A Dual-Platform Approach to Methane Emission Monitoring in Feedlots Using Open-Path Lasers and UAVs
Bibliographic record
Abstract
Abstract. Methane, a potent greenhouse gas (GHG), has a global warming potential 28 times greater than carbon dioxide over a 100-year timeframe. Within the livestock sector, beef feedlots are significant sources of methane emissions, primarily due to enteric fermentation of the housed animals. Accurate quantification of these feedlot emissions is essential to refine livestock emission factors and improve the accuracy of GHG inventories. This is particularly important for supporting global climate initiatives, such as the Global Methane Pledge and the broader net-zero emissions target. Although several methodologies have been developed to estimate methane emissions from feedlots, the potential of uncrewed aerial vehicles (UAVs) for this purpose remains underexplored. This study bridges that gap by deploying a UAV equipped with a methane sensor at 20 m altitude over a beef feedlot in Southern Alberta. In parallel, three open-path lasers (OPLs) with retroreflectors are used to capture upwind and downwind methane concentrations. An in-feedlot weather station and a 3D sonic anemometer provide supporting meteorological and atmospheric turbulence information. Methane emission rates are then estimated using the atmospheric dispersion model, WindTrax. Preliminary results from the UAV measurements revealed spatial variation of methane concentrations, with hotspot locations more easily identified during calm wind conditions (wind speeds between 0.16 and 4 m/s). Meteorological factors significantly influenced methane dispersion patterns, emphasizing their role in detecting localized emission sources. The consistency of clustering patterns during low wind speeds highlights the UAV's capability of detecting methane concentrations at high-spatiotemporal resolution. This study demonstrate the potential of UAV to complement fixed-ground based OPLs, offering a scalable approach for methane monitoring. This research provides critical insights for enhancing Measurement, Reporting, and Verification (MRV) frameworks, contributing to effective methane mitigation strategies in livestock management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".