Analysing patterns of forest cover change and related land uses in the Tano-Offin forest reserve in Ghana: Implications for forest policy and land management
Why this work is in the frame
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Bibliographic record
Abstract
Forest cover change is a major contributing factor to global environmental change. Whereas several studies have focused on the general land use and land cover dynamics, we focus on analysing forest cover change patterns in a protected landscape taking into consideration how other land categories are increasing at the expense of the forest. In this study, we analyse forest cover change patterns and associated proximate land use factors between 1987 and 2017 using Landsat images from the Tano-Offin Forest Reserve (TOFR) in Ghana. Using the Random Forest machine learning algorithm, we classified the images into forest, developed land, and agricultural land. The study finds that forest cover losses are 1.9 and 1.4 times the amount of forest cover gains in 1987–2002 and 2002–2017, respectively. We find that even though the forest cover is more likely to recover from the agricultural land, land developers mostly targeted the agricultural land. The focus of Ghana's Forest and Wildlife Policy and the underlying process of forest cover change in the TOFR suggest that a country's forest policy should focus on a combination of diverse and spatially explicit proximate factors that are likely to threaten the integrity of forests.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it