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Predicting forest fire kernel density at multiple scales with geographically weighted regression in Mexico.

Norma Angélica Monjarás-Vega1, Carlos Ivan Briones-Herrera1, Daniel José Vega-Nieva1

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This study used geographically weighted regression (GWR) to map fire density in Mexico. Kernel density prediction with GWR showed better performance, suggesting a 15-20 km scale for effective fire management decisions.

Keywords:
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Area of Science:

  • Environmental Science
  • Spatial Analysis
  • Fire Ecology

Background:

  • Understanding human and environmental drivers of fire occurrence is crucial for effective fire management.
  • Limited research exists on multi-scale spatial fire patterns and driver relationships, especially at regional to national levels.
  • Spatial non-stationarity in fire occurrence-driver relationships requires further investigation across various scales.

Purpose of the Study:

  • To predict spatial fire occurrence patterns at regional and national scales in Mexico.
  • To utilize geographically weighted regression (GWR) to model fire density using both regular grid and kernel density approaches.
  • To analyze fire density prediction at multiple spatial resolutions (5-50 km) for both dependent and independent variables.

Main Methods:

  • Employed geographically weighted regression (GWR) for spatial prediction of fire density.
  • Calculated fire density using two methods: regular grid density and kernel density.
  • Tested multiple spatial resolutions (5-50 km) for both fire density and predictor variables.

Main Results:

  • GWR demonstrated superior performance in predicting kernel density compared to regular grid density, evidenced by better goodness of fit and reduced residual correlation.
  • Model performance improved with increasing kernel density search radius (bandwidth), with predictive capacity saturating around 15-20 km.
  • This study is the first to apply GWR for predicting fire kernel density and to incorporate multi-scale analysis for both dependent and independent variables.

Conclusions:

  • The 15-20 km spatial scale appears optimal for operational fire prevention and suppression decision-making, balancing predictive accuracy and spatial detail.
  • Kernel density estimation combined with GWR offers a robust approach for spatial fire occurrence prediction.
  • Further research is recommended to validate these findings in different geographical contexts.