Mapping Land Use and Land Cover Change Detection Using Supervised Maximum Likelihood Classification of Multi-Temporal Landsat Imagery: A Case Study of Nakuru County
Bibliographic record
Abstract
Monitoring land use and land cover (LULC) change is crucial for analyzing the socio-economic and environmental effects of land development and management. This research aims to explore the dynamics of urban growth and LULC changes in Nakuru County, Kenya, over a decade from 2014 to 2024. Supervised Maximum Likelihood Classification (MLC), a popular remote-sensing technique, was utilized for the multi-temporal analysis of Landsat 8 Operational Land Imager (OLI) data acquired from the United States Geological Survey (USGS) Earth Explorer website. Five dominant land-cover classes were distinguished, including built-up areas, bare land, sparse vegetation, dense vegetation, and water bodies. The findings reveal that rapid urbanization and agricultural expansion are the primary forces behind LULC changes, resulting in significant loss of green spaces, forest cover, and water resources. These alterations have led to ecosystem disruption and increased environmental stress throughout Nakuru County. The results underscore the urgent need for sustainable land-use planning and management practices that consider the implications of urban growth. Integrating remote-sensing data into decision-making processes is crucial for formulating policies that effectively mitigate land degradation and promote environmentally sustainable urban development in rapidly expanding regions. The findings provide spatially explicit evidence to guide sustainable land management policies under Kenya's Vision 2030 and United Nations Sustainable Development Goals (SDGs 11 and 15).
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".