Field experiment demonstrates the potential utility of satellite-derived reflectance indices for monitoring regeneration of boreal forest communities
Bibliographic record
Abstract
Large-scale reforestation efforts require scalable and affordable monitoring technologies to track progress. Boreal forests disturbed by oil and gas development provide an opportunity to utilize ongoing reclamation efforts to assess new monitoring technologies with potential utility to other regions. In this study, we tested the application of reflectance indices derived from high spatial resolution satellite imagery to monitor early-stage reforestation. We installed a randomized nursery experiment in Alberta, Canada wherein the total stocking density and relative abundance of four common boreal tree species were manipulated. Satellite imagery from Maxar's WorldView-2 and WorldView-3 satellites were collected over the experimental plots annually from 2017 through 2020. From these reflectance data, we calculated one texture index, three greenness indices, and one index that integrated both texture and greenness. Finally, two sets of regression models were developed, one of which included only manipulated stem density while the other set included both the density and the mean height of stems in a given year. In the first year of the experiment, we detected no significant differences in any reflectance index across the range of manipulations. Thereafter, all indices showed significant differences, with the integrated texture-greenness index demonstrating the best relative performance among those tested. Our results illustrate the potential for satellite data to yield information regarding tree density and height during the critical early years of restoration. We suggest specific steps that future researchers could use extend this work to other regions and enable potential adoption of this monitoring approach at scale.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".