Monitoring vegetation recovery after China's May 2008 Wenchuan earthquake using Landsat TM time‐series data: a case study in Mao County
Bibliographic record
Abstract
Abstract The Wenchuan earthquake (Richter scale 8) on 12 May 2008 in southwestern China caused widespread ecosystem damage in the Longmenshan area. It is important to evaluate natural vegetation recovery processes and provide basic information on ecological aspects of the recovering environment after the earthquake. To circumvent the weather limits of remote sensing in the Wenchuan earthquake‐hit areas, and to meet the need for regional observation analyses, three Landsat TM images pre‐ and post‐earthquake in Mao County were used for analysis. Post‐earthquake normalized difference vegetation index (NDVI) values were compared to pre‐earthquake values with an NDVI‐based index differencing method to determine the extent to which the vegetation was damaged in relation to the pre‐earthquake pattern, and the rate of recovery was evaluated. The spatial characteristics of vegetation loss and natural recovery patterns were analyzed in relation to elevation, slope and aspect. The results indicated that severely damaged sites occurred mainly in river valleys, within a range of 1,500–2,500 m elevation and on slopes of 25–55°. The distance from rivers, rather than the distance from active faults, controls the damage patterns. After 1 year of natural regeneration, 36 % of the destroyed areas showed a decrease in NDVI value, 28.8 % showed very little change, 19.1 % showed an increase, and 16.1 % also increased with a recovery rate greater than 100 %. Moreover, there is a good correlation between recovery rate and both slope and elevation, but recovery patterns in the damaged area are complicated. Our results indicate that natural recovery in this arid valley is a slow process.
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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 | 0.001 | 0.001 |
| 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 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".