# Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping
## 4 Discussion
### Forest/Non-Forest Mask:
For the \"Union\" approach, the producer accuracy increases up to 0.71 suggesting that SAR and optical map results are highly complementary. This is also confirmed by the area statistics. Of the 331.79 ha of disturbance area in the \"Union\" disturbance map, only 42.70 ha are detected by both \"S1 Only\" and the \"Optical Only\" approach. This is only about one-fifth of the recalculated adjusted disturbed forest area (225.61 ha). The remaining area is only detected by one of the two approaches. The area statistics also show that the NDVI and NDII/7 thresholds used for the optical approach in Peru are slightly too conservative. Only 153.90 ha are detected as disturbed, which is 30% less than the calculated adjusted area. The producer accuracy of the NF class in Peru is quite low (0.6), suggesting that parts of the forest area in the benchmark FNF mask are indeed not tree-covered at the beginning of the change detection window. The accuracies at this subset are lower than the overall accuracy of the FNF mask for the entire Peru test site. This can be explained by the higher complexity of land use at the subset. It is also partly a problem of the different minimum mapping units used for generating and validating the FNF mask and the disturbance detection. Misclassifications in the benchmark FNF map can make up a large part of the overall error in disturbance detection. This error can be more important than the errors related to the change detection methodology, since the overall area percentage of forest disturbances in one year is only very small. This highlights the importance of using accurate and up-to-date benchmark forest masks in operational forest disturbance monitoring. When we remove the NF class in the second validation approach, the overall and the user accuracies improve considerably. For the \"S1 Only\" approach, the user accuracy of the disturbed forest increases from 0.64 to 0.95, as many of detected changes turned out to have been non-forest already at the start of the change detection period. These areas are vegetated and often used for agriculture. In the optical images their spectral signals are quite similar to those of forest and are thus misclassified as forest and no change is detected. However, the S1 approach detects a SAR shadow that is related to an existing forest border. In Peru, the S1 disturbance detection starts with the same date as the optical approach. A shadow is detected in the first S1 image of the time series and, thus, a change is written to the change map, even though the shadow was already present before. In Gabon, of the 15,723 ha detected as disturbed forest in the \"Union\" approach, only 5241 ha are detected by both of the approaches. However, in Gabon the \"Optical Only\" approach detects a much larger disturbed area than the \"S1 Only\" approach--13,763 ha vs. 7201 ha. This effect is mostly caused by the large deforestation area for a palm oil plantation. The applied S1 disturbance detection approach is not suitable for such large disturbance areas, as the detected shadows on both sides are too far apart to reconstruct the disturbance area with a convex envelope boundary operator. Also very small or very narrow patches are difficult to detect with the S1 approach used in this study. If the gap is too small, the distinct shadow is lost and detection fails. Also thin and elongated disturbance patches stretching in the east-west direction are difficult to detect, as the sensor is side-looking from east or west due to its near-polar orbit. It can also be expected, that due to their smaller pixel size, the S-2 results are better than the results only based on Landsat data. Shortcomings in all optical approaches are problems with regrowth and the selection of appropriate thresholds. Regrowth and cloud cover are the main limitations of optical disturbance monitoring in the tropics, as discussed in the Introduction.