The impact of land use and land cover on land surface temperature in an Indian riverine town over a decade and how it varied post-lockdown
Bibliographic record
Abstract
Abstract In towns and cities in developing countries, negligence in consistently regulating the growth of urban sprawl is commonplace. The purpose of the study was to analyze spatiotemporal changes in land use land cover (LULC) and their impact on land surface temperature (LST) in Balurghat, Dakshin Dinajpur district, West Bengal, India. The results revealed a decrease in the vegetation cover (64–44%) and an increase in the built-up area (14–39%) from 2012 to 2022. Over the study period, built-up regions and bare land had the highest temperatures, ranging from 20.6°C to 24.96°C, and waterbodies had the lowest temperatures, ranging from 17.85°C to 20.47°C. From 2012 to 2017, LST exhibited an increasing trend. However, after the lockdown, LST declined slightly in 2022. The mean LST variations in the study area from 2012 to 2022, presenting a pre- and post-pandemic scenario, were also highlighted in this study. Furthermore, this study emphasized the correlation analysis between LST and four spectral indices, which are the Normalized Difference Built-up Index (NDBI), the Normalized Difference Vegetation Index (NDVI), the Soil Adjusted Vegetation Index (SAVI), and the Modified Normalized Difference Water Index (MNDWI). Multiple linear regression (MLR) containing NDVI and MNDWI with LST has been consistently the best-fit model for 2012, 2017 and 2022. These models have been established using various statistical tools, primarily the Akaike information criterion (AIC) model selection and the Inflation Factor (VIF). The results provide a framework for sustainable urban design and development, which can serve as a resource for policymakers and increase public understanding.
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 | 0.000 | 0.001 |
| 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".