Forest Disturbance and Restoration in China's North-South Transition Zone: A Case from the Funiu Mountains
Bibliographic record
Abstract
Accurate monitoring and assessment of forest disturbance and recovery dynamics are essential for sustainable forest management, particularly in ecological transition zones. This study analyzed forest disturbance and recovery patterns in China’s Funiu Mountains from 1991 to 2020 by integrating the LandTrendr algorithm with space-time cube analysis. Using Landsat time series data and the Geodetector method, we examined both the spatiotemporal characteristics and driving factors of forest change across three periods. The results showed that (1) between 1991 and 2020, the study area experienced 131.19 km2 of forest disturbance and 495.88 km2 of recovery, with both processes most active during the 1990s; (2) spatiotemporal analysis revealed that both disturbance and recovery patterns were predominantly characterized by cold spots, suggesting relatively stable forest conditions despite localized changes; (3) human activities were the primary drivers of forest disturbance in the early period, while forest recovery was consistently influenced by the combined effects of topographic conditions and precipitation. Additionally, forest fires emerged as an important factor affecting both disturbance and recovery patterns after 2010. These findings enhance our understanding of forest dynamics in transition zones and provide empirical support for regional forest management strategies. The results also highlight the importance of considering both spatial and temporal dimensions when monitoring long-term forest changes.
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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".