This paper presents an educational experiment that merges art and science to deepen landscape architecture students' comprehension of scientific data. It explores tools and methodologies aimed at fostering more intuitive insights into data, bridging both analytical and artistic readings and expression. The study investigates techniques for representing landscapes and vegetation vitality using point clouds and vegetation indices, examining methodologies to stimulate the emergence of novel forms and morphologies in landscape architecture in artistic expressive ways. Through the analysis of airborne LiDAR data and utilization of near-infrared signatures, such as NDVI, the paper illustrates how invisible data can enhance vegetation assessment. Additionally, it evaluates the application of node-based visual programming, particularly Geometry Nodes in Blender, as an intuitive interface for handling scientific data, offering an alternative to Grasshopper in digital landscape architecture education.