# A SAR-Based Index for Landscape Changes in African Savannas
## Materials and Methods
### Data and Pre-Processing
#### 2.2.2 Pre-Processing and Collection of Samples  
ALOS data was obtained as Level 1.1 products in Slant Range Complex format [85; 86]. Pre-processing included radiometric calibration to radar brightness (Beta Naught, \(\beta^{\circ}\)[87]), multi-looking (n = 2), terrain flattening to normalize radiometric effects caused by different incidence angles (Flattened Gamma Naught, \(\gamma^{\circ}\)[88]) and Range-Doppler terrain correction to adjust topographic distortions using a digital elevation model (1 Arc-Second SRTM) [89]. All rasters were resampled to a common ground resolution of 30 m. No speckle filtering was applied in order to conserve image texture as accuracies of classifications based on SAR texture alone are reported to decrease when speckle was removed [90; 91]. In order to derive additional information layers we derived image textures based on the concept of Grey-Level Co-Occurrence Matrix (GLCM) [92] consisting of Cluster Prominence, Cluster Shade, Correlation, Difference Of Entropies, Difference Of Variances, Energy, Entropy, Grey-Level Nonuniformity, HT10, Haralick Correlation, High Grey-Level Run Emphasis, IC1, IC2, Inertia, Inverse Difference Moment, Long Run Low Grey-Level Emphasis, Low Grey-Level Run Emphasis, Mean, Run Length Non-uniformity, Run Percentage, Short Run High Grey-Level Emphasis, Short Run Low Grey-Level Emphasis, Sum Entropy, Sum Variance, and Variance. Each of these 25 textures was calculated for kernels of 3, 9, and 15 pixels in order to extract patterns emerging at different spatial scales, leading to a total of 75 SAR texture layers per analyzed year. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Year/\(\Delta\)t**} & \multicolumn{1}{c}{**Date**} & \multicolumn{1}{c}{**Sensor**} & \multicolumn{1}{c}{**Comment**} \\ \hline
**2007**/18 days & 24 December 2006 & ALOS PALSAR & \multirow{2}{*}{SLC-off} \\  & 11 January 2007 & Landsat ETM+ & \\ \hline
**2008**/2 days & 27 December 2007 & ALOS PALSAR & \multirow{2}{*}{SLC-off} \\  & 29 December 2007 & Landsat ETM+ & \\ \hline
**2009**/18 days & 29 December 2008 & ALOS PALSAR & \multirow{2}{*}{SLC-off} \\  & 16 January 2009 & Landsat ETM+ & \\ \hline
**2010**/30 days & 2 December 2009 & Landsat ETM+ & \\  & 1 January 2010 & ALOS PALSAR & \\ \hline
**2011**/20 days & 19 February 2011 & ALOS PALSAR & \multirow{2}{*}{SLC-off} \\  & 11 March 2011 & Landsat ETM+ & \\ \hline
**2015**/0 days & 23 January 2015 & ALOS-2 & \multirow{2}{*}{} \\  & 23 January 2015 & Landsat OLI/TIRS & \\ \hline
**2016**/6 days & 22 January 2016 & ALOS-2 & \multirow{2}{*}{} \\  & 28 January 2016 & Landsat OLI/TIRS & \\ \hline
**2017**/15 days & 2 March 2017 & ALOS-2 & \multirow{2}{*}{} \\  & 15 February 2017 & Landsat OLI/TIRS & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Satellite imagery used in this study. Landsat data was obtained as Level-1T (terrain corrected) products. All rasters were radiometrically corrected by applying conversion top of atmosphere (TOA) reflectance and dark object subtraction (DOS) [93; 94]. Six LULC classes were defined for the analysis: (1) urban areas, (2) bare soil, (3) bare rock, (4) grassland, (5) shrubland, and (6) forest. We did not include water as a separate class since the study area does not feature any permanent water bodies and the few remaining temporary flooded areas are covered by the forests of Wadi Aouada. Each class is represented by training areas which were automatically derived from the Landsat scenes. We utilized indices and threshold values from several studies to define areas which characterize the corresponding class to a best possible degree in each scene. These are listed in Table 2. The selection of training samples underlies two steps. First, representative areas for every class are derived in each year observed (see Table 1). Areas which are assigned to the same class throughout all scenes were then considered as stable over time and transparent according to the corresponding LULC.