Variation in understory and canopy reflectance during stand development in Finnish coniferous forests
Bibliographic record
Abstract
Inherent variability in the spectral properties of boreal forests complicates the retrieval of canopy properties such as canopy leaf area index from satellite images. Understanding the drivers of this variability could help provide better estimates of desired canopy cover properties. Field plot data from the Finnish National Forest Inventory and Landsat thematic mapper (TM) images were used to investigate the variation in canopy and understory reflectance during stand development in coniferous boreal forests. Spectral data for each plot were obtained from the Landsat pixel within which the plot center coordinates fell. Nonlinear unmixing was used to estimate the bidirectional reflectance factors (BRFs) of the “sunlit understory” and “canopy and shaded ground” components by site fertility and stand development classes. A forest albedo model was used to estimate the contribution of diffuse radiation reflected downwards from the canopy to the sunlit understory component. The sunlit understory BRF in the near-infrared spectral band decreased as the site fertility decreased and the forest matured, whereas the sunlit understory BRFs in the red and shortwave-infrared spectral bands concurrently increased. The BRFs of the canopy and shaded ground component decreased slightly during stand development, mostly in the near-infrared spectral band. Adding the diffuse contribution to the sunlit understory component changed the estimated component BRFs only a little (0.1%–1.7%) compared with those obtained using a linear mixing assumption. This effect was largest in the near-infrared spectral band and smallest in the red spectral band. For Norway spruce plots, the measured and estimated forest variables were well correlated with the BRFs in all of the studied spectral bands, but for the Scots pine plots, the correlations were notably weaker. Results show a greater importance of the fraction of visible sunlit understory on forest reflectance in Scots pine than in Norway spruce forests.
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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 | 0.001 | 0.000 |
| 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) | 0.000 | 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".