The Use of Multipolarized Spaceborne SAR Backscatter for Monitoring the Health of a Degraded Mangrove Forest
Bibliographic record
Abstract
To determine whether multipolarized spaceborne synthetic aperture radar could be used to monitor the health of a mangrove forest, leaf area index, as well as other biophysical parameter data, from stands dominated by white mangrove (Laguncularia racemosa) and located within a degraded mangrove forest were examined in relation to backscatter coefficients from ENVISAT synthetic aperture radar scenes. The results indicate that polarization and, to a lesser extent, incident angle play a significant role in the ability to estimate both leaf area index and mean tree height. No significant linear coefficients of determination were observed between the recorded parameters and the backscatter coefficient from any of the copolarized scenes. With regards to leaf area index, r2 values of 0.82 and 0.73 were calculated for the cross-polarized data at two incident angles. For mean tree height, the linear coefficient of determination was much higher for the smaller incident angle data than for the larger incident angle data. No significant relationships were identified for stem density, basal area, or mean diameter at breast height. It is postulated that the inability of the copolarized ENVISAT advanced synthetic aperture radar data to differentiate between dead mangrove stands and healthy ones is the result of equally high backscatter resulting from strong scattering from trunk–ground double bounce and crown volume, respectively.
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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 | 0.000 | 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".