# Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping
## 4 Discussion
### Forest/Non-Forest Mask:
A recent study on forest loss monitoring from 2000-2014 in the Congo Basin with Landsat data showed that in Gabon on average only 1.1 cloud-free observations were available per year [64]. Regrowth cannot properly be detected with such a sparse time series. Several forest change detection maps and accuracy analyses for both Peru and Gabon have been produced by other research teams in recent years. Most of them are based on Landsat data and validations are mostly carried out at Landsat pixel level, i.e., \(30\times 30\) m [8; 64]. In Peru, a Landsat-based national analysis of forest cover loss from 2000 to 2010 revealed that the majority of gross forest cover loss (92.2%) was attributed to clearing for agriculture and tree plantations. The rest was due to natural disturbance, mostly represented by flooding and river meandering (6.0%), and fires (1.5%). The classification was based on training data derived primarily from Landsat composites and forest cover loss was mapped at 30 m Landsat pixel scale. Validation was performed while using a point-based sampling design at Landsat pixel level (\(30\times 30\) m) [2]. The pixels are visually analyzed for forest cover loss while using Landsat (2000) and RapidEye (2011) data at 5 m spatial resolution. The reported producer accuracy for forest loss is 75.4%. This is significantly higher than our \"Optical Only\" producer accuracy at the Peru test site of 38.2% and similar to the combined map producer accuracy of 71.0%. We assume that the observed differences are primarily related to sampling unit size-\(30\times 30\) m for the analysis vs. \(10\times 10\) m in our study-and the complexity of land use at our test site subset. The reference plot area we use for the validation is only 11.11% of that used in [2] and our reference data set includes very small disturbances (0.01 ha) that are neglected in the other study. Peru was also one of the first countries used to demonstrate and validate the near real-time GFW HTFA (Global Forest Watch--Humid Tropical Forest Alerts) methodology [8]. Detected forest loss was grouped by patch size: Loss detection consisting of a single Landsat pixel (approximately 0.1 ha) totaled only 4% of total detected loss, 33% of detected loss patches were less than one hectare, and 63% were larger or equal one hectare in size. The reported user accuracies on a Landsat-pixel level of \(30\times 30\) m are between 86.5%, when including boundary pixels, and 96.2% with boundary pixels removed [8]. Producer accuracies are not provided in [8], but most of the omitted samples were from tree cover loss patches \(<10\) ha (= less than 100 Landsat pixels). For a better comparison between our results and the GFW dataset we also derived mapping accuracies for the GFW dataset for the exact same validation plots and time frame at the Peru test site. The dataset is based on the GFW version 1.6 ([[https://eartheenginepartners.appspot.com/science-2013-global-forest/download_v1.6.html](https://eartheenginepartners.appspot.com/science-2013-global-forest/download_v1.6.html)]([https://eartheenginepartners.appspot.com/science-2013-global-forest/download_v1.6.html](https://eartheenginepartners.appspot.com/science-2013-global-forest/download_v1.6.html))) and includes all changes of 2016 (\"lossyear\" = 2016) clipped with our FNF mask of March 2016. Thus, the time frame for disturbance detections is identical to the observation period we used in this study. Therefore, the results can be directly compared. For geometric congruency, we also performed a geometric adjustment to the S-2 geometry with the same parameters as used for the adjustment of the original Landsat 8 data. Table 7 shows the GFW validation results. The user accuracies of disturbances are quite comparable between our approaches (\"S1 Only\": 0.95, \"Optical Only\": 0.963, \"Union\": 0.941 from Table 5) and GFW (0.969 from Table 7), especially the \"Optical Only\" result is very similar, which makes sense, as GFW is also a result based on optical data. The same is true for the overall accuracy values.