Retrieving crown leaf area index from an individual tree using ground-based lidar data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Light detection and ranging (lidar) sensors, both at the terrestrial and airborne levels, have recently emerged as useful tools for three-dimensional (3D) reconstruction of vegetated environments. One such terrestrial laser scanner (TLS) is the Intelligent Laser Ranging and Imaging System (ILRIS-3D). The objective of this research was to develop approaches to use ILRIS-3D data to retrieve structural information of an artificial tree in a controlled laboratory experiment. The key crown-level structural parameters investigated in this study were gap fraction, leaf area index (LAI), and clumping index. Measured XYZ point cloud data from a systematically pruned tree were sliced to retrieve laser pulse return density profiles, which subsequently were used to estimate gap fraction, LAI, and clumping index. Gap fraction estimates were cross-validated with traditional methods of histogram thresholding of digital photographs (r2 = 0.95). LAI estimates from lidar data were corrected for the confounding effects of woody material and nonrandom foliage distribution and then compared with direct LAI measurements (r2 = 0.98, RMSE = 0.26). The methods developed in this research provide valuable lessons for application to field-level TLS data for structural parameter retrievals. Successful demonstration of analysis protocols to extract crown-level structural parameters like gap fraction, LAI, and clumping index from TLS data will be important for detailed assessments of 3D canopy radiative transfer modeling and likely will lead to more robust inversion algorithms.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it