Comparison of a regional-level habitat index derived from MERIS and MODIS estimates of canopy-absorbed photosynthetically active radiation
Bibliographic record
Abstract
Earth observation data and approaches are increasingly being utilized to improve our insights into the ecological processes that influence biological diversity. Physically based indices such as the fraction of absorbed photosynthetically active radiation (fAPAR) intercepted by vegetation are particularly useful in describing variations in productivity and seasonality that can, in turn, be related to species abundances and distributions. The increasing availability of time series of fAPAR data from 2000 from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, as well from other satellites, such as ENVISAT, has resulted in a motivation to extend techniques, originally developed for MODIS biophysical data, to other sensors. In this letter we investigate and demonstrate the application of the dynamic habitat index (DHI) methodology to the MEdium Resolution Imaging Spectrometer (MERIS) Global Vegetation Index (MGVI) across the over 1 million km2 province of Ontario, Canada. Results indicate the three DHI components varied significantly in their magnitude, principally because of MODIS fAPAR estimates being larger than those observed by MERIS fAPAR. However the relationship was, in general, temporally stable across the years and the residuals tended to be spatially consistent. We conclude that following inter-calibration the production of consistent indicators of habitat and biodiversity from different data sources is possible, thus supporting global terrestrial ecological research, policy support and management.
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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 | 0.000 |
| 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 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".