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Published December 19, 2025 | Version v1.0.0
Project deliverable Open

Pinus-sylvestris-Forgenius

Authors/Creators

  • 1. ROR icon Philipps University of Marburg
  • 2. ROR icon University of Freiburg

Contributors

Project leader:

Work package leader:

  • 1. INRA Provence-Alpes-Cote d'Azur
  • 2. ROR icon Philipps University of Marburg
  • 3. CSIC-INIA-CIFOR

Description

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 Innovation
This 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.

### Repository
The full dataset, workflows, and documentation are available in the GitLab repository:
[PopNigra - Forgenius gitlab Repository](https://gitlab.uni-marburg.de/fb17/ag-opgenoorth/pinus-sylvestris-forgenius).

### Reproducibility
All 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.

### Acknowledgments
This 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.

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Additional details

Funding

European Commission
FORGENIUS - Improving access to FORest GENetic resources Information and services for end-USers 862221

Dates

Available
2025-12-19
Date the deliverable and dataset were made publicly available on Zenodo.

Software

Repository URL
https://gitlab.uni-marburg.de/fb17/ag-opgenoorth/pinus-sylvestris-forgenius
Programming language
R
Development Status
Active

References

  • Dandois, J.P.; Olano, M.; Ellis, E.C. Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure. Remote Sens. 2015, 7, 13895-13920. https://doi.org/10.3390/rs71013895
  • Ludwig, M.; M. Runge, C.; Friess, N.; Koch, T.L.; Richter, S.; Seyfried, S.; Wraase, L.; Lobo, A.; Sebastià, M.-T.; Reudenbach, C.; Nauss, T. Quality Assessment of Photogrammetric Methods---A Workflow for Reproducible UAS Orthomosaics. Remote Sens. 2020, 12, 3831. https://doi.org/10.3390/rs12223831
  • Roussel, J.R., Auty, D., Coops, N. C., Tompalski, P., Goodbody, T. R. H., Sánchez Meador, A., Bourdon, J.F., De Boissieu, F., Achim, A. (2020). lidR : An R package for analysis of Airborne Laser Scanning (ALS) data. Remote Sensing of Environment, 251 (August), 112061. doi:10.1016/j.rse.2020.112061.
  • Jean-Romain Roussel and David Auty (2024). Airborne LiDAR Data Manipulation and Visualization for Forestry Applications. R package version 4.1.3. https://cran.r-project.org/package=lidR
  • Dalponte, M. and Coomes, D. A. (2016), Tree-centric mapping of forest carbon density from airborne laser scanning and hyperspectral data. Methods Ecol Evol, 7: 1236–1245. doi:10.1111/2041-210X.12575. DEM France https://geoservices.ign.fr/telechargement
  • Meyer, H., Reudenbach, C., Hengl, T., Katurji, M., Nauss, T. (2018): Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation. Environmental Modelling & Software, 101, 1-9. https://doi.org/10.1016/j.envsoft.2017.12.001
  • Meyer, H., Reudenbach, C., Wöllauer, S., Nauss, T. (2019): Importance of spatial predictor variable selection in machine learning applications - Moving from data reproduction to spatial prediction. Ecological Modelling. 411. https://doi.org/10.1016/j.ecolmodel.2019.108815
  • Meyer, H., Pebesma, E. (2021). Predicting into unknown space? Estimating the area of applicability of spatial prediction models. Methods in Ecology and Evolution, 12, 1620– 1633. https://doi.org/10.1111/2041-210X.13650
  • Meyer, H., Pebesma, E. (2022): Machine learning-based global maps of ecological variables and the challenge of assessing them. Nature Communications, 13. https://www.nature.com/articles/s41467-022-29838-9
  • Goralogia, G.S., Howe, G.T., Brunner, A.M. et al. Overexpression of SHORT VEGETATIVE PHASE-LIKE (SVL) in Populus delays onset and reduces abundance of flowering in field-grown trees. Hortic Res 8, 167 (2021). https://doi.org/10.1038/s41438-021-00600-4
  • Benavides, R.; Montes, F.; Rubio, A.; Osoro, K. Geostatistical modelling of air temperature in a mountainous region of Northern Spain. Agric. Meteorol. 2007, 146, 173–188.
  • Bianchi, E.; Villalba, R.; Viale, M.; Couvreux, F.; Marticorena, R. New precipitation and temperature grids for northern Patagonia: Advances in relation to global climate grids. J. Meteorol. Res. 2016, 30, 38–52
  • Pebesma, E.J., 2004. Multivariable geostatistics in S: the gstat package. Computers & Geosciences, 30: 683-691.
  • Gräler, B., E. Pebesma and G. Heuvelink, 2016. Spatio-Temporal Interpolation using gstat. The R Journal 8(1), 204-218
  • Bivand, R. S., Pebesma, E. J., & Gómez-Rubio, V. (2008). Applied Spatial Data Analysis with R (2008th ed.). Springer. Ch. 8 "Interpolation and Geostatistics