Foundations of Burkina Faso’s Great Green Wall: Vegetation Growth in the Sahel Since 1990
Bibliographic record
Abstract
The importance of the Sahel region as a barrier between the lush lands of sub-Saharan Africa and the unforgiving Sahara Desert has been known for decades. However, this region has not remained impervious to desertification, a process in which vegetation ceases to grow due to changing climate and poor agriculture practices, amongst other factors. In 2006, the African Union conceived a plan to halt the advancing desert, a wall of greenery stretching from coast to coast dubbed the Great Green Wall. Since its inception, the ambitious project has been widely criticized for its slow progression, and its utility has been questioned. This study is seeking to quantify vegetation growth before the Great Green Wall’s launch and after it to evaluate the importance of the project. With the Landsat satellites imaging the Earth since 1972, a large archive of imagery is available for examination. By conducting a change detection analysis on images acquired between 1990 and 2020, vegetation growth can be measured through the project’s duration as well as prior to it. Image differencing was used to detect vegetation loss and growth in four time intervals since 1990. These results were then coupled with unsupervised classifications that identified land uses. Between 1990 and 2002, a period preceding the Great Green Wall, massive vegetation loss was observed. The following period, between 2002 and 2007, saw massive growth, undoing much of previous time interval’s loss. While growth was again slightly outpaced by loss between 2007 and 2014, 2014 to 2020 saw vegetation growth soaring again. While the study’s methods allowed for the quantification of vegetation change between 1990 and 2020, a correlation between the Great Green Wall and these findings cannot be established without additional data such as precipitation records or local observations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".