New Approaches in Estimating Rubberwood Standing Volume Using Airborne Hyperspectral Sensing
Bibliographic record
Abstract
This paper focuses on mapping and quantifying the rubberwood plantation’s volume by using the UPM-APSB’s AISA airborne hyperspectral sensor in Lebuh Silikon, Universiti Putra Malaysia, Serdang, Malaysia. The image and field spectral knowledge of the individual standing rubber tree was obtained by image analysis and field work, respectively. The correlations between the different crown area of rubber trees and the diameter at breast height with the ancillary data were obtained. A supervised classification method based on Spectral Angle Mapper (SAM) and spectral matching knowledge was presented and applied to classify the entire image leading towards an estimation of rubberwood volume for the study area. It shows that the rubberwood volume of an old matured unmanaged rubber plantation can easily be predicted with high accuracy obtained through this approach. A total of 20 rubber tree samples were identified, quantified and mapped representing a standing rubberwood volume of 91.44 m3 /ha and a mapping accuracy of 89.84%. The study demonstrated that UPM-APSB’s AISA airborne hyperspectral sensor is capable of mapping individual rubber trees and estimate the standing ruberwood volume with an acceptable accuracy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 teacher head, 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".