Remotely piloted aircraft imagery for automatic tree counting in forest restoration areas: a case study in the Amazon
Bibliographic record
Abstract
Throughout the world, restoration of degraded areas (RDA) is not only a global but also a local challenge. In this context, the Brazilian government committed itself to restore 12 million hectares of forests by 2030. RDA monitoring customarily depends on extensive fieldwork to collect data on all individuals planted. As remotely piloted aircrafts (RPAs) can reduce costs and time of fieldwork activities, studying this technology is therefore timely given. A crucial metric for RDA is the number of trees established in the area. Methods using RPAs on automatic tree counting showed good accuracy using algorithms based on the canopy height model (CHM), which is the difference between a digital surface model (DSM) and a digital terrain model (DTM). However, obtaining a DTM demands an extra computational processing step and may require field control points or manually delimiting objects on the surface. The study presented here proposes and evaluates a semi-automated methodology for counting trees directly on DSM in RDAs in the Amazon using RPA coupled with a red–green–blue standard photographic sensor. The DSM method obtained good overall accuracy and F-score indexes, superior to the CHM method for all study areas even when overall accuracy was low for both methods.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".