A non-destructive approach to estimate buttress volume using 3D point cloud data
Bibliographic record
Abstract
Buttressed trees have one of the largest sources of variation in volume or biomass estimates in tropical forests. Buttresses provide mechanical support for trees and offer other essential ecological functions such as nutrient acquisition. Here, we use an Alpha Shape Algorithm (ASA) based on a 3D point cloud to estimate the volume of 30 buttressed trees collected using Terrestrial Photogrammetry (TP). We also calculated the buttresses volume using allometric models developed using the Diameter Above the Buttress (DAB) and the Diameter computed from non-convex (Darea130) and convex area (Dconvex130) at breast height (1.3 m). To demonstrate the broader generalization of our allometric models, we validated the developed models using independent data obtained by Terrestrial Laser Scanning (TLS) and destructive measurement. Volume estimated by the ASA showed a high agreement with the reference volume acquired by the Smalian formula (RRMSE of 0.08 and R2 = 0.99 regardless of species effect). Our results suggest that the DAB seems to be the most advanced predictor for volume, with the lowest Akaike information criterion (AIC) of -62.4 than the Darea130 (49.2)and the Dconvex130 (30.3). At the same time, the DAB (RRMSE of 0.2) and Darea130 (RRMSE of 0.2) show similar performance when validated with independent data sets. Our results indicate that the ASA is more reliable and efficient than allometric models for buttress modelling. Our results also provide a solid foundation for buttress modelling, as we use more buttressed trees (45) for allometric model development than previous studies. Furthermore, the proposed non-destructive method can help to correct the bias in present and past estimates of volume and biomass of large trees, which are keystone components to understanding biomass allocation and dynamics in tropical forests.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".