Examining the effects of sampling point densities on laser canopy height and density metrics
Bibliographic record
Abstract
Forest resource managers rely on the information extracted from forest resource inventories to manage forests sustainably and efficiently, thereby supporting more precise decision-making. Light detection and ranging (LiDAR) is a relatively new technology that has proven to enhance forest resource inventories. However, the relationship between LiDAR sampling point density (which is directly related to acquisition and processing costs) and accuracy and precision of forest variable estimation has not yet been established across a range of forest ecosystems. In this study, 2 airborne LiDAR surveys using the same sensor, but configured with disparate parameters, were carried out over the York Regional Forest near Toronto, Canada producing 2 data sets characterized by different sampling point densities. The effects of 2 sampling point densities on 23 laser canopy height and density metrics typically used in forest studies at the plot level were examined with comparisons grouped by first and last return data. The minimum (h min ) and maximum (h max ) laser canopy heights were statistically different for first and last returns. The proportion of laser returns (i.e., canopy density) in the upper (d 1 ) and lower (d 10 ) range of laser canopy heights was statistically different for the first returns, whereas only a single canopy density metric was different for the last returns (d 9 ). These results suggest that changes in sampling point density (due to changes in scan angle and altitude) only affect laser canopy height and density metrics that are characterized by the small percentage of returns from the very top (h max ; d 1 ) and base of the canopy (h min ; d 10 ) (i.e., those metrics that characterize the tail ends of the distributions of laser canopy heights). Consequently, higher sampling point densities may add little value to current LiDAR forest research or operations at the stand level, as metrics derived from the canopy profile can be implemented for biophysical variable estimation. Implications for forest management are in terms of identifying which aspects of LiDAR project design: a) impact the quality and cost-effectiveness of derived FRI information; b) should be specified within a LiDAR request for proposals; or c) scrutinized within LiDAR project tender documentation. Key words: LiDAR, airborne laser scanning, FRI, canopy profile, biophysical variables, sampling density
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".