## 3 Methods
### Supervised Land Cover Classification  
Land cover across the Reserve was classified using the process depicted in Figure 2. We first randomly partitioned our supervised data into two categories, a training set and a validation set. Polygons representing approximately 80% of the area for each class were included in the training set (Table 2), while polygons accounting for the remaining 20% were reserved as an independent validation set. Polygons representing sites that were visited during fieldwork were prioritized to be included in the validation data set. Supervised data added via visual interpretation of aerial imagery but not verified via ground-truthing were only used as validation data in cases where the field verified polygons did not reach the 20% threshold. This was done to ensure that validation used field-verified data whenever possible. In the final classified map, only the upland forest class contained polygons that were used as validation but were not visited in the field. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & \multicolumn{6}{c}{**Training Polygons**} & \multicolumn{6}{c}{**Validation Polygons**} \\ \cline{2-11}  & **FD** & **VI** & **Total** & **Pixels** & \
\begin{tabular}{c} **Area** \\ **(m2)** \\ \end{tabular} & **FD** & **VI** & **Total** & **Pixels** &
\begin{tabular}{c} **Area** \\ **(m2)** \\ \end{tabular} \\ \hline \hline Barren & 18 & 4 & 22 & 763 & 76,300 & 5 & 0 & 5 & 159 & 15,900 \\ Riverine forest & 6 & 13 & 19 & 2635 & 263,500 & 4 & 0 & 4 & 133 & 13,300 \\ Upland forest & 0 & 29 & 29 & 4161 & 416,100 & 5 & 3 & 8 & 727 & 72,700 \\ Grass _Acacia_ & 5 & 4 & 9 & 250 & 25,000 & 2 & 0 & 2 & 50 & 5000 \\ Grass _Balanites_ & 7 & 8 & 15 & 7303 & 730,300 & 3 & 0 & 3 & 1190 & 119,000 \\ Tall grass & 25 & 0 & 25 & 5462 & 546,200 & 6 & 0 & 6 & 959 & 95,900 \\ Medium grass & 30 & 0 & 30 & 2311 & 231,100 & 7 & 0 & 7 & 485 & 48,500 \\ Short grass & 18 & 0 & 18 & 1440 & 144,000 & 4 & 0 & 4 & 321 & 32,100 \\ Shrub & 18 & 7 & 25 & 1668 & 166,800 & 6 & 0 & 6 & 369 & 36,900 \\ Water & 4 & 20 & 24 & 799 & 79,900 & 5 & 0 & 5 & 170 & 17,000 \\ Emergent wetland & 1 & 11 & 12 & 1371 & 137,100 & 3 & 0 & 3 & 141 & 14,100 \\ Wet meadow & 4 & 14 & 18 & 1675 & 167,500 & 4 & 0 & 4 & 176 & 17,600 \\ \hline Grand Total & 136 & 110 & 246 & 29,838 & 2,983,800 & 54 & 3 & 57 & 4880 & 488,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of supervised data. Field data (FD) polygons were visited in research vehicles (ground-truthed). 190 of the original 233 FD polygons were included in the final training dataset. Polygons added via visual interpretation (VI) were not ground-truthed, but rather were identified via visual interpretation of remotely sensed imagery (PASLAR-2, Sentinel-1, and Sentinel-2) only. Figure 1: Great care had to be taken in delineating supervised data (polygons) to account for spatial misalignment of data collected by different sensors. Here, a patch of _Euclea_ shrubs is shown using **(a)** Sentinel-2 band 4 (image captured 11 July 2018) and **(b)** Sentinel-1 cross-polarization (VH; image captured 13 July 2018). We aimed to limit the polygons to pixels where the patch, or feature, of interest, as shown by each sensor, overlapped. The training dataset and imagery stack were used to predict land cover for each pixel in the study area using the machine learning algorithm Random Forests [40; 41] in R version 3.6.1 [42]. The training data were selected from the training data polygons by selecting 100 random pixels from each land cover class. Random Forests uses both a random sample of the training data and a random subset of predictors (image bands) to create a decision tree that best classifies the data. This is repeated multiple times until a \"forest\" of decision trees is generated. Each decision tree generates a \"vote\" for the most likely land cover class for the given pixel, and the pixel is assigned to whichever land cover class receives the most votes. We used 500 trees in our classifier and used the default node size of one.