Modelling the vegetation–climate relationship in a boreal mixedwood forest of Alberta using normalized difference and enhanced vegetation indices
Bibliographic record
Abstract
The present study compared two popular vegetation indices (VIs), the Normalized Difference Vegetation Index (NDVI) from the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) and the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) for monitoring the temporal responses of vegetation to climate over a boreal mixedwood forest of central-eastern Alberta. Linear and nonlinear regressions and an artificial neural network (ANN), called the Back Propagation Neural Network (BPNN), were used to elucidate the influence of climatic variables (precipitation, temperature, potential evapotranspiration and aridity index) on the VIs. These climate variables were used either individually or in certain combinations as predictors to these models. It was found that using multiple climate variables as predictors predicts the VI more accurately than using a single climate variable. Furthermore, the BPNN was found to be more efficient than the linear and nonlinear regressions at modelling the VI–climate relationship. For both VIs, BPNN using precipitation, temperature, potential evapotranspiration and aridity index as predictors modelled the VI–climate relationship very accurately in both the calibration and the validation stages. This study demonstrated a promising potential for monitoring the patterns of terrestrial vegetation productivity from climate variables in a boreal mixedwood forest of western Canada.
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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".