Evaluating Restoration Success on Lyell Island, British Columbia Using Oblique Videogrammetry
Bibliographic record
Abstract
Abstract In degraded ecosystems where the impact on wildlife and the destruction of natural systems is high, restoration becomes a critical component of recovery. Monitoring restoration activities plays a key role in determining end points for restoration and assessing effectiveness. Appropriate monitoring of major systems, particularly in assessing vegetation reestablishment and slope stabilization, requires a long‐term commitment to annual assessment of change and improvement over time. However, intrinsic factors built into government or public management systems, such as budgeting and staffing limitations, limit the ability for long‐term monitoring of critical restoration projects. In the research reported in this article, we devised and assessed a new remote method for assessing restoration success and tested it on restoration and monitoring requirements in Lyell Island, British Columbia. We developed a system (the oblique data fusion system [ODFS]) to extract spatial information from oblique aerial video imagery. The ODFS enables low‐cost change detection and database updates at a range of operational scales. System tests show an absolute spatial accuracy on the order of ±2.1 m. The evaluation, based on digitized historical data, ground surveys, and the ODFS‐derived data, indicates that the landslide rate (new area/year) tapered off following treatment; after 5 years it had been reduced by a factor of 3 relative to the background rate. The recovery is deemed sufficient to initiate secondary restoration tasks. The evaluation demonstrates the accuracy and utility of the ODFS for long‐term monitoring of landscape restoration efforts, particularly in remote areas. In conclusion, this new innovative method shows considerable promise for park managers.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".