Welcome to my central repository for research projects, publications, datasets, and open‑source geospatial workflows.
This landing page links to all of my GitHub project repositories and Zenodo‑archived research artifacts, including work with spaceborne lidar (ICESat‑2, GEDI), mobile lidar, urban forestry, and machine learning for vegetation assessment.
Repository: https://github.com/justinn-j-jones/Spaceborne-lidar-for-vegetation-assessment
Zenodo DOI: https://doi.org/10.5281/zenodo.19099501
Description:
Instructional materials and Jupyter notebooks demonstrating workflows with ICESat‑2 ATL08, GEDI L2A/L2B, Google Earth Engine, and machine learning for vegetation structure modeling.
Repository: https://github.com/justinn-j-jones/Toward_fully_automated_urban_tree_inv_-_UFUG-2026
Zenodo DOI: https://doi.org/10.5281/zenodo.19121499
Publication DOI: https://doi.org/10.1016/j.ufug.2026.129362
Description:
Supporting notebooks and workflows for urban tree inventory using mobile lidar (MLS), lidR, TreeLS, and automated tree‑feature extraction.
This repository serves as a high‑level index for my research work.
Rather than storing code directly here, this page links to dedicated repositories for each project, each containing:
- Notebooks
- Code (Python / R)
- Documentation
- Sample datasets
- Zenodo DOIs
- Citation files
- Publication links (when applicable)
This approach provides clean project separation while maintaining a single, easy‑to‑navigate landing page.
- (Future project placeholder)
- (Thesis‑related workflows)
- (Google Earth Engine scripts)
- (ICESat‑2 and GEDI comparison tools)
- (Machine learning models for canopy height prediction)
This repository will continue to be updated as new workflows, datasets, and publications are released.
Justinn J. Jones
Graduate Assistant – Research
Texas A&M University, LASERS Lab
ORCID: https://orcid.org/0009-0007-5032-6657
Lab Website: https://lasers.tamu.edu/
My research focuses on:
- Spaceborne and airborne lidar
- Automated tree inventory
- Urban and forest ecology
- Machine learning for vegetation assessment
- Open‑source geospatial analysis
- Cloud‑based remote sensing workflows
If you have questions, collaboration requests, or would like to use any of these workflows in your research or teaching:
📧 justinn.j.jones@tamu.edu
Or open an issue in the corresponding GitHub project repository.
All repositories linked here include individual licenses (typically MIT License) allowing reuse, modification, and distribution.
When using any project, please cite the associated Zenodo DOI and publication (if applicable).