Ecological restoration success on the Loess Plateau of China: A qualitative and quantitative exploration based on rephotography
Bibliographic record
Abstract
Abstract Ecological restoration programs such as the “Grain for Green Project” (GFGP) have significantly reduced soil erosion and increased vegetation cover on the Loess Plateau (LP) of China over the last decades. The LP has become the paragon of ecological restoration and soil and water conservation across the world and has been highlighted by numerous reports in the literature. However, there is a lack of “seeing is believing” evidence (that is, a picture is worth a thousand words) depicting the effectiveness of the ecological restoration on the LP. Rephotography (repeat photography) was used in this study to compare historical and current photographs of the same locations to explore the landscape changes associated with ecological restoration. Twenty‐nine photo pairs between 1925 and 2021 were compiled and combined with satellite imagery and the deduced Normalized Difference Vegetation Index (NDVI) between 1986 and 2021. This allowed the establishment of a rephotography library reflecting the achievements of ecological remediation and reconstruction on the LP. This in turn provides intuitive and detailed evidence for the ecological environment construction of the LP and resonates with the notion of “lucid waters and lush mountains being invaluable assets”. In addition to presenting a great opportunity to investigate landscape change over time, which is difficult to discern using other approaches, the study provides a scientific reference and a basis for government decision‐making. The potential use of rephotography for calibrating remote sensed data was also discussed. Good agreement was found between the NDVI derived from rephotography of large areas and NDVI derived from satellite imageries, which may facilitate the accurate reconstruction of time series NDVI.
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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 | 0.000 | 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 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".