Burned vegetation recovery trajectory and its driving factors using satellite remote-sensing datasets in the Great Xing’An forest region of Inner Mongolia
Bibliographic record
Abstract
Forest fire is one of the most important factors that alter a forest ecosystem’s biogeochemical cycle. Large-scale distributed burned areas lose their original vegetation structure and are more impacted by climate change in the vegetation recovery process, thus making it harder to restore their original vegetation structure. In this study, we used historical Landsat imagery and the LandTrendr algorithm in the Google Earth Engine platform to study and identify post-fire stages in the Great Xing’An Range of Inner Mongolia. Moreover, we categorized different post-fire vegetation recovery trajectories. The usefulness of spectral indices was also evaluated in the study region. We applied the Geodetector model to analyze the driving factors of the burned area vegetation regeneration process. The results show that burn severity and earth–atmosphere hydrological cycle are two main impacting factors in the short term after the fire (e.g. 5–6 years). Other climatical conditions affect vegetation recovery, including prolonged vegetation recovery process, hydrothermal circulation process and topographical conditions, seasonally frozen soil, freeze–thaw processes, and climate events. This study improves understanding of the dynamic successional processes in the burned area and the driving factors. Also, the outcomes can facilitate and support sustainable forest management of the Great Xing’An Range.
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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 | 0.001 | 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".