Assessing human-caused wildfire ignition likelihood across Europe

Abstract. This study features a cohesive modelling approach of human-caused wildfire ignitions applied to a set of representative regions in terms of fire activity across Europe (pilot sites, PS). Our main goal was to develop a common approach to model human-caused ignition probability at a fine-grained spatial resolution (100 m) and identify the main drivers of ignitions. Specifically, we (i) ascertain which factors influence ignitions in each PS; (ii) deliver a spatial-explicit representation of ignition probability, and (iii) provide a framework for comparison with regional-scale models among PS. To do so, we calibrated Random Forest models from historical fire records compiled by local fire agencies, and geospatial layers of land cover, accessibility, population density and dead fine-fuel moisture content (DFMC). Models were built individually for each PS, comparing them with a full model constructed from all PS. Furthermore, special attention was given to the effect of spatial autocorrelation in model performance. All models achieved sufficient predictive performance (Areas Under the Receiver Operating Characteristic Curve (AUCs) from 0.70 to 0.89). For all PS models, the yearly anomaly in DFMC was the most influential variable. Among human-related factors, distance to the Wildland Urban Interface emerged as the most relevant variable, followed by proximity to roads, population density, and the fraction of wildland coverage. The performance of the full model achieved an AUC value of 0.81, with mean DFMC and anomaly being the main ignition factors, modulated by distance to roads and population density. The local performance of the full model dropped by 0.10 for AUC in both Southern Sweden and Attica (Greece) regions. The wildfire occurrence models developed in this study are essential for understanding wildfire ignition hazard and may help implement integrated wildfire risk management strategies and mitigation policies in fire-prone EU landscapes.

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Publication Details

Journal
Natural hazards and earth system sciences
Published
2025-11-28
DOI
https://doi.org/10.5194/nhess-25-4713-2025
Primary Topic
Fire effects on ecosystems
Type
article
Field-Weighted Citation Impact
0.00

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article

Assessing human-caused wildfire ignition likelihood across Europe

Cristina Vega‐García, Johan Sjöström, Fermín Alcasena, Palaiologos Palaiologou et al.
Natural hazards and earth system sciences
Fire effects on ecosystems
article

Assessing human-caused wildfire ignition likelihood across Europe

Cristina Vega‐García, Johan Sjöström, Fermín Alcasena, Palaiologos Palaiologou, Carmen Bentué‐Martínez, Luís Mário Ribeiro, Pere Gelabert, Christopher Marrs, Adrián Jiménez-Ruano, Marcos Rodrigues, Emilio Chuvieco, Clara Ochoa
article en

Abstract

Abstract. This study features a cohesive modelling approach of human-caused wildfire ignitions applied to a set of representative regions in terms of fire activity across Europe (pilot sites, PS). Our main goal was to develop a common approach to model human-caused ignition probability at a fine-grained spatial resolution (100 m) and identify the main drivers of ignitions. Specifically, we (i) ascertain which factors influence ignitions in each PS; (ii) deliver a spatial-explicit representation of ignition probability, and (iii) provide a framework for comparison with regional-scale models among PS. To do so, we calibrated Random Forest models from historical fire records compiled by local fire agencies, and geospatial layers of land cover, accessibility, population density and dead fine-fuel moisture content (DFMC). Models were built individually for each PS, comparing them with a full model constructed from all PS. Furthermore, special attention was given to the effect of spatial autocorrelation in model performance. All models achieved sufficient predictive performance (Areas Under the Receiver Operating Characteristic Curve (AUCs) from 0.70 to 0.89). For all PS models, the yearly anomaly in DFMC was the most influential variable. Among human-related factors, distance to the Wildland Urban Interface emerged as the most relevant variable, followed by proximity to roads, population density, and the fraction of wildland coverage. The performance of the full model achieved an AUC value of 0.81, with mean DFMC and anomaly being the main ignition factors, modulated by distance to roads and population density. The local performance of the full model dropped by 0.10 for AUC in both Southern Sweden and Attica (Greece) regions. The wildfire occurrence models developed in this study are essential for understanding wildfire ignition hazard and may help implement integrated wildfire risk management strategies and mitigation policies in fire-prone EU landscapes.

Natural hazards and earth system sciencesVol. 25(11)
Agricultural University of Athens (GR), Universitat de Lleida (ES), Universidad Pública de Navarra (UPNA) (ES), Universidad de Alcalá (ES), Universidad de Zaragoza (ES), RISE Research Institutes of Sweden (SE), University of Coimbra (PT), Technische Universität Dresden (DE)
European Commission, Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación
Openalex Percentile: Top 65%
Fire effects on ecosystems
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