## 6 Conclusions  
This study represents the first study to our knowledge to use remotely sensed data to accurately classify discrete classes of grass height among other diverse land cover types in a tropical savanna-woodland landscape mosaic, which is highly spatially heterogeneous and temporally dynamic. We combined multi-date radar imagery (PALSAR-2 and Sentinel-1) and optical imagery (Sentinel-2) from a single season with training data from ground-truthing (i.e., field data) and image interpretation to assign land cover at a fine spatial resolution (\(10\times 10\) m) using a machine learning algorithm, Random Forests [40]. We reserved 20% of our supervised data for validation, allowing us to assess and report user's, producer's, and overall accuracy and subsequently add and refine supervised data to improve classification in an iterative process.  
The resultant map was highly accurate, achieving user's and producer's accuracies ranging from 66%-100% and an overall map accuracy of 86%. In addition to classifying a wide variety of land cover types, from open water to forests, we were able to distinguish discrete grass heights (short, medium, and tall) with user's accuracies of 83%, 82%, and 85%, respectively. Furthermore, confusion between grassland and non-grassland land cover occurred at low rates of 1% (omission) and 4% (commission). Overall, most of the confusion in classification occurred between shrubs, forests, and grasslands dotted with _Acacia_ trees or shrubs. However, confusion was still low.  
We expect that this fine-resolution, highly accurate land cover map of an ecologically important protected area will inform wildlife managers and allow researchers to address new questions regarding habitat preference and land cover change over time. Furthermore, these methods can be repeated or expanded upon for implementation in other mixed grassland ecosystems.  
The following are available online at [[http://www.mdpi.com/2072-4292/12/7/1086/s1](http://www.mdpi.com/2072-4292/12/7/1086/s1)]([http://www.mdpi.com/2072-4292/12/7/1086/s1](http://www.mdpi.com/2072-4292/12/7/1086/s1)), Table S1: blank field data sheet.  
Conceptualization, O.S.B.S., J.C.J., and Z.M.L.; methodology, M.J.B., Z.M.L., and M.E.M.; field data collection, O.S.B.S., J.C.J., and Z.M.L.; software, M.J.B. and M.E.M.; validation, M.J.B. and M.E.M.; formal analysis, O.S.B.S. and M.J.B.; resources, K.E.H. and L.L.B.-C.; data curation, O.S.B.S., J.C.J., M.J.B., Z.M.L., and M.E.M.; writing--original draft preparation, O.S.B.S., writing--review and editing, O.S.B.S., J.C.J., M.J.B., Z.M.L., M.E.M., K.E.H., and L.L.B.-C.; visualization, O.S.B.S., J.C.J., and M.J.B.; project administration, O.S.B.S. and Z.M.L.; funding acquisition, O.S.B.S., J.C.J., M.J.B., Z.M.L., M.E.M., K.E.H., and L.L.B.-C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by NSF grants OISE1853934 and IOS1755089 to K.E.H. Ground truthing was made possible by travel funding from the Ecology, Evolutionary Biology, and Behavior (EEBB) program (EEBB Summer Fellowship and EEBB Travel Fellowship) and the Graduate School (Research Enhancement Award) at Michigan State University. The PALSAR-2 imagery was provided through Grant # APS3120 to [PERSON], under the Japan Aerospace Exploration Agency's 6 th Research Announcement for the Advanced Land Observing Satellite-2 (ALOS-2). Remote sensing training was made possible by MichiganView through AmericaView grant #AV18-MI-01 to MTRI.  
We thank the Kenyan National Commission for Science, Technology and Innovation, the Kenya Wildlife Service, the Narok County Government, the Mara Conservancy, [PERSON], [PERSON], and the Naibosho Conservancy for permissions to conduct this research.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.