Investigating ALOS PALSAR interferometric coherence in central Siberia at unfrozen and frozen conditions: implications for forest growing stock volume estimation
Bibliographic record
Abstract
This paper investigates the impact of freezing on the properties of the magnitude of the interferometric coherence |γ| in central Siberia and discusses its implications for forest growing stock volume (GSV) estimation in the boreal zone. Eighty-seven acquisitions were employed and approximately 300 interferograms were generated. Accordingly, a high statistical credibility of the obtained results can be presumed. The temporal baselines of the interferograms ranged from 46 days to 2.5 years. The random volume over ground model was applied to support the interpretation of the observations. Compared with unfrozen conditions, we observed increased coherence over open areas, decreased coherence over dense forest, decreased spread of coherence, and improved correlation between |γ| and GSV during a frozen state. At frozen conditions, the experimental data showed no proof that the perpendicular baseline B⊥ impacted |γ| over dense forest, whereas at unfrozen conditions an impact was detected. Consequently, at frozen conditions, the temporal decorrelation was the major source of decorrelation and obstructed the detection of volume decorrelation effects. Nevertheless, |γ| acquired at frozen state exhibits potential for GSV mapping. The relationship between GSV and |γ| can be described with an average coefficient of determination R2 of 0.6. Saturation occurs at about 250 m3/ha.
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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 | 0.001 |
| 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) | 0.001 | 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".