Remote Sensing-Based Deep Learning Approach for Identifying Burned Forest Areas

In this study, the burnt areas and intensity of forest fires that occurred in the Samandağ region of Hatay between September 5-10, 2020, are mapped. Analyses were carried out using deep learning, remote sensing, and satellite data from Sentinel 2. With Sentinel 2 satellite photos of the research locations, an image dataset for deep learning was constructed. Then, using deep learning approaches, a deep learning model was developed, trained using the photos in the dataset, and successfully tested. Images from Sentinel 2 were used to produce the Normalized Burn Ratio(NBR) and Burnt Area Index for Sentinel 2 (BAIS2) indices using the results of a new deep learning model. Calculating the Difference Normalized Burning Intensity (dNBR) and Burnt Area Index for Difference Sentinel-2 (dBAIS2) values for the discrepancies between these indices before and after the fire allowed for categorization and determination of the fire area. The deep learning approach burnt area indexes, and General Directorate of Forestry (GDF) fire registration slips were compared, and it was established that the new deep learning model was more effective at locating burned forest areas than the indexes. In identifying the burnt forest areas, the new model has a proportionate accuracy of 98.36% in the Samandağ study region.