Airborne lidar intensity correction for mapping snow cover extent and effective grain size in mountainous terrain
Bibliographic record
Abstract
Differentially mapping snow depth in mountain watersheds from airborne laser altimetry is a valuable hydrologic technique that has seen an expanded use in recent years. Additionally, lidar systems also record the strength of the returned light pulse (i.e. intensity), which can be used to characterize snow surface properties. For near-infrared lidar systems, return intensity is relatively high over snow and inversely related to the effective grain size, a primary control on snow albedo. Raw intensity is also sensitive to laser range and incidence angle, however, and requires a correction for snow property retrieval that is especially pertinent in mountainous terrain. Here, we describe a workflow to correct the intensity using the plane trajectory, lidar scan angle, and lidar-derived topography. As a proof of concept for snow retrievals, we apply the workflow to an airborne 1064 nm lidar flight over a snow-covered mountain basin in the Colorado Rockies. Corrected intensity was empirically related to reflectance before delineating snow extent and retrieving grain size. Relative to the traditional snow classification derived from optical imagery, the lidar-derived snow extent covered 5.4% more area due to the fine resolution point cloud and absence of shadows common in optical imagery. The lidar-derived grain size retrievals had a MAE of 32 µm compared to those from field spectroscopy, which translated to a 1% error in snow albedo. We found high incidence angles yielded an overcorrection in intensity that introduced a high bias in the grain size distribution and, therefore, suggest using an incidence angle threshold (40°). Developing methods specifically for quantitative snow surface property retrievals from lidar intensity is timely and relevant as aerial lidar is increasingly being used to map snow depth for hydrologic and cryospheric studies.
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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".