Forest fire probability zonation using dNBR and machine learning models: a case study at the Similipal Biosphere...

Forests play a vital role in environmental balance, supporting biodiversity and contributing to atmospheric purification. However, forest fires threaten this balance, making the identification of forest fire probability (FFP) areas crucial for effective mitigation. This study assesses forest fire trends and susceptibility in the Similipal Biosphere Reserve (SBR) from 2012 to 2023 using four machine learning models-extreme gradient boosting tree (XGBTree), AdaBag, random forest (RF), and gradient boosting machine (GBM). A forest fire inventory was created using the delta normalized burn ratio (dNBR) index, and 19 conditioning factors were incorporated after rigorous collinearity testing. FFP maps were generated and evaluated using ROC-AUC, MAE, MSE, and RMSE metrics. The frequency ratio (FR) model was also applied to assess the importance of variables. The results show that approximately 40.85% of the study area is high to very high susceptible to forest fires, with the RF model achieving the highest accuracy (AUC = 0.965). An average analysis across all models revealed that high susceptibility areas accounted for 23.08% of the study area, the largest among all classes. Moderate susceptibility zones covered 16.19%, while very high susceptibility areas comprised 18.23%. Interestingly, very low and low susceptibility zones together represented 42.50%, indicating a large portion of the area is at relatively low fire risk. Temporal analysis identified 2021 as the peak year for fire incidents, with 94.72% of the fires occurring during March and April. The buffer zone experienced the highest number of incidents, with a significant anthropogenic influence. Using the FR model, variable importance analysis showed that land use and land cover (LULC), NDVI, and NDMI were the most influential factors in fire susceptibility. This study contributes to forest fire management by integrating the dNBR index with machine learning models and FR analysis to generate precise FFP maps. These findings provide valuable insights for policymakers and conservationists, enabling targeted interventions in high-risk zones and enhancing fire management strategies to reduce the impact of forest fires.