# Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping
## 4 Discussion
### Forest/Non-Forest Mask:
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{10}{c}{Peru disturbance map validation including NF class:} \\ \hline  & \multicolumn{3}{c}{User Accuracy} & \multicolumn{3}{c}{Producer Accuracy} & \multicolumn{3}{c}{Overall Accuracy} \\ \hline class & S1 & Optical & Union & S1 Only & Optical & Union & S1 & Optical & Union \\  & Only & Only & & Only & Only & Only & Only & Only & Union \\ \hline F & 0.821 & 0.773 & 0.852 & 0.949 & 0.946 & 0.950 & 0.819 & 0.788 & 0.842 \\ D & 0.644 & 0.788 & 0.640 & 0.461 & 0.382 & 0.710 & & & \\ NF & 0.842 & 0.842 & 0.842 & 0.590 & 0.569 & 0.602 & & & \\ \hline \multicolumn{10}{c}{Peru disturbance map validation only within the forest area (method validation):} \\ \hline  & \multicolumn{3}{c}{User Accuracy} & \multicolumn{3}{c}{Producer Accuracy} & \multicolumn{3}{c}{Overall Accuracy} \\ \hline class & S1 & Optical & Union & S1 Only & Optical & Union & S1 & Optical & Union \\  & Only & Only & & Only & Only & Only & Only & Union \\ \hline F & 0.957 & 0.925 & 0.985 & 0.998 & 0.998 & 0.998 & 0.956 & 0.926 & 0.983 \\ D & 0.950 & 0.963 & 0.941 & 0.459 & 0.332 & 0.710 & & & \\ \hline \hline \end{concentrated around villages and oil palm plantations in the South. These site characteristics largely explain the higher FNF map accuracies at the Gabon site. The different methodologies that are used to derive the FNF maps could also explain some of the observed errors. The Peru FNF map is based on 13 images spanning 12 months. Some changes that occurred towards the end of the time window might not have been accounted for with the applied majority approach, even if a temporal weighting is applied. **Forest Disturbance Detection:**  
When interpreting the forest disturbance results, it has to be considered that forest disturbances are quite different in character at both of the test sites. Forest disturbances at the Peru test site are characterized by the logging of mostly secondary forests for subsequent agricultural use, which is in-line with other findings [2]. There is almost no selective logging of individual trees, but the size of forest disturbance areas is mostly small (60% are smaller than 0.5 ha). At the Gabon site, we find three different disturbance types: large-area deforestation for oil palm plantations in the South, small scale logging for subsequent agricultural/urban use near villages, and a large amount of industrial selective logging and logging roads in the forest concession areas. Gabon contributes 4.7 \(\pm\) 0.9% of total forest loss in the Congo basin [64]. Selective logging is by far the most prominent forest disturbance type in Gabon, which is different to the other Congo Basin countries, where small scale clearing for agriculture is the most important driver of forest loss [64]. In the final disturbance map of the combined approach, 85% of the detected forest disturbance areas are smaller than 0.2 ha and 94% are smaller than 0.5 ha. 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 that are larger than 0.09 ha [8] to 1 ha [81] and, thus, only focus on deforestation and do not include small and patchy forest degradation. In terms of area changes, a large deforestation for oil-palm plantations contributes approximately 55% of the total forest change area. When comparing the accuracies based on the stratified sampling of the three approaches in Peru-\"S1 Only\", \"Optical Only\", and \"Union\"-we find highest overall accuracies for the \"Union\" approach (0.84), but differences in overall accuracy are small (\"S1 Only\": 0.82, \"Optical Only\": 0.79). When comparing SAR and optical results for the forest disturbance class, the \"Optical Only\" approach shows higher user accuracy and lower producer accuracy than the \"S1 Only\" approach. However, the producer accuracy is lower than 0.5 for both approaches showing high omission errors for both data types.