Improving precision of field inventory estimation of above ground biomass through an alternative view on plot biomass
Bibliographic record
Abstract
Abstract We contrast a new continuous approach (CA) to estimation of plot level above-ground biomass (AGB) in forest inventories with the current approach of deriving the AGB estimate exclusively from the tree-level AGB predicted for each tree in a plot; henceforth called DA (discrete approach). In CA the AGB in a forest is modelled as a continuous surface and the AGB estimate for a fixed area plot is computed as the integral of the AGB surface taken over the plot. Hence with CA, the portions of biomass in plot-trees that extend across a plot perimeter is ignored while the biomass from trees outside the plot reaching inside the plot is added. We use a sampling simulation with data from a fully mapped 2 ha area to illustrate, that important differences in plot-level AGB estimates can emerge, and that one should expect CA-based estimates of AGB to be less variable than with the DA, which translates to a higher precision of estimates from field plots: in our case study, for a target precision of estimation of 5%, the required sample size was 27% lower for small plots of 100m2 when using the CA and 10% lower for larger plots of 1700m2. We discuss practical issues to implementing CA in field inventories and discuss the expected potential for applications that model biomass from remote sensing data.
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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".