Monitoring the ecological and socio-economic impacts of wildfires using traditional approaches requires significant financial resources, time, and sampling expertise. However, not only are resources scarce, but the spatial and temporal extent of forest fires may make it impractical to assess large areas over time. As a result, fire monitoring initiatives are often not realized. This makes the remote sensing approach an intriguing option for fire protection managers and decision makers due to its ability to measure large areas and its temporal capabilities. In this study, burn spectral indices derived from Landsat 8 (difference normalized vegetation index (dNDVI) and difference normalized burn ratio (dNBR)) were used to assess the ecological and socio-economic impacts of forest fire based on an existing land use land cover dataset. The relationships between estimated fire severity/area and, environmental and anthropogenic factors were also evaluated. The results show that more than 700 hectares of forest and other land use categories were burnt. Fires adversely affect high forests, thickets, degraded forests, and most cultivation and rural areas. The study also shows a moderate positive relationship between burn severity and pre-fire vegetation (R2 = 0.48 and R2 = 0.49 for dNDVI and dNBR, respectively). This result suggests, that fuel amount is the main driver of burn severity during the fire season in this particular ecosystem. Topography has been shown to affect the fire behavior in the study area, where fires occurred primarily on elevations averaging 400-800 meters above mean sea level. In addition, there is a weak positive relationship between population density and burnt area. This is typical of certain areas where fire increases with increasing population density, and falls when the burning exceeds a threshold. This study has shown that Landsat 8 data-derived burn spectral indices (dNDVI and dNBR) have a high potential for the spatial analysis of wildfire. The study recommends an improvement in the application of remote sensing to detect, monitor, and assess fire extent and patterns. These in return will protect and improve lives and properties for sustainable development.