Application of the class-balancing strategies with bootstrapping for fitting logistic regression models for post-fire tree mortality in South Korea

Abstract

We aimed to tackle a common problem in post-fire tree mortality where the number of trees that survived surpasses the number of dead trees. Here, we investigated the factors that affect Korean red pine (Pinus densiflora Siebold & Zucc.) tree mortality following fires and assessed the statistical effects of class-balancing methods when fitting logistic regression models for predicting tree mortality using empirical bootstrapping (B = 100,000). We found that Slope, Aspect, Height, and Crown Ratio potentially impacted tree mortality, whereas the bark scorch index (BSI) and diameter at breast height (DBH) significantly affected tree mortality when fitting a logistic regression with the original dataset. The same variables included in the fitted logistic regression model were observed using the class-balancing regimes. Unlike the imbalanced scenario, lower variabilities of the estimated parameters in the logistic models were found in balanced data. In addition, class-balancing scenarios increased the prediction capabilities, showing reduced root mean squared error (RMSE) and improved model accuracy. However, we observed various levels of effectiveness of the class-balancing scenarios on our post-fire tree mortality data. We still suggest a thorough investigation of the minority class, but class-balancing scenarios, especially oversampling strategies, are appropriate for developing parsimonious models to predict tree mortality following fires.