Comparative evaluation of Landsat8 and Sentinel2 images to prepare a fire occurrence map in Arasbaran

The valuable Arasbaran forest is a complex and dynamic ecosystem that has always been subject to extensive fires. The purpose of the present research is to utilize the technology of remote sensing and geographic information system and the technical abilities of the Google Earth Engine system in order to prepare a fire occurrence map in the rangelands and forests of Arasbaran. In order to choose the appropriate method and type of satellite between Sentinel2 and Landsat8, the separability index was used. Accordingly, the RdNBR differential index was selected among the different indicators of fire detection to prepare the final map of the last 9 years and the cumulative fire map. Based on the accuracy assessment of the resulting map quantitatively, 84% of the actual fire points recorded by the General Directorate of Natural Resources and Watershed of East Azarbayjan province were placed at a distance of 200 meters from fire polygons extracted from satellite images, which showed the high accuracy of the fire map. The field visit also showed a good match among the fire areas resulting from the processing of satellite images with the existing situation of the region. The current research showed the high potential of these two satellites as well as the extraordinary ability and facilities of the Google Earth Engine system in providing a huge amount of remotely sensed data and advanced processing on them to prepare fire occurrence maps. The advantages of Landsat8 compared to Sentinel2 are having thermal bands and more time series. The spatial and radiometric resolution of both are almost similar, and the low-temporal resolution of Landsat8 will be compensated by combining it with Landsat 9 images. Therefore, in line with the results of similar studies, Landsat8 is generally preferable to Sentinel2. For the correct and scaled spatial data from Arasbaran region, it is suggested to create an integrated and large-scale Geo-database due to the lack of accuracy.