Photosynthesis is the amazing ability of plants transforming solar into chemical binding energy. Currently, we have a limited understanding of plants’ physiological and evolutionary answers to environmental factors as for example solar radiation, water stress, and temperature. Collecting all necessary data results in high-dimensional time series that are hard to explore and analyze. In this project, we apply dimension reduction techniques as one tool of the Visual Analytics approach reducing the information overload and enabling insights. In the video loop above, we show the evolution of an optimization algorithm computing the best two-dimensional embedding of the high-dimensional input data. The optimization algorithm is called t-distributed stochastic neighbor embedding and tries to preserve local neighborhoods in low-dimensional space. Coloring reflects the clustering of similar environmental and photosynthesis situations of trees in a typical Swiss mountain forest on the Lägeren ridge. For further information about this data set please refer to this detailed description.