Assessing Chilgoza Pine (Pinus gerardiana) forest fire severity: Remote sensing analysis, correlations, and predictive modeling for enhanced management strategies

Forest fires represent a critical global threat to both humans and ecosystems. This study examines the intensity and impacts of Chilgoza (Pinus gerardiana) Pine Forest fires by using advanced remote sensing techniques comprising Normalized Burn Ratio (NBR) and Difference Normalized Burn Ratio (dNBR) analyses based on Landsat 9 datasets. The study highlights the severe effect of these fires, resulting in noteworthy losses of livestock and private properties and widespread damage to 10,156.53 acres of the Chilgoza Pine Forest. A comprehensive variable correlation analysis is conducted to gain deeper insights into the influencing factors causing forest fires. Spearman's Rank Correlation Coefficient was used to assess the association between burnt and unburnt areas and various independent factors. The analysis reveals compelling evidence of significant correlations with forest fire prevalence. This study found moderate negative (-0.532, p < 0.05) and positive (0.513, p < 0.05) correlations with elevation and Land Surface Temperature (LST), respectively, and a weak positive correlation (0.252, p < 0.05) with a Wind Speed (V). To predict forest fire susceptibility and better understand the contributing factors, three machine learning models, Random Forest (RF), XGBoost, and logistic regression, are applied to assess variable importance scores. Among the considered factors, LST is the most critical variable, with consistently high variable importance scores (100%, 96%, and 59%) across all three models. Wind Speed (V) also proved influential in all models, with variable importance scores of 78%, 83%, and 61% for RF, XGBoost, and logistic regression, respectively. Moreover, elevation significantly influences the frequency of forest fires, as evidenced by variable importance scores ranging from 26% to 100%. Comparatively, the Random Forest model outperforms XGBoost and Logistic Regression in predicting forest fire vulnerability. During the training stage, the Random Forest (RF) model achieves an impressive classification accuracy of 99.1%, followed by XGBoost with 94.5% and Logistic Regression with 85.6%. On evaluation with the validation dataset, the accuracies remain promising, with RF at 96.4%, XGBoost at 91.1%, and Logistic Regression at 84.6%. Based on the Random Forest model, the identified high-risk sites offer valuable insights for proactive fire management and prevention strategies. This study provides a robust predictive model and a comprehensive understanding of forest fire severity and impacts. Future research should consider climate change scenarios and account for human activities to enhance fire behavior predictions and risk assessment models.