Wetland mapping with multi-temporal sentinel-1 & -2 imagery (2017 – 2020) and LiDAR data in the grassland natural region of alberta
Bibliographic record
Abstract
ABSTARCTIn the Grassland Natural Region (GNR) of southern Alberta, wetlands are relatively small-sized disconnected prairie pothole marshes, swamps, and shallow open water habitats often surrounded by grasslands, parkland forests, agricultural lands, and urban areas. These wetlands are susceptible to climatic variability, resulting in temporally and spatially dynamic habitats that are difficult to map accurately. This study hypothesizes that seasonal synthetic aperture radar (SAR) and optical imagery will capture temporal variations of wetlands in the spring/summer and fall months of 2017, 2018, 2019, and 2020. We propose that these data combined with topographic variability offered by LiDAR-derived topographic wetness index (TWI) shall result in the accurate delineation of the wetlands. Using a combination of open-access government databases, we generated ground and training data to develop the classification models and perform accuracy assessments. The wetland map products’ overall accuracy results ranged from 63.2% to 75.7%. The pixel-based random forest (RF) classified dataset (Dataset 5 – multi-temporal (2017–2020) S1 SAR (VH) and S2 optical (B8 and B11) bands fused with TWI) had the highest overall accuracy (75.6%). The RF result significantly outperformed similar CART (Classification and Regression Trees) and SVM (Support Vector Machine) classifications, which had overall accuracies of 67.4% and 63.2%, respectively. In addition, the RF optimal wetland product had the best combination of F-score values for wetland and upland classes: 0.61 (marsh), 0.82 (open water), 0.75 (swamp), and 0.8 (uplands). Overall, the methodology adopted in this study is promising for mapping the spatial distribution of wetland habitats across the seasonally dynamic GNR of Alberta.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".