Forest vertical structure characterization using ground inventory data for the estimation of forest aboveground biomass
Bibliographic record
Abstract
A common method for estimating forest biomass is to measure forest height and apply allometric equations. However, changing forest density or structure heterogeneity increases the variability of the known allometric relationship. Here, we investigated the potential of allometric relationships based on vertical forest structure for biomass inversions with a global potential. First, vertical biomass profiles, which were calculated from ground forest inventory data, were used to model forest vertical structure. Then, a vertical structure ratio based on Legendre polynomials was proposed as a structural descriptor and its sensitivity to biomass was evaluated. Finally, we developed a structure-to-biomass inversion expression that could be extrapolated for aboveground biomass estimations. This is a case study based on inventory data from the Traunstein and Ebersberg test sites, two temperate forests located in southeastern Germany with different forest structural conditions. Results from the structure-to-biomass inversion algorithm show a clear improvement with respect to traditional height-to-biomass expressions, with increasing correlation factor (r2) from 0.52 to 0.73 for Traunstein and from 0.51 to 0.76 for Ebersberg and reducing the root mean square errors from 75.32 to 47.56 Mg·ha−1 and from 73.25 to 48.31 Mg·ha−1, respectively.
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 | 0.001 | 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".