Estimating above-ground biomasss using lidar remote sensing
Bibliographic record
Abstract
Previous forest research using time-of-flight lidar suggests that there exists some quantile of the distribution of laser canopy heights that could provide an estimate of various forest biophysical properties. The results presented here not only support this theory, but also extend it by suggesting that a quantile of the distribution of all laser heights could provide estimates of aboveground biomass for forests with similar stand structure. Tolerant northern hardwood forests, composed predominantly of mature sugar maple (Acer saccharum Marsh.) and yellow birch (Betula alleghaniensis Britton), were surveyed using an ALTM 1225 (Optech Inc.) in August 2000. Field data for 49 circular plots, each 400 m2 in area, were collected in July 2000. Using site-specific allometric equations, total aboveground biomass and biomass components (i.e., stem wood, stem bark, live branches, and foliage) were derived for each plot. Three laser height metrics were derived from the lidar data: (i) maximum laser height; (ii) mean laser height; and (iii) mean laser height calculated from lidar returns filtered based on a threshold applied to the intensity return data LhIR). LhIR was identified as the best predictor of total aboveground biomass (R2 = 0.85) and biomass components (R2 between 0.84 to 0.85) when all plot types were considered.
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 | 0.001 |
| 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".