This Zenodo entry provides a DOI for the repository associated with the Forgenius (https://www.forgenius.eu/) project, expanding on the methodology established in the FORGENIUS-PP project by Dr. Chris Reudenbach. The project focuses on monitoring *Populus nigra* in forest ecosystems using low-cost UAV systems and integrated workflows. ### Key Contributions- **Data**: UAV-acquired images (RGB and multispectral), geospatial datasets, soil moisture and temperature measurements, and derived environmental variables.- **Methods**: Automated R workflows for reproducible data processing and analysis, including canopy structure modeling, tree crown delineation, and classification of green catkins using machine learning.- **Deliverables**: Comprehensive project documentation, including workflows, fieldwork protocols, and analytical results. ### Scope and InnovationThis project covers complex phenological and environmental dynamics across study sites in La Alfranca (Spain) and Vienna (Austria). UAV flights and ground-based measurements achieved centimeter-level georeferencing accuracy, enabling high-resolution analyses of forest structure and soil properties. Innovative methods, such as Ordinary Kriging with External Drift (OKED), were used to derive soil moisture and temperature maps, while advanced machine learning algorithms optimized the classification of green catkins. ### RepositoryThe full dataset, workflows, and documentation are available in the GitLab repository:[PopNigra - Forgenius gitlab Repository](https://gitlab.uni-marburg.de/fb17/ag-opgenoorth/popnigra-forgenius). ### ReproducibilityAll R scripts and workflows are designed to ensure reproducibility, enabling users to replicate the study's methods and results. Detailed comments and bibliographic references are included for ease of use. ### AcknowledgmentsThis project was conducted under the supervision of Prof. Dr. Lars Opgenoorth and Dr. Chris Reudenbach at Philipps-Universität Marburg, as part of the Horizon Europe framework.