## 4 Discussion  
Our study has important contributions for the remote sensing of disturbance in the savanna environment. As far as we know, this is the first investigation using univariate and multivariate approaches that evaluates the detection of disturbance in the Brazilian savannas from a set of VIs that respond differently to biophysical attributes of vegetation. In addition, our investigation demonstrates for the first time the performance of each VI and ensemble VIs to detect two types of disturbance (savanna clearing and fire). Finally, it shows the existence of a seasonal pattern of occurrence between these two drivers of disturbance in the MATOPIBA region. When compared to the available projects of _Cerrado_ monitoring that are mostly based on visual interpretation of satellite data, the current approach is automated and does not require training samples to detect disturbance. This is very important because we have observed a large reflectance variability over the savanna physiognomies in response to different types and intensities of disturbance. In this context, our approach using CCDC and ensemble VIs can optimize the generation of early warning alerts of disturbance to be further inspected in the field. Our data-driven approach can be extended into other savanna areas from Brazil, since the selected site is representative of the different patterns of land use, vegetation physiognomy, and geomorphology observed in a great part of the biome. Our findings showed that the most important VIs to detect savanna clearing and fire used NIR and SWIR Landsat bands on their formulations (e.g., NBR, NBR2, and MSI). This result is not a surprise, since the first two VIs have been designed to detect burned areas [29]. Moreover, MSI is sensitive to changes in canopy water that may result from gradual processes of vegetation degradation [5; 28]. It also expresses modifications in non-photosynthetic vegetation left over in the soil surface after events of savanna clearing (e.g., dry leaves, branches, and twigs). Our findings also agree with results obtained by [PERSON] et al. [42], who showed the importance of the SWIR-derived VIs (e.g., NBR and NBR2) to detect clearing in highly seasonal areas of Brazilian savannas. When using the Breaks For Additive Season and Trend (BFAST) algorithm over a savanna site located in southeastern Brazil, [PERSON] et al. [43] obtained an overall accuracy ranging from 54.7% to 59.6% for different VIs. This accuracy was approximately consistent with results obtained in our work with the use of single VIs. On the other hand, it was comparatively lower than the accuracy reported for our ensemble approach (71.2%). From a visual comparison between the VI disturbance maps, we observed that their similarity depended on the intensity of disturbance and on the spectral range of VI operation. For instance, most VIs detected intense disturbance, while VIs operating at a similar spectral interval (e.g., NDVI and SAVI) produced maps that were more similar. The ensemble approach using CCDC took advantage of these aspects. Using the CCDC algorithm, the ensemble VI approach had a better performance for detecting overall disturbance than the use of single VIs. When compared to NBR2, the ensemble VIs increased the overall accuracy of detection from 65.9% to 71.2%. This result indicates an advantage of the CCDC over other time-series algorithms that do not run with multiple input data [23; 44; 45; 46]. However, even for these algorithms, our results are useful when indicating single VIs that deserve attention and testing over other savanna areas of the world. In the literature, it is very difficult to compare the results of different products generated from distinct approaches, satellite data, and sensor specifications. For instance, our approach uses a data-driven procedure with Landsat data, while the DETER _Cerrado_ program involves visual interpretation of medium spatial resolution CBERS/WFI and endmember-fraction images.