Assessment of LiDAR snow depth measurements and the spatiotemporal variability of the snowpack in a forested watershed on Vancouver Island
Bibliographic record
Abstract
,This study uses repeat airborne LiDAR surveys to assess snow distribution within complex forested terrain in Russell Creek, a 33 km2 sub-basin of the Tsitika watershed, on northern Vancouver Island. LiDAR surveys used within this study were acquired in 2022 and 2023, with 4 – 5 flights per year timed with the intent to capture maximum snow depth, to the completion of the melt season. In addition, two separate bare earth models were used to assess error by acquisition date (2017 vs 2023). Over 2000 manual snow depth measurements were taken over the course of this study via a) cardinal plots within six different forest cover types and b) weather station snow courses conducted on gravel roads. Mean difference (MD) of the averaged manual and LiDAR measurements were overall lowest for the weather station snow courses (-29 to 13 cm) and highest in the harvested (juvenile and regenerating) plots (-39 to 134 cm). Analysis of the LiDAR bare earth point returns showed that juvenile and regenerating plots both had a low percentage of pixels with ground returns – average of 24 and 35% respectively - a result of the tall (~ 2 meter) and complex ground vegetation. Error was reduced in the juvenile forest plots (-22 to 29 cm) when LiDAR snow depth was processed with an alternative bare earth model acquired in 2017, in which the average coverage of ground returns was much greater (96%). Total snow storage within Russell Creek was relatively similar (1000 m) of the watershed - comprised of old growth forest and alpine cover types – store the majority (56 – 82%) of the snow. At high elevations (1400 – 1700 m) in the watershed, the snow volumes were much greater (28 – 44%) when processed with the 2023 bare earth model compared to the 2017. An additional component to this study was a paired control-treatment approach to assess the impacts of forest harvest on a) the bare earth model and b) snow distribution. Evaluation of pre- (2020) and post-harvest (2023) snow free models showed a slight (0.15 m) bias within treatment sites due to changes to the ground surface. After bias correction, the rate of increase in snow water equivalence between the pre-and post-harvest snow years in the treatment sites (1.4 – 4.2) doubled the control (0.7 – 1.7). Overall, these results highlighted the importance of the bare earth model to accurately measure snow, especially within complex forested and harvested watersheds.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".