Derivation and assessment of forest-relevant polarimetric indices using RCM compact-pol data
Bibliographic record
Abstract
Three radar polarimetric indices, Radar Vegetation Index (RVI), Canopy Structure Index (CSI) and Radar Forest Degradation Index (RFDI)—normally associated with quad-pol (QP) synthetic aperture radar (SAR) sensors—were derived using compact polarimetry (CP) data from the Radarsat Constellation Mission (RCM). Indices were generated over a 10,000-hectare temperate mixedwood forest containing a range of complex forest structures. For comparative purposes, the same indices were generated using Radarsat-2 QP data. Agreement between CP and QP indices were assessed across broad vegetated land cover types, at the forest stand-level wherein ground plots and airborne LiDAR data were available, and at the pixel level within validation stands representing different forest types. Agreement was consistently strong for the RVI and weak for the CSI, with agreement stronger when generalized to the stand level. Indices were more informative on differences between vegetation types (e.g. forest and open wetland) than between forest types with different structures. With a radar nominal off-nadir incidence angle at 38°, RCM CP indices had narrower dynamic ranges compared to Radarsat-2 QP indices, especially the CSI, contributing to a lower level agreement for the CSI. CP data enables derivation of RVI, CSI and RFDI indices simultaneously, and provides large spatial coverage and capacity for dense time-series collection, features which are unavailable from current QP sensors.
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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 | 0.000 | 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 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".