Modelling the Contributions of Riparian Vegetation and Topography to Stream Shade Using <scp>LiDAR</scp> and Conventional Digital Elevation Data
Bibliographic record
Abstract
ABSTRACT Stream temperature is widely considered the master variable in stream ecosystems. One of the key drivers of diel and seasonal stream temperature variability is the solar radiation received at the stream surface, which can be influenced by shading associated with both larger scale topographic features and riparian vegetation. In this study, a stream shade model was developed that uses LiDAR point cloud data to model shading by riparian vegetation, including canopy overhang, and conventional elevation data to model stream shading by topography. The model was applied to a dominantly north–south oriented river flowing in a floodplain within a mountain valley. When compared with shade interpreted from PlanetScope visual imagery, the model predicted stream shade at the point scale with 92% agreement. Sources of error were attributed to pixel and azimuth band size, which can be refined within the model arguments, although at the cost of increased processing time. The shade model was re‐run after virtually rotating the reach by 90° and 270° clockwise to evaluate the effect of valley orientation. Peak reach‐wide sunlight exposure occurred approximately 2 h later in the day when the stream reach was rotated 90°, and produced greater shading from mid‐morning to mid‐afternoon. Further work should test the model on smaller streams using ground‐based oblique or drone‐based photography to provide ground‐truthing, particularly to assess the accuracy of predicted shade below over‐hanging vegetation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".