Post-Fire Burned Area Detection Using Machine Learning and Burn Severity Classification with Spectral Indices in İzmir: A SHAP-Driven XAI Approach

This study was conducted to precisely map burned areas in fire-prone forest regions of İzmir and analyze the spatial distribution of wildfires. Using Sentinel-2 satellite imagery, burn severity was first classified using the dNBR and dNDVI indices. Subsequently, machine learning (ML) algorithms—RF, XGBoost, LightGBM, and AdaBoost—were employed to classify burned and unburned areas. To enhance model performance, hyperparameter optimization was applied, and the results were evaluated using multiple accuracy metrics. This study found that the RF model achieved the highest performance, with an overall accuracy of 98.0% and a Kappa coefficient of 0.960. In comparison, classification based solely on spectral indices resulted in overall accuracies of 86.6% (dNBR) and 81.7% (dNDVI). A key contribution of this study is the integration of Explainable Artificial Intelligence (XAI) through SHapley Additive exPlanations (SHAP) analysis, which was used to interpret the influence of key spectral and environmental variables in burned area classification. SHAP analysis made the model decision processes transparent and identified dNBR, dNDVI, and SWIR/NIR bands as the most influential variables. Furthermore, spatial analyses confirmed that variations in spectral reflectance across fire-affected regions are critical for accurate burned area delineation, particularly in heterogeneous landscapes. This study provides a scientific framework for post-fire ecosystem restoration, fire management, and disaster strategies, offering decision-makers data-driven and effective intervention strategies.