Applying unmanned aerial vehicles (UAVs) to map shrubland structural attributes in northern Patagonia, Argentina
Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs) have gained attention for forestry applications in recent years. These technologies provide ultrahigh-resolution spatial data for detailed mapping of forest structure, among other forestry applications. UAVs have mainly been tested in high-value timber stands, but little is known about their performance in other woody ecosystems such as shrublands that also provide key ecosystem services. Field measurements in shrublands are time-consuming, so UAVs could be used instead to provide data for shrubland management and conservation. We tested whether UAVs could map common structural attributes in shrublands of northern Patagonia. We specifically evaluated the capability of UAV point clouds for mapping (i) canopy height, (ii) stand density, (iii) basal area, and (iv) volume. The agreement with the field measurements was satisfactory (R2 was up to 0.95 and relative root mean square error (rRMSE) ranged between 12% and 39%) and comparable with those found for coniferous forests in similar studies. This study is a first attempt to characterize the structure of Patagonian shrublands using UAV data. Despite the challenges and methodological aspects that need to be solved, our results encourage the use of UAVs in these types of ecosystems.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".