Optical sensors are widely used in the detection and analysis of forest fires. However, in regions affected by dense smoke clouds during fires or areas with persistently cloudy weather due to climatic conditions, conducting analyses using optical imagery becomes challenging. Therefore, this study focuses on the detection and monitoring of forest fires using InSAR Coherence data. Prior to the fire detection/monitoring analyses, forest fire incidents in the Turkey/İzmir region between 2012 and 2024 were collected from the NASA FIRMS platform. In addition to these data, maps of slope, aspect, curvature, and drainage were derived from the DEM of the İzmir region. Urban areas in İzmir were identified using CORINE data, road networks were obtained from OSM datasets, temperature and wind data were retrieved from ERA-5 datasets, and precipitation data were sourced from the CHIRPS dataset. Using these collected parameters, forest fire susceptibility maps for the İzmir region were generated through the Frequency Ratio method. The resulting map revealed a direct correlation between increasing slope and heightened forest fire susceptibility in the region. Additionally, the urban parameter demonstrated a high frequency ratio in the analyses, indicating its contribution to fire risk, and areas near settlements were observed to exhibit high-risk levels on the map. Following the generation of the fire susceptibility map, a forest fire in the İzmir/Yamanlar Mountain area was investigated using the InSAR Coherence method. The analyses showed that forest fires can be detected/monitored through the changes observed in InSAR Coherence histograms and coherence-based maps. Furthermore, it was observed that the application of a threshold value to the coherence histogram enabled the classification of forested areas in the region.