Characterizing Vegetation Response to Climatic Variations in Hovsgol, Mongolia Using Remotely Sensed Time Series Data
Bibliographic record
Abstract
One of the challenges faced by forest managers is the inability to quickly interpret forest ecosystem attributes and vegetation responses to climate change. This research aims to address this challenge by characterizing the phenological metrics and evaluating the temporal and spatial dynamics of vegetation over 12 years (2000-2011) under climate change effects in Hovsgol, Mongolia. Time series Normalized Difference Vegetation Index (NDVI) was used as an indicator to monitor vegetation response in the area. The effects of climatic variations on vegetation growth were considered through the relationship between climatic variables and NDVI. Results indicate that the growing season commonly starts in late April and ends in late October with full growth by July, and as a consequence of climate change in the area, the growing season in recent years seems to be beginning earlier. Plant stress caused by higher temperature was the most significant contributor to earlier vegetation green up since NDVI, length, and starting point of the growing season strongly depend on air temperature. Analysis of spatio-temporal heterogeneity indicates some areas with highly dynamic NDVI, particularly in the western part of the Hovsgol Lake, the high mountainous areas, and the Darhad valley. Our results suggest that temperature variations mainly determine the pattern of vegetation responses in the Hovsgol area.
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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".