Modelling wildfire activity in wildland–urban interface (WUI) areas of Sardinia, Italy
Carla Scarpa
A , Mario Elia B , Marina D’Este B , Michele Salis A * , Marcos Rodrigues C , Bachisio Arca A , Pierpaolo Duce A , Maria Antonella Francesca Fiori D and Grazia Pellizzaro A
A
B
C
D
Abstract
Wildfire frequency, magnitude and impacts in wildland–urban interface (WUI) areas are increasing in the Mediterranean Basin.
We investigated the role played by socio-economic, vegetation, climatic, and zootechnical drivers on WUI wildfire patterns (area burned and wildfire ignitions) in Sardinia, Italy.
We defined WUI as the 100-m buffer area of the anthropic layers. We created a comprehensive and multi-year dataset of explanatory variables and wildfires, and then trained a set of models and evaluated their performances in predicting WUI fires. We used the best models to assess the single variable’s importance and map wildfire patterns.
Random Forest and Support Vector Machine were the best performing models. In broad terms, wildfire patterns at WUI were influenced by socio-economic factors and herbaceous vegetation types.
Machine learning models can be useful tools to predict wildfire ignitions and area burned at WUI in Mediterranean areas.
Improved knowledge of the main drivers of wildfires at WUI in fire-prone Mediterranean areas can foster the development or optimisation of wildfire risk reduction and prevention strategies.
Keywords: driving factors, fire management, fire regimes, risk, machine learning models, Mediterranean areas, spatial patterns, Wildland–Urban Interface.
References
Achu A, Thomas J, Aju CD, Gopinath G, Kumar S, Reghunath R (2021) Machine-learning modelling of fire susceptibility in a forest-agriculture mosaic landscape of southern India. Ecological Informatics 64, 101348.
| Crossref | Google Scholar |
Ager AA, Preisler HK, Arca B, Spano D, Salis M (2014) Wildfire risk estimation in the Mediterranean area. Environmetrics 25(6), 384-396.
| Crossref | Google Scholar |
Alcasena FJ, Salis M, Ager AA, Castell R, Vega-García C (2017) Assessing wildland fire risk transmission to communities in northern Spain. Forests 8(2), 30.
| Crossref | Google Scholar |
Aldersley A, Murray SJ, Cornell SE (2011) Global and regional analysis of climate and human drivers of wildfire. Science of The Total Environment 409(18), 3472-3481.
| Crossref | Google Scholar | PubMed |
Ascoli D, Moris JV, 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(2), 177-188.
| Crossref | Google Scholar |
Badia A, Serra P, Modugno S (2011) Identifying dynamics of fire ignition probabilities in two representative mediterranean wildland-urban interface areas. Applied Geography 31, 930-940.
| Crossref | Google Scholar |
Bar-Massada A (2021) A comparative analysis of two major approaches for mapping the wildland-urban interface: a case study in California. Land 10(7), 679.
| Crossref | Google Scholar |
Bar-Massada A, Syphard AD, Stewart S, Radeloff VC (2012) Wildfire ignition- distribution modelling: a comparative study in the Hurone-Manistee National Forest, Michigan, USA. International Journal of Wildland Fire 22(2), 174-183.
| Crossref | Google Scholar |
Bar-Massada A, Stewart SI, Hammer RB, Mockrin MH, Radeloff VC (2013) Using Structure Locations as a Basis for Mapping the Wildland Urban Interface. Journal of Environmental Management 128, 540-547.
| Crossref | Google Scholar | PubMed |
Bar-Massada A, Alcasena F, Schug F, Radeloff VC (2023) The wildland–urban interface in Europe: spatial patterns and associations with socioeconomic and demographic variables. Landscape and Urban Planning 235, 104759.
| Crossref | Google Scholar |
Bot K, Borges JG (2022) A systematic review of applications of machine learning techniques for wildfire management decision support. Inventions 7(1), 15.
| Crossref | Google Scholar |
Bouillon C, Fernandez Ramiro MM, Sirca C, Fierro Garcia B, Casula F, Vila B, Long Fournel M, Pellizzaro G, Arca B, Tedim F, Trebini F, Derudas A, Canè S (2014) A Tool for Mapping Rural-Urban Interfaces on Different Scales. In ‘Advances in Forest Fire Research, Chapter 3 – Fire Management’. (Ed. DX Viegas) pp. 611–625. (University of Coimbra) 10.14195/978-989-26-0884-6_70
Chas-Amil ML, Prestemon JP, McClean CJ, Touza J (2015) Human-ignited wildfire patterns and responses to policy shifts. Applied Geography 56, 164-176.
| Crossref | Google Scholar |
Curt T, Borgniet L, Ibanez T, Moron V, Hély C (2015) Understanding fire patterns and fire drivers for setting a sustainable management policy of the New-Caledonian biodiversity hotspot. Forest Ecology and Management 337, 48-60.
| Crossref | Google Scholar |
Curt T, Fréjaville T, Lahaye S (2016) Modelling the spatial patterns of ignition causes and fire regime features in southern France: implications for fire prevention policy. International Journal of Wildland Fire 25(7), 785-796.
| Crossref | Google Scholar |
Del Giudice L, Arca B, Scarpa C, Pellizzaro G, Duce P, Salis M (2021) The wildland-anthropic interface raster data of the Italy–France maritime cooperation area (Sardinia, Corsica, Tuscany, Liguria, and Provence-Alpes-Côte d’Azur). Data in Brief 38, 107355.
| Crossref | Google Scholar | PubMed |
D’Este M, Giannico V, Lafortezza R, Sanesi G, Elia M (2021) The Wildland-Urban Interface Map of Italy: A Nationwide Dataset for Wildfire Risk Management. Data in Brief 38, 107427.
| Crossref | Google Scholar | PubMed |
EEA_2012 - Corine Land Cover (2012) Available at https://www.eea.europa.eu/data-and-maps/data/clc-2012-raster/link [accessed 25 March 2024]
Elia M, Giannico V, Lafortezza R, Sanesi G (2019) Modeling Fire Ignition Patterns in Mediterranean Urban Interfaces. Stochastic Environmental Research and Risk Assessment 33, 169-181.
| Crossref | Google Scholar |
Elia M, D’Este M, Ascoli D, Giannico V, Spano G, Ganga A, Colangelo G, Lafortezza R, Sanesi G (2020) Estimating the probability of wildfire occurrence in Mediterranean landscapes using artificial neural networks. Environmental Impact Assessment Review 85, 106474.
| Crossref | Google Scholar |
Elia M, Giannico V, Ascoli D, Argañaraz JP, D’Este M, Spano G, Lafortezza R, Sanesi G (2022) Uncovering current pyroregions in Italy using wildfire metrics. Ecological Processes 11, 15.
| Crossref | Google Scholar |
Fernández-García V, Beltrán-Marcos D, Calvo L (2023) Building patterns and fuel features drive wildfire severity in wildland-urban interfaces in southern Europe. Landscape and Urban Planning 3, 231.
| Crossref | Google Scholar |
Fick SE, Hijmans RJ (2017) WorldClim 2: new 1‐km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37(12), 4302-4315.
| Crossref | Google Scholar |
Galiana-Martin L, Herrero G, Solana J (2011) A wildland-urban interface typology for forest fire risk management in Mediterranean areas. Landscape Research 36, 151-171.
| Crossref | Google Scholar |
Gan J, Jarrett A, Gaither CJ (2015) Landowner response to wildfire risk: adaptation, mitigation or doing nothing. Journal of Environmental Management 159, 186-191.
| Crossref | Google Scholar | PubMed |
Ganteaume A, Camia A, Jappiot M, San-Miguel-Ayanz J, Long-Fournel M, Lampin C (2013) A review of the main driving factors of forest fire ignition over Europe. Environmental Management 51, 651-662.
| Crossref | Google Scholar | PubMed |
Ganteaume A, Barbero R, Jappiot M, Maillé E (2021) Understanding future changes to fires in southern Europe and their impacts on the wildland-urban interface. Journal of Safety Science and Resilience 2, 20-29.
| Crossref | Google Scholar |
Gao B, Shan Y, Liu X, Yin S, Yu B, Cui C, Cao L (2024) Prediction and driving factors of forest fire occurrence in Jilin Province, China. Journal of Forestry Research 35(1), 21.
| Crossref | Google Scholar |
Garson GD (1991) Interpreting Neural-Network Connection Weights. AI Expert 6(4), 46-51.
| Google Scholar |
Gigović L, Pourghasemi HR, Drobnjak S, Bai S (2019) Testing a new ensemble model based on SVM and random forest in forest fire susceptibility assessment and its mapping in Serbia’s Tara National Park. Forests 10(5), 408.
| Crossref | Google Scholar |
Grünig M, Seidl R, Senf C (2023) Increasing aridity causes larger and more severe forest fires across Europe. Global Change Biology 29(6), 1648-1659.
| Crossref | Google Scholar | PubMed |
Jain P, Coogan SCP, Subramanian SG, Crowley M, Taylor S, Flannigan MD (2020) A Review of Machine Learning Applications in Wildfire Science and Management. Environmental Reviews 505, 478-505.
| Crossref | Google Scholar |
Jain P, Castellanos-Acuna D, Coogan SC, Abatzoglou JT, Flannigan MD (2022) Observed increases in extreme fire weather driven by atmospheric humidity and temperature. Nature Climate Change 12(1), 63-70.
| Crossref | Google Scholar |
Jones MW, Abatzoglou JT, Veraverbeke S, Andela N, Lasslop G, Forkel M, Smith ALP, Burton C, Betts RA, van der Werf GR, Sitch S, Canadell JG, Santín C, Kolden C, Doerr SH, Le Quéré C (2022) Global and regional trends and drivers of fire under climate change. Reviews of Geophysics 60(3), e2020RG000726.
| Crossref | Google Scholar |
Koutsias N, Martínez-Fernández J, Allgöwer B (2010) Do factors causing wildfires vary in space? Evidence from geographically weighted regression. GIScience & Remote Sensing 47(2), 221-240.
| Crossref | Google Scholar |
Kuhn M (2008) Building predictive models in R using the caret package. Journal of Statistical Software 28, 1-26.
| Crossref | Google Scholar |
Lampin-Maillet C, Jappiot M, Long M, Bouillon C, Morge D, Ferrier JP (2010) Mapping Wildland-Urban Interfaces at Large Scales Integrating Housing Density and Vegetation Aggregation for Fire Prevention in the South of France. Journal of Environmental Management 91, 732-741.
| Crossref | Google Scholar | PubMed |
Mancini LD, Corona P, Salvati L (2018a) Ranking the Importance of Wildfires’ Human Drivers through a Multi-Model Regression Approach. Environmental Impact Assessment Review 72, 177-186.
| Crossref | Google Scholar |
Mancini LD, Elia M, Barbati A, Salvati L, Corona P, Lafortezza R, Sanesi G (2018b) Are wildfires knocking on the built-up areas door. Forests 9, 234.
| Crossref | Google Scholar |
Mansuy N, Miller C, Parisien MA, Parks SA, Batllori E, Moritz MA (2019) Contrasting human influences and macro-environmental factors on fire activity inside and outside protected areas of North America. Environmental Research Letter 14(6), 064007.
| Crossref | Google Scholar |
Milanović S, Marković N, Pamučar D, Gigović L, Kostić P, Milanović SD (2020) Forest fire probability mapping in eastern Serbia: logistic regression versus random forest method. Forests 12(1), 5.
| Crossref | Google Scholar |
Modugno S, Balzter H, Cole B, Borrelli P (2016) Mapping Regional Patterns of Large Forest Fires in Wildland–Urban Interface Areas in Europe. Journal of Environmental Management 172, 112-126.
| Crossref | Google Scholar | PubMed |
Molina-Terrén DM, Xanthopoulos G, Diakakis M, Ribeiro L, Caballero D, Delogu GM, Viegas DX, Silva CA, Cardil A (2019) Analysis of Forest Fire Fatalities in Southern Europe: Spain, Portugal, Greece and Sardinia (Italy). International Journal of Wildland Fire 28(2), 85-98.
| Crossref | Google Scholar |
Ochoa C, Bar-Massada A, Chuvieco E (2024) A European-scale analysis reveals the complex roles of anthropogenic and climatic factors in driving the initiation of large wildfires. Science of The Total Environment 917, 170443.
| Crossref | Google Scholar | PubMed |
Oliveira S, Oehler F, San-Miguel-Ayanz J, Camia A, Pereira JMC (2012) Modeling Spatial Patterns of Fire Occurrence in Mediterranean Europe Using Multiple Regression and Random Forest. Forest Ecology and Management 275, 117-129.
| Crossref | Google Scholar |
Oliveira S, Moreira F, Boca R, San-Miguel-Ayanz J, Pereira JMC (2013) Assessment of Fire Selectivity in Relation to Land Cover and Topography: A Comparison between Southern European Countries. International Journal of Wildland Fire 23, 620-630.
| Crossref | Google Scholar |
Oliveira R, Oliveira S, Zêzere JL, Viegas DX (2020a) Uncovering the perception regarding wildfires of residents with different characteristics. International Journal of Disaster Risk Reduction 43, 101370.
| Crossref | Google Scholar |
Oliveira S, Gonçalves A, Benali A, Sá A, Zêzere JL, Pereira JM (2020b) Assessing risk and prioritizing safety interventions in human settlements affected by large wildfires. Forests 11, 859.
| Crossref | Google Scholar |
Pais S, Aquilué N, Campos J, Sil Â, Marcos B, Martínez-Freiría F, Domínguez J, Brotons L, Honrado JP, Regos A (2020) Mountain farmland protection and fire-smart management jointly reduce fire hazard and enhance biodiversity and carbon sequestration. Ecosystem Services 44, 101143.
| Crossref | Google Scholar |
Pham BT, Jaafari A, Avand M, Al-Ansari N, Dinh Du T, Yen HPH, Phong TV, Nguyen DH, Le HV, Mafi-Gholami D, Prakash I (2020) Performance evaluation of machine learning methods for forest fire modeling and prediction. Symmetry 12(6), 1022.
| Crossref | Google Scholar |
Pourtaghi ZS, Pourghasemi HR, Aretano R, Semeraro T (2016) Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques. Ecological Indicators 64, 72-84.
| Crossref | Google Scholar |
Radeloff VC, Hammer RB, Stewart SI, Fried JS, Holcomb SS, McKeefry JF (2005) The Wildland-Urban Interface in the United States. Ecological Applications 15, 799-805.
| Crossref | Google Scholar |
Ribeiro LM, Rodrigues A, Lucas D, Viegas DX (2020) The Impact on Structures of the Pedrógão Grande Fire Complex in June 2017 (Portugal). Fire 3, 57.
| Crossref | Google Scholar |
Rihan M, Bindajam AA, Talukdar S, Naikoo MW, Mallick J, Rahman A (2023) Forest fire susceptibility mapping with sensitivity and uncertainty analysis using machine learning and deep learning algorithms. Advances in Space Research 72(2), 426-443.
| Crossref | Google Scholar |
Rodrigues M, De la Riva J (2014) An insight into machine-learning algorithms to model human-caused wildfire occurrence. Environmental Modelling & Software 57, 192-201.
| Crossref | Google Scholar |
Rodrigues M, De la Riva J, Fotheringham S (2014) Modeling the Spatial Variation of the Explanatory Factors of Human-Caused Wildfires in Spain Using Geographically Weighted Logistic Regression. Applied Geography 48, 52-63.
| Crossref | Google Scholar |
Rodrigues M, Jiménez-Ruano A, Peña-Angulo D, De la Riva J (2018) A Comprehensive Spatial-Temporal Analysis of Driving Factors of Human-Caused Wildfires in Spain Using Geographically Weighted Logistic Regression. Journal of Environmental Management 225, 177-192.
| Crossref | Google Scholar | PubMed |
Rodrigues M, Zúñiga-Antón M, Alcasena F, Gelabert P, Vega-Garcia C (2022) Integrating geospatial wildfire models to delineate landscape management zones and inform decision-making in Mediterranean areas. Safety Science 147, 105616.
| Crossref | Google Scholar |
Rodrigues M, Camprubí ÀC, Balaguer-Romano R, Megía CJC, Castañares F, Ruffault J, Fernandes PM, de Dios VR (2023) Drivers and implications of the extreme 2022 wildfire season in Southwest Europe. Science of The Total Environment 859, 160320.
| Crossref | Google Scholar | PubMed |
Rodriguez-Jimenez F, Fernandes PM, Fernández-Guisuraga JM, Alvarez X, Lorenzo H (2023) Drivers and trends in the size and severity of forest fires endangering WUI areas: a regional case study. Forests 14(12), 2366.
| Crossref | Google Scholar |
Salis M, Arca B, Alcasena-Urdiroz F, Massaiu A, Bacciu V, Bosseur F, Caramelle P, Dettori S, Fernandes de Oliveira AS, Molina-Terren D, Pellizzaro G, Santoni PA, Vega-Garcia C, Duce P (2019) Analyzing the recent dynamics of wildland fires in Quercus suber L. woodlands in Sardinia (Italy), Corsica (France) and Catalonia (Spain). European Journal of Forest Research 138, 415-431.
| Crossref | Google Scholar |
Salis M, Arca B, Del Giudice L, Palaiologou P, Alcasena-Urdiroz F, Ager A, Fiori M, Pellizzaro G, Scarpa C, Schirru M, Ventura A, Casula M, Duce P (2021) Application of Simulation Modeling for Wildfire Exposure and Transmission Assessment in Sardinia, Italy. International Journal of Disaster Risk Reduction 58, 102189.
| Crossref | Google Scholar |
Salis M, Del Giudice L, Jahdi R, Alcasena-Urdiroz F, Scarpa C, Pellizzaro G, Bacciu V, Schirru M, Ventura A, Casula M, Pedes F, Canu A, Duce P, Arca B (2022) Spatial Patterns and Intensity of Land Abandonment Drive Wildfire Hazard and Likelihood in Mediterranean Agropastoral Areas. Land 11(11), 1942.
| Crossref | Google Scholar |
Salis M, Del Giudice L, Alcasena-Urdiroz F, Jahdi R, Arca B, Pellizzaro G, Scarpa C, Duce P (2023) Assessing cross-boundary wildfire hazard, transmission, and exposure to communities in the Italy-France Maritime cooperation area. Frontiers in Forests and Global Change 6, 1241378.
| Crossref | Google Scholar |
San-Miguel-Ayanz J, Durrant T, Boca R, Maianti P, Libertà G, Jacome Felix Oom D, Branco A, De Rigo D, Suarez-Moreno M, Ferrari D, Roglia E, Scionti N, Broglia M, Onida M, Tistan A, Loffler P (2023) ‘Forest Fires in Europe, Middle East and North Africa 2022’. JRC135226. (Publications Office of the European Union: Luxembourg) 10.2760/348120
Schug F, Bar-Massada A, Carlson AR, Cox H, Hawbaker TJ, Helmers D, Hostert P, Kaim S, Kasraee NK, Martinuzzi S, Mockrin MH, Pfoch KA, Radeloff VC (2023) The global wildland–urban interface. Nature 621(7977), 94-99.
| Crossref | Google Scholar | PubMed |
Sharma LK, Gupta R, Fatima N (2022) Assessing the predictive efficacy of six machine learning algorithms for the susceptibility of Indian forests to fire. International Journal of Wildland Fire 31(8), 735-758.
| Crossref | Google Scholar |
Sirca C, Casula F, Bouillon C, García BF, Fernández Ramiro MM, Molina BV, Spano D (2017) A Wildfire Risk Oriented GIS Tool for Mapping Rural-Urban Interfaces. Environmental Modelling & Software 94, 36-47.
| Crossref | Google Scholar |
Spadoni GL, Moris JV, Vacchiano G, Elia M, Garbarino M, Sibona E, Tomao A, Barbati A, Sallustio L, Salvati L, Ferrara C, Francini S, Bonis E, Dalla Vecchia I, Strollo A, Di Leginio M, Munafò M, Chirici G, Romano R, Corona P, Marchetti M, Brunori A, Motta R, Ascoli D (2023) Active governance of agro-pastoral, forest and protected areas mitigates wildfire impacts in Italy. Science of The Total Environment 890, 164281.
| Crossref | Google Scholar | PubMed |
Tonini M, Parente J, Pereira MG (2018) Global assessment of rural–urban interface in Portugal related to land cover changes. Natural Hazards and Earth System Sciences 18(6), 1647-1664.
| Crossref | Google Scholar |
Tonini M, D’Andrea M, Biondi G, Esposti SD, 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.
| Crossref | Google Scholar |
UDS_2008 - Sardinia Land Use Map (2008) Carta Uso Suolo 2008. Sardegna Geoportale Available at http://www.sardegnageoportale.it/index.php?xsl=2420&s=40&v=9&c=14480&es=6603&na=1&n=100&esp=1&tb=14401 [accessed 25 March 2024]
Vacchiano G, Foderi C, Berretti R, Marchi E, Motta R (2018) Modeling anthropogenic and natural fire ignitions in an inner-alpine valley. Natural Hazards and Earth System Sciences 18(3), 935-948.
| Crossref | Google Scholar |
Viegas DX, Allgöwer B, Koutsias N, Eftichidis G (2003) Fire Spread and the Wildland Urban Interface Problem. In ‘International Workshop on Forest Fires in the in the WUI, WARM Project’, 15–16 May, Athens, Greece. pp. 93–103. Available at https://www.fria.gr/WARM/chapters/warmCh12Viegas.pdf
Vilar L, Camia A, San-Miguel-Ayanz J, Martín MP (2016a) Modeling Temporal Changes in Human-Caused Wildfires in Mediterranean Europe Based on Land Use-Land Cover Interfaces. Forest Ecology and Management 378, 68-78.
| Crossref | Google Scholar |
Vilar L, Gómez I, Martínez-Vega J, Echavarría P, Riaño D, Martín MP (2016b) Multitemporal modelling of socio-economic wildfire drivers in Central Spain between the 1980s and the 2000s: comparing generalized linear models to machine learning algorithms. PLoS One 11(8), e0161344.
| Crossref | Google Scholar |
Wang SSC, Qian Y, Leung LR, Zhang Y (2021) Identifying key drivers of wildfires in the contiguous US using machine learning and game theory interpretation. Earth’s Future 9(6), e2020EF001910.
| Crossref | Google Scholar | PubMed |
