Stability of surface LIDAR height estimates on a point and polygon basis
Bibliographic record
Abstract
LIDAR has been demonstrated as a tool for remotely sensing information on the vertical structure of forests. The Scanning LIDAR Imager of Canopies by Echo Recovery (SLICER) records data on canopy height, vertical structure, and ground elevation. Based upon the sensor configuration for this study, the vertical resolution of the SLICER is approximately 1m, with a horizontal resolution of approximately 9m, with five adjacent footprints resulting in an approximate 45m wide swath. Information on the height of trees within forest stands is an important attribute in forest inventories. The ability to remotely sense height information for forest inventory purposes may allow for procedures such as up-date, audit, calibration, and validation. Prior to applying remote estimates of height in an inventory context the consistency of the estimates at locations and over areas is assessed. Locations which have more than one LIDAR observations from differing flight over-passes allow for an assessment of the stability of point height estimates. To assess the stability of area estimates, the height estimates from multiple flight lines through individual forest inventory polygons are compared. For the boreal forest conditions present in our central Saskatchewan study area the following conclusions are made. On a point stability basis, LIDAR observations are found to vary little when separation distances between points are small. On a polygon basis, considering both between and within line standard deviations, the within polygon variability in LIDAR heights is well captured by collecting data over any portion of a polygon.
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 | 0.016 |
| 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 | 0.001 | 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".