## 3 Results
### Validation of the Forest Loss Detections  
To validate the observed forest loss detection in Guatem and Mato Grosso, a stratified random sampling method was used, as described by [PERSON] [23], at the scale of the processed Sentinel-2 tiles. Two main strata were selected in Guatem and Mato Grosso, comprising areas of forest cover loss and unchanged forest. In addition, a third stratum was defined that included areas of potential forest loss around the identified forest change. This zone serves to reduce omission errors that may occur in vegetative transition zones and around large strata [24; 25; 26]. An inner zone of potential forest loss of 20 m (i.e., 2 pixels) was defined around the areas of forest change to reduce omission errors during validation. A total of 600 stratified random points (200 points per stratum) per area were examined, and accuracy metrics, including Cohen's Kappa [27], were calculated. Visual validation was carried out by interpreting a pair of before and after images around the time of the first detection of the forest loss, as well as for a stratified random sample of unchanged forest areas for comparison.  
The accuracies of the forest loss detections in Guatem and Mato Grosso for the period 2019 to 2022 were quantified as 86.3% and 85.5% overall accuracy, respectively (Table 5). About 229,140 ha of forest in Guatem and 110,480 ha of forest in Mato Grosso were lost between 2019 and the first half of 2022, while about 442,196 ha and 295,927 ha of forest remained unchanged. Forest change patches were distributed throughout the areas of interest, with most patches concentrated along forest edges. The patch sizes of forest changes in Guatem ranged from 0.01 ha to 7011.89 ha and for Mato Grosso from 0.01 ha to 12,846.41 ha.  
The use of zones of potential forest loss around the forest loss detections increased the accuracy of the validation for Guatem and Mato Grosso by 2.4% and 3.5%, respectively. These differences in accuracy reflect the importance and effectiveness of the buffer method and are consistent with previous findings that showed an increase in validation accuracy [25; 28]. Finally, for Mato Grosso, the kappa coefficient was \(\kappa\) = 0.71, and for Guatem, \(\kappa\) = 0.72, showing a'substantial agreement' according to [PERSON]'s interpretation [27]. [PERSON] suggested that \(\kappa\) values \(\leq\) 0 showed no agreement, 0.01-0.20 showed none to slight agreement, 0.21-0.40 a fair agreement, 0.41-0.60 a moderate agreement, 0.61-0.80 a substantial agreement, and 0.81-1.00 almost perfect agreement.  
\begin{table}
\begin{tabular}{c c c c} \hline Guatem & & & \\ OA = 86.3\% & No Change & Change & User Accuracy \\ \(\kappa\) = 0.71 & & & \\ \hline No Change & 193 & 7 & 96.5\% \\ Change & 48 & 152 & 76\% \\ Producer Accuracy & 80.1\% & 95.6\% & \\ \hline Mato Grosso, Brazil & & & \\ OA = 85.5\% & No Change & Change & User Accuracy \\ \(\kappa\) = 0.72 & & & \\ \hline No Change & 187 & 13 & 93.5\% \\ Change & 45 & 155 & 77.5\% \\ Producer Accuracy & 80.6\% & 92.3\% & \\ \hline \end{tabular}
\end{table}
Table 5: Confusion matrices showing the forest loss detection algorithm’s overall accuracy of the forest change detections and the areas of no detected change, stating user accuracy, and producer accuracy in Guatem (top) and Mato Grosso, Brazil (bottom), between 2022 and a median image composite of 2019. OA = overall accuracy, \(\kappa\) = kappa coefficient.