In this paper it is presented the results of a study aimed at analyzing the evolution of land cover—particularly vegetation—using remote sensing time series. The focus is on monitoring vegetation changes in the Passaúna basin, an important water supply source for Curitiba, Brazil. Vegetation in this basin plays a key role in ensuring both the quantity and quality of water available to the population. The study employed an unsupervised classification scheme based on the Normalized Difference Vegetation Index (NDVI) and a binary encoding approach. It is used a hybrid method, combining classification results from multiple dates. The core of the methodology involves deriving indicators of seasonal or annual pixel variation by analyzing several images from the same year, and then comparing these indicators across different years. This approach enhances the ability to detect seasonal land cover variations, which improves the identification of land cover classes. Using multiple observations per year proved especially effective in distinguishing vegetation types. The analysis aimed at detecting significant land cover changes, with an emphasis on vegetation loss and recovery. The binary encoding technique facilitated the mapping of land cover evolution, particularly changes associated with the filling of the Passaúna reservoir, and helped pinpoint their locations. A key advantage of this method is that it does not require training sample selection to classify the data. Because NDVI is a normalized index, it was used variation ranges to separate certain land cover classes at each time point. The potential for accurate discrimination increased, combining multiple dates within a year, thanks to the integration of seasonal dynamics. From a hydrological perspective, the land cover changes observed between 1988 and 2018 were substantial, though the system has since shown signs of stabilization. The creation of the reservoir led to changes such as the emergence of new agricultural areas around the water body. At the same time, denser vegetation increased in the upper basin. These changes significantly affect infiltration rates and potential surface runoff, highlighting the hydrological impact of land cover dynamics in the region.