## 3 Methods
### Supervised Land Cover Classification
The Random Forests classifier was deemed optimal for this study based on its high classification accuracy and relatively low processing time. Additional benefits of Random Forests include its insensitivity to missing data [29], such as pixels obscured by cloud cover [31] and nonpredictive input data, its capability for classifying datasets with many variables and relatively few training data [29; 41], and the fact that it is easy to use [40] and allows for parallel processing [41]. Figure 2: An overview of the land cover classification process. We reserved 20% of the polygons from the supervised dataset for validation, prioritizing field-verified sites. These validation polygons were not used in creating the Random Forests classifier. The other 80% of the supervised data and the multi-sensor composite stack imagery were input into a Random Forests classifier. The validation data were then used to construct a confusion matrix to assess accuracy. Visual inspection of the resultant land cover map and the confusion matrix informed subsequent refinement, addition, and deletion of supervised data and combination of classes. After classification, the validation data were compared to the predictions of Random Forests to assess accuracy. The overall accuracy is the total number of correctly identified validation pixels divided by the total number of validation pixels. Within each land cover type, the producer's accuracy is the number of pixels correctly identified as the given land cover type divided by the total number of validation pixels of that class. In other words, we determined the percentage of pixels in a given class that were correctly identified. As producer's accuracy increases, omission error decreases. The user's accuracy is derived by dividing the number of pixels correctly identified as a given land cover type by the total number of pixels assigned to that class, whether they were correctly identified or not. In other words, for each given class, we determined the probability that a pixel identified as that class truly does belong to that class. User's accuracy, also known as reliability, increases as commission error decreases. Results are presented in an error, or confusion, matrix (Table 3). Our mapping goal was to achieve a minimum of 75% producer's and user's accuracies for each land cover type and an overall accuracy higher than 75%. Based on the resultant map and confusion matrix, additional training polygons were added or removed via visual interpretation of high-resolution satellite imagery. A few field data had to be removed as the area of the land cover was too small to cover the minimum mapping area. Field-verified polygons that had been mapped using aerial imagery to define boundaries had to be buffered inward to avoid having mixed pixels within the scale of our 10 m resolution imagery. Additional riverine and upland forest polygon classes had to be added with visual interpretation of aerial imagery. After revision, data were once again assigned to training or validation data sets (prioritizing field-verified polygons for validation), and the Random Forests algorithm was run again. We aggregated the _Croton_ shrub and _Euclea_ shrub classes into a single shrub class. Between iterations of Random Forests, if a class received additional training data (i.e., from visual interpretation), then the polygons in that class were once again randomly split. Therefore, for the classes requiring additional training polygons, validation data in the final classification may have been used in a prior run as training data. The most important classes of grass height were not affected, as we could not use visual interpretation. In the final classification iteration, we utilized a new image stack with different dates designed to avoid confusion with the burned areas. Given the change in predictor variables, we believe the final run can be considered independent of previous runs.