Abstract

The spatial relationships between lightning-induced fire ignition and topography, vegetation, climate, and weather were analyzed in the region of East Macedonia and Thrace, northeastern Greece. The study was based on reported lightning-induced ignitions during the 2009 fire period. Lightning data for the same period was provided by the ZEUS lightning detection network operated by the National Observatory of Athens, while fire statistics were obtained from the official records of the Greek Fire Service. A total of 198 lightning strike events (66 fire ignitions and 132 non-fire events) were used for model development. Statistical models based on Logistic Regression (LR) and random forests (RF) were developed to estimate the probability of lightning-induced fire using topography, climate, weather, and vegetation indices as predictor variables. According to the analysis results, the probability of an area being affected by lightning-induced fire is primarily determined by the Normalized Difference Vegetation Index (NDVI) and the accumulated precipitation in 24 h equal to or less than 2.5 mm expressed by Dry Thunderstorm (DT) day occurrence in this dataset. The logistic regression model achieved an area under the ROC curve of 0.94 and an overall classification accuracy of 91.9%, while the random forest model produced an Out-Of-Bag (OOB) error rate of 3.0%. Although the models have not been subjected to independent validation and include a single year’s data, the results demonstrate high internal classification performance and provide valuable insights into the primary drivers of fire ignition following lightning strikes in the study region. The outcomes of the present study will be useful in assessing spatially explicit fire risk, the planning and coordination of efforts to identify high-fire-risk areas, and designing long-term fire management and climate change adaptation strategies.

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