Impacts of climate change and human activities on vegetation NDVI in China’s Mu Us Sandy Land during 2000–2019
Bibliographic record
Abstract
There are many ecologically fragile areas similar to China’s Mu Us Sandy Land in the world, which are facing ecological and environmental problems, and improvement of its vegetation cover is essential to those regions’ sustainable development. In this study, spatiotemporal patterns in the Sandy Land’s vegetation cover between 2000 and 2019 were monitored using the Normalized Difference Vegetation Index (NDVI) data (MOD13A1-NDVI). Correlation analyses of regional climate change (precipitation and temperature) and NDVI-related land cover parameters, and quantified respective contribution rates using the residual analysis, indicated that: ( i ) accounted for 43.5% of the Sandy Land by area, zones of significant improvement in vegetation cover occurred predominantly in the east and southeast. In contrast, the Sandy Land’s central and northwest regions, accounting for 56.5% of their area, showed little change in vegetation coverage. ( ii ) in terms of overall trends in vegetation improvement, interannual changes in vegetation cover were highly spatially consistent: vegetation coverage was high in the east and south, but low in the central and western regions. ( iii ) within the Sandy Land, a correlation existed between NDVI and precipitation, and between NDVI and temperature, with the former being the stronger with a positive correlation across 99% of the Sandy Land. ( iv ) since zones with unchanged land cover contributed 85% of the change in NDVI, changes in the Sandy Land’s NDVI values were not related to changes in land cover types, but rather to the improvement of vegetation within land cover types. ( v ) the contribution rate of human activities to vegetation improvement was 62.68%, while that of climate change was 37.32%. These results can hopefully provide support for local government in the development of an ecologically sound environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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 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".