Forest Cover Indicator Based on Multi-scale Remote Sensing Information
Bibliographic record
Abstract
Forests cover nearly half of Canada's landmass. While forested lands are often viewed as areas of wood production, forests also provide wildlife habitat and ecosystem mechanisms to clean air and water, and sequester carbon. Measuring the area of forested land in Canada on a regular basis provides an indicator of the availability of these important ecosystem services. This study examines the capability of coarse spatial resolution satellite data to quantify forest cover based on crown closure estimates. Field data, high and medium resolution remote sensing imageries, and a canopy radiative transfer model (Five-Scale) are used to assess the mapping potential of 1-km data, such as AVHRR, VGT and MODIS. The main challenge of the research is in the transition zone between boreal forest and the tundra, where few inventory data are available and the trees are found in clusters. The results will be used in a Canada-wide forest indicator that aimed at monitoring yearly changes in the forest extend due in part to forest fires, insect defoliation, regrowth and changes due to climate change. Different methodologies and initial results of this ongoing activity are presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| 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) | 0.002 | 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".