Evaluation of MODIS-based Vegetation Restoration After the 2008 Wenchuan Earthquake
Bibliographic record
Abstract
Mountainous vegetation recovery after major earthquakes has been significant for preventing post-seismic soil erosion and geo-hazards. Magnitude 7.9 Wenchuan earthquake struck western Sichuan, China in 2008, caused salient number of geological hazards and caused major vegetation damage. This recovery process could be a very long and fluctuating. And Remote sensing has been an important method of vegetation restoration monitoring. This study aims to use remote sensing technology data to analyze the post-seismic vegetation damage and recovery situation of the 2008 Wenchuan earthquake over years to 2020, and find the relevant factors affecting the restoration of ecological vegetation. This paper examined the vegetation recovery processes following the 2008 Wenchuan earthquake using 16-day interval MODIS normalized difference vegetation index time series from 2000 to 2020. It has been found that the vegetation recovery rate generally increased by years, the entire study area has recovered 49.89% by 2020. In addition, by combining remote sensing imagery and geographic information data, we also found that the heavily affected vegetation areas are mainly located along the southern part of the earthquake surface rupture, where have a very high slope which mainly over 60 degrees. It makes this part having higher probabilities to experiences secondary natural hazards and a fluctuating vegetation recovery rate. Through this research, it can be concluded that remote sensing is an effective method for monitoring vegetation dynamics in a long series. For soil and soil retention and ecological vegetation protection of landslides after the earthquake, it should be more concerned about the areas where have steep slope that over 60 degrees.
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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 | 0.001 | 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 | 0.000 | 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) | 0.001 | 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 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".