Boreal Wetland Mapping by UAV to Upscale Greenhouse Gas Emissions
Bibliographic record
Abstract
Wetlands are the largest environmental sources of methane, and interannual changes in wetland methane fluxes explain most of the variability in the global flux. Despite their importance, global wetland maps, a key component of methane models, are inaccurate for at least three reasons: (1) Their temporal variability is poorly suited for static maps; (2) Optical remote sensing cannot penetrate foliage, making water hard to identify; and (3) satellites cannot resolve their fine-scale features. Furthermore, small, unmapped water bodies may emit methane disproportionately to their size due their shallow depths inhibiting bacterial oxidation from the water column and their large perimeter: volume ratios, which introduce the potential for organic matter input and plant-mediated fluxes from shorelines. However, in boreal regions, there is conflicting evidence on the effects of water body size on methane and carbon dioxide fluxes. Here, we measure methane emissions in lakes and wetlands in an Arctic-Boreal delta and compare to open water and vegetated area with the goal of improving methane emission estimates in this region. We expect small, shallow, and vegetated wetlands to produce more methane than those bordering deeper lakes. To test this hypothesis, we map wetlands in the Peace-Athabasca Delta, a 5,000 km2 inland delta in northern Alberta, Canada containing abundant open and vegetated wetlands. We use airborne remote sensing from three sources: (1) High-resolution (<5 cm pixel) unmanned aerial vehicle (UAV) imagery, (2) Coincident L-band synthetic aperture radar (SAR) from NASA’s UAVSAR airborne imaging system, and (3) 2017 AirSWOT Ka-band interferometric SAR with color-infrared imagery. With a wavelength of 23.8 cm, UAVSAR L-band returns are ideal for mapping vegetated wetlands due to double-bounce backscatter between vegetation and the water surface. Combining two field campaigns of flux chamber gas sampling from over twenty lakes, walked shoreline surveys, and over 70 thousand UAV photos, we present a collection of wetland maps and a methodology for efficiently mapping them from UAV. We then upscale methane and carbon dioxide emissions to the scale of the delta and compare to existing estimates. These results will help improve greenhouse gas emission estimates for boreal zone wetlands.
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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".