Using VEGNET In-Situ Monitoring LiDAR (IML) to Capture Dynamics of Plant Area Index, Structure and Phenology in Aspen Parkland Forests in Alberta, Canada
Bibliographic record
Abstract
The use of ceptometers and digital hemispherical photographs to estimate Plant Area Index (PAI) often include biases and errors from instrument positioning, orientation and data analysis. As an alternative to these methods, we used an In-Situ Monitoring LiDAR system that provides indirect measures of PAI and Plant Area Volume Density (PAVD) at a fixed angle, based on optimized principles and algorithms for PAI retrieval. The instrument was installed for 22 nights continuously from September 26 to October 17, 2013 during leaf-fall in an Aspen Parkland Forest. A total of 85 scans were performed (~4 scans per night). PAI measured decreased from 1.27 to 0.67 during leaf-fall, which is consistent with values reported in the literature. PAVD profiles allowed differentiating the contribution of PAI per forest strata. Phenological changes were captured in four ways: number of hits, maximum cumulative and absolute PAI values, time series of PAVD profiles and PAI values per forest strata. We also found that VEGNET IML Canopy PAI and MODIS LAI values showed a similar decreasing trend and differed by 2%–15%. Our results indicate that the VEGNET IML has great potential for rapid forest structural characterization and for ground validation of PAI/LAI at a temporal frequency compatible with earth observation satellites.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".