Real-world tree attribute input data for 3D fuel modelling in FastFuels from Oregon, USA, 2019
https://doi.org/10.5285/bc67144d-91c4-446d-8691-248a793bf4f4
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This dataset is available under the terms of the Open Government Licence
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This dataset consists of high-resolution individual tree attributes specifically formatted for 3D fuel modeling and physics-based fire simulation. The primary data resource is a structured inventory of forest vegetation, where each entry represents an individual tree. Key variables include precise spatial coordinates (X, Y, Z), Diameter at Breast Height (DBH), and Total Height (TH). These parameters serve as the foundational inputs required to synthesize complex 3D fuel beds within the FastFuels modeling environment that can be later be used as inputs for wildfire simulation softwares FIRETEC and QUIC-Fire.
The data was extracted from terrestrial 3D LiDAR point clouds of a Pinus ponderosa plot scanned with a Riegl-VZ400i TLS scanner in Sycan Marsh, Oregon, USA during the summer of 2019. This resource was developed to provide "ground truth" fuel data for the validation and calibration of next-generation wildfire models, enabling researchers to simulate fire behaviour-such as crown fire initiation and heat flux-with higher physical fidelity.
The data was extracted from terrestrial 3D LiDAR point clouds of a Pinus ponderosa plot scanned with a Riegl-VZ400i TLS scanner in Sycan Marsh, Oregon, USA during the summer of 2019. This resource was developed to provide "ground truth" fuel data for the validation and calibration of next-generation wildfire models, enabling researchers to simulate fire behaviour-such as crown fire initiation and heat flux-with higher physical fidelity.
Publication date: 2026-06-03
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Format
Comma-separated values (CSV)
Spatial information
Study area
Spatial representation type
Tabular (text)
Spatial reference system
WGS 84
Spatial resolution
100 metres
Temporal information
Temporal extent
2019-07-08 to 2019-07-12
Provenance & quality
Point cloud acquisition was done between 8th and 12th July 2019 in a 200x200 m2 study plot of a ponderosa pine woodland in Sycan Marsh preserve, Oregon, USA.
TLS point cloud sampling was performed along a 50 m grid to create a synoptic scan. The point clouds have been acquired with a RIEGL-VZ400i TLS system with a laser sampling rate 750 kHz and a vertical sampling density of 0.21 degrees, mounted on a tripod at a minimum height of 1.5 m. Point cloud was georeferenced using the integrated L1 GPS receiver, compass and inclination sensors of the TLS system and ground control points whose coordinates were recorded were recorded using an RTK/Differential GPS receiver/base combination.
The raw scans were pre-processed using Riegl's proprietary software RiSCAN PRO to assign metadata (scanner position, scan ID, date/time), align all scans into a common coordinate system and divide the total study area in 40 x 40 m2 plots (n = 25 plots). These resulting plots were finally stored into LAZ files.
LAZ files were processed using 3DFin software (https://github.com/3DFin/3DFin) to compute forest inventory metrics. Then, a random sample of 150 trees across the plot were extracted. Real-world coordinates (X, Y), Diameter at Breast Height (DBH) and tree Total Height (TH) are provided for each tree.
TLS point cloud sampling was performed along a 50 m grid to create a synoptic scan. The point clouds have been acquired with a RIEGL-VZ400i TLS system with a laser sampling rate 750 kHz and a vertical sampling density of 0.21 degrees, mounted on a tripod at a minimum height of 1.5 m. Point cloud was georeferenced using the integrated L1 GPS receiver, compass and inclination sensors of the TLS system and ground control points whose coordinates were recorded were recorded using an RTK/Differential GPS receiver/base combination.
The raw scans were pre-processed using Riegl's proprietary software RiSCAN PRO to assign metadata (scanner position, scan ID, date/time), align all scans into a common coordinate system and divide the total study area in 40 x 40 m2 plots (n = 25 plots). These resulting plots were finally stored into LAZ files.
LAZ files were processed using 3DFin software (https://github.com/3DFin/3DFin) to compute forest inventory metrics. Then, a random sample of 150 trees across the plot were extracted. Real-world coordinates (X, Y), Diameter at Breast Height (DBH) and tree Total Height (TH) are provided for each tree.
Licensing and constraints
This dataset is available under the terms of the Open Government Licence 
Cite this dataset as:
Cabo, C.; Laino, D.; Doerr, S.H.; Parsons, R.A.; Hudak, A.T.; Santin, C. (2026). Real-world tree attribute input data for 3D fuel modelling in FastFuels from Oregon, USA, 2019. NERC EDS Environmental Information Data Centre. https://doi.org/10.5285/bc67144d-91c4-446d-8691-248a793bf4f4
Related
Correspondence/contact details
Other contacts
Publisher
NERC EDS Environmental Information Data Centre
info@eidc.ac.uk
Rights holder
Swansea University
Additional metadata
Keywords
Funding
Natural Environment Research Council Award: NE/T001194/1
Agencia Estatal de Investigacion Award: PID2021-126790NB-I00
Department of Defense Strategic Environmental Research and Development Program Award: RC20-1025
Department of Defense Strategic Environmental Research and Development Program Award: RC23-7626
Agencia Estatal de Investigacion Award: PID2021-126790NB-I00
Department of Defense Strategic Environmental Research and Development Program Award: RC20-1025
Department of Defense Strategic Environmental Research and Development Program Award: RC23-7626