Quantification of landscape change from satellite remote sensing
Bibliographic record
Abstract
Satellite remote sensing data and methods can be used to develop maps of large areas at different times in order to assess changes in forest ecosystem patterns and processes. Such maps are useful in understanding wildlife populations and habitat, forest biodiversity, and forest productivity. They may be important in ecological monitoring programs at multiple spatial and temporal scales, and could include assessment of structural aspects of the landscape, such as forest or habitat fragmentation. Quantification and measurement of landscape structure depend on the definition of landscape classes or patches, defined on the basis of more or less homogeneous elements, which differ in some measurable way from neighbouring patches. In this paper, we review some of the issues, and provide examples using satellite remote sensing data, in the quantification of landscape structure in two Canadian forests. The link between landscape structure and biodiversity is provided through the emergence of ecological understanding of species richness, species-habitat or niches, and metapopulation dynamics. Key words: forest disturbance, landscape metrics, satellite remote sensing, forest fragmentation, monitoring, biodiversity, change detection
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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 | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".