Using InSAR Coherence to Map Stand Age in a Boreal Forest
Bibliographic record
Abstract
The interferometric coherence parameter γ estimates the degree of correlation between two Synthetic Aperture Radar (SAR) images and can be influenced by vegetation structure. Here, we investigate the use of repeat-pass interferometric coherence γ to map stand age, an important parameter for the study of carbon stocks and forest regeneration. In August 2009 NASA’s L-band airborne sensor UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar) acquired zero-baseline data over Quebec with temporal separation ranging between 45 min and 9 days. Our analysis focuses on a 66 km2 managed boreal forest and addresses three questions: (i) Can coherence from L-band systems be used to model forest age? (ii) Are models sensitive to weather events and temporal baseline? and (iii) How is model accuracy impacted by the spatial scale of analysis? Linear regression models with 2-day baseline showed the best results and indicated an inverse relationship between γ and stand age. Model accuracy improved at 5 ha scale (R2 = 0.75, RMSE = 5.3) as compared to 1 ha (R2 = 0.67, RMSE = 5.8). Our results indicate that coherence measurements from L-band repeat-pass systems can estimate forest age accurately and with no saturation. However, empirical model relationships and their accuracy are sensitive to weather events, temporal baseline, and spatial scale of analysis.
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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 | 0.001 |
| 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".