Mapping and monitoring indicators of terrestrial biodiversity with remote sensing
Bibliographic record
Abstract
Biodiversity is a complex concept incorporating genes, species, ecosystems, composition, structure and function. The global scientific and political community has recognized the importance of biodiversity for human well-being, and has set goals and targets for its conservation, sustainable use, and benefit sharing. Monitoring biodiversity will help meet conservation goals and targets, yet observations collected in-situ are limited in space and time, which may bias interpretations and hinder conservation. Remote sensing can provide complementary datasets for monitoring biodiversity that are spatially comprehensive and repeatable. However, further research is needed to demonstrate, for various spatial scales and regions, how remotely sensed datasets represent different aspects of biodiversity. The overall goal of this dissertation is to advance the mapping and monitoring of biodiversity indicators, globally and within Canada, through the use of remote sensing. This research produced maps that were rich with spatially explicit, spatially continuous data, filling gaps in the availability and spatial resolution or scalability of information regarding ecosystem function (primary productivity) at global scales, tree species composition at regional scales (Saskatchewan, Canada), and ecosystem structure at local scales (coastal British Columbia, Canada). Further, the remotely sensed indicator datasets proposed and tested in each of the research chapters are repeatable, ecologically meaningful, translate to specific biodiversity targets globally and within Canada, and are calculable at multiple spatial scales. Challenges and opportunities for fully implementing these or similar remotely sensed biodiversity indicators and indicator datasets at a national level in Canada are discussed, contributing to the advancement of biodiversity monitoring science.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".