USING DISCRETE LASER PULSE RETURN INTENSITY TO MODEL CANOPY TRANSMITTANCE
Bibliographic record
Abstract
Five comparable airborne lidar datasets were collected over a mixed wood site on five separate occasions throughout a single growing season to capture changing canopy transmittance conditions. Using the small footprint discrete pulse intensity return data, the vertical pulse power distribution was reconstructed for 30 plots each containing 5 digital hemispherical photo (DHP) stations. Canopy gap fraction was calculated for the 150 DHP images collected coincident with the lidar data and used as validation for overhead canopy transmittance. By modifying a Beer-Lambert approach, we relate the ratio of lidar intensity-based ground return power / total return power to the canopy gap fraction. The results are compared to the commonly cited and utilised ground-tototal returns ratio. It is found that for the mixed wood environment studied, a lidar intensity-based power distribution ratio provides a slightly higher coefficient of determination with DHP gap fraction (r 2 = 0.92) than does the often used ground-to-total return ratio approach (r 2 = 0.86). Moreover, if the intensity power distribution ratio is modified to account for two-way pulse transmission losses within the canopy, the model requires no calibration and provides a 1:1 estimate of the overhead (solar zenith) gap fraction. The premise of the study is that the interaction between forest canopy and laser pulses emitted from an airborne lidar (light detection and ranging) mapping system can be considered in some ways analogous to the interaction of direct beam solar radiation with canopy covered environments. We examine the reconstructed vertical pulse power distribution returned from a commercial small footprint discrete pulse airborne laser scanning system and relate properties of the distribution to canopy structural and radiative transfer characteristics. In particular, we compare published gap fraction (P) algorithms to new algorithms that utilize the return intensity information. From the algorithms tested we develop a non-parameterized quasi-physical model of the spatiotemporal variation in canopy gap fraction for a mixed forest landscape. For the purpose of this analysis we make the assumption that overhead gap fraction (P) and overhead transmittance (T) are equivalent.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".