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Satellite Image-Based Analysis of the Greening Impact on the Formation of an Urban Heat Island (UHI) in Abu Dhabi City

2014· article· en· W2625538491 on OpenAlexvenueno aff
Salem Issa, Nazmi Saleous

Bibliographic record

VenueArab world geographer · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban Heat Island Mitigation
Canadian institutionsnot available
Fundersnot available
KeywordsUrban heat islandVegetation (pathology)Abu dhabiMultispectral imageGeographyEnvironmental sciencePhysical geographyVegetation IndexSatelliteAbundance (ecology)Land coverSatellite imageryRemote sensingLand useNormalized Difference Vegetation IndexMeteorologyEcologyClimate change

Abstract

fetched live from OpenAlex

Two Landsat scenes (acquired on 21 May 1986 and 19 May 2000) were used to examine differences in surface temperature inside and outside the city of Abu Dhabi and to examine a potential relationship between vegetation abundance and drop in temperatures. Land cover maps of Abu Dhabi city and its surroundings spanning the period 1986–2000 were created using multispectral classification, and vegetation abundance maps using the vegetation fraction index and surface temperature maps from Landsat were created to study their spatial relationships using GIS-based multivariate statistical analysis. Differences in surface temperature between urban areas and their non-urban surroundings were also studied to assess any urban heat island (UHI) effect. A map showing the magnitude of UHI effect was created and linked to the vegetation abundance map. Results show that the UHI effect in major UAE urban centres is minimal; however, more investigation is needed to confirm this hypothesis for other UAE cities.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.788

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.008
GPT teacher head0.220
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2014
Admission routes1
Has abstractyes

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