Modeling Forest Lightning Fire Occurrence in the Daxinganling Mountains of Northeastern China with MAXENT

Abstract

Forest lightning fire is a recurrent and serious problem in the Daxinganling Mountains of northeastern China. Information on the spatial distribution of fire danger is needed to improve local fire prevention actions. The Maxent (Maximun Entropy Models), which is prevalent in modeling habitat distribution, was used to predict the possibility of lightning fire occurrence in a 1 × 1 km grid based on history fire data and environment variables in Daxinganling Mountains during the period 2005–2010.We used a jack-knife test to assess the independent contributions of lightning characteristics, meteorological factors, topography and vegetation to the goodness-of-fit of models and evaluated the prediction accuracy with the kappa statistic and AUC (receiver operating characteristic curve) analysis. The results showed that rainfall, number of strikes and lightning current intensity were major factors, and vegetation and geographic variable were secondary, in affecting lightning fire occurrence. The predicted model performs well in terms of accuracy, with an average AUC and maximum kappa value of 0.866 and 0.782, respectively, for the validation sample. The prediction accuracy also increased with the sample size. Our study demonstrated that the Maxent model can be used to predict lightning fire occurrence in the Daxinganling Mountains. This model can provide guidance to forest managers in spatial assessment of daily fire danger.