The Potential of the Least-Squares Spectral and Cross-Wavelet Analyses for Near-Real-Time Disturbance Detection within Unequally Spaced Satellite Image Time Series
Bibliographic record
Abstract
Near-real-time disturbance detection within the remotely sensed time series has become a crucial task in many environmental applications that can help policymakers and responsible authorities to make rapid decisions and proper actions. Although there are several techniques for the near-real-time monitoring of time series, their reliability in regions with higher latitudes are not yet assessed, particularly in regions with consistent data gaps in certain time periods and with large observational uncertainties. A new method is proposed that determines a stable history period from which the least-squares spectral analysis can detect and classify the changes in newly acquired data. To validate the effectiveness of the method, both simulated and real-world vegetation time series obtained for a region in northern Alberta, Canada, are used, where there are consistent data gaps from November to April each year due to the availability of valid Landsat satellite imagery and climate conditions. Furthermore, the least-squares cross-wavelet analysis is applied to demonstrate how the temperature and precipitation time series can be used for assessment of the results. The proposed method is fast, does not rely on any interpolation methods, leaves the data gap as is, considers the observational uncertainties, and does not depend on thresholds.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".