Remote sensing of boreal forest biophysical and inventory parameters: a review
Bibliographic record
Abstract
The growing need to manage vegetation resources at regional and global spatial scales has led to the increased use of remote sensing technologies among forestry scientists and managers for use in their investigation and supervision of forested landscapes. With an array of extant and developing airborne and satellite sensors, as well as multiple analysis techniques, there is a need to discern the most acceptable methods with which to examine remotely sensed imagery for forest ecosystem parameters. We have reviewed the literature on the remote sensing of boreal forests in order to determine if common frameworks for monitoring and assessing change in forest biophysical and inventory parameters could be developed. Other important remote sensing techniques such as those for change detection and land cover identification were also examined. Our review examined studies documented within the scientific literature that involved the extraction of information on forest parameters with remote sensing instruments. Primarily, we focused on studies within the boreal forest, which plays an important part in the Earth-atmosphere system and contributes significantly to the global economy through forest-derived products and resources. Additionally, the boreal forest has served as the primary investigation site for many important forest remote sensing discoveries. In this review, we considered the forest parameter that was examined, the remote sensing platform used, and the calibration and validation accuracies for the study. The most effective methodology for examining each parameter, considering spatial scale, is described. In general, a fusion of multiple sensors provided the most accurate approach for parameter extraction; however, this may not be the most appropriate methodology for all studies, due to spatial and temporal considerations. In each case, we attempted to consider which methodology works the best in a variety of scenarios.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".