Mapping the condition of mangroves of the Mexican Pacific using C-band ENVISAT ASAR and Landsat optical data
Why this work is in the frame
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Bibliographic record
Abstract
To determine whether spaceborne C-band SAR data could be used alone, or in conjunction with optical data, for accurately mapping mangrove forests of the Mexican Pacific, four scenes of dual-polarized ENVISAT ASAR data, at two incidence angles, were collected for the Teacapán-Agua Brava-Las Haciendas estuarine-mangrove complex. Several combinations of these ASAR data were classified to determine the most optimal arrangement for mangrove mapping. In addition, corresponding Landsat TM data were classified using the same training sites. The overall accuracy in mapping these mangroves did improve when more than one polarization mode was employed. In general, the higher incidence angle data (~41º vs ~23º) provided better results. In all circumstances, the optical data alone provided higher classification accuracies. When contained as one mangrove class, the highest overall accuracy achieved using the ASAR data was 54% as compared to 76% for the optical data. When considering four separate mangrove classes, representing the four conditions typical of this system (dead, poor condition, healthy, tall healthy), overall accuracies dropped to 45% and 63%, respectively. With the limited penetration of C-band into canopies, it was difficult to separate healthy and tall healthy mangrove from palm and other terrestrial forests using the ASAR data. In addition to confusion amongst the four mangrove classes, the dead mangrove stands created considerable misclassification as they were readily misidentified with water and saltpan areas in the optical data and with agricultural lands in the ASAR data procedure. Given the advantage of ASAR for identifying dead stands from open water and saltpan, these data were then used in conjunction with the optical data to reduce the misclassification of these areas.
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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 | 0.000 | 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 it