# Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping
## 2 Material and Methods
### Main Processing
#### 2.4.2 Disturbance Detection  
Optical data  
Both of the study areas are located in the humid tropics, where the seasonality of the spectral signal of forests is much less pronounced than for dry tropical forests or temperate forests. Therefore, the applied change detection method does not need to account for seasonality as in the dry tropics [77],  
Figure 3: Overall workflow. and we can apply straightforward thresholding approaches. Single spectral bands usually show higher variance in time series than indices, which is why indices are commonly used as input data to times series analyses. Our initial tests revealed that NDVI and NDII/ (Normalized Difference Infrared Index with Landsat band 7: (band4-band7) / (band4 + band7)) show the best overall accuracies for forest disturbance detection. However, the Landsat 8 surface reflectance data in Peru and Gabon showed artifacts in the optical bands that, according to the USGS, were caused by the Global Climate Modeling (GCM) grid's aerosol values not being correctly interpolated to the Landsat grid. We use the NDII7 index as input to disturbance mapping with Landsat 8 instead of NDVI to bypass this radiometric issue, as NDII7 does not include the optical bands. The NDVI (for Sentinel-2) and NDII7 (for Landsat 8) are individually calculated for all pre-processed and radiometrically adjusted images for the time window 10.03.2016 to 31.12.2016 at the Peruvian test site and for the time window 15.12.2015 to 08.04.2017 at the Gabonese test site. An additional eight weeks are also processed for the optical data disturbance confirmation. All of the pixels with NDVI and NDII7 values exceeding the thresholds indicate a forest disturbance and are therefore named \"disturbance indicator\" (see Figure 3). The forest disturbance detection workflow is described in [52] and follows recommendations from other operational approaches [8]. It is based on the temporal behavior of index values and a thresholding approach. From reference data plots, we derive a threshold value for the NDVI and the NDII7, which separates the forested and non-forested areas. When an index value falls below the defined threshold, it is considered to be a preliminary potential forest disturbance detection. At this point, it is still unknown if this detection represents a real forest disturbance, or just remnants of an undetected cloud, a radiometric artifact or simply a false classification in the benchmark FNF map. Therefore, single outlier detection is only considered as a \"possible disturbance\". We then developed a confirmation tool to analyze these preliminary disturbance detections in time. Subsequent observations are used to either confirm the disturbance detection if their spectral values are also below the defined threshold or to reject the detection if spectral values of subsequent images are above the valid threshold. If three consecutive outliers are detected, the forest disturbance is confirmed and added to the final disturbed forest output file (referred to as \"Optical Only\" in subsequent text). The final optical data based forest disturbance file is a 10 m raster product that sums all of the confirmed changes within the forest area for the observation period. A minimum mapping unit of 0.04 ha is applied. The near real time forest disturbances detection method based on SAR data has been described in detail in [47] and was successfully tested in Peru. Classical methods for SAR based forest change detection are based on the hypothesis that the radar backscatter decreases when forest disturbances occur. However, backscatter does not necessarily decrease, because soil moisture and/or trees that remain on the ground can also lead to an increase of the radar backscatter [46]. To overcome this problem, the new method [47] is based on the detection of radar shadowing. Shadowing occurs in radar images because of the particular side-looking viewing geometry of radar systems. A shadow in a radar image is an area that cannot be reached by any radar pulse.