Ultra-high resolution assessment of potential impacts of vegetation shadows on satellite-derived spectral signals from small thermokarst lakes in the boreal forest-tundra transition zone (subarctic Canada)
Bibliographic record
Abstract
Warming of the circumpolar north is accelerating permafrost thaw, with implications for landscapes, hydrology, ecosystems and the global carbon cycle. In subarctic Canada, abrupt permafrost thaw is creating widespread thermokarst lakes. Little attention has been given to small waterbodies with area less than 10,000 m2, yet these are biogeochemically more active than larger lakes. Additionally, the landscapes where they develop show intense shrubification and terrestrialization processes, with increases in area and height of shrub and tree communities. Tall vegetation that is colonizing waterbody margins can cast shadows that impact productivity, thermal regime and the water spectral signal, which in satellite data generates pixels with mixed signatures between sunlit and shaded surfaces. We undertook UAV surveys using optical and multispectral sensors at long-term monitoring sites of the Center for Northern Studies (CEN) in subarctic Canada, from the sporadic (SAS/KWAK) to the discontinuous (BGR) permafrost zones in the boreal forest-tundra transition zone. This ultra-high spatial resolution data enabled spectral characterization and 3D reconstruction of the study areas. Ultra-high resolution digital surface models were produced to model shadowing at satellite overpass time (WorldView, PlanetScope and Sentinel-2). We then analyzed the impacts of surrounding vegetation and cast shadows on lake surface spectral reflectance derived from satellite imagery. Ultra-high resolution UAV data allows generating accurate shadow models and can be used to improve the assessment of errors and accuracy of satellite data analysis. Particularly, we identify different spectral signal impacts of cast shadows according to lake color, which highlight the need for special attention of this issue onto lakes with more turbidity. This research is funded by the Portuguese Foundation for Science and Technology (FCT) under the project THAWPOND (PROPOLAR), by the Centre of Geographical Studies (FCT I.P. UIDB/00295/2020 and UIDP/00295/2020), with additional support from ArcticNet (NCE), Sentinel North (CFREF) and CEN and is a contribution to T-MOSAiC. PF is funded by FCT (SFRH/BD/145278/2019).
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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 | 0.001 | 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".