# Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping
## 4 Discussion
### Forest/Non-Forest Mask:
Producer accuracy of disturbances for the \"Optical Only\" result is a bit lower than for the GFW result, which is probably due to the very conservative thresholds selected in the assessments. When comparing the GFW result to the \"Union\" approach however, the producer accuracy of \"Union\" is much higher (0.71--Table 5) than that of the GFW data set (0.377--Table 7). This clearly shows the added value of including the additional S-1 data. Figure 8 provides a visual comparison of the \"Union\" mapping result and the GFW mapping result. For Gabon, a recent study compares the forest change estimates from 2000 to 2010 between national forest maps and the Global Forest Change (GFC) map from the University of Maryland [81]. The results indicate that net deforestation is not significantly different to 0, which suggests that the loss of forest is counter balanced by regeneration. The MMU of 1 ha used by GFC in the change analysis [81] strongly differs to that of our presented study, where we detect and validate forest disturbances at a MMU of 0.04 ha. At this spatial level, we also detect small forest disturbances that are typical of industrial selective logging. Selective logging is the most prominent forest disturbance type in Gabon, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Class** & **F** & **D** & **Total** & **Map Area [ha]** & **Weights** & \\ \hline F & 331 & 22 & 353 & 5805.32 & 0.963 & \\ D & 1 & 31 & 32 & 225.61 & 0.037 & \\ Total & 333 & 53 & 385 & 6030.93 & 1.000 & \\ \hline Class & F & D & Total & User & Producer & Overall \\ \hline F & 0.903 & 0.060 & 0.963 & 0.938 & 0.999 & 0.939 \\ D & 0.001 & 0.036 & 0.037 & 0.969 & 0.377 & \\ Total & 0.904 & 0.096 & 1.000 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Global Forest Watch changes of 2016 (version 1.6). Figure 8: Comparison of S1 and optical “Union” disturbance detections (**left**) and Global Forest Watch disturbance detections (**right**) (March 2016-end of December 2016). accounting for more than 60% of forest disturbances [64]. At a 1 ha MMU such small forest disturbance features cannot be detected and, therefore, also not included in the analysis. Despite or even because of the aforementioned shortcomings, S-1 and optical data are highly complementary. Combining the two data sets significantly increases the accuracy, especially the producer accuracy of disturbances and, as other studies have demonstrated, it also allows for a more rapid detection needed for near real-time alerting [3]. The results show that some forest disturbance patches are indeed detected by both sensor types and related mapping approaches. However, different types of disturbances are often only captured by only one sensor, which confirms the benefit of sensor combination. The contributions of optical and SAR data to the forest disturbance mapping success are variable and they depend on the land use and forest characteristics of a specific site. The dependencies are primarily based on type and typical height of forest (primary, secondary); weather and atmospheric conditions; topography; dominant LU types outside of the forest; change drivers (selective logging, plantations, agriculture, shifting cultivation, fire) and related size of disturbance plots; data availability (ascending, descending for SAR); density of the time series; and regrowth speed, and orientation of the satellite (especially S1) with respect to the disturbance site.