Vegetation disturbances characterization in the Tibetan Plateau from 1986 to 2018 using Landsat time series and field observations
Bibliographic record
Abstract
Abstract Disturbances in vegetated land could dramatically affect the process of vegetation growth and reshape the land cover state. The overall greenup of vegetation on the Tibetan Plateau (TP) has almost served as a consensus to date. However, we still lack consistent acquisitions on the timing, the spatial patterns, and the temporal frequency of vegetation disturbance over the TP, limiting the capacity for planning land management strategies. Therefore, we explored the spatiotemporal pattern and variation of vegetation disturbances across the TP during the past decades and analyzed the disturbance agents. We utilized 37-year Landsat time series images and field observations coupled with a temporal segmentation approach to characterize the spatiotemporal pattern of vegetation disturbances across the TP for the period 1986–2018. The results from this study revealed that 75.71 M ha (accounting for 29.34% of TP’s area) vegetation area underwent at least one disturbance, of which 8.44 M ha area ever experienced large-scale disturbances (disturbance area greater than 0.9 ha and disturbance magnitude (the difference between the spectral value of pre-disturbance and that of post-disturbance) over 0.2). Further, the spatial distributions of these large-scale disturbances varied over time: before 2002, the disturbed sites were evenly distributed over the southeast part of the TP probably induced by overgrazing and unscientific livestock management, while after 2002, most disturbances were concentrated in the south of the Yarlung Tsangpo, mainly caused by anthropogenic activities, such as urban area, roadways, railway, and water control projects. This study presents an effort to characterize vegetation disturbances and their variations over the past decades on the TP, which provides crucial insights toward a complete understanding of vegetation dynamics and its causal relationship with human activities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 teacher head, 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".