# A SAR-Based Index for Landscape Changes in African Savannas
## 4 Discussion
### Methods
were not possible due to security reasons but using optical data from Landsat grants for independency of the training and validation process from the actual image classification was possible. Also worth noting, optical data does not have to be complete or cloud free. As only the areas which consistently fulfilled the criteria listed in Table 2 throughout all images were used for possible locations of the randomized samples, we could flexibly deal with cloud coverage, burnt areas and missing scan lines in Landsat ETM+ products. Smaller misclassifications occurred between classes with similar response to the radar signal as shown in Tables 3 and 4. These could, however, be compensated by the creation of hexagon grid cells as a main unit of analysis. Each of them aggregates information of 1280 pixels and represents their percentages share as one value of natural resources. This reduces the effect of ambiguities at the transition between two LULC classes and facilitates both the visual inspection and interpretation of results. Detailed changes at borders between two classes are no longer visible in this approach but since the fact that savanna ecosystems often consist of a mosaic of continuous LULC this is neither possible nor considered as reasonable. Shifts in phenology could, however, been better handled with an investigation over a longer time span. The longer the time-series of images, the more stable is the approach as it more clearly reveals outliers along the investigated period. ESA's Sentinel-1 mission lays a solid foundation for multi-decade investigations as it was designed to continue the archived data of ERS and Envisat. All of them operate at C-band wavelengths and a comparable spatial resolution. One crucial point is the definition of investigated LULC classes and their corresponding contribution to both environmental integrity and human security (see Table 5). As this has to be done at the beginning of the study, changes in the numbers of investigated classes can, of course, strongly affect the later results. If interest is placed on the change of a certain type of LULC, the definition of classes should be appropriate and respectively balanced in this context. Also, the expert-based weights have large influence on the evaluation of the changes. It is therefore advisable to include the expertise of as many people as possible, preferably from different domains (local population, humanitarian units, authorities, and other stakeholders). This not only provides for a balanced assessment of weights but also prevents the abuse of weights to intentionally target desired results. As [PERSON] & [PERSON] argue, research on the impacts of refugee camps upon their environment is needed but cannot act as the only argument for repatriation in political debates [PERSON] & [PERSON] (2013). Lastly, the proposed variability can be a valuable measure for both the quality and the long-term information content of results. However, a way of normalization has to be performed when comparing index values along a temporal sequence. We used a rather basic model of change vectors as it met our requirements for the comparison of eight images. If longer or more detailed time-series are employed, the concept of variability has to be refined according to variation at different temporal scales--such as the differentiation between seasonal, inter-annual, and overall variation of a grid cell.