Burned area (BA) mapping of a forest after a fire is required for its management and the determination of the impacts on ecosystems. Different remote sensing sensors and their combinations have been used due to their individual limitations for accurate BA mapping. This study analyzes the contribution of different features derived from optical, thermal, and Synthetic Aperture Radar (SAR) images to extract BA information from the Turkish red pine (Pinus brutia Ten.) forest in a Mediterranean ecosystem. In addition to reflectance values of the optical images, Normalized Burn Ratio (NBR) and Land Surface Temperature (LST) data are produced from both Sentinel-2 and Landsat-8 data. The backscatter of C-band Sentinel-1 and L-band ALOS-2 SAR images and the coherence feature derived from the Interferometric SAR technique were also used. The pixel-based random forest image classification method is applied to classify the BA detection in 24 scenarios created using these features. The results show that the L-band data provided a better contribution than C-band data and the combination of features created from Landsat LST, NBR, and coherence of L-band ALOS-2 achieved the highest accuracy, with an overall accuracy of 96% and a Kappa coefficient of 92.62%.

Background: Forests have an extremely important place in the ecosystem in terms of ensuring social and environmental balance. The biggest danger for forests that have this importance is forest fires due to various reasons. It is extremely important to estimate the formation and behavior characteristics of fires in terms of combating forest fires. Using the satellite images obtained with the developing technology for this purpose provides great convenience in the detection of the fire areas and the severity of the fire affected. In this study, forest fire that occurred in the Zeytinköy region of Muğla province was investigated using remotely sensed images. According to the reference data provided by the General Directorate of Forestry (GDF), 425 hectares of area was destroyed by fire. In this study, it is aimed to extract burn scar by applying seven vegetation indexes on Sentinel-2 and Landsat-8 satellite images. Additionally, forest fire areas have been determined with the object-based classification technique. Results: As a result of the study, when the obtained results are compared with the values obtained from GDF, it is determined that object based analysis of Sentinel-2 provided the highest accuracy with 98.36% overall accuracy and 0.976 kappa statistics. Comparing the results of spectral indices of Sentinel-2 and Landsat-8, Sentinel-2 resulted better results in all indices. Among the indices RdNBR and dNDVI obtained better results than other indices with Sentinel-2 and Landsat-8, respectively. Conclusions: In general, it has been determined that Sentinel-2 data is more suitable than Landsat-8 satellite images for determining Turkish red pine forest fired areas. Red and near infrared based images can be used for rapid mapping of fired areas. The results also indicated that the indices provided by multi-temporal Sentinel-2 data can assist forest management for rapid monitoring of fire scars and also for evolution of reforestation after fire.