A multi‐sensor approach to wetland flood monitoring
Bibliographic record
Abstract
Abstract The Peace–Athabasca Delta, Canada, is a 3900 km2 freshwater wetland complex, characterized by numerous river channels, lakes and wetland basins. Periodic flooding of the wetland basins is necessary to maintain the productivity in the delta. The delta experienced a 22 year drying trend between 1974 and 1996, resulting in considerable changes in water boundaries. Availability of water is the dominant mechanism driving the ecosystem response. As such, accurate and frequently updated maps of floodwater extent and vegetation types are necessary for proper wetland management. Owing to the large size, remoteness, and dynamic nature of the delta, flood mapping is only feasible using remote sensing. This paper evaluates the use of radar and visible/infrared satellite imagery for mapping the extent of flooded wetland areas. The extent of standing water in the delta during May 1996 and May 1998 was mapped using RADARSAT and SPOT imagery. The RADARSAT scenes, the SPOT scenes, and a combination of the two were, for each year, classified into open water, flooded vegetation, and non‐flooded land using a Mahalanobis distance classifier. When the 1996 RADARSAT scene and the 1996 SPOT scene were classified separately, they resulted in Kappa coefficients of 70% and 66% respectively. The accuracy increased to 92% when the RADARSAT and the SPOT scenes were combined and classified together. Classification of the 1998 RADARSAT scene and the 1998 SPOT scene resulted in accuracies of 76% and 80% respectively, whereas a combination of the two scenes resulted in an accuracy of 92%. The results from this study indicate that the information from radar and visible/infrared satellite imagery is complementary and that flood mapping in wetland areas can be achieved with higher accuracy if the two image types are used in combination. Copyright © 2002 John Wiley & Sons, Ltd.
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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 | 0.001 | 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".