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Method for Recording Broadband High Resolution Emission Spectra of Laboratory Lightning Arcs
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Evaluating Lightning-Caused Fire Occurrence Using Spatial Generalized Additive Models: A Case Study in Central Spain.

José Ramón Rodríguez-Pérez1, Celestino Ordóñez2, Javier Roca-Pardiñas3

  • 1GEOINCA Research Group, Universidad de León, Ponferrada, León, Spain.

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|April 30, 2020
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Summary
This summary is machine-generated.

Lightning-caused forest fires in Spain are most strongly linked to the prevalence of coniferous woodlands. Understanding these vegetation-based risks helps regional governments develop effective long-term fire management strategies.

Keywords:
Lightning-caused fireslightning fire occurrencespatial effectspatial generalized additive models

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Area of Science:

  • Ecology
  • Forestry
  • Environmental Science

Background:

  • Wildfire occurrence is influenced by spatial scale.
  • Regional-scale modeling is crucial for understanding wildfire dynamics.
  • Lightning is a significant ignition source for forest fires.

Purpose of the Study:

  • To analyze factors influencing lightning-caused forest fires in Castilla y León, Spain.
  • To compare the predictive performance of various statistical models.
  • To identify key drivers of wildfire risk at a regional scale.

Main Methods:

  • A 4x4 km grid cell approach was used to assess fire presence/absence.
  • Biophysical variables (vegetation, terrain, climate) and lightning characteristics were analyzed.
  • Five prediction methods were compared: GLM, RFM, GAM, GAMs, and AUREG.

Main Results:

  • The Generalized Additive Model with a spatial trend function (GAMs) was the most effective model.
  • Coniferous woodland percentage showed a positive, nonlinear association with fire probability.
  • Vegetation type was a stronger predictor than topography, climate, or lightning characteristics.

Conclusions:

  • Forest fire risk in the region is primarily driven by vegetation composition, specifically coniferous woodlands.
  • The findings support targeted forest fuel and fire management strategies.
  • The selected GAMs model provides valuable insights for regional fire agencies.