Spatial and Temporal Change of Land Cover in Protected Areas in Malawi: Implications for Conservation Management
Bibliographic record
Abstract
Protected areas (PAs) transform over time due to natural and anthropogenic processes, resulting in the loss of biodiversity and ecosystem services. As current and projected climatic trends are poised to pressurize the sustainability of PAs, analyses of the existing perturbations are crucial for providing valuable insights that will facilitate conservation management. In this study, land cover change, landscape characteristics, and spatiotemporal patterns of the vegetation intensity in the Kasungu National Park (area = 2445.10 km2) in Malawi were assessed using Landsat data (1997, 2008 and 2018) in a Fuzzy K-Means unsupervised classification. The findings reveal that a 21.12% forest cover loss occurred from 1997 to 2018: an average annual loss of 1.09%. Transition analyses of the land cover changes revealed that forest to shrubs conversion was the main form of land cover transition, while conversions from shrubs (3.51%) and bare land (3.48%) to forest over the two decades were comparatively lower, signifying a very low rate of forest regeneration. The remaining forest cover in the park was aggregated in a small land area with dissimilar landscape characteristics. Vegetation intensity and vigor were lower mainly in the eastern part of the park in 2018. The findings have implications for conservation management in the context of climate change and the growing demand for ecosystem services in forest-dependent localities.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".