Tropical PeatLand Forest Biomass Estimation Using Polarimetric Parameters Extracted from RadarSAT-2 Images
Bibliographic record
Abstract
This paper was aimed at estimating the forest aboveground biomass (AGB) in the Central Kalimantan tropical peatland forest, Indonesia, using polarimetric parameters extracted from RadarSAT-2 images. Six consecutive acquisitions of RadarSAT-2 full polarimetric data were acquired and polarimetric parameters were extracted. The backscattering coefficient ( σ o ) for HH, HV, VH, and VV channels was computed respectively. Entropy (H) and alpha ( α ) were computed using eign decomposition. In order to understand the scattering behavior, Yamaguchi decomposition was performed to estimate surface scattering ( γ s u r f ) and volume scattering ( γ v o l ) components. Similarly following polarimetric indices were computed; Biomass Index (BMI), Canopy Structure Index (CSI), Volume Scattering Index (VSI), Radar Vegetation Index (RVI) and Pedestal Height ( p h ). The PolSAR parameters were evaluated in terms of their temporal consistency, inter-dependence, and suitability for forest aboveground biomass estimation across rainy and dry conditions. Regression analysis was performed between referenced biomass measurements and polarimetric parameters; VSI, H, RVI, p h , and γ v o l were found significantly correlated with AGB. Biomass estimation was carried out using significant models. Resultant models were validated using field-based AGB measurements. Validation results show a significant correlation between measured and referenced biomass measurements with temporal consistency over the acquisition time period.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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 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".