Analysis of Spatio-Temporal Dynamics of Land Use in the Bouba Ndjidda National Park and Its Adjacent Zone (North Cameroun)
Bibliographic record
Abstract
We evaluated the dynamics of land use in the Bouba Ndjidda National Park (BNNP) and adjacent areas, in northern Cameroon. Using a maximum likelihood supervised classification of satellite images from 1990 to 2016, coupled with field and a socio-economic survey, we performed a robust land-use classification. Between 1990 and 2016, the area included eight classes of land use, with the largest in 1990 being the woody savannah (42.9%) followed by the gallery forest (20.2%) and the clear forest (16.3%). Between 1990 and 1999, the gallery forest lost 64.8% of its area mostly to the benefit of woody savannahs. Between 1999 and 2016, the largest loss of area was that of the clear forest, which decreased generally by 43.2% in favor of woody savannah. Rates of increase of crop field areas were 59.6% and 78.8% respectively for the periods of 1990 to 1999 and 1999 to 2016 to the detriment of woody savannahs. We attribute the changes in land use observed mainly to the increasing human population and associated agriculture, overgrazing, fuelwood harvesting and bush fires. The exploitation of non-timber forest products and climatic factors may also have changed the vegetation cover. We recommend the implementation of farming techniques with low impact on the environment such as agroforestry.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".