USING SATELLITE IMAGERY TO ESTIMATE THE RATE OF VEGETATION COVER IN THE WATERSHED OF CHOTT CHERGUI -WILAYA OF EL BAYADH (HIGH STEPPE PLAINS OF ALGERIA)
Bibliographic record
Abstract
Several factors influence the extent of water erosion: the length and the gradient of the slope, soil texture, the extent of vegetation cover. However it is the soil cover that remains the dominant issue on the effective response against erosion by protective the surface facing the erosive forces of raindrops and runoff. Our work aims to estimate the rate of vegetation recovery using satellite imagery in a semiarid region of the watershed of the Chott Chergui within the wilaya of El Bayadh. It is estimated the overall rate of recovery of vegetation on the site and its linking with the values of the normalized difference vegetation index (NDVI) corresponding to the image. The result has developed a map of three classes of vegetation cover, the first is completely denuded of vegetation, it is sandy areas, with rocky outcrops, or areas of buildings. The second vegetation with a less than 10 %, which corresponds to natural vegetation growing at altitudes moderately important to mountainsides. The third class has a recovery rate above 10% representing the agricultural parcels along the banks of the wadis, or reforested areas (that cover more than 30 %).
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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 | 0.000 |
| 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 | 0.000 | 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".