Measuring river planform changes from remotely-sensed data: A Monte-Carlo approach to assess the impact of spatially-variable geometric error.
Bibliographic record
Abstract
A majority of European rivers have been extensively affected by diverse anthropogenic activities, including e.g. channelization, regulation and sediment mining. Against this background, the planimetric analysis based on remotely-sensed data is frequently used to evaluate historical planform changes, eventually leading to quantification of migration rates. However, geometric spatially-variable (SV) error inherently associated with these data can result in poor or even misleading interpretation of measured changes, especially on mid-sized rivers. We therefore address the following issue: What is the impact of spatially-variable error on the quantification of surfacic river planform changes? Our test river corresponds to a 20 m wide meandering sub-tributary of the Upper Rhine, the Lower Bruche. Within four, geomorphologically-diverse sub-reaches, the active channel is digitised using diachronic orthophotos (1950; 1964) and the SV-error affecting the data is interpolated with an Inverse Distance Weighting technique based on an independent set of ground control points. As a second step, the main novelty of our approach consists in running Monte-Carlo (MC) simulations to randomly translate active channels according to the interpolated SV-error. This eventually allows to display the relative margin of error (RME) associated with measured eroded and/or deposited surfaces for each sub-reach through MC simulations, illustrating the confidence level in the respective measurements of our river planform changes. Our results suggest that (i) SV-error strongly affects the significance of measured changes and (ii) the confidence level might be dependent not only on magnitude of changes but also on their shapes. Taking SV-error into account is strongly recommended, regardless of the remotely-sensed data used. This is particularly true for mid-sized rivers and/or low amplitude river planform changes, especially in the aim of their sustainable management and/or restoration. Finally, our methodology is transferrable to different fluvial styles.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".