Empowering ecological remote sensing learning: The imageRy R package to help students and instructors

Remote sensing is widely used in ecology to estimate key properties such as biodiversity, species distributions, and habitat dynamics. The increasing availability of free satellite imagery has expanded its use in teaching, but has also introduced challenges related to data handling, workflow fragmentation, and the interpretation of analytical outputs. To address these issues, we introduce the \texttt{imageRy} R package, a pedagogically oriented framework that integrates data, analytical functions, and visualization within a single reproducible environment. Rather than focusing solely on the implementation of standard remote-sensing methods (e.g., spectral indices, classification, spatial variability, and multivariate analysis), \texttt{imageRy} emphasizes the connection between spatial outputs and their statistical interpretation. The main contribution of the package lies in the integration of statistical visualization as a native analytical layer within remote-sensing workflows. Functions for ridgeline plots, boxplots, and barplots enable users to move seamlessly from maps to quantitative summaries of data structure, including class separability, variability, and relative abundance. This dual perspective links pixel-level information to aggregated ecological interpretations, improving both analytical clarity and pedagogical effectiveness. By coupling controlled in-package datasets with a unified analytical and visualization framework, \texttt{imageRy} reduces technical variability, enhances reproducibility, and supports a structured transition from introductory learning to more advanced remote-sensing applications. The package is therefore positioned as a conceptual and computational bridge between spatial analysis and statistical interpretation in ecological informatics.