Stability of Surface LiDAR Height Estimates on a Point and Polygon Basis(<Special Issue>Silvilaser)
Bibliographic record
Abstract
Airborne scanning LiDAR (Light Detection and Ranging) data has significant potential to update, audit, calibrate, and validate operational stand-level forest inventories by providing information on canopy height, vertical structure, and ground elevation. However, using LiDAR data as an operational data source in a sampling context requires repeatable and consistent attribute estimation (i.e. height), from data collected over several acquisition flight lines. We examined the consistency of LiDAR height estimates obtained from the Scanning LiDAR Imager of Canopies by Echo Recovery (SLICER) instrument over Jack pine (Pinus banksiana, var. Lamb) and black spruce (Picea mariana, var. Mill.) forest stands in central Saskatchewan, Canada. Two analyses were undertaken: first, estimated tree heights derived from pairs of LiDAR returns, acquired from multiple flight lines and within 9m of a single LiDAR footprint, were compared to assess the consistency of height estimates (point stability); secondly, height estimates from multiple flight lines within individual forest inventory polygons were compared to assess the consistency of within-polygon estimates of tree height (polygon stability). The point stability analysis indicated that over all forest classes estimates of height were consistent, with 94% of LiDAR returns (n=15,896) having a pair-wise height difference within ±5m. On a polygon basis, both between- and within-flight line standard deviations were considered. Results indicated that the within-polygon variability in estimated tree heights was captured by LiDAR data collected over any portion of a polygon. This result suggests that the inventory polygons are homogenous with regards to height (and related variability) and may be characterized with LiDAR, independent of actual flight path.
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 | 0.003 | 0.012 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".