Phenology and vegetation change measurements from true colour digital photography in high Arctic tundra
Bibliographic record
Abstract
Manual collection of accurate phenology data is time-consuming and expensive. In this study, we investigate whether repeat colour digital photography can be used (1) to identify phenological patterns, (2) to identify differences in vegetation due to experimental warming and site moisture conditions, and (3) as a proxy for biomass. Pixel values (RGB) were extracted from images taken of permanent plots in long-term warming experiments in three tundra communities at a high Arctic site during one growing season. The Greenness Excess Index (GEI) was calculated from image data at the plot scale (1 × 1 m) as well as for two species, Dryas integrifolia and Salix arctica. GEI values were then compared to corresponding field-based phenology observations. GEI and Normalized Difference Vegetation Index (NDVI) values from a paired set of true colour and infrared images were compared with biomass data. The GEI values followed seasonal phenology at the plot and species scale and correlated well with standardized observations. GEI correlated well with biomass and was able to detect quantitative differences between warmed and control plots and the differences between communities due to site-specific moisture conditions. We conclude that true colour images can be used effectively to monitor phenology and biomass in high Arctic tundra. The simplicity and affordability of the photographic method represents an opportunity to expand observations in tundra ecosystems.
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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 | 0.000 |
| 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) | 0.000 | 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".