## 6 Conclusions  
This research study presents a novel procedure for medium-high resolution burned areas mapping, taking advantage of the spectral and temporal characteristics of S2 MSI data, and adopts a four-step procedure based on expert-derived rules and thresholds. The selected advanced image analysis techniques were used to develop an automated and transferable procedure that relies on a change detection approach, based on the identification of structural changes in vegetation cover, and exploits complementary information through a region-growing algorithm.  
The case study of Italy wildfires in the year 2017 exhibits a burned area of around 1400 km\({}^{2}\), with the proposed methodology generating a commission error of around 25% and an omission error of around 40%. The NBR spectral index reported the best classification performances, but BAIS2 and BAIS2\(\alpha\) showed similar performances.  
Even though the proposed procedure was applied to the Italian territory only, the algorithm provides opportunities for extending the medium-high resolution mapping of burned areas at wider scales, offering a benchmark for the development of new operational downstreaming services at national and continental levels based on Copernicus data, with the aim of providing reliable information for the systematic monitoring of wildfires, which is of interest to a wide range of communities.  
The following are available online at [[http://www.mdpi.com/2072-4292/11/6/622/s1](http://www.mdpi.com/2072-4292/11/6/622/s1)]([http://www.mdpi.com/2072-4292/11/6/622/s1](http://www.mdpi.com/2072-4292/11/6/622/s1)). Table S1: Spectral characteristics of Sentinel-2 MSI sensor; Table S2: Layers used to generate the raster mask; Figure S1: Sensitivity analysis for the maximum number of iterations used in Step C (growing region); Figure S2: (a) Distribution of differences in days between the post-fire date identified from Sentinel-2 observations and the fire date reported in the EFFIS \"MODIS burnt areas\" product; (b) distribution of differences in days between the post-fire date identified from Sentinel-2 observations and the post-fire date used to produce the Copernicus EMS rapid mapping activations; Figure S3: Distribution of spectral index values in the identified burned areas with respect to the Copernicus EMS fire grading classes; Figure S4: Distribution of dLAI values in the identified burned areas with respect to the Copernicus EMS fire grading classes; Figure S5: Distribution of misclassified burned area pixels with respect to the Corine Land Cover classes (source: CLC 2012).  
This research received no external funding.  
This work contains modified Copernicus Sentinel data and Copernicus Service information (2018). Sentinel-2 MSI data used were available at no cost from ESA Sentinels Scientific Data Hub. Copernicus Sentinel-2 data were processed at level 2A by CNES for the THEIA Land data center. Successive data processing was performed through the RUS (Research and User Support for Sentinel) on-line platform, designed to promote the uptake of the Sentinel data. The author would like to acknowledge the contribution of [PERSON] and three anonymous reviewers for their valuable comments improving the manuscript quality. The author is grateful to the many individuals working on the development of free and open-source software for supporting the sharing of knowledge.  
The author declares no conflict of interest.