Responses of the Carbon Storage and Sequestration Potential of Forest Vegetation to Temperature Increases in Yunnan Province, SW China
Bibliographic record
Abstract
The distribution of forest vegetation and forest carbon sequestration potential are significantly influenced by climate change. In this study, a map of the current distribution of vegetation in Yunnan Province was compiled based on data from remote sensing imagery from the Advanced Land Observing Satellite (ALOS) from 2008 to 2011. A classification and regression tree (CART) model was used to predict the potential distribution of the main forest vegetation types in Yunnan Province and estimate the changes in carbon storage and carbon sequestration potential (CSP) in response to increasing temperature. The results show that the current total forest area in Yunnan Province is 1.86 × 107 ha and that forest covers 48.63% of the area. As the temperature increases, the area of forest distribution first increases and then decreases, and it decreases by 11% when the temperature increases from 1.5 to 2 °C. The mean carbon density of the seven types of forest vegetation in Yunnan Province is 84.69 Mg/ha. The total carbon storage of the current forest vegetation in Yunnan Province is 871.14 TgC, and the CSP is 1100.61 TgC. The largest CSP (1114.82 TgC) occurs when the temperature increases by 0.5 °C. Incremental warming of 2 °C will sharply decrease the forest CSP, especially in those regions with mature coniferous forest vegetation. Semi-humid evergreen broad-leaved forests were highly sensitive to temperature changes, and the CSP of these forests will decrease with increasing temperature. Warm-hot coniferous forests have the greatest CSP in all simulation scenarios except the scenario of a 2 °C temperature increase. These results indicate that temperature increases can influence the CSP in Yunnan Province, and the largest impact emerged in the 2 °C increase scenario.
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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 | 0.000 | 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".