Vegetation Cover Monitoring in the Abu Dhabi Region Using GIS and Remote Sensing, 1973–2010
Bibliographic record
Abstract
Change detection plays a vital role in the gathering of accurate information about the behaviour of the landscape over time. This recent study used various historical Landsat satellite images to detect changes occurring in the vegetation cover in the Abu Dhabi Region from 1973 to 2010. Three 1973 MSS Landsat imagery scenes, three 1986 TM Landsat imagery scenes, three 1992 TM Landsat imagery scenes, and six 2010 ETM+ Landsat imagery scenes covering the study area were used to build normalized difference vegetation index (NDVI) time series data by applying the integration of remote sensing and a geographic information system. Post-classification techniques were then used to analyze and interpret the vegetation index throughout the study period. It was found that the vegetation cover increased by about nine times during the study period because of the ongoing efforts by the Abu Dhabi government to improve and develop agriculture and green areas in the region. Moreover, it was also apparent that the vegetatio...
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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 | 0.001 |
| 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 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".