Satellite data have become an essential tool in environmental monitoring and ecosystem assessment. This study investigates the application of unsupervised classification to characterize the spatio-temporal dynamics of vegetation in southwestern Madagascar, a region highly vulnerable to climatic variability. MODIS Collection MOD13Q1 products were selected despite their relatively coarse spatial resolution, due to their dense temporal coverage, enabling the analysis of a long time series from 2001 to 2024. The methodological framework is based on clustering pixels according to their monthly growth profiles derived from the Normalized Difference Vegetation Index (NDVI). Seasonal variations, including the wet and dry seasons, were explicitly considered. To ensure robustness, results from K-means clustering were cross-validated with Hierarchical Ascendant Classification (HAC), allowing us to compare and consolidate class stability. The classification identified seven distinct profile classes, reflecting both seasonal phenological patterns and dominant vegetation cover types. These results provide crucial insights for spatio-temporal monitoring and mapping of ecosystems, contributing to improved environmental surveillance in the region. Overall, the study demonstrates the effectiveness of unsupervised classification in extracting meaningful information from satellite time series. By offering a detailed understanding of vegetation dynamics over two decades, this approach highlights valuable opportunities for sustainable management and conservation of natural resources in southwestern Madagascar.