Vegetation loss and recovery analysis from the 2015 Gorkha earthquake (7.8 Mw) triggered landslides
Bibliographic record
Abstract
The 2015 Gorkha earthquake (7.8 Mw) triggered thousands of landslides in the highlands of central Nepal, causing widespread vegetation damage. After the earthquake, several attempts were made by the government to recover damaged vegetation; however, the efficacy of artificial restoration (from public finance) vs. self-ecological restoration is unknown. We analyze the vegetation recovery process of the areas impacted by the 2015 Gorkha earthquake landslides with a dual-lens: (1) remote sensing and (2) public finance and policy. Using remote sensing, Vegetation Recovery Rate (VRR) is estimated from the normalized difference vegetation index (NDVI) from Landsat imagery between 2015 and 2021. Then public finance data is analyzed to compare the efficacy of vegetation recovery from the artificial vs. self-ecological restoration. The study examines fourteen severely impacted districts from the Gorkha earthquake in 2015. Out of 24,826 landslides triggered by the earthquake, ~95% of vegetation damage was caused by 13,670 large landslides (with area >0.09 ha). A total of 8651.58 ha of vegetation was lost due to landslides induced by the 2015 Gorkha earthquake. About 4442 ha (51%) of such lost vegetation has been restored so far. Only 9.5% of this restored vegetation was due to artificial restoration, while the remaining 90.5% was by self-ecological restoration process in protected areas. Furthermore, VRR analysis showed that at least nine years are required to restore vegetation cover to the pre-earthquake level (R2 =0.91). The government had invested 3.73 million USD in this duration for artificial restoration. Our findings suggest that strict protection promotes self-ecological restoration, an effective tract for vegetation recovery, over artificial interventions. Findings provide insights for plausible decision-making in restoring lost vegetation due to earthquake-triggered landslides.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".