Study on Global Vegetation Dynamics Based on Remote Sensing Big Data
Bibliographic record
Abstract
Vegetation is one of themostimportant factors in maintaining the Earth's ecological environment and has significant value of theecosystem. Vegetation monitoring is an important means of detecting dynamic changes in vegetation. The Remotely sensed NDVI (Normalized DifferenceVegetation Index) which isbased on the absorption of light by plants can respond well to changes in vegetation dynamics, thus becoming a commonly used indicator in large-scale monitoring. In this study, the global PKU GIMMS NDVI was used as the data source (two images per month, a total of 360 global images) for the period from 2001 to 2015. First, the global spatial distribution of vegetation NDVI was analyzed by averaging theNDVI over the fifteen-year period. Then the trend of vegetation NDVI was analyzed using a one-way linear regression model. The results of the study showed that vegetation NDVI was higher in regions such as Russia, South America, Central Africa and Southeast Asia. Regions with a significant increase in the rate of change of vegetation NDVI, such as Russia, the Czech Republic, China, the United States and Brazil, and regions with a significant decrease in the rate of change of vegetation NDVI, such as Kazakhstan, Nigeria, Canada and Argentina, were found.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".