Remote Sensing of Landslide-Generated Sediment Plumes, Peace River, British Columbia
Bibliographic record
Abstract
Quantifying the contribution of sediment delivered to rivers by landslides is needed to assess a river’s sediment load in regions prone to mass wasting. Monitoring such events, however, remains difficult. This study utilised six years of remotely sensed imagery (PlanetScope and RapidEye, Imagery courtesy of Planet Labs, Inc., San Francisco, CA, USA), topographic surveys, and field observation to examine a hydro-geologically controlled, retrogressive landslide near a tributary to the Peace River, British Columbia. The slide has been active since 2014, delivering large amounts of sediment to the Peace River, visible in a persistent plume. Here, we quantify the landslide’s sediment contribution to the Peace River, assess the hydro-meteorological drivers of plume variability, and test whether plume activity can be directly linked to landslide activity for monitoring purposes. Our results show that the landslide on average delivered 165,000 tonnes of sediment per year, a seven-fold increase of the tributary’s regular load and near half of the Peace River’s load at this location. Due to continuous erosion of landslide material, sediment supply is steady and fuelled by repeated failures. Using thresholding, the identification of ‘high’ plume activity was possible, which positively correlated with the water level in a nearby reservoir, a proxy for the state of groundwater in this region. We reason that ‘high’ plume activity is linked to increased groundwater pressure because landslide activity is groundwater-controlled and failures fuel sediment delivery to the Peace River. Using readily available imagery, it is thus possible to monitor the activity of this recurrent landslide when field data are difficult to obtain.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".