There is need to identify ecosystems that support richer assemblages of biological species in order to preserve habitats and protect the greatest number of species. Remotely sensed data hold tremendous potential for mapping species habitats and indicators of biological diversity, such as species richness. Landscape level habitat analysis using remotely sensed data and Geographical Information Systems (GIS) has the potential to aid in explaining species richness patterns at fine-scale resolutions. We used Landsat Thematic Mapper (TM) image and GIS as well as field data to classify habitat types in the Maasai Mara ecosystem, Kenya. The accuracy of the resulting habitat map was assessed and indices of habitat diversity computed. We then determined the relationship between large mammal species richness and habitat diversity indices, and investigated whether this relationship is sensitive to changes in spatial scale (extent and grain size). Statistical analyses show that species richness is positively correlated with habitat diversity indices and changes of scale in calculations of habitat diversity indices influenced the strength of the correlation. The results demonstrate that mammalian diversity can be predicted from habitat diversity derived from satellite remotely sensed data.