## 6 Conclusions  
This study highlights the use of a One-Class Support Vector Machine classifier for burned area mapping using automated sample selection based on active fires. The procedure was applied to the Brazilian Savanna using Project for On-Board Autonomy--Vegetation (PROBA-V) reflectance and Visible Infrared Imaging Radiometer Suite (VIIRS) active fire data. Nevertheless, we must highlight that although the present study has used PROBA-V images and VIIRS active fire data, the approach may be easily adapted to other data sources.  
Validation results using Landsat reference data, and a comparison with the MCD64A1 burned area product (MODIS/Terra and Aqua Burned Area Monthly L3 Global 500 m) showed a higher accuracy of the current algorithm in the Brazilian Cerrado. In general, the AQM-PROBA presented lower omission error compared with the MCD64A1 product, but the latter displayed lower commission errors. However, the AQM-PROBA displayed more balanced values of omission and commission and, consequently, a bias closer to 1 and a higher correlation with the fire reference data. Higher similarity was also observed between reference and AQM-PROBA for fire scars smaller than 100 ha.  
One of the limitations of the presented methodology is the spectral limitation of the PROBA-V sensor, which has only four channels. Several studies have shown that the spectral space of short wave infra-red and middle infra-red channels in savanna areas is advantageous, especially at the 2.1 and 3.9 \(\upmu\)m wavelengths, respectively [69; 70; 71; 72; 73]. Thus, the implementation of the approach presented herein with spectral indexes presenting greater separability between burned and unburned surfaces may improve the results. Considering that burned areas in the Cerrado tend to show an increase in temperature after the fire occurrence [74], another alternative to improve burned area detection may involve the use of thermal data.  
Finally, the proposed methodology based on the use of one-class algorithms opens new perspectives on fire scar detection, since current burned area algorithms based on hybrid approaches are still hampered by the accuracy of active fire products. Moreover, the results of this study have demonstrated that the technique of automated sample selection based on active fires as proposed here produced accurate and promising results for burned area mapping, circumventing the need for human intervention in sample acquisition.  
The authors acknowledge the support of the Science without Borders Program from CNPq 407459/2013-8, grants 215158/2014-8 and 150625/2017-0; the FAPESP/FCT Project Brazilian Fire-Land-Atmosphere System (BrFLAS) 1389/2014 and 2015/01389-4; and the scientific grant FAPERJ (E-26/203.174/2016). The first author also acknowledges CAPES for the grants to a Doctoral program of Teacher Training of IFSULDEMINAS. The Centro de Estudos Florestsia (CEF) is a research unit funded by Fundacao para a Ciencia e Tecnologia I.P. (FCT), Portugal (UID/AGR/00239/2013).  
[PERSON] and [PERSON] developed the AQM-PROBA algorithm and performed the validation approach. [PERSON] established the methodology for the validation section and compiled the manuscript. [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] reviewed the paper and contributed to the interpretation of the results.  
The authors declare no conflict of interest.