Estimation of Above-Ground Biomass over Boreal Forests in Siberia Using Updated In Situ, ALOS-2 PALSAR-2, and RADARSAT-2 Data
Bibliographic record
Abstract
The estimation of above-ground biomass (AGB) in boreal forests is of special concern as it constitutes the highest carbon pool in the northern hemisphere. Particularly, monitoring of the forests in the Russian Federation is important as some regions have not been inventoried for many years. This study explores the combination of multi-frequency, multi-polarization, and multi-temporal radar data as one key approach to provide an accurate estimate of forest biomass. The data from L-band Advanced Land Observing Satellite 2 (ALOS-2) Phased Array L-Band Synthetic Aperture Radar 2 (PALSAR-2), together with C-band RADARSAT-2 data, were applied for AGB estimation. Backscatter coefficients from L- and C-band radar were used independently and in combination with a non-parametric model to retrieve AGB data for a boreal forest in Siberia (Krasnoyarskiy Kray). AGB estimation was performed using the random forests machine learning algorithm. The results demonstrated that high estimation accuracies can be achieved at a spatial resolution of 0.25 ha. When the L-band data alone were used for the retrieval, a corrected root-mean-square error (RMSEcor) of 29.4 t ha−1 was calculated. A marginal decrease in RMSEcor was observed when only the filtered L-band backscatter data, without ratio and texture, were used (29.1 t ha−1). The inclusion of the C-band data reduced the over and underestimation; the bias was reduced from 5.5 t ha−1 to 4.7 t ha−1; and a RMSEcor of 30.2 t ha−1 was calculated.
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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) | 0.001 | 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 | 0.000 |
| 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".