Comparison of remote sensing and ground-based methods for determining residue burn pile wood volumes and biomass
Bibliographic record
Abstract
Following forest harvest, residues left on site are often piled and burned. Quantification of residue piles is required in many jurisdictions to estimate billable waste, harvest efficiency, smoke emissions, C budgets, and available bioenergy biomass. Piled residues and harvested wood were measured on four blocks of a second-growth Douglas-fir forest in coastal British Columbia. Pile wood volumes were determined by two ground-based methods (a Waste and Residue Survey (WRS) and a geometric equation (EEP) used for smoke emissions) and by two geospatial methods using orthophotography (GIS) and LiDAR. Eight sample piles were disassembled to determine plot densities (PD) and packing ratios (PR). Plot density did not differ between the WRS and GIS method. Packing ratios of the EEP were 50% of that of the LiDAR method. LiDAR pile bulk volume and PR were negatively correlated. Choice of LiDAR digital elevation model had no significant effect on pile bulk volumes. The WRS method underestimated pile areas by 50% and the EEP method overestimated pile bulk volumes by two times. Pile wood volumes from the WRS method (20.0 m3·ha−1 (SE 2.8)) were 30%–50% of those for all other methods (46.1–60.4 m3·ha−1). The EEP method overestimated wood volume if nonspecific PRs were used. The residues to harvest ratio for the WRS method (3.3% (SE 0.45)) was lower than for all other methods (7.5%–9.6%). Total pile wood biomass from LiDAR was 25.4 t dry mass·ha−1 (SE 5.9). Ground-based methods are still required to determine PD or PR. Since their estimation depends on the method used to determine pile area or bulk volume, and may vary with pile size and type, they remain the major source of uncertainty in determining pile wood volumes. However, use of remote sensing techniques to determine the area or bulk volume of all piles in a cutblock can improve estimates of pile wood volumes and biomass.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".