## 2 Data and Methodology
### Continuous Change Detection and Classification (CCDC)  
The selected VIs of Table 1 were used individually as input data (univariate approach) and in an ensemble approach (multivariate) for the detection of disturbance with the CCDC algorithm on the GEE platform. Thus, the algorithm was run eight times: one run for each of the seven VIs and another run for the ensemble approach (VIs together as  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**VIs** & **Equation** & **Reference** \\ \hline Enhanced Vegetation Index (EVI) & \(2.5\times((\text{NIR}-\text{Red})/(\text{NIR}+6\times\text{Red}-7.5\times\text{ Blue}+1))\) & [36] \\ Green-Red Normalized Difference (GRND) & \((\text{Green}-\text{Red})/(\text{Green}+\text{Red})\) & [37] \\ Moisture Stress Index (MSI) & \((\text{NIR}-\text{SWIR1})/(\text{NIR}+\text{SWIR1})\) & [38] \\ Normalized Burn Ratio (NBR) & \((\text{NIR}-\text{SWIR2})/(\text{NIR1}+\text{SWIR2})\) & [29] \\ Normalized Burn Ratio2 (NBR2) & \((\text{SWIR1}-\text{SWIR2})/(\text{SWIR1}+\text{SWIR2})\) & [29] \\ Normalized Difference Vegetation Index (NDVI) & \((\text{NIR}-\text{Red})/(\text{NIR}+\text{Red})\) & [39] \\ Soil-Adjusted Vegetation Index (SAVI) & \(1.5\times(\text{NIR}-\text{Red})/\text{NIR}+\text{Red}+0.5)\) & [40] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vegetation indices (VIs) used in the CCDC algorithm for detecting disturbance over the savannas.  
seven input variables). The ensemble approach explored indirectly the distinct spectral-biophysical information provided by the seven VIs for disturbance detection. The CCDC is an online algorithm that uses a data-driven statistic threshold (adjusted for each pixel) to detect changes in the time series [24]. An online approach means that a model is fitted to observations from the beginning to the end of the time series, being updated as new observations become available. Thus, if new observations over the time series do not diverge widely from the fitted model, they are added to the time series before updating the model.  
The time series modeled by CCDC have components of overall value (mean VI throughout the time series), seasonality, and trend. In our study, Least Absolute Shrinkage and Selection Operator (LASSO) regression fitting estimated the coefficients of the model. After testing different parameters in pixels with available information of disturbance, the model based on the overall value was then selected for analysis. When we considered single VIs as input data for CCDC, a disturbance caused by clearing or fire was flagged when the difference between observed and predicted values by the model exceeded three times the normalized root mean square error (RMSE) for three consecutive observations. In the ensemble approach, the algorithm averages and normalizes such differences (observed minus predicted) by three times the RMSE for all VIs. A disturbance was marked in the time series when the result was larger than one for three consecutive observations. This number of observations was set to avoid the pseudo-detection of disturbance caused by noise in the time series [34]. While noise factors tend to be ephemeral, land cover change is more persistent through time. The results of the disturbance detection that occurred in 2019 were evaluated.