Monitoring Vegetation Change in Tozeur Oases in Southern Tunisia by Using Trend Analysis of MODIS NDVI Time Series (2000–2016)
Bibliographic record
Abstract
Oasis ecosystems are highly vulnerable to environmental changes. To determine the state of vegetation in these ecosystems, monitoring systems must be provided with data on cultivated areas. These data can be obtained in part by using satellite observation systems with high and moderate spatial resolution and high temporal repetitiveness; these systems offer a synoptic vision that makes them a particularly appropriate information source for effectively estimating such data. In this study, we describe an approach to monitor the changing dynamics of Tozeur oases in southwestern Tunisia. To this end, we used a time series decomposition method (seasonal and trend decomposition using loess) to extract the trends from a multi-year time series at the scale of a geographical point (250 m × 250 m pixel) across the MOD13Q1 time series (2000–2016) of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, at a 250-m spatial resolution time series. These methods were tested with the final aim of setting up an oasis monitoring system based on the analysis of time signatures obtained from MODIS images. The results showed that it was possible to identify the main types of irrigated perimeters present in the Djerid region and retrospectively trace their recent development history.
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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 | 0.002 |
| 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".