# Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping
## 3 Results
### Results of Forest Disturbance Mapping in Peru  
The three different disturbance results from the \"S1 Only\" approach, the \"Optical Only\" approach, and the \"Union\" approach show very different results in terms of overall detected disturbed forest area. This is shown in Figure 7 and Table 2. The \"Union\" approach detects almost twice the area of the \"Optical Only\" approach. The overlapping area of the \"S1 Only\" and \"Optical Only\" disturbance maps (yellow areas in Figure 7) is only 70.88 ha, which explains why the \"Union\" disturbance area is significantly larger than in the two single sensor based disturbance maps. \begin{table}
\begin{tabular}{c|c c c c|c c} \hline \hline  & \multicolumn{2}{c}{**User Accuracy**} & \multicolumn{2}{c}{**Producer Accuracy**} & \multicolumn{2}{c}{**Overall Accuracy**} \\ \hline class & Peru & Gabon & Peru & Gabon & Peru & Gabon \\ \hline F & 0.944 & 0.947 & 0.968 & 0.994 & 0.930 & 0.988 \\ NF & 0.867 & 0.893 & 0.785 & 0.884 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Forest/Non-Forest mask validation Peru and Gabon. \begin{table}
\begin{tabular}{c c} \hline \hline
**Mapping Approach** & **Detected Area of Forest Disturbances in ha** \\ \hline S1 Only & 248.77 \\ Optical Only & 153.90 \\ S1/Optical Union & 331.79 \\ S1/Optical Intersect Area & 70.88 \\ Adjusted Area Estimate (Reference) & 225.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Area of detected forest disturbances at the Peru validation site for each mapping approach. Figure 7: Comparison of forest disturbance detection results in Peru showing the high complementarity of S-1 and optical data. Top left: “S1 Only” map; top right: “’Optical only” map; bottom left: Union of “S1 Only” and “Optical Only” maps (yellow areas are detected by both approaches); bottom right: enlarged area of “Union” map (black rectangle). We use two different approaches to estimate the final disturbance map accuracies. The first accuracy assessment approach is targeted to analyze detection accuracy by disturbance patch size. Disturbance patch size is a critical issue in forest disturbance mapping but it is seldom addressed in publications on forest monitoring. Many studies only map disturbances larger than 0.5 ha and thus only focus on deforestation and do not include small and patchy forest degradation. In our analysis, a disturbance patch is considered detected when \(>\) 10% of the disturbed patch area is mapped. The results for Peru are shown in Table 3; those for Gabon are shown in Table 4.  
The second accuracy assessment is based on the stratified random sampling approach described in detail in Section 2.4.4. Tables 5 and 6 provide summaries of the accuracy estimates. They include two analyses per test site. The first analysis also includes the non-forest areas, thus it reflects the accuracy of the map. The second analysis is only carried out for plots that are forest at the beginning of the disturbance detection window. Non-forest areas and all misclassifications of the FNF map are removed. This reflects the accuracy of the change detection method. Broadly speaking, the first analysis is a map validation, while the second analysis is a method validation. The validations are based on 575 points (including NF) and 385 points (only forest) for Peru, and 855 points (including NF) and 691 points (only forest) for Gabon. For Peru, we differentiated three classes, where F = undisturbed forest; D = disturbed forest; NF = non-forest at the beginning of the disturbance detection window. For Gabon, the disturbed class was split in two classes that were based on the area of disturbance plots: D\({}_{\text{large}}\) = disturbed forest area \(\geq\) 25 ha; D\({}_{\text{small}}\) = disturbed forest area \(<\) 25 ha. Large area disturbances are mostly related to new palm oil plantations. At the Peru subset, there are no disturbances larger than 25 ha, therefore no subdivision was made. The mapped area of the strata and their respective weights are derived from the \"Union\" disturbance map.