Using lidar to assess impacts of forest harvest landings on vegetation height by harvest season and the potential for recovery over time
Bibliographic record
Abstract
Tree regeneration and growth is generally reduced at forest harvest landing areas because of significant soil compaction, but it is commonly believed that harvesting in winter can reduce these impacts and that recovery occurs naturally with time. We used lidar data to assess differences in vegetation height between landing and general harvest areas across 79 sites in northern Minnesota, United States, that had been harvested in either summer/fall or winter and between 2 and 175 months since harvest. Vegetation height was significantly lower at landing areas compared with general harvest areas; however, there was no effect of harvest season on the difference (p = 0.50), indicating that impacts occur during all seasons. There was a significant (p < 0.01) positive relationship between the difference in vegetation height and time, regardless the harvest season, providing evidence that recovery occurs across a wide range of conditions within our time period of assessment. Sites with three landings present had the lowest relative landing area and also had the lowest differences in vegetation height between landing and general harvest areas, demonstrating the potential for optimized landing configurations to minimize impacts to growth. Based on our findings, landing areas should be kept as small as reasonably possible during all seasons of harvest, but the need for active reclamation practices is probably not warranted given that recovery occurs within the first few decades after harvest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".