Modelling, Characterizing, and Monitoring Boreal Forest Wetland Bird Habitat with RADARSAT-2 and Landsat-8 Data
Bibliographic record
Abstract
Earth observation technologies have strong potential to help map and monitor wildlife habitats. Yellow Rail, a rare wetland obligate bird species, is a species of concern in Canada and provides an interesting case study for monitoring wetland habitat with Earth observation data. Yellow Rail has highly specific habitat requirements characterized by shallowly flooded graminoid vegetation, the availability of which varies seasonally and year-to-year. Polarimetric Synthetic Aperture Radar (SAR) in combination with optical data should, in theory, be a great resource for mapping and monitoring these habitats. This study evaluates the use of RADARSAT-2 data and Landsat-8 data to characterize, map, and monitor Yellow Rail habitat in a wetland area within the mineable oil sands region. Specifically, we investigate: (1) The relative importance of polarimetric SAR and Landsat-8 data for predicting Yellow Rail habitat; (2) characterization of wetland habitat with polarimetric SAR data; (3) yearly trends in available habitat; and (4) predictions of potentially suitable habitat across northeastern Alberta. Results show that polarimetric SAR using the Freeman–Durden decomposition and polarization ratios were the most important predictors when modeling the Yellow Rail habitat. These parameters also effectively characterize this habitat based on high congruence with existing descriptions of suitable habitat. Applying the prediction model across all wetland areas showed accurate predictions of occurrence (validated on field occurrence data), and high probability habitats were constrained to very specific wetland areas. Using the RADARSAT-2 data to monitor yearly changes to Yellow Rail habitat was inconclusive, likely due to the different image acquisition times of the 2014 and 2016 images, which may have captured seasonal, rather than inter-annual, wetland dynamics. Polarimetric SAR has proved to be very useful for capturing the specific hydrology and vegetation structure of the Yellow Rail habitat, which could be a powerful technology for monitoring and conserving wetland species habitat.
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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 | 0.001 | 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) | 0.000 | 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".