LAUSR

In this letter, we propose an approach based on the use of Sentinel-2 spectral indices and self-organizing map (SOM) to automatically map burned areas and burned severity. These analyses were performed on a test area in Chania, located in Crete, affected by a fire (around 200 ha) that occurred from July 13, 2018 to July 28, 2018. The investigated area is characterized by heterogeneous land cover types made up of natural and agricultural lands. To identify different levels of fire severity without using fixed thresholds, we applied SOM to the three spectral indices normalized difference vegetation index (NDVI), normalized burn ratio (NBR), and burned area index for sentinel (BAIS) used to enhance burned areas. This is a particular critical issue because fixed threshold values are generally not suitable for fragmented landscapes, vegetation types, and geographic regions different from those for which they were devised. To cope with this issue, the methodological approach herein proposed is based on three steps: 1) indices computation; 2) maps of the difference of the three indices computed using the data acquired from prefire and postfire occurrences; and 3) unsupervised classification obtained processing all the difference maps using the SOM. The obtained results were validated using an independent data set, which showed high correlation with satellite-based fire severity.