Nonlinear trends of vegetation changes in different geomorphologic zones and land use types of the Yangtze River basin, China
Bibliographic record
Abstract
Abstract How land use changes and geomorphologic zones impact on vegetation nonlinear trends remains unclear in economically developed areas with complicated terrain. This paper explores the nonlinear trends of vegetation changes with normalized vegetation index (NDVI) in the Yangtze River basin, China, and further analyzes the effect of geomorphologic zones and land use changes on the nonlinear trend. The results show that: (1) Although monotonic increasing is the main trend type of vegetation NDVI (32.46%), reversal trends from decreasing to increasing and from increasing to decreasing account for 11.87% and 24.95%, respectively. (2) The vegetation change is mainly monotonically increasing in low altitude and relief zones, while that is mainly a reversal trend in high altitude and relief zones, indicating an increased risk of vegetation degradation with altitude and relief increasing. (3) The trends in most land use types are mainly monotonically increasing, but those in urban, forest, and grassland are mainly from increasing to decreasing, with area percentages as high as 32.29%, 27.25%, and 35.97%, indicating degradation risk. (4) The conversion of cropland and wetland to forestland has greatly promoted the vegetation restoration. However, a risk of vegetation degradation exists in land conversions between grassland and other land use types, especially the afforestation of grassland. Over all, considering the effects of both geomorphic zones and land use changes can help us better explore the driving of the nonlinear trends of vegetation changes and understand the process of vegetation dynamics.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".