Monitoring of a National-Scale Indirect Indicator of Biodiversity Using a Long Time-Series of Remotely Sensed Imagery
Bibliographic record
Abstract
Remote sensing provides continuous, large-area coverage that supports synoptic, consistent, and repeatable monitoring of vegetation and, therefore, can be used to derive indirect indicators of biodiversity. We used a 21-year archive of Advanced Very High Resolution Radiometer (AVHRR) (1987–2007) data to assess changes in an indirect indicator of biodiversity, the Dynamic Habitat Index (DHI), which has proven useful in predicting patterns of species richness and abundance at broad spatial scales (>1 km2). The index has 3 components reflecting integrated vegetation greenness, vegetation seasonality, and minimum vegetated cover. We used a Theil–Sen nonparametric rank-based test and computed the proportion of cells with positive or negative DHI trends within both the Canadian protected area network and the Canadian ecoprovinces. In general, the smaller protected and natural areas close to urban development had relatively larger proportions of trending DHI components over the 21-year period, than the larger, more remote protected regions. Most protected areas and ecoprovinces observed an overall increase in integrated greenness and a reduction in vegetation seasonality over the analysis period. We conclude that the ability to derive trends from long time-series of remote sensing data helps focus local biodiversity monitoring programs by guiding actions to areas of the greatest observed change.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".