Extracting tree crown properties from ground-based scanning laser data
Bibliographic record
Abstract
The spatial organization of above-ground plant material plays an important role in controlling not only plant functional activities like photosynthesis and evapotranspiration, but also the photo-vegetation interactions. To improve our understanding of such interactions, the acquisition of highly detailed information about the 3D architecture of individual plants and communities of plants is required. Recently, Light detection and ranging (LiDAR) sensors, both at the ground and the airborne-level, have emerged as useful tools for mapping 3D plant structure. One such ground-based instrument is the Intelligent Laser Ranging and Imaging System (ILRIS 3D), which was developed at Optech Incorporated. This laser scanner, generates a 3D digital reconstruction of any scene, by actively emitting laser pulses and recording the time elapsed for the return of a pulse, thereby measuring the distance of any given object. It is the objective of this research to utilize the ILRIS 3D to measure structural, and biophysical information of individual trees for use as direct inputs into complex radiative transfer models. The key parameters under investigation are crown dimensions (i.e. shape, area, and volume), crown-level gap fraction (GF) and crown- level leaf area index (LAI). The ILRIS 3D was used to acquire 3D point clouds of an artificial 6' Ficus tree, in a controlled laboratory environment. Measured XYZ point cloud data was segmented to retrieve laser pulse return density profiles, which subsequently were used to estimate gap fraction and LAI . Gap fraction estimates were cross-validated with traditional methods of histogram thresholding of digital photographs (r2= 0.96). Crown LAI estimates were compared with the actual values (r2= 0.95, RMSE = 0.45). The next challenge was to implement the developed algorithms to real crowns, namely olive (Olea europaea L.) orchards in southern Spain. Individual tree-level ILRIS 3D data was collected from 24 structurally diverse crowns. Crown dimensional profiles were extracted for ILRIS data that was collected from a horizontal view (i.e ground-based) and a nadir view (i.e from platform 12 meters above ground). Preliminary retrievals from the olive orchards dataset is described here, while current ongoing field measurements are being conducted to validate the findings. Successful demonstration of extracting crown-level structural parameters like gap fraction and LAI from ground-based LiDAR will be important new information that can be used for detailed radiative transfer modeling in olive orchards and likely lead to more robust inversion algorithms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| 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) | 0.001 | 0.001 |
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".