Urban heat island relation with land use land cover change in Hetauda sub-metropolitan city of Nepal
Bibliographic record
Abstract
Urban areas are expanding globally at the expense of natural productive land which affects the quality of life of urban residents. Hetauda sub-metropolitan city of Nepal has been undergoing rapid urban growth for the last few decades causing local climatic effects such as land surface temperature (LST) variation. Thus, exploring spatio-temporal changes in land use, land cover (LULC), and urban heat island (UHI) analysis could be an effective means of exposing local environmental issues caused by anthropogenic activities. Development in thermal Remote Sensing and Geographic Information System (GIS) has enabled the monitoring of spatial LST, UHI, and its correlation to LULC. We used Landsat 8 OLI/TIRS satellite data and a supervised classification algorithm for land use land classification for the years 1995, 2008, and 2018 in Arc map software. The spatial pattern of LST was obtained through mathematical calculation of the thermal band of Landsat images. Correlation analysis was applied to explore the relationship between LST, LULC types, and LUCL indices. The LST was higher for urban/built-up and cultivated land use types. There was approximately 4°C mean LST variation for all three years of study. The regression analysis showed a positive correlation of urban/built-up with the Normal Difference Built-Up Index (NDBI) however a negative correlation with the Normal Difference Vegetation Index (NDVI) which implies that green structure weakens the UHI effects while urban/built-up areas strengthen the UHI. Overall, the study can be useful for urban planners in sustainable urban planning and management as well as to raise public awareness of climate change and the warming effect.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".