Semi-arid and savanna-type (SAST) systems in East Africa have unique plant species compositions and characteristics that make quantifying this area's seasonality and inter-annual variability difficult. Phenoregion classification offers a way to use seasonality of vegetation growth dynamics to help understand the phenology of complex landscapes. Here, we used Normalized Difference Vegetation Index (NDVI) time series from the Landsat 8 imagery to map phenoregions in scenes centered around national parks from Mt. Kenya National Park (Kenya) to Limpopo National Park (Mozambique) to assess whether landscape-scale controls on phenology are consistent across the region or if they differ on a latitudinal gradient. We used MODIS Land Cover to assess land cover composition in each phenoregion, and discriminant analysis to determine the role that elevation, slope and aspect play in driving phenological differences. There was no clear latitudinal pattern seen in land cover or geologic composition. Most of the site's phenoregions showed no unique composition of either of the variables, meaning that land cover or geology type did not help in differentiating phenoregions. The discriminant analysis showed that topography was a strong predictor of many of the phenoregions, however, these also did not reveal any clear latitudinal pattern. Using seasonality of the NDVI time series to generate phenoregions provides different and even in some cases more ecologically relevant information, compared to past studies that use only land cover to generate ecoregions. With a significant population of humans and animals that live in and depend on SAST ecosystems, it is important to better understand vegetation processes and the factors that affect them as climate change becomes an increasingly pertinent issue in dry systems.
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