Mapping the vegetation disturbances over the Tibetan Plateau
Bibliographic record
Abstract
The Tibetan Plateau is one of the most sensitive areas responding to global environmental changes, especially global climate change, and has thus been deemed an important indicator of global change. The vegetated areas in Tibetan Plateau are expected to respond to environmental change because vegetation cover is a key part of the ecosystem. However, the vegetation disturbance behavior in the region remains poorly understood. Since the various change detection algorithms perform differently across complex natural systems, the combination of different approaches is currently a mainstream solution for better quantification of vegetation disturbances. The main objective of this study was to map the vegetation disturbances across the Tibetan Plateau using satellite data and a combination of change detection algorithms. We applied an ensemble strategy and satellite data to map the three decades of vegetation disturbances over the Tibetan Plateau. The two leading disturbance detection algorithms (Continuous Change Detection and Classification algorithm, CCDC; Landsat-based detection of Trends in Disturbance and Recovery algorithm, LandTrendr) were involved in the ensemble strategy with a Random Forests-based fusion for aggregating the classifiers. The reference data were taken from a total of 15,680 manually interpreted Landsat pixels, including 1,739 disturbed vegetation points, 3,696 stable vegetation points, and 10,245 non-vegetation points. It is found that a total area of about 105.83 M ha has experienced vegetation disturbance with considerable spatial variability across the Tibetan Plateau over the past three decades, and large differences among the disturbance patches were found. The identified unexpected scale of vegetation disturbance can further facilitate the understanding of the dramatic ecological changes in the ecologically fragile Tibetan Plateau region in response to climate change and more frequent human activities.
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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 | 0.001 | 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".