Abstract. This paper presents the results of a study aimed at analyzing land cover evolution—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 crucial role in ensuring both the quantity and quality of water available to the population. The study employed an unsupervised classification approach based on the Normalized Difference Vegetation Index (NDVI), combined with a binary encoding technique. A hybrid method was used, integrating classification results from multiple dates. The core of the methodology involved deriving indicators of seasonal or annual pixel variation by analyzing several images from the same year and comparing these indicators across different years. This approach enhanced the detection of seasonal land cover variations, thereby improving the identification of land cover classes. Using multiple observations per year proved especially effective in distinguishing different vegetation types. The analysis aimed to detect significant land cover changes, with particular emphasis on vegetation loss and recovery. The binary encoding technique facilitated the mapping of land cover evolution, especially 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 for data classification. Because NDVI is a normalized index, variation ranges were used to separate certain land cover classes at each time point. The integration of multiple dates within a year increased the potential for accurate discrimination by capturing seasonal dynamics. From a hydrological perspective, the land cover changes observed between 1988 and 2018 were substantial, although the system has since shown signs of stabilization. The creation of the reservoir led to the emergence of new agricultural areas around the water body, while 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.
Centeno et al. (Fri,) studied this question.