Prediction of Vegetation Change by Discrete Wavelet Decomposition Based on Remote Sensing Time Series Images
Bibliographic record
Abstract
The development of remote sensing technology has accumulated a large number of remote sensing image time series data for human monitoring of surface vegetation change, which provides a basis for vegetation change prediction.In order to improve the prediction accuracy of vegetation change, this paper uses discrete wavelet to decompose remote sensing image sequences at multiple scales, to explore the difference of influence of different temporal scale change characteristics on vegetation spatio-temporal change prediction, and find the best decomposition scale for vegetation change prediction.In this paper, the research object is the MODIS 13Q1 EVI image data of Hunan Province from 2001 to 2021.The discrete wavelet is adopted to obtain multi-scale vegetation trend components and detailed component sequences, and then complete the LSTM modeling prediction and comparison.The following are the experimental findings: the predictive ability of the discrete wavelet decomposition sequence group is better than that of the original EVI time series to varying degrees.The order of prediction accuracy is: monthly scale > seasonal scale > annual scale > original EVI time series.Thus, it is of reference significance to the research of application scenarios of change prediction of other regionalized variables with multi-scale characteristics.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".