# Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping
## 2 Material and Methods
### Main Processing
#### Evaluation Concept for Disturbance Mapping  
We use two different approaches to validate the disturbance maps: a plot-based approach based on digitized disturbance patches and an area-based approach based on stratified random sample points. Both validation approaches are meaningful for different assessments. The plot-based approach is viable for example in an alert system, where it is more important to pinpoint the location, where something is happening than delineating the exact outline. The area-based approach is needed e.g., in the frame of REDD reporting, where the disturbed forest area is needed as activity data. For the plot-based approach, we calculate the detected area percentage for each reference plot. A plot is considered to be correctly detected if a user defined percentage of its area is detected. For Peru and Gabon, we use a 10% threshold. If a percentage of the plot area higher than 10% is detected by the disturbance mask, the plot is considered correctly identified. For Peru, the 148 reference disturbance plots cover an area of roughly 108 ha. In terms of plot size, only 34 of the 142 plots are larger than 1 ha and 35 are smaller than 0.2 ha. For Gabon, the 362 reference disturbance plots cover an area of 6420 ha. Figure 4: Illustration of the S-1 C-band SAR shadowing effect at the border between forests and deforested area. Left: schematic illustration, middle: 10 m C-band S-1 SAR image before forest disturbance, right: 10 m C-band S-1 SAR image after forest disturbance: the arrows point at the new shadows. In terms of plot size, 51 plots are larger than 1 ha and 166 are smaller than 0.2 ha. These patches are an arbitrary subset based on available VHR data and they do not necessarily represent the whole test site. The area-based approach allows for the computation of user, producer, and overall accuracy based on the representative areas of reference plots in each stratum (non-forest, undisturbed forest, and disturbed forest). We performed a visual interpretation of HR and VHR (RapidEye, Worldview-2, Spot-6) data in order to obtain reliable ground truth data for this assessment. For Peru, we used RapidEye data from 14 August 2016, ArcGIS Basemap (WorldView-2) data from 20.06.2015, Google Earth imagery from 2015 and 2018, and all Sentinel-2 data from 10.01.2016 to 25.12.2016. The area-based validation was carried out at a sub-region covering 88.33 km\({}^{2}\) for which RapidEye data was available. For Gabon, we visually interpreted a combination of RapidEye, WorldView and Spot-6 data and the full Sentinel-2 time series for the time window 12/2015-4/2017. The validation was carried out at the full extent. The overall number of plots and the plots per stratum are based on recommendations for land cover accuracy estimation [79; 80]. The disturbance result from the \"Union\" combination was used to estimate the map areas per stratum. For Peru, we sampled a total of 575 reference points within a subset of the map (see Figure 6) with 75 sample points for the stratum \"D\" (disturbed forest), 114 sample points for stratum \"NF\" (non-forest) and 384 sample points for stratum \"F\" (undisturbed forest). The respective area weights are provided in Table 10. For Gabon, we sampled a total of 855 reference points within 14 subset areas of the map, for which also VHR imagery was available. The 855 total plots are composed of 112 sample points for stratum \"Dlarge\" (disturbed forest areas \(\geq\) 25 ha), 81 sample points stratum \"Dsmall\" (disturbed forest areas \(<\) 25 ha), 121 sample points for stratum \"NF\" (non-forest) and 541 sample points for stratum \"F\" (undisturbed forest). Table 11 provides the respective area weights. Figure 5: Forest/non-forest (FNF) evaluation in Peru: example of the systematic random sampling design. For the area-based accuracy assessment, we calculate the confusion matrices for the three, respectively, four, different strata and for all sensor specific disturbance maps--i.e., \"S1 only\", \"Optical only\", and \"Union\".