Spatio-Temporal Dynamics of Land Use and Land Cover Change in the Agricultural Plains of Cul-de-Sac, Maribahoux, and Léogâne (1997–2024): An Analysis Using Remote Sensing and Landscape Metrics
Bibliographic record
Abstract
In Haiti, uncontrolled urbanization is increasing pressure on agricultural landscapes, compromising both their ecological integrity and productivity. This study examines spatio-temporal land-use changes across three agricultural plains, Cul-de-Sac, Maribahoux, and Léogâne, between 1997 and 2024, using Landsat imagery and landscape metrics of composition (percentage of landscape, PLAND) and configuration (largest patch index, LPI). The findings reveal a rapid expansion of built-up areas, primarily at the expense of farmland. In the Cul-de-Sac plain, built-up areas and bare soil grew by 152%, from 41.26 km2 to 104.11 km2, while agricultural land became highly fragmented (LPI dropping from 94.51% to 57.63%). In Maribahoux, urbanization was more moderate, partly offset by a temporary rise in woody vegetation that peaked at 20.04% in 2022 before declining. The Léogâne plain experienced a 17.38 km2 increase in built-up areas and bare soil, alongside a slight decrease in woody vegetation. Population density showed limited differences in Maribahoux and Léogâne, but marked disparities in Cul-de-Sac, where landscape transformation was more pronounced. These findings highlight increasing fragmentation of agricultural landscapes, threatening ecological connectivity and functionality, and stress the urgent need for land-use planning that curbs urban growth, protects farmland, and safeguards biodiversity.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".