Land Use and Land Cover Change as an Indicator of Watershed Urban Development in the Kenyan Lake Victoria Basin
Bibliographic record
Abstract
The socio-economic and ecological value of Lake Victoria is threatened by significant regional development and urbanization. This study analyzed spatial-temporal land use/land cover changes in the Kenyan Lake Victoria basin from 1978–2018 using Landsat 3, 4-5 and 8 imagery, with a view to identifying the extent and potential impacts of urbanization on the basin. Supervised image classification was undertaken following the Maximum Likelihood algorithm to generate land use/land cover maps at ten-year intervals. Results indicate that the basin is characterized by six main land use/land cover classes namely, agricultural land, water bodies, grasslands and vegetation, bare land, forests and built-up areas. Further, the results indicate that the basin has experienced net increases in built-up areas (+97.56%), forests (+17.30%) and agricultural land (+3.54%) over the last 40 years. During the same period, it experienced net losses in grassland and vegetation (-37.36%), bare land (-9.28%) and water bodies (-2.19%). Generally, the changing landscapes in the basin are characterized by conversion of natural environments to built-up environments and driven by human activities, urban populations and public policy decisions. The study therefore recommends the establishment of a land use system that creates a balance between the ecological realm and sustainable development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.000 | 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 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".