## 4 Results  
We combined a 10 m resolution imagery stack (dual date PALSAR-2, dual date Sentinel-1, and single date Sentinel-2) with training and validation data to assign land cover of the Masai Mara National Reserve using a Random Forests classifier. The resultant map (Figure 3) had an overall accuracy of 86%. The producer's accuracies for individual land cover classes ranged from 75% to 100% and the user's accuracies ranged from 66% to 100% (Table 3). This map will be made publicly and freely available as a Tagged Image File Format, compatible with ArcGIS and QGIS, via the Michigan Tech Research Institute (Ann Arbor, MI, USA) website. Recall that the producer's accuracy is the likelihood with which a validation pixel for a given class was correctly classified, whereas the user's accuracy is the likelihood that a pixel assigned to a given class truly represents that class. The highest class-level accuracy was obtained for water, such as permanent water holes and rivers, which had producer's and user's accuracies of 99% and100%, respectively. Most of the observed confusion occurred between riverine forest, upland forest, shrub, and grass _Acacia_, but this error was not substantial; this confusion increased class-level omission error by a maximum of 21% (riverine forest) and commission error by a maximum of 22% (shrub). Not surprisingly, medium grass had the lowest user's accuracy (82%) of the grass heights due to confusion between tall and short grasses. That is, slightly more confusion occurred between tall and medium grass and between medium and short grass, than between tall and short grass. The three grass height land covers were all identified with accuracies of 82% or higher, with accuracies as high as 85% (user's) and 88% (producer's) obtained (Table 3). Variable importance of our image bands was quantified using the mean decrease in accuracy metric (Figure 4) [41]. We looked at the importance of all the image bands to the overall classification, and we also looked at the importance of bands to classifying the three grass height land cover types. Values represent the loss in out-of-bag classification accuracy for each input band if that band had been excluded or permutated. Cross-polarized backscatter from L-band PALSAR-2 collected on 13 July 2018 was the most important band for the full classification as well as for the grass classes individually. The cross-polarized component of radar backscatter is typically due to significant volume scattering and  
Figure 3: Land cover map of the Masai Mara National Reserve, with an overall accuracy of 86%. is especially sensitive to biomass, so the importance of the variable for discriminating grass height, shrub, and forest is not surprising. Sentinel-2 red edge bands were also important, which highlights the strength of those bands for distinguishing vegetation types. Of some interest is the relatively low importance of C-band Sentinel-1 data. [PERSON] and [PERSON] (2015) also found Sentinel-1 to have limited value in assessing grass height in a study region in Italy. The most common land cover across the Reserve was tall grass (31.0%), followed by medium grass (19.7%). Short grass covered 10.19% of the study area, similar to grass _Acacia_ (12.0%), grass _Balanites_ (11.0%), and shrub (8.8%) (Table 4). Collectively, grass constituted 60.9% of the entire study area. The rarest classes were water (0.3%), followed by wetlands (1.3% emergent wetland and 1.1% wet meadow), and forests (1.1% riverine forest and 0.9% upland forest). Visual inspection of the map (Figure 3) corroborates our personal observations that upland forest primarily occurs along the Oloololo Escarpment, which runs along the western boundary of the Reserve, atop inselbergs, and sometimes on hills, particularly those with human settlements, including tourist lodges. Riverine forest is found along the Mara River, with some also appearing along the Talek River and other small rivers. Shrubs were over four times as widespread as riverine and upland forest combined.