# A SAR-Based Index for Landscape Changes in African Savannas
## Materials and Methods
### Index of Landscape Change  
As described in the previous section, pixel-based image classifications based on SAR data can reveal smaller misclassifications due to the lack of spectral diversity. These can, of course, lead to false conclusions in multi-temporal approaches when wrongly classified pixels may appear as change in land-use or land cover. Furthermore, changes from one class into another within single pixels do not allow for implications on the state of the environment, nor do they reveal the consequences of these changes for man. For this reason, [PERSON] et al. proposed a weighted index which estimates the impact of LULC changes within larger aggregated units in terms of environmental integrity and human security [104]. It is uses expert-based weightings which determine its ecologic and social-economic value of each LULC class so that changes can directly be interpreted as a percentage of resource depletion for different sectors in the study area. This weighted natural resource depletion index (NRD\({}_{\text{w}}\)) was originally designed for very high-resolution data which needed to be aggregated at a larger level in order to overcome the limitations of pixel-based approaches for change detection. We transferred this concept on to the SAR data to compensate the deficiencies of our classification resulting from lack of spectral diversity, as reported in Section 2.3. The weights were defined by two regional experts with ecological and humanitarian background as shown in Table 5. They refer to environmental integrity (EnvInt) and human security (HumSec) which were then averaged to a mean value of abundant natural resources (NR) of each pixel. As our study addresses the human-related aspects of landscape change, we used a ratio of 0.35/0.65 for the calculation of NR to place emphasis on their socio-economic impact. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **2007** & **2008** & **2009** & **2010** & **2011** & **2015** & **2016** & **2017** \\ \hline Urban area & 100.0\% & 100.0\% & 100.0\% & 100.0\% & 98.5\% & 99.2\% & 100.0\% & 100.0\% \\ Bare soil & 82.6\% & 84.5\% & 84.7\% & 85.4\% & 82.1\% & 76.1\% & 79.3\% & 82.5\% \\ Bare rock & 97.3\% & 95.3\% & 99.3\% & 97.2\% & 94.1\% & 95.9\% & 94.0\% & 95.9\% \\ Grassland & 63.1\% & 61.4\% & 64.6\% & 62.8\% & 64.7\% & 57.8\% & 59.3\% & 57.6\% \\ Shrubland & 83.7\% & 84.8\% & 85.7\% & 82.3\% & 82.5\% & 80.5\% & 80.5\% & 78.4\% \\ Forest & 89.8\% & 82.3\% & 90.6\% & 92.9\% & 89.7\% & 84.2\% & 93.2\% & 87.3\% \\ \hline \end{tabular}
\end{table} 
Table 4: User’s accuracy for the texture-based classification of SAR images using the Random Forest classifier. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **2007** & **2008** & **2009** & **2010** & **2011** & **2015** & **2016** & **2017** \\ \hline Urban area & 89.3\% & 91.3\% & 90.7\% & 92.0\% & 89.3\% & 86.0\% & 90.7\% & 70.53\% \\ Bare soil & 92.0\% & 90.7\% & 92.0\% & 93.3\% & 92.0\% & 89.3\% & 86.7\% & 77.88\% \\ Bare rock & 96.0\% & 95.3\% & 96.0\% & 94.0\% & 96.0\% & 94.0\% & 93.3\% & 90.91\% \\ Grassland & 83.3\% & 80.7\% & 88.7\% & 83.3\% & 82.0\% & 94.0\% & 76.7\% & 82.23\% \\ Shrubland & 75.3\% & 70.7\% & 76.0\% & 77.3\% & 75.3\% & 68.7\% & 76.7\% & 82.64\% \\ Forest & 70.7\% & 71.3\% & 70.7\% & 70.0\% & 69.3\% & 67.3\% & 64.0\% & 66.92\% \\ \hline \end{tabular}
\end{table} 
Table 3: Producer’s accuracy for the texture-based classification of SAR images using the Random Forest classifier. \begin{table}
\begin{tabular}{c c c c} \hline
**Class** & **EnvInt 35\%** & **HumSec 65\%** & **NR 100\%** \\ \hline Urban area & 0.00 & 0.50 & 0.313 \\ Bare soil & 0.30 & 0.35 & 0.331 \\ Bare rock & 0.20 & 0.25 & 0.231 \\ Grassland & 0.50 & 0.45 & 0.469 \\ Shrubland & 0.70 & 0.75 & 0.731 \\ Forest & 0.95 & 0.90 & 0.919 \\ \hline \end{tabular}
\end{table} 
Table 5: Importance of the analyzed land-use and land cover classes on environmental integrity (EnvInt) and human security (HumSec) for the calculations of an integrated value of natural resources (NR).