Shrub characterization using terrestrial laser scanning and implications for airborne LiDAR assessment
Bibliographic record
Abstract
Sagebrush-steppe ecosystems across the United States Intermountain West are experiencing major structural and functional changes. Scientists and managers need effective technologies to understand such dynamic changes across broad spatial scales. We tested the capacity of terrestrial laser scanning (TLS) to automatically determine structural information of individual shrubs (principally Artemisia tridentata) and shrub canopies in eastern Washington, USA. Because current airborne LiDAR systems have technological constraints that may limit their utility in shrub-dominated ecosystems, we used the TLS data both in their basic form and to simulate high resolution discrete-return airborne LiDAR data with sample densities of 4 and 16 points m−2. Through spatial wavelet analysis we automatically detected the locations of up to 78% of all individual shrubs identified in the field and up to 88% of shrubs with a crown diameter > 1.5 m. Shrub height and canopy cover derived from TLS data were significantly correlated with field measurements (respectively, r2 = 0.94 and 0.51, p < 0.001, α = 0.05). Automated detection of individual shrub crown area using the high resolution simulated airborne LiDAR dataset was also significantly correlated with field measurements (r2 = 0.47, p ≤ 0.01). Our results indicate that TLS data are useful for automatic quantification of shrub structure and provide a glimpse to the utility of the next generation of small-footprint airborne LiDAR instruments in quantifying shrub biophysical parameters across broad areas.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| 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".