Urban Heat Island Effect Detection in the Al ‘Ain Region in 2000 and 2019
Bibliographic record
Abstract
The study evaluates the extent of land use, land cover, and land surface temperature change between August 2000 and August 2019 in the Al ‘Ain region in the southeast of the United Arab Emirates using Landsat satellite images. The satellite imageries have been classified by both unsupervised and supervised classification methods using ENVI software. In an unsupervised technique, the ISODATA clustering algorithm will be used for the classification. The resulting image will be used as a reference and for understanding the distribution of pixels with different digital numbers. In the supervised classification method, the maximum likelihood algorithm will classify the image based on the region of interest (training sets) provided by the user based on the field knowledge. Changes in land use/land cover between 2000 and 2019 were quantified using post-classification analysis in a geographic information system. Followed by atmospheric correction and LST retrieval. The results have shown a dramatic change in land cover and an obvious increase in land surface temperature over the 19 years’ study period. The composition of land use/landcover features significantly influences the magnitude of land surface temperature, and the percent cover of the urban area had an unexpected inverse effect. In contrast, the percent of vegetation is the most fundamental factor in reducing land surface temperature. Using the topical approach, the researchers suggest that the leadership can directly minimize the urban heat island effect in Al ‘Ain city by keeping the cooling effects of urban greenery.
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".