This paper presents the analysis of a time series of hyperspectral images collected with the Hyperion sensor on board EO-1 to demonstrate how hyperspectral imaging can be used for studying seasonal variations of vegetation cover over the Guánica Dry Forest in Puerto Rico. The approach is based on a local unmixing procedure that splits the hyperspectral scene into tiles and performs endmember extraction on each tile. The main assumption is that within a tile, a single spectral signature is an adequate representation of an endmember. Local endmember signatures from each tile are then clustered to extract endmember classes that better account for endmember spectral variability across the scene and provide a better global description of the full forest scene. Within a scene, abundances are computed using all extracted spectral endmembers and the abundance of an endmember class is computed as the sum of the abundances for the spectral endmembers belonging to that class. Variations in abundance maps are used to understand seasonal changes in forest cover. The procedure was performed using eleven near-cloud-free Hyperion images collected in different months in 2008. Results from the analysis agreed with published knowledge of the phenological changes for this forest. Correlation analyses with NDVI and rainfall time series are used to understand variations in coverage of certain endmember classes with weather. Mangrove was shown to be uncorrelated with rainfall, whereas the upland forest endmember was highly correlated with rain. This study shows the potential for unmixing methods to exploit hyperspectral data for temporal analysis.