Analysis of the Effects of Drought on Vegetation Cover in a Mediterranean Region through the Use of SPOT-VGT and TERRA-MODIS Long Time Series
Bibliographic record
Abstract
The analysis of vegetation dynamics and agricultural production is essential in semi-arid regions, in particular as a consequence of the frequent occurrence of periods of drought. In this paper, a multi-temporal series of the Normalized Difference of Vegetation Index (NDVI), derived from SPOT-VEGETATION (between September 1998 and August 2013) and TERRA-MODIS satellite data (between September 2000 and August 2013), was used to analyze the vegetation dynamics over the central region of Tunisia in North Africa, which is characterized by a semi-arid climate. Products derived from these two satellite sensors are generally found to be coherent. Our analysis of land use and NDVI anomalies, based on the Vegetation Anomaly Index (VAI), reveals a strong level of agreement between estimations made with the two satellites, but also some discrepancies related to the spatial resolution of these two products. The vegetation’s behavior is also analyzed during years affected by drought through the use of the Windowed Fourier Transform (WFT). Discussions of the dynamics of annual agricultural areas show that there is a combined effect between climate and farmers’ behavior, leading to an increase in the prevalence of bare soils during dry years.
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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 | 0.001 | 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 | 0.000 | 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 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".