Dataset for Fine Fuels and Vegetation Point Clouds from Close-Range Structure-from-Motion
Summary & Purpose
Rangelands and semi-arid ecosystems are subject to increasing changes in ecologic makeup from a collection of factors. In much of the northern Great Basin of the western United States, rangelands invaded by exotic annual grasses such as cheatgrass (Bromus tectorum) and medusahead (Taeniatherum caput-medusae) are experiencing an increasingly short fire cycle, which is compounding and persistent. Improving and expanding ground-based field methods for measuring above-ground biomass (AGB) may enable more sample collections across a landscape and over succession regimes, and better harmonize with other remote sensing techniques. Developments and increased adoption of uncrewed aerial vehicles and instrumentation for vegetation monitoring are enabling greater understanding of vegetation in many ecosystems. Research towards understanding the relationship of traditional field measurements with newer aerial platforms in rangeland environments is growing rapidly, and there is increasing interest in exploring the potential use both to quantify AGB and fine fuel load at pasture and landscape scales. Our study here uses relatively inexpensive handheld photography with custom sampling frames to collect and automatically reconstruct 3D-models of the vegetation within 0.2 m2 quadrats (n = 288). Next, we examine the relationship between volumetric estimates of vegetation to compare with biomass. We found that volumes calculated with 0.5 cm voxel sizes (0.125 cm3) most closely represented the range of biomass weights. We further develop methods to classify ground points, finding a 2% reduction in predictive ability compared to using the true ground surface. Overall, our reconstruction workflow had an R2 of 0.42, further emphasizing the importance of high-resolution imagery and reconstruction techniques. Ultimately, we conclude that more work is needed of increasing extents (such as from UAS) to better understand and constrain uncertainties in volumetric estimations of biomass in ecosystems with high amounts of invasive annual grasses and fine fuel litter.
Date of Publication or Submission
1-12-2024
Recommended Citation
Enterkine, Josh; Hojatimalekshah, Ahmad; and Glenn, Nancy F.. (2024). Dataset for Fine Fuels and Vegetation Point Clouds from Close-Range Structure-from-Motion [Data set]. Retrieved from https://doi.org/10.18122/bcal_data.8.boisestate
DOI
https://doi.org/10.18122/bcal_data.8.boisestate
Funding Citation
This work is supported by the National Institute of Food and Agriculture, US Department of Agriculture [2019-68008-29914].
Single Dataset or Series?
Dataset
Data Format
*.ply; *.xlsx; *.csv; *.cpg; *.dbf; *.prj; *.sbn; *.sbx; *.shp; *.xml; *.shx; *.docx; *.m; *txt
File Size
46.3 GB
Data Attributes
This dataset is the structure of vegetation contained within 0.40 meter by 0.50 meter sample frames. Initially collected via handheld imaging with a Sony a6000 and a Nikon Coolpix AW120, and processed into pointclouds using Structure-from-Motion (photogrammetry). They are in local coordinates and the units are in meters. Additionally, the code is included that was used to classify the ground surface and voxelize the pointclouds for comparing with biomass. Biomass tables are included.
Time Period
2020-2021
Privacy and Confidentiality Statement
Boise State is explicitly compliant with federal and state laws surrounding data privacy including the protection of personal financial information through the Gramm-Leach-Bliley Act, personal medical information through HIPAA, HITECH and other regulations. All human subject data (e.g., surveys) has been collected and managed only by personnel with adequate human subject protection certification.
Use Restrictions
Users are free to share, copy, distribute and use the dataset; to create or produce works from the dataset; to adapt, modify, transform and build upon the dataset as long as the user attributes any public use of the dataset, or works produced from the dataset, referencing the author(s) and DOI link. For any use or redistribution of the dataset, or works produced from it, the user must make clear to others the license of the dataset and keep intact any notices on the original dataset.
Disclaimer of Warranty
BOISE STATE UNIVERSITY MAKES NO REPRESENTATIONS ABOUT THE SUITABILITY OF THE INFORMATION CONTAINED IN OR PROVIDED AS PART OF THE SYSTEM FOR ANY PURPOSE. ALL SUCH INFORMATION IS PROVIDED "AS IS" WITHOUT WARRANTY OF ANY KIND. BOISE STATE UNIVERSITY HEREBY DISCLAIMS ALL WARRANTIES AND CONDITIONS WITH REGARD TO THIS INFORMATION, INCLUDING ALL WARRANTIES AND CONDITIONS OF MERCHANTABILITY, WHETHER EXPRESS, IMPLIED OR STATUTORY, FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT.
IN NO EVENT SHALL BOISE STATE UNIVERSITY BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF INFORMATION AVAILABLE FROM THE SYSTEM.
THE INFORMATION PROVIDED BY THE SYSTEM COULD INCLUDE TECHNICAL INACCURACIES OR TYPOGRAPHICAL ERRORS. CHANGES ARE PERIODICALLY ADDED TO THE INFORMATION HEREIN. COMPANY AND/OR ITS RESPECTIVE SUPPLIERS MAY MAKE IMPROVEMENTS AND/OR CHANGES IN THE PRODUCT(S) AND/OR THE PROGRAM(S) DESCRIBED HEREIN AT ANY TIME, WITH OR WITHOUT NOTICE TO YOU.
BOISE STATE UNIVERSITY DOES NOT MAKE ANY ASSURANCES WITH REGARD TO THE ACCURACY OF THE RESULTS OR OUTPUT THAT DERIVES FROM USE OF THE SYSTEM.
Comments
Dataset Contributors
Monica Vermillion, Thomas Van Der Weide, Sergio A. Arispe, William J. Price, and April Hulet