Decomposing Arctic Land Cover - Implications of heterogeneity and scale for the estimation of energy fluxes in \nArctic tundra landscapes
Bibliographic record
Abstract
The rapid rate of environmental change in the Arctic alters the exchange of water, carbon, and energy fluxes between the land surface and the atmosphere with global impacts on ecosystems and climate. This thesis investigates the effect of mixed satellite signals on land cover mapping and on the estimation of latent heat fluxes, QE, and land surface temperature (LST) in three Arctic tundra environments in the Lena Delta (Siberia, Russia), on Bathurst Island (Canadian High Arctic), and the Barrow Peninsula (Alaska, USA). Land cover maps were derived from optical and radar remote sensing data with resolutions of 4m or better to decompose satellite mixed pixels with resolutions of 17m (CHRIS/PROBA) and 30m (Landsat5-TM). Downscaling \nland/water cover via Landsat surface albedo increased the total water surface area of the Lena Delta from 13% to 20%. Ponds, i. e., water bodies with a surface area smaller than 10^4 m, made over 95% of the total number of water bodies at all sites. Water body size-distributions deviated from a power law function for \nponds and very large lakes which could only be detected with high-resolution water body mapping. Maximum spatial differences of up to 22 W/m^2 for QE and 10°C for LST were associated with fair weather perios dominated by high net radiation and little precipitation. Uncertainties of �35% would arise in Landsat-based QE mapping, and of �30% in MODIS-based LST mapping when subpixel land cover heterogeneities are not considered. \nResults of this thesis highlight the importance of integrating detailed field studies with multi-scale remote sensing data to determine fine-scale spatial differences in energy fluxes over larger areas in Arctic tundra landscapes. Land cover maps with spatial resolutions of 2m or better are necessary to ensure the quality and representativeness of land cover statistics. This thesis proposes to compile improved subpixel land cover statistics in different Arctic ecosytems to facilitate upscaling of the surface energy balance as well as carbon fluxes to larger-scale grids. This is a crucial task regarding the great uncertainty associated \nwith the global estimation of feedbacks between the Arctic surface and the atmosphere under a changing climate.
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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 | 0.001 | 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".