Vegetation shadow casts impact remotely sensed reflectance from permafrost thaw ponds in the Subarctic forest-tundra zone.
Bibliographic record
Abstract
Abstract Thermokarst lakes and ponds are a common landscape feature resulting from permafrost thaw, but their intense greenhouse gas emissions are still poorly constrained as a feedback mechanism for global warming because of their diversity, abundance and remoteness. Thermokarst waterbodies may be small and optically diverse, posing specific challenges for optical remote sensing regarding detection, classification and monitoring. This is especially relevant when accounting for external factors that affect water reflectance, such as scattering and vegetation shadow casts. In this study in the forest-tundra zone of northern Canada, we evaluated the effects of shadowing across optically diverse waterbodies. We used ultra-high spatial resolution multispectral data and digital surface models obtained from Unmanned Aerial Systems for modelling and analyzing shadow effects on water reflectance at Earth Observation satellite overpass time. Our results show that shadowing causes variations in reflectance, reducing the usable area of remotely sensed pixels for waterbody analysis in small lakes and ponds. The effects were greater on brighter and turbid inorganic thermokarst lakes embedded in postglacial silt-clay marine deposits and littoral sands, where the mean reflectance decrease was from − 51 to -70%, depending on the wavelength. These effects were also dependent on lake shape and vegetation height and were amplified in the cold season due to low solar elevations. Remote sensing will increasingly play a key role in assessing thermokarst lake responses and feedbacks to global change, and this study shows the magnitude and sources of optical variations caused by shading that need to be considered in future analyses.
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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 | 0.000 | 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".