# A SAR-Based Index for Landscape Changes in African Savannas
## 4 Discussion
### Methods  
On the technical side the following points can be highlighted. As the first findings made by [PERSON] et al. (2013) indicated, the application of the NRDw concept from [PERSON] et al. (2014) is transferable to radar imagery of medium spatial resolution. This study was able to provide a framework more robust towards smaller misclassifications as they often occur in SAR data which is still flexible enough to be based on different sensors or to be transferred to other regions. The results created reflect different aspects of landscape changes: namely environmental integrity and human security which can be weighted according to the expertise and the needs of the user. The final maps include information on both spatial and temporal variation of landscape changes but are still easy to read and interpret. These maps highlight regions which require special attention and allow a more differentiated dealing with the outcomes. ALOS PALSAR proved to be suitable for long-term landscape monitoring due to its long wavelength interaction with volume scatterers for the discrimination of different types of vegetation. It would have been interesting to observe how the study area changed annually in this perspective between 2011 and 2015 but no accessible L-band data is available for this period. It is however expected that other SAR sensors with similar archives such as ERS, Radarsat, and Envisat bear the same potential, especially at the temporal scale. Regarding seasonality, additional data at the middle or end of the rainy season would surely have substantially increased the validity of the approach as it could have been used to depict a clearer image of each year's resources. However, this data was not available for all the investigated years. Future approaches surely should consider using a combination of several scenes at crucial points in the study area's phenology which can be determined by the variation of NDVI, for example ([PERSON] et al., 2012; [PERSON] et al., 2013; [PERSON] et al., 2014). This would also decrease the weight of textural information in favor of additional seasonal effects. However, the used textures were able to discriminate all selected LULC classes to a sufficient degree. As Tables 3 and 4 demonstrate, Grassland was the class with lowest accuracies due to its indifferent degree of coverage in the study area and comparably low interaction with the L-band signal. Using these large numbers of input rasters surely requires classifiers based on machine-learning which extract the most valuable information. The random forest classifier performed efficient but other approaches, such as support vector machines (SVM) or artificial neuronal networks (ANN), can be employed as well for these kinds of analyses, especially when phenological variation is additionally introduced as suggested above ([PERSON] et al., 2013; [PERSON] et al., 2014). The automatic and literature-based selection of training areas proved to increase the study's credibility as it reduces the possible bias of manual sampling collection. In our case, field investigations \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **2007–2008** & **2008–2009** & **2009–2010** & **2010–2011** & **2011–2015** & **2015–2016** & **2016–2017** & **r**(NRDw) \\ \hline
**NRDw** & 3.36 & \(-0.66\) & \(0.28\) & \(-3.14\) & \(-7.55\) & \(6.80\) & \(2.833\) & \(1.00\) \\
**Aprec** & \(-37.7\) & \(-55.7\) & \(97.7\) & \(-49.9\) & \(-74.6\) & \(15.8\) & \(-\)* & \(0.47\) \\
**SAR d** & 12 & 12 & 18 & 88 & 1078 ** & 8 & \(12\) & \(-0.61\) \\ \hline \end{tabular}  
* Changes in precipitation could not be assessed between 2016 and 2017 as no all-season records were available at the time of the study. ** In order to not distort the statistics, the temporal baseline of four years was excluded from the calculation of r.  
\end{table} 
Table 7: NRDw net values for the investigated time-steps compared to trends in precipitation (Aprec) and temporal baselines of the used SAR images (SAR d).