A time series algorithm was being presented that classifies vegetation in the Njoro Watershed, Kenya, according to the shapes of temporal normalized difference vegetation index (NDVI) profiles representing growing cycles for different vegetation. We present a two-step approach that includes noise reduction using discrete Fourier filtering and a clustering algorithm that uses the Fourier components of magnitude and phase to identify phenological differences. The classification considers possible variations in shape that may be imposed by climate, soil, topography, or human impacts. The primary input to the classification is a user-defined set of reference cycles to which pixels are assigned depending on a set of shape criteria. The output is a consistent classification of NDVI cycles representing vegetation classes with similar phenologies. The algorithm allows the creation of classification mosaics without typical boundary offsets and temporally comparable classification products. It identifies vegetation more accurately than single image classification methods, because it exploits the temporal variability in spectral reflectance due to phenological responses. We produced a classified land cover map at two hierarchical levels with five classes in a level I classification and seven classes in a level II classification that represents a refinement of the level I data. These map products compare favorably to previously published land cover maps that were developed using more standard supervised classification. The level I map has an overall accuracy of 94% compared to field data, while the level II map as an overall accuracy of 77%.