Analysis of airborne LiDAR surveys to quantify the characteristic morphologies of northern forested wetlands
Bibliographic record
Abstract
A new technique for quantifying the geomorphic form of northern forested wetlands from airborne LiDAR surveys is introduced, demonstrating the unprecedented ability to characterize the geomorphic form of northern forested wetlands using high‐resolution digital topography. Two quantitative indices are presented, including the lagg width index (LWI) which objectively quantifies the lagg width, and the lateral slope index (LSI) which is a proxy measurement for the dome shape or convexity of the wetland ground surface. For 14 forested wetlands in central Ontario, Canada, northwestern Ontario, Canada, and northern Minnesota, United States, these indices were systematically correlated to metrics of topographic setting computed from LiDAR digital elevation models. In particular, these indices were strongly correlated with a Peatland Topographic Index (PTI, r2 = 0.58 and r2 = 0.64, respectively, p < = 0.001) describing the relative influence of upslope contributing area on the hydrology and biogeochemistry of individual wetlands. The relationship between PTI and the LWI and LSI indices was interpreted as geomorphic evolution in response to the spatially varying influence of upslope runoff on subsurface hydrochemistry. Spatial patterns of near‐surface pore water chemistry were consistent with this interpretation. Specifically, at four wetland sites sampled extensively for pore water chemistry, the mean and variance of near‐surface pore water methylmercury (MeHg) concentrations were higher within the zone of enhanced upland‐wetland interactions, as inferred from the LiDAR‐derived LWI estimates. Use of LiDAR surveys to measure subtle topographic gradients within wetlands may therefore help quantify the influence of upland‐wetland interactions on biogeochemical cycling and export in northern forested landscapes.
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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 | 0.000 | 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) | 0.000 | 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".