## 3 Results
### Variations in Accuracy Metrics per VI, Ensemble VIs, and Type of Disturbance  
The CCDC algorithm detected 1.24 \(\pm\) 0.12 Mha of total disturbance (clearing plus fire) in 2019. This result is based on the ensemble approach. The other VI-area estimates presented approximately similar results, since the estimates were based on a stratified estimator. This number represents an area equivalent to 41.8% of the native vegetation of our site. The overall accuracy of detection ranged from 51.2 \(\pm\) 4.2% for GRND to 65.9 \(\pm\) 4.2% for NBR2, and increased to 71.2 \(\pm\) 4.0% for ensemble VIs (Table 2). The best single VIs for detecting total disturbance were therefore metrics that used NIR, SWIR-1, and SWIR-2 reflectance bands of the Landsat instruments on their equations (NBR2, NBR, and MSI in Table 1).  
Figure 5: Landsat time series examples of disturbance in 2019 in seasonal profiles of **(a)** NDVI (savanna clearing in July) and **(b)** NBR (disturbance by fire in September). In both figures, dashed lines indicate the seasonal cycles of the savannas during the rainy and dry seasons of the 2017–2019 period. In **(a)**, savanna clearing is followed by vegetation regrowth after October 2019.  
For ensemble VIs, the producer's accuracy varied from 35.0% (high omission error) to 97.3% (low omission error) for the disturbance and non-disturbance classes, respectively (Table 2). On the other hand, user's accuracy ranged from 90.2% to 67.5%, indicating a low commission error for detecting disturbance. In Table 2, the contrast between producer's and user's accuracies occurs because of the differences in the proportion of the disturbed and non-disturbed classes. The disturbance class had higher user's accuracy (small commission error) and lower producer's accuracy (large omission error) than the predominant non-disturbance class in the scene. The use of an unbiased estimator accommodates the effects of map classification errors in the area estimates. Compared to the use of single VIs, a practical effect of the ensemble CCDC approach was to reduce the omission error and slightly increase the commission error (Figure 6).  
When we considered the performance of detection of each VI or ensemble VIs per type of disturbance, we observed higher accuracies over cleared areas than over burned areas (Figure 7). NBR2 had the highest accuracy values for detecting fire (34.0%) and clearing (55.2%) disturbance. Such values increased to 36.7% and 71.4%, respectively, when ensemble VIs were used as input data to CCDC (Figure 7).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Input Data**} & \multirow{2}{*}{**OA (\%)**} & \multirow{2}{*}{**95\% CI**} & **PA (\%)** & **UA (\%)** & **PA (\%) Non-** & **UA (\%) Non-** \\  & & & **Disturbance Class** & **Disturbance Class (\%)** & **Disturbance Class (\%)** & **Disturbance Class Class** \\ \hline Ensemble VIs & 71.2 & 4.0 & 35.0 & 90.2 & 97.3 & 67.5 \\ NBR2 & 65.9 & 4.2 & 17.5 & 97.6 & 99.7 & 63.4 \\ NBR & 64.9 & 4.3 & 13.7 & 96.0 & 99.6 & 63.0 \\ MSI & 61.0 & 4.3 & 7.8 & 97.0 & 99.8 & 59.7 \\ EVI & 59.6 & 4.4 & 5.1 & 98.9 & 100 & 58.7 \\ SAVI & 57.9 & 4.3 & 4.5 & 97.4 & 99.9 & 57.1 \\ NDVI & 57.9 & 4.3 & 4.5 & 97.4 & 99.9 & 57.1 \\ GRND & 51.2 & 4.2 & 1.7 & 93.3 & 99.9 & 50.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overall accuracy (OA) with confidence intervals (CI) for the CCDC detection of disturbance over savannas using different vegetation indices (VIs) and ensemble VIs. Producer’s (PA) and User’s (UA) accuracies are also indicated for the disturbance and non-disturbance classes.  
Figure 6: Variations in commission and omission errors for the CCDC detection of disturbance (clearing plus fire), using different vegetation indices (VIs) and ensemble VIs.