## 4 Discussion
Even considering the limitations of such a comparison, we observed that 73.14% of our reference samples detected as disturbed sites by CCDC were also assigned as savanna-cleared areas by the DETER _Cerrado_. On the other hand, when we compared our CCDC-detected reference samples of fire disturbance with the burned areas mapped by the 250-m MODIS FireCCI v.5.1 product, the agreement was only 40.7% on a per-pixel basis. The differences in agreement between the clearing and burning products are explained by different factors. For instance, there is a greater duration of the signals of clearing disturbance over the OLI/Landsat-8 images when compared to burning events. Overall, our results showed that the CCDC accuracy was generally higher for detecting clearing than for mapping burned areas, but the contrary was observed for the accuracy of the actual date of disturbance occurrence. As mentioned before, the great availability of cloud-free images in the predominant period of savanna clearing favors the record of three consecutive observations in the CCDC algorithm for the assignment of change. On the other hand, fire comprises more abrupt events than savanna clearing, which may include gradual processes of degradation. Such processes add uncertainties in establishing the exact date of vegetation suppression. Another interesting result obtained here is the existence of a seasonal pattern of disturbance occurrence in MATOPIBA. Our results showed the predominance of savanna clearing between April and July, and of disturbance caused by fire between August and October. They were consistent with reports published by [PERSON] and [PERSON] (2016) on the fire regime in the Brazilian savannas. According to them, the lack of fire management causes wildfires over native vegetated areas, especially during the late dry season (August-October) and near the agricultural fields. From 2000 to 2018, the new agricultural frontier of MATOPIBA was responsible for approximately 40% of the total cleared areas of the _Cerrado_ biome and for 57% of the burned areas, as indicated by data of the MapBiomas and INPE's projects of savanna monitoring ([PERSON], 2016). Finally, our study has some constraints to consider in future investigations of savannas. For instance, we did not consider other algorithms in the analysis of disturbance. Recent studies have proposed also the use of an ensemble of time series algorithms to monitor land cover changes ([PERSON], 2016). By comparing four dense time series algorithms using simulated data, [PERSON] et al. (2018) discussed the advantages and limitations of each approach. They demonstrated the advantage of CCDC in working with multiple input variables for providing robust detection of land covers, especially when high quantities of missing data occur in the time series. This is the case with our datasets, which have distinct frequencies of satellite observations in the rainy and dry seasons of MATOPIBA due to cloud cover. In this context, we did not consider the inclusion of other satellites than Landsat (e.g., MSI/Sentinel-2) to produce denser time series. This would require strategies of data harmonization that may add other uncertainties in the data analysis. The number of images was still a limiting factor for disturbance detection in MATOPIBA, adding commission errors during the rainy season and omission errors at the end of the dry season. This limiting factor also added uncertainties in the definition of the date of disturbance occurrence. In spite of these constraints, our results reinforce the importance of data-driven procedures for the fast generation of near real-time warning alerts of disturbance to be carefully inspected in the images and further checked in the field. Therefore, future studies should consider hybrid satellite datasets to produce denser time series. By increasing the number of consecutive observations, the accuracy of disturbance detection by CCDC will probably increase.