## 1 Introduction  
Savannas in Brazil comprise a global hotspot of biodiversity that has already lost half of the native vegetation cover to crops and pastures [1, 2]. Locally known as the _Cerrado_, the savannas are the second largest biome in the country and the second largest terrestrial source of carbon emissions after the Amazon biome [3, 4]. Savannas show gradients ranging from grasslands to woodlands, having species well adapted to natural and human-induced fires as well as to the strong seasonality of precipitation [5, 6, 7]. In addition to its biodiversity, the _Cerrado_ is also very important for agriculture, and has half of the croplands in Brazil [8, 9, 10]. The recent expansion of soybean over native vegetation of the northern portion of the _Cerrado_ (new agricultural frontier) has increasedthe number of disturbance events in this region [11; 12; 13; 14; 15]. As in other regions of the _Cerrado_, clearing and fire are the main drivers of disturbance. However, the seasonal pattern of occurrence of these two types of disturbance is not entirely understood. The comprehension of the relationships between fire and clearing over each year requires further studies. For land management, fire is a common practice that generally invades the native vegetation areas. Although part of the savanna vegetation is adapted to fire, an excessive number of events may cause biodiversity loss and land degradation [16; 17]. Fire is also used in recently cleared areas to burn and clean the non-photosynthetic vegetation left on the ground after disturbance (e.g., rest of leaves, branches, and trunks). To monitor vegetation disturbance caused by clearing and fire in the _Cerrado_, remote sensing programs have been developed in Brazil. The use of satellite imagery is perhaps the only way for large-scale monitoring of this biome [18]. An example of these initiatives is the project for monitoring the _Cerrado_ developed by the Brazilian National Institute of Space Research (INPE), which is mostly based on the visual interpretation of several satellite data. The objective of the INPE's project is to provide annual rates and warning alerts of savanna clearings [19]. Monitoring disturbance in the _Cerrado_ is part of the strategies used to mitigate greenhouse gas emissions and to conserve biodiversity. For instance, if a similar agreement to the Amazonian Soy Moratorium is adopted to the _Cerrado_, such types of remote sensing programs can adequately support the major exporting companies to not buy and finance soybean production from new savanna-cleared areas. This would probably reduce savanna-clearing rates, as was observed in the last decade for tropical deforestation in the Amazon region. However, satellite monitoring of human-induced disturbance in the _Cerrado_ is not a trivial task. It still requires the development and testing of new approaches for the detection of disturbance and the reduction of uncertainties in the data analysis. Some factors that affect detection are: the distinct resilience of savanna vegetation types to disturbance events; the significant changes in satellite spectral response due to vegetation phenology; the natural soil exposure due to the presence of more open canopies in the savanna formations; and the frequency of satellite observations affected by cloud cover. To reduce the influence of vegetation phenology on clearing detection, vegetation indices (VIs) calculated in a fixed period of each year (e.g., dry season) can be used as input data for algorithms. When compared to band reflectance, VIs can reduce data variability associated with the geometry of data acquisition and with terrain illumination or topographic effects. This is especially valid for VIs with a band reflectance normalization on their formulations [20; 21; 22]. Algorithms like the Landsat-based detection of Trends in Disturbance and Recovery (LandTrendr) retrieve the annual rates of disturbance from Landsat time series [16; 23]. In this case, early warning alerts of disturbance cannot be generated.