Wildfires increasingly affect ecological systems, rural livelihoods, infrastructure, and emergency-management capacity across Southeast Europe, yet national assessments often treat active-fire detections and mapped burn scars as separate evidence streams. This study examined the spatial distribution, intensity, and territorial consequences of open-space fires in Serbia during June-August 2025 by integrating 5,233 NASA Fire Information for Resource Management System (FIRMS) records with 269 burned-area features from the European Forest Fire Information System (EFFIS). MODIS and VIIRS active-fire detections were filtered to retain nominal- and high-confidence observations and were combined with administrative boundaries in ArcGIS Pro. Spatial Join/Intersect procedures assigned observations to municipalities, while Kernel Density Estimation with a 10-km search radius and Natural Breaks classification identified national and local concentration patterns. The results showed marked spatial heterogeneity. Smederevo, Bor, Kuršumlija, and Podujevo formed the highest-frequency municipal group, whereas the largest burned-area classes were concentrated primarily in the southern and southeastern municipalities of Žitorađa, Kuršumlija, Preševo, and Leskovac. Density modeling identified two principal cores: a southern Toplica-Pčinja concentration and an agricultural concentration around Smederevo, with secondary activity in eastern Serbia. Fire radiative power patterns distinguished a frequent, lower-intensity agricultural model from high-energy mountain-forest and mixed industrial-forest models. Integrating FIRMS detections with EFFIS polygons provided a more complete interpretation than either source alone by linking where intense thermal activity occurred with the spatial extent of subsequent land-cover damage. The findings support differentiated prevention, satellite-based early warning, accessibility modeling, and intermunicipal data exchange. Because no direct statistical model of meteorological variables was conducted, climatic explanations are interpreted as contextually plausible associations rather than tested causal effects.