Spatial variability in tropical forest leaf area density from multireturn lidar and modeling
Bibliographic record
Abstract
Abstract Leaf area index and leaf area density profiles are key variables for upscaling from leaves to ecosystems yet are difficult to measure well in dense and tall forest canopies. We present a new model to estimate leaf area density profiles from discrete multireturn data derived by airborne waveform light detection and ranging (lidar), a model based on stochastic radiative transfer theory. We tested the method on simulated ray tracing data for highly clumped forest canopies, both vertically homogenous and vertically inhomogeneous. Our method was able to reproduce simulated vertical foliage profiles with small errors and predictable biases in dense canopies (leaf area index = 6) including layers below densely foliated upper canopies. As a case study, we then applied the method to real multireturn airborne lidar data for a 50 ha plot of moist tropical forest on Barro Colorado Island, Panama. The method is suitable for estimating foliage profiles in a complex tropical forest, which opens new avenues for analyses of spatial and temporal variations in foliage distributions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".