Using drone mapping to evaluate error of plot-based field surveys and its effects on moderate spatial resolution remote sensing retrieval of lichen cover
Bibliographic record
Abstract
Effective plot-based field sampling involves a trade-off between implementation efficiency and sample error. Optimal field sampling therefore requires quantifying the sample error under various sampling designs. For remote sensing applications, it is also important to understand how field sample error and training sample size (the number of pixels) affect the retrieval of surface properties. In this research, drone imagery was used to simulate field plots and investigate plot sampling error for forage lichen cover in relation to plot size, number of plots, and sampling strategy. The effect of this error on remote sensing-based lichen cover retrieval was evaluated using varying training sampling sizes in two different study regions in northern Canada. Results showed that cover with high spatial variability increased the number of plots or plot size required to achieve a specified level of error. For lichen cover retrieval at moderate spatial resolution (10–30 m), field sampling (plot size and number of plots) did not have as significant of an effect as regional differences (spectral separability of cover types), sensor, and the number of pixels used for model training. This plot simulation approach using drone images can be applied to other surface properties and regions to provide field sampling guidance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".