Mapping wood area in forests from ground lidar and estimating their light interception using radiative transfer modeling
Bibliographic record
Abstract
• Two structurally contrasting broadleaf forest were surveyed with ground lidar. • The wood silhouette indices are estimated using two different modeling approaches. • The absorbed PAR and NIR by leaves and wood is estimated from a ray tracing model. • Wood is found to absorb about 30–35 % of NIR energy, with implications for energy balance. Leaves in forests are commonly believed to shade many stems and branches, and thus most of the absorption of photosynthetically active radiation (PAR) within a canopy is done by leaves. Near-infrared radiation (NIR) on the other hand is not used in photosynthesis, and leaf level absorptance of NIR is much lower than it is for PAR. Still, temperate broadleaf canopies absorb about 50–70 % of incoming NIR, and how this absorption is partitioned between leaves and woody structures is unclear. Here, I show that of the NIR absorbed within the canopy space, woody structures contribute about 30–35 %. The results also confirm that leaves account for about 90 % of the absorbed PAR within the canopy space. To establish these figures, I used ground lidar measurements to map leaf area and stem and branch area in 3D in two structurally contrasting broadleaf forests, and from radiative transfer modeling I estimated the fractions of PAR and NIR absorbed by leaves and wood based on illumination conditions measured at each site over multiple years. The findings have implications for the development of land surface models that consider the storage of heat by woody biomass in forests as part of the canopy energy balance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".