Monitoring Vegetation, Water, and Land Surface Temperature in Dubai and Muscat Over Three Time Periods
Bibliographic record
Abstract
The goal of this study is to monitor the changes caused by climate change on vegetation's health, land surface temperature, and water bodies in Dubai, UAE, and Muscat, Oman, focusing on three different years: 2018, 2021, and 2024.The monitoring procedure is carried out using Landsat 8, which is known for its high-resolution imagery.The satellite was used to capture several images for the proposed years; then, the images will be processed using ERDAS Imagine software, which is an advanced remote sensing tool.The software was initially used to classify imagery using supervised algorithms.Subsequently, it was used to assess vegetation's health, land surface temperature, and water availability using the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and Normalized Difference Water index (NDWI), respectively.Results indicate that Dubai underwent a rapid increase in vegetation cover over the years, which perfectly aligns with the city's vision.On the other side, Muscat faced an inconsistent variation in vegetation cover; a rise occurred between 2018 and 2021, followed by a fall between 2021 and 2024.Additionally, both cities experienced a gradual decrease in LST values, which highlights the cooling effect of vegetation expansion.However, both cities faced a decrease in NDWI values, indicating lower water availability over the years.These findings show the critical role of satellite monitoring in future planning for urban development and vegetation expansion.
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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 | 0.000 |
| 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 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".