Do we miss the hot spots? – The use of very high resolution aerial photographs to quantify carbon fluxes in peatlands
Bibliographic record
Abstract
Abstract. Accurate determination of carbon balances in heterogeneous ecosystems often requires the extrapolation of point based measurements. The ground resolution (pixel size) of the extrapolation base, e.g. a land-cover map, might thus influence the calculated carbon balance, in particular if biogeochemical hot spots are small in size. In this paper, we test the effects of varying ground resolution on the calculated carbon balance of a boreal peatland consisting of hummocks (dry), lawns (intermediate) and flarks (wet surfaces). The generalizations in lower resolution imagery led to biased area estimates for individual micro-site types. While areas of lawns and hummocks were stable below a threshold resolution of ~60 cm, the maximum of the flark area was located at resolutions below 25 cm and was then decreasing with coarsening resolution. Using a resolution of 100 cm instead of 6 cm led to an overestimation of total CO2 uptake of the studied peatland area (approximately 14 600 m2) of ~6% and an underestimation of total CH4 emission of ~11%. To accurately determine the surface area of scattered and small-sized micro-site types in heterogeneous ecosystems (e.g. flarks in peatlands), a minimum ground resolution appears necessary. In our case this leads to a recommended resolution of 25 cm, which can be derived by conventional airborne imagery. The usage of high resolution imagery from commercial satellites, e.g. Quickbird, however, is likely to underestimate the surface area of biogeochemical hot spots. It is important to note that the observed resolution effect on the carbon balance estimates can be much stronger for other ecosystems than for the investigated peatland where the relative hot spot area of the flarks is very small and their hot spot characteristics with respect to CH4 and CO2 fluxes is rather modest.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".