A high resolution burned area detector for Sentinel-2 and Landsat-8

Fires are disruptive events that should be carefully studied. To this date, the monitoring at large scale of fire events is mainly performed using low spatial resolution sensors such as MODIS and VIIRS and ancillary data like active fire maps. However, the data produced by the new generation of spaceborne multispectral missions can be used for burned area detection at a finer spatial and temporal scale with respect to existing methods. In this paper we present a method that analyzes time series of optical images acquired by Landsat-8 and Sentinel-2 to detect burned areas in a fully automatic and unsupervised way. The method analyzes each image in the time-series to extract Candidate Burned Areas (CBAs). CBAs are analyzed accounting for the temporal correlation to reduce the number of false alarms. In particular, only pixels that have a temporal profile consistent with the physical event of a fire are selected. Moreover, the time variable is used to compute the confidence of the detection. The method has been tested on a time-series of the Attica region, Greece, which was affected by large fires in 2018. Preliminary experimental results showed that the method correctly identified the burned areas of the region.