Eaton Canyon in California serves as the focal point for a comprehensive post-wildfire ecological impact assessment. This study employs an approach integrating satellite imagery from the European Space Agency's Sentinel constellation to study an area of 271.49 〖km〗^2. The data encompasses both radar and multispectral data, offering a multi-dimensional view of the affected landscape. The analysis leverages the power of the Random Forest Algorithm. Firstly, three widely used indices—the Difference Normalized Burn Ratio (dNBR), Relative Burn Ratio (RBR), and Relative Difference Normalized Burn Ratio (RdNBR)—were calculated and compared based on their accuracy and Kappa Index. Secondly, we developed a fusion approach to create a precise fire severity map by classifying the affected area into distinct severity classes. Thirdly, a separate fusion approach was developed utilizing the Normalized Difference Vegetation Index (NDVI), Radar Vegetation Index (RVI), and Modified Normalized Difference Vegetation Index (MNDVI) to analyze the distribution of vegetation before and after the wildfire. The results showed a perfect 100% accuracy and Kappa Index in all the predictions. A percentage of 56.79% did not burn, due to the topography of the canyon creating natural firebreaks. Areas classified as low severity (13.49%) showed minimal damage with minimal tree mortality. Moderate- to low-severity areas (5.79%) represented regions with partial crown burn and some tree mortality. Moderate- to high-severity areas (3.57%) showed significant tree mortality. Finally, high-severity areas (20.36%), characterized by complete tree mortality and significant loss of vegetation cover, were largely concentrated in specific sections of the canyon, likely influenced by factors such as slope and fuel type. These findings provide valuable information for post-fire ecological recovery efforts and future land management strategies in Eaton Canyon and similar fire-prone landscapes.