Assessing Temporal and Spatial Variations of Vegetation Degradation in Southwest China Based on Multi-Source Remote Sensing Data
Bibliographic record
Abstract
Southwest China is an ecologically fragile area in China. Understanding temporal and spatial variations of vegetation degradation can help to formulate measures of ecosystem protection and ecological function restoration. In this study, we evaluated vegetation dynamics and identified vegetation degradation and its spatial patterns in Southwest China, based on land cover, DEM, and GLASS LAI datasets from 2001 to 2017. The key results are: (1) Though the average LAI in Southwest China showed an insignificant trend during the study period, based on grid-scale analysis, significant declines in LAI indicating vegetation degradation were identified in some areas such as western Sichuan, western and central Yunnan, and western Tibet; (2) about 10.75% of the vegetation experienced significant degradation during the study period in Southwest China; (3) degraded vegetation was mostly distributed in high elevation areas, and about 43% degraded vegetation was located in areas with the elevation between 3500m and 5000m; (4) the dominant degraded vegetation types included grassland, alpine vegetation, shrubland, and coniferous forest. Our findings can provide valuable management implications for vegetation restoration and ecological protection in Southwest China.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".