Urban Heat Island and Environmental Degradation Analysis Utilizing a Remote Sensing Technique in Rapidly Urbanizing South Asian Cities
Bibliographic record
Abstract
Rapid urbanization in South Asian cities has triggered significant changes in land use and land cover (LULC), degrading natural biophysical components and intensifying urban heat islands (UHIs). This study investigated the impact of LULC changes on land surface temperature (LST) and the role of biophysical indicators in enhancing urban resilience to thermal extremes. We used Landsat satellite imageries from 1993 to 2023, conducted a comprehensive analysis of LULC changes, and estimated LST variations at 6-year intervals in the Dhaka, Gazipur, and Narayanganj districts in Bangladesh. Afterward, we performed statistical analysis upon employing correlation, regression, and principal component analysis (PCA) techniques to summarize information. The results reveal that 339.13 km2 worth of urban expansion has occurred in last 30 years, with an average annual growth rate of 3.5%, accompanied by a substantial reduction in water bodies (−139.17 km2) and vegetation cover. Consequently, summer temperatures exceeded approximately 36.52 °C in dense urban areas. Also, the results highlighted the strong influence of built-up areas (BSI and SAVI) on LST, while vegetation (NDVI) and water indices (NDWI) exhibited a negative association. The findings emphasize the urgency of integrating green infrastructure and deploying sustainable urban planning policies to mitigate the potential adverse impacts of scattered urbanization in the face of climate change.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".