A SAR fine and medium spatial resolution approach for mapping the Brazilian Pantanal
Bibliographic record
Abstract
The objective of this research was to utilize a dual season set of L-band (ALOS/PALSAR) and C-band (RADARSAT-2 and ENVISAT/ASAR) imagery, a comprehensive set of ground reference data, and a hierarchical object-oriented approach to 1) define the diverse habitats of the Lower Nhecolândia subregion of the Pantanal at both a fine spatial resolution (12.5 m), and a relatively medium spatial resolution (50 m), thus evaluating the accuracy of the differing spatial resolutions for land cover classification of the highly spatially heterogeneous subregion, and, 2) to define on a regional scale, using the 50 m spatial resolution imagery, the wetland habitats of each of the hydrological subregions of the Pantanal, thereby producing a final product covering the entire Pantanal ecosystem. The final classification maps of the Lower Nhecolândia subregion were achieved at overall accuracies of 83% and 72% for the 12.5 m and 50 m spatial resolutions, respectively, defining seven land cover classes. In general, the highest degree of confusion for both fine and medium resolution Nhecolândia classifications were related to the following issues: 1) scale of habitats, for instance, capoes, cordilheiras, and lakes, in relation to spatial resolution of the imagery, and 2) variable flooding patterns in the subregion. Similar reasons were attributed to the classification errors for the whole Pantanal. A 50 m spatial resolution classification of the entire Pantanal wetland was achieved with an overall accuracy of 80%, defining ten land cover classes. Given the analysis of the comparison of fine and relatively medium spatial resolution classifications of the Lower Nhecolândia subregion, the authors concluded that significant improvements in accuracy can be achieved with the finer spatial resolution dataset, particularly in subregions with high spatial heterogeneity in land cover.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".