Mapping Wetlands and Land Cover Change with Landsat Archives: The Added Value of Geomorphologic Data
Bibliographic record
Abstract
While classification of remote sensing images has proven useful to quantify wetland losses, wetland mappers have always faced challenges, such as dealing with variable responses of wetlands to meteorological conditions and the obstruction of ground by the canopy in forested wetlands. In this paper, we investigate the added value of using geomorphological data, namely hillslope geometry (through Dikau shapes) and soil drainage classes, as ancillary datasets to map the evolution of wetland cover over the last 35 years. We developed an object-based image analysis method, through a case study in Quebec. Two sets of land cover scenarios (spring and fall) were generated from Landsat archives for 1978, 1985, 1992, 2001, and 2014. Results show that the global accuracy was improved by 19% to 35% when using the geomorphological data, especially soil drainage classes, confirming that their use as ancillary data significantly contributed to the classification process. However, differences were noted between spring and fall scenarios. The wetland cover of the study watershed decreased by 8%–53% between 1978 and 2014 at the expense of an increase in urban areas. Meanwhile, the agricultural land cover decreased (72%–83%) throughout the study period, while forests, water, and bare soil remained stable.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".