Analysis of Desertification in the Upper East Region (UER) of Ghana Using Remote Sensing, Field Study, and Local Knowledge
Bibliographic record
Abstract
Remote sensing (RS) techniques based on multispectral satellite-acquired data have demonstrated an unequalled potential to detect, quantify, monitor, and map land degradation. However, RS data alone do not provide information on how land degradation affects the socio-political aspects and the economy of the population living in the affected regions. We developed the Continuous Cycle of Land Degradation (CCoLD) to quantify the severity of the land degradation in the Upper East Region (UER) of Ghana and combined it with the RS-based Normalized Difference Vegetation Index (NDVI) using Global Inventory Modeling and Mapping Studies (GIMMS) NDVI, ground data, and food-production data. In addition, we carried out a field study in the UER, a semi-arid transitional region that plays an important food-production role in Ghana, and compared the results with multi-temporal RS imagery. As well as the general ground measurements, the field study included questionnaires asking local residents to assess the impact of land degradation on their quality of life. The RS data show widespread localized degradation; the field study, supported by crop-production data, also suggests overall extensive land degradation. However, field evidence suggests ecological succession where locally adapted horsetail grasses were displaced by environmentally efficient, short-lived, quick-maturing, and dense grasses. A convergence of evidence suggests that land degradation is in the advanced stage and that more focused, community-based efforts would be needed to combat land degradation and restore the ecosystem's integrity.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".