Quantifying downed coarse woody material and residual forest basal area following retention harvesting in northeastern Minnesota using Landsat sensor data
Bibliographic record
Abstract
Retention harvesting shows great promise for restoring and maintaining forest structural and compositional diversity. However, economical, comprehensive monitoring is needed to advance understanding of the effectiveness of these management strategies through time. We investigate multitemporal winter Landsat sensor data (capturing snow ground cover at 7.6 cm and 106.7 cm depths) as a tool for discriminating between and providing regional estimates of both residual forest basal area (BA) and downed coarse woody material (DCWM) volume following retention harvesting in Minnesota, USA. Measurements from 34 ground plots were used with Landsat predictor variables to estimate these two biophysical forest parameters. According to similar studies, results for DCWM volume estimation are considered adequate, with an R2adj = 0.54 and absolute RMSE (RMSEa) = 19.02 m3·ha−1. Residual forest BA estimates were similar: total BA R2adj = 0.55 (RMSEa = 1.85 m2·ha−1), hardwood BA R2adj = 0.67 (RMSEa = 1.23 m2·ha−1), and conifer BA R2adj = 0.52 (RMSEa = 0.94 m2·ha−1). Use of winter Landsat imagery was key to quantifying these important forest biophysical parameters — a tool that carries the potential to transform our understanding of the impact of human and natural disturbance regimes on northern forest ecosystems.
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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) | 0.000 | 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".