Effect of Land Use Changes on the Urban Surface Temperature in Umuahia Town, Southeast, Nigeria
Bibliographic record
Abstract
The land surfaces of hot-humid tropical urban areas are exposed to significant levels of solar radiation. Increased heat gain adds to different land surface temperature profiles in cities, resulting in different thermal discomfort thresholds. Using multi-temporal (1986, 2001, and 2017) landsat data, this study examined the impact of land use change on urban temperature profiles in Umuahia, Nigeria. The findings revealed that over time, built-up regions grow in surface area and temperature at the expense of other land use. The transfer matrix, showed that approximately 59.88 percent of vegetation and 8.23 percent of bareland were respectively changed into built up during the course of 31 years. The highest annual mean temperature in built-up regions was 21.50°C in 1986, 22.20°C in 2001, and 26.01°C in 2017. Transect profiles across the landuses reveals that surface Temperature rises slowly around water/vegetation and quickly over built-up and bare land area. The study observed drastic changes in land cover with a corresponding increase in surface temperature for the period between 1986 and 2017 with consistent decrease in water bodies and bare land in the study area. Overall, the spatio-temporal distribution of surface temperature in densely built up areas was higher than the adjacent rural surroundings, which is evidence of Urban Heat Island. The impact of landuse change on urban surface temperature profiles could provide detailed data to planners and decision makers in evaluating thermal comfort levels and other risk considerations in the study area.
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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 | 0.000 | 0.000 |
| 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".