New Standardized Terrestrial Laser Scanning Dataset Transforms Forest Research Across Europe
A major new open-access resource has been released that promises to accelerate research in forest ecology, remote sensing, and climate science. The publication details a harmonized collection of terrestrial laser scanning data from 121 forest plots spanning five European regions. Released in June 2026, the dataset includes high-resolution 3D point clouds and detailed tree-level attributes, all acquired with consistent protocols using a RIEGL VZ-400i scanner.
Lead authors Miriam Herrmann, Marius Derenthal, Ephraim Amos Schmidt-Riese, Marion Stellmes, Růžena Janoutová, Florian Arendholz, Daria Alison Bäte, Jonas Ernst, Arvin Fakhri, Piet Jaki, Florian Katerndahl, Fabian Kempfer, Chiara Mansi, Johann Meindl, Barbora Navrátilová, Florian Plewnia, Thomas Ruhtz, Marius Scholl, Asad Waseem, and Fabian Fassnacht collaborated across institutions including Freie Universität Berlin and partners in Germany, Czechia, and beyond. The work appears in Data in Brief and is freely available at https://www.sciencedirect.com/science/article/pii/S2352340926005731, with supporting files on Zenodo.
Why Terrestrial Laser Scanning Matters for Modern Forestry
Terrestrial laser scanning, or TLS, uses ground-based lidar systems to create precise three-dimensional maps of forest structure. Unlike traditional field inventories that rely on tape measures and clinometers, TLS captures millions of points per plot in minutes, revealing canopy height, stem diameter, branching patterns, and understory density with centimetre accuracy. Researchers use these point clouds to model biomass, assess biodiversity, and monitor changes over time. The technology supports applications from carbon accounting under international climate agreements to precision forestry practices that optimise timber yield while protecting ecosystems.
European forests face mounting pressures from climate change, pests, and land-use shifts. Consistent, large-scale datasets like this one enable cross-regional comparisons that single-site studies cannot provide. Harmonisation—aligning coordinate systems, scan densities, and attribute extraction methods—removes barriers that previously made combining data from different campaigns difficult.
Details of the 121-Plot European Dataset
The collection covers 50 m × 50 m plots distributed across five measurement campaigns. Each plot yields a high-resolution point cloud at approximately 1 cm spacing, together with derived metrics such as tree height, diameter at breast height, crown volume, and species classification where available. The harmonised format includes georeferenced LAS files and accompanying tabular data in standard formats. Total volume exceeds several terabytes, making it one of the largest publicly available TLS forest resources to date.
Plots represent a range of forest types including managed conifer stands, mixed deciduous woodlands, and old-growth remnants. Geographic spread spans Central and Northern Europe, capturing gradients in climate, soil, and management intensity. This diversity supports studies on how forest structure responds to environmental variation and human intervention.
Photo by Qamar Mahmood on Unsplash
Harmonisation Process and Quality Assurance
Creating a usable multi-site dataset required careful alignment of acquisition parameters and post-processing pipelines. All scans used the same scanner model and similar scan patterns. Raw data underwent co-registration, noise filtering, and ground-point classification using shared software tools. Tree segmentation followed a common workflow that identifies individual stems and delineates crowns. Quality checks included manual verification of a subset of plots and comparison against independent field measurements.
The resulting attributes are stored in consistent units and coordinate reference systems. Metadata files document exact scan dates, weather conditions, and any site-specific adjustments. This level of documentation allows future users to assess fitness for purpose and replicate analyses.
Potential Applications in Research and Industry
Forestry researchers can now train machine-learning models on thousands of individual trees without collecting new field data. Ecologists gain tools to quantify structural diversity linked to habitat quality. Climate modellers can refine estimates of above-ground biomass and carbon stocks at landscape scales. Industry partners in timber, conservation, and carbon markets benefit from standardised benchmarks for validating remote-sensing products derived from airborne or satellite platforms.
Early adopters have already used similar TLS collections to develop algorithms that automatically detect tree species or predict growth rates. The new dataset extends these opportunities across borders, supporting collaborative projects funded by Horizon Europe and national research councils.
Implications for Academic Careers and Training
The release coincides with growing demand for specialists who can handle large geospatial datasets. Universities are expanding programmes in remote sensing, geospatial data science, and environmental informatics. Students and early-career researchers gain immediate access to real-world data for theses, dissertations, and publications. Faculty can design courses around the dataset, teaching skills in point-cloud processing, statistical modelling, and reproducible research practices.
Postdoctoral positions and research assistant roles increasingly list experience with lidar or forest structure analysis as desirable qualifications. Institutions seeking to strengthen interdisciplinary teams may find this dataset a catalyst for new collaborations between forestry departments, computer science groups, and climate research centres.
Photo by Marcin Jozwiak on Unsplash
Access, Licensing, and Community Engagement
The dataset is published under an open licence that permits reuse with attribution. Download links appear on both the ScienceDirect page and the Zenodo repository. Users are encouraged to cite the original paper and share derived products. A dedicated GitHub organisation hosts example scripts for visualisation and basic analysis, lowering the barrier for new users.
Workshops and webinars planned for autumn 2026 will introduce the data to broader audiences. Feedback channels allow contributors to report issues or suggest improvements for future releases.
Future Outlook and Next Steps
Plans are already underway to expand the collection with additional campaigns and integrate it with airborne lidar and satellite observations. Researchers anticipate combining the TLS data with genetic, soil, and meteorological records to build comprehensive digital twins of European forests. Such integrated resources will support scenario modelling for policy decisions on reforestation, biodiversity targets, and climate adaptation.
As open science practices mature, datasets of this scale become foundational infrastructure. They reduce duplication of effort, accelerate discovery, and train the next generation of scientists in data-intensive methods. The European forest research community now has a shared reference point that strengthens both basic science and applied solutions.