# Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping
## 4 Discussion  
### Forest/Non-Forest Mask:  
The benchmark FNF maps show good overall accuracies for both the Peru (93.0%) and the Gabon test site (98.8%), but the omission and commission errors for the non-forest class are larger than 10% at both test sites. The overall accuracies for Gabon are similar to those reported by other studies: 98.1% for the national forest map of year 2000 and 95.9% for the Global Forest Watch dataset of year 2000 evaluated at a 1 ha minimum mapping unit and \(>30\%\) tree cover [81]. The accuracies are generally lower at the Peru site, with an omission error of 21.5% for the non-forest class. The lower accuracies in Peru seem to be related to a lower total percentage of forest area and a higher complexity of land use at the test site. Non-forest areas at the Peru test site make up 20% of the total area, which is a much higher percentage than at the Gabon test site with 5.6%. Large parts of the forested area are composed of secondary forests with rapid regrowth rates and the overall percentage of forest changes is higher than in Gabon. The primary cause of forest loss in the Loret and San Martin region of Peru where the test site is located is clearing for agriculture and pastures and large-scale industrial oil-palm plantations [2]. A rapid regrowth with shrubs, bushes, and low height trees is very common soon after agricultural clearing. Even from visual interpretation of VHR imagery it is often difficult to decide if an area is already forest regrowth or still non-forest. Non-forest areas are mostly characterized by different forms of agricultural use, but also by shrublands and grasslands. The situation is different in Gabon, where 88.5% of the entire country is covered by forests and large parts of the forest are still primary forests [81]. Large parts of the test site in Gabon are characterized by homogeneous forests. Non-forest areas are not distributed evenly at the Gabon test site, but they are \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{10}{c}{Gabon 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 & Union & Only & Only & Union \\ \hline F & 0.826 & 0.901 & 0.941 & 0.989 & 0.985 & 0.987 & 0.837 & 0.892 & 0.930 \\ D\({}_{\text{large}}\) & 0.982 & 0.925 & 0.929 & 0.316 & 0.739 & 0.904 & & & \\ D\({}_{\text{small}}\) & 0.778 & 0.352 & 0.469 & 0.388 & 0.150 & 0.392 & & & \\ NF & 0.942 & 0.942 & 0.942 & 0.497 & 0.580 & 0.593 & & & \\ \hline \multicolumn{10}{c}{Gabon 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 & 0.882 & 0.935 & 0.977 & 0.985 & 0.966 & 0.973 & 0.883 & 0.912 & 0.955 \\ D\({}_{\text{large}}\) & 1.000 & 0.961 & 0.963 & 0.376 & 0.795 & 0.928 & & & \\ D\({}_{\text{small}}\) & 0.800 & 0.528 & 0.613 & 0.610 & 0.396 & 0.675 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Disturbance map validation for Gabon.