## 1 Introduction
For instance, [PERSON] [21] assessed active fire temporal continuity over Brazil using the Geostationary Operational Environmental Satellite (GOES) hotspot product and indicated that a reduction of 15% in fire counts occurs due to cloud obscuration. The approach described in this paper only uses active fire data to select burned pixels (positive training samples), avoiding the need to collect unburned (negative) training pixels. Collection of positive training samples can be used in a positive--only classification approach [26], and is particularly interesting for mapping burned areas since training sample collection is normally a difficult task when considering the unburned area class. However, few studies have applied this technique for burned area mapping. In a comparative study among Maximum Likelihood (ML), Binary Support Vector Machine (SVM) and One-Class Support Vector Machine Description (OC-SVDD) aimed at burned area identification, the authors of [27] reported that SVM and OC-SVDD produced good results for a broader range of sample sizes than ML. Although the SVM yields better accuracy, the authors mention as an advantage of OC-SVDD that it does not require training samples from unburned areas. However, burned area training data are manually collected, impairing its use in an automated burned area algorithm. [PERSON] et al. [28] proposed mapping burned areas applying a one-class classifier and obtained reasonable results using a kernel sparse representation model to represent burned samples. However, in both studies, training data for the single class of interest was collected manually, which increases the classification cost, making it dependent on human intervention for sample acquisition. Besides eliminating the subjectivity of the human intervention, the fully automatic approach is advantageous for maximizing image data processing efficiency for large area coverage and data volume in a timely and cost-effective way, enabling operational delivery of burned area products. Here, we explore the suitability of the One-Class Support Vector Machine classifier for burned area classification and mapping in the Cerrado region and propose using active fire data to automatically collect burned area training samples, circumventing the need for human intervention and increasing the degree of automation of the entire classification procedure. The procedure was applied to 300-m spatial resolution imagery from the Project for On-Board Autonomy-Vegetation (PROBA-V) and Visible Infrared Imaging Radiometer Suite (VIIRS) active fire data (375 m spatial resolution). This algorithm, hereafter designated AQM-PROBA (from \"_Area Queimada_\", meaning Burned Area, in Portuguese), is based on VIIRS active fire data which are used to automatically extract burned area training samples from PROBA-V reflectance imagery. Then, those samples are used as input to a one-class support vector machine classifier, which only requires positive (i.e., burned area) training data [29]. Our goal is to generate a completely automated methodology, capable of producing results with accuracy levels in the range required by users of global fire data products, such as climate and vegetation researchers, as well as land managers and policy-makers [13]. The accuracy of our results was assessed using reference data derived from Landsat-8 OLI data over the Cerrado region and finally compared with MODIS standard burned area product.