Using Digital Camera and Eddy Covariance Data to Track Vegetation Phenology and Carbon Dioxide Fluxes in the Badain Jaran Desert
Bibliographic record
Abstract
Abstract Understanding on relationships between seasonality of vegetation phenology and photosynthesis is lacking for desert ecosystems. We used digital camera (i.e., PhenoCam) to monitor the phenology of forest (i.e., 2 sites with one being closer to a lake) and grassland (i.e., 1 site) ecosystems in the Badain Jaran Desert, China. The vegetation phenology was quantified using vegetation indices calculated from the red, green, and blue digital numbers in images obtained by the PhenoCams. Additionally, various meteorological variables were continuously measured, and gross primary production (GPP) was obtained using the eddy covariance technique at the grassland site. The difference between the phenological periods extracted from the PhenoCam images and the artificial visual method was small (≤6 days), indicating that the digital camera can effectively obtain desert vegetation phenology. The key meteorological factors affecting changes in the vegetation indices were identified, with temperature being the most important factor (i.e., correlation coefficients = 0.4–0.8 and p‐value < 0.001 for all three study sites). Although precipitation showed weak correlation with the vegetation index (correlation coefficient = 0.18–0.14, p‐value < 0.01), rapid increases in the vegetation index were observed in response to precipitation events. Vegetation indices were strongly correlated with GPP variations at the grassland, and the strongest correlation was observed in the green‐up stage (correlation coefficient = 0.67 to 0.85, p‐value < 0.001). The highest GPP lagged about 1 month behind the peak in the vegetation indices in summer (June–August). Our results can markedly improve the knowledge of desert ecosystem processes and aid in assessing the influence of future climate changes in drylands.
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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 | 0.000 | 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".