This study evaluates Sentinel-2 imagery for wildfire detection and post-fire assessment by comparing spectral indices (NDVI, BAI, BAIS2, CSI, NBR) using the separability index and accuracy metrics validated with PlanetScope data. The differenced Normalized Burn Ratio (dNBR) was most effective, achieving 90.7% accuracy and a Kappa of 0.8. Future research should explore machine learning methods, test indices across ecosystems, assess temporal stability, develop field validation databases, and integrate Sentinel-2 with other sensors like SAR and UAVs to improve fire mapping and overcome cloud cover issues.