Satellite imagery time series offer a powerful means to detect and analyze both short- and long-term changes in the environment. In particular, the availability of open-access data from missions like Landsat (since 1972) and Sentinel (since 2015) has significantly enhanced our ability to study these changes. This workshop aims to explore the use of time series of indices derived from satellite imagery for analyzing various types of land cover changes using the programming language R. The workshop covered essential preprocessing steps, including outlier removal and handling missing observations, to ensure the quality of the data. Participants learned how to effectively model time series using different methods. Additionally, the workshop provided insights into detecting trends and breaks within the time series data. The analysis focused on a range of objects and encompass both abrupt and gradual changes. Examples of the types of changes that were explored include urban growth or vegetation succession.