanzadeh O, Nachappa TG, Gholamnia K, Blaschke T, Ghamisi P (2022) A Google Earth Engine Approach for Wildfire Susceptibility Prediction Fusion with Remote Sensing Data of Different Spatial Resolutions. Remote Sensing 14, 672
| A Google Earth Engine Approach for Wildfire Susceptibility Prediction Fusion with Remote Sensing Data of Different Spatial Resolutions.Crossref | GoogleScholarGoogle Scholar |
Tedim F, Remelgado R, Borges C, Carvalho S, Martins J (2013) Exploring the occurrence of mega-fires in Portugal. Forest Ecology and Management 294, 86–96.
| Exploring the occurrence of mega-fires in Portugal.Crossref | GoogleScholarGoogle Scholar |
Tedim F, Leone V, Amraoui M, Bouillon C, Coughlan MR, Delogu GM, Fernandes PM, Ferreira C, McCaffrey S, McGee TK, Parente J, Paton D, Pereira MG, Ribeiro LM, Viegas DX, Xanthopoulos G (2018) Defining Extreme Wildfire Events: Difficulties, Challenges, and Impacts. Fire 1, 9
| Defining Extreme Wildfire Events: Difficulties, Challenges, and Impacts.Crossref | GoogleScholarGoogle Scholar |
Tonini M, D’Andrea M, Biondi G, Degli Esposti S, Trucchia A, Fiorucci P (2020) A Machine Learning-Based Approach for Wildfire Susceptibility Mapping. The Case Study of the Liguria Region in Italy. Geosciences 10, 105
| A Machine Learning-Based Approach for Wildfire Susceptibility Mapping. The Case Study of the Liguria Region in Italy.Crossref | GoogleScholarGoogle Scholar |
Trucchia A, Meschi G, Fiorucci P, Gollini A, Negro D (2022) Defining Wildfire Susceptibility Maps in Italy for Understanding Seasonal Wildfire Regimes at the National Level. Fire 5, 30
| Defining Wildfire Susceptibility Maps in Italy for Understanding Seasonal Wildfire Regimes at the National Level.Crossref | GoogleScholarGoogle Scholar |
Turco M, Bedia J, Di Liberto F, Fiorucci P, von Hardenberg J, Koutsias N, et al. (2016) Decreasing Fires in Mediterranean Europe. PLoS One 11, e0150663
| Decreasing Fires in Mediterranean Europe.Crossref | GoogleScholarGoogle Scholar |
Turco M, Rosa-Cánovas JJ, Bedia J, Jerez S, Montávez JP, Llasat MC, Provenzale A (2018) Exacerbated fires in Mediterranean Europe due to anthropogenic warming projected with non-stationary climate-fire models. Nat Commun 9, 3821
| Exacerbated fires in Mediterranean Europe due to anthropogenic warming projected with non-stationary climate-fire models.Crossref | GoogleScholarGoogle Scholar |
United Nations Environment Programme (2022) Spreading like Wildfire – The Rising Threat of Extraordinary Landscape Fires. A UNEP Rapid Response Assessment. (Nairobi) Available at https://www.unep.org/resources/report/spreading‐wildfire‐rising‐threat‐extraordinary‐landscape‐fires
Verde JC, Zêzere JL (2010) Assessment and validation of wildfire susceptibility and hazard in Portugal. Natural Hazards and Earth System Sciences 10, 485–497.
| Assessment and validation of wildfire susceptibility and hazard in Portugal.Crossref | GoogleScholarGoogle Scholar |
World Bank Group (2021) Metadata Climate Change Knowledge Portal (CCKP). Available at https://climateknowledgeportal.worldbank.org/media/document/metatag.pdf [accessed 5 December 2022]
Yamazaki D, Ikeshima D, Tawatari R, Yamaguchi T, O’Loughlin F, Neal JC, Sampson CC, Kanae S, Bates PD (2017) A high accuracy map of global terrain elevations. Geophysical Research Letters 44, 5844–5853.
| A high accuracy map of global terrain elevations.Crossref | GoogleScholarGoogle Scholar |
