Evaluating Riparian Function Using LiDAR-Derived Vegetation Intactness and Cattle Accessibility in the Cariboo Region, British Columbia
Bibliographic record
Abstract
Riparian zones provide vital ecological services, such as maintaining water quality, supporting biodiversity, and stabilizing stream banks; however, these areas in British Columbia’s Cariboo Natural Resource Region are increasingly pressured by forestry practices, cattle grazing, and private land management. Traditional cumulative effects assessments rely predominantly on indirect indicators of disturbance potential, which may inaccurately represent actual conditions, making direct quantification of riparian vegetation intactness and cattle accessibility critical for effective management. This study utilized high-resolution Light Detection and Ranging (LiDAR) data and spatial datasets to directly assess riparian conditions across four watersheds (approximately 20 km² each), addressing three objectives: (1) quantifying vegetation intactness within riparian zones on private land and Indian Reserves compared to adjacent crown land; (2) modeling cattle accessibility to riparian areas within active range tenures based on slope and vegetation density; and (3) evaluating the effectiveness of vegetation retention within forestry cutblock buffers (harvested areas less than 40 years old). Results revealed significant disparities in vegetation intactness, with crown land riparian buffers showing substantially higher mean canopy heights (7.54 m) than private and Indian Reserve lands (2.78 m). Approximately 50–60% of riparian areas within range tenures were highly accessible to cattle, with an additional 30–40% classified as medium accessibility, underscoring potential ecological risks from grazing. While 79.7% of forestry riparian buffers retained vegetation, their average canopy height (5.80 m) was significantly lower than surrounding areas (7.82 m), suggesting compromised ecological integrity. These findings emphasize the importance of leveraging remote sensing data in cumulative effects assessments, providing precise management guidance to enhance riparian protection strategies and policy efficacy.
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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 | 0.001 | 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".